Journal of Distribution Science (유통과학연구)

Korea Distribution Science Association (한국유통과학회)
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Measuring Trusts And The Effects On The Consumers' Buying Behavior

  • Received : 2020.01.10
  • Accepted : 2020.03.05
  • Published : 2020.03.30
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Abstract

Purpose: Trust plays an important role in e-commerce because consumers perceive more risk involved with this type of shopping than traditional way of shopping. Trust is defined as complex and multidimensional issue. This paper argues that trust should be considered to have two important components as trust belief and trust intention, in which trust belief has three components as competence, integrity and benevolence. Research design, data and methodology: This study examines the relationship between retailer website quality (web design, navigation, information), reputation and risk toward trust. In addition, trust and risk toward buying behavior are also considered, leading to customer satisfaction. The paper is conducted on a sample of 594 customers with direct experience of online shopping in Vietnam. Both confirmatory factor analysis (CFA) and a structural equation model (SEM) were utilised. Results: Empirical findings from this paper indicate that trust is high order construct. Website quality and reputation have positive impacts on customers' trust. Trust has a positive relationship to buying behavior and customers' satisfaction while perceived risk has negative relationship to buying behavior. In contrast, a relationship between perceived risk and trust is not supported in this study. Conclusions: Improving reputation and website quality (especially information) may increase customers' trust and eventually lead to purchase decision.

Keywords

1. Introduction

Researchers and practitioners are concerned about trust as it is an important factor that influences and drives customers to make purchase decisions, especially on internet where there are risks for them to take action. More than 70 thousand publications about online trust over the last five years (using google scholar search from 2015- 2019). Moreover, Bauman and Bachmann (2017) provided a comprehensive review on consumer online trust to confirm the importance of the topics that researchers should discuss. From the perspective of customers, online shopping is convenient, hassle free. Customers are also able to find different products easier on online (Sarkar, 2011). However, customers have to cope with issues like financial risk, product risk, security risk, social risk, psychological risk, and time risk (Ariffin, Mohan, & Goh, 2018; Tham, Dastane, Johari, & Ismail, 2019). Trust is considered as factors which help customers make decisions and mitigate customers' perceived risk (Pappas, 2016).

Studies have been conducted to demonstrate that retailers need to build customers‘ trust in order to be able to run a successful business online (Pappas, 2016). The value of trust is in the effect of the trust on the relationship and buying decisions between buyers and sellers (Gupta, Yadav, & Varadarajan, 2009). Trust plays the more important role for retailers in e-commerce in comparison with the traditional brick and mortar stores, because consumers perceive more risks when they cannot see the stores‘ presence physically and they are not able to touch or check the products before they make the buying decisions. Instead, consumers search for information about product on website therefore the e-retailer‘s website quality is a base for customer to lay trust on.

Although several studies agreed that trust is complicated and should be considered as a multidimensional issue (Doney & Cannon, 1997; Ganesan, 1994), other studies suggested that trust is conceptualized, measured and manipulated as a simple concept (Gefen, Karahanna, & Straub, 2003; Nguyen & Bui, 2019b, 2019a). Does trust as multidimensional construct captured all variances and is better to explain customers' behavior?

In this paper, the concept of trust will be examined and measured as a multi-dimensional issue to determine and explain its effect on the customers‘ buying behavior. Trust will be viewed through the lenses of intentions, beliefs, competence, benevolence and integrity. The aim of this study is to suggest a measurement for trust and to examine the role of trust in relationship with the retailer‘s reputation, website quality, consumer‘s perceived risk and buying behavior. We expect that the findings achieved from this paper will help researchers understand better about the concept of trusts as well as its relationship with perceived risk, reputation, website quality and initial purchase. Practitioners also benefit from this study by understanding the role of trusts and how to build trusts for e-retailers particularly for potential and new customers.

2. Literature review

2.1. Trust, initial trust and on-going trust

Previous studies have developed the concept of trust as (1) a set of beliefs related to integrity, benevolence and competency of partners (Doney & Cannon, 1997; Ganesan, 1994; Gefen & Silver, 1999) or (2) a general belief that counterparts can be trusted, and willingness to accept the risk and vulnerable results when trusting their partners (trust intention) (Gefen et al., 2003; Mayer, Davis, & Schoorman, 1995; McKnight & Chervany, 2006). Moreover, customers usually rely on their perceptions and beliefs over their counterpart characteristics such as integrity, competence and benevolence to develop expectations about their trustee‘s future behaviors (Dunn & Schweitzer, 2005; Le & Ngo, 2012; Palmatier, Jarvis, Bechkoff, & Kardes, 2009). Thus, the concept of trust should be measured as multi-dimensional issue with components including trust intention and belief, in which trust belief has three components: integrity, benevolence, and competence (e.g. McKnight, Choudhury, & Kacmar, 2002a). Competence is defined as ability that the trustee can do what the trustor needs and wants, benevolence is about trustee caring and motivation to act in the trustor‘s favours, and integrity is about trustee honesty and commitment (Bhattacherjee, 2002; Butler, 1991; Mayer et al., 1995).

McKnight, Choudhury, and Kacmar (2002b) defined initial trust as the situation where consumers trust the e-retailer without prior experience. Initial trust is different from on-going trust where on-going trust is based on prior experience. Initial trust is based on different signal to form trust towards counterpart and initial trust can only exist in a short time (Euijin & Tadisina, 2007). In this paper, we collect data from people who make the first purchase at retailers‘ website, hence the construction of trust we measure represents the initial trust. Previous studies measured trust as a simple concept. As such, it contradicts with the theories and definition of trusts.

2.2. Retailer’s website quality and consumers’ trusts

A retailer‘s website with familiar design and navigation will attract the consumers. The customers will spend time in the website, feel comfortable, get accustomed to the information, and eventually trust the e-retailer. Similarly, the information provided on the website will provide the same effects as those from website design and web navigation. Information provided will convince the consumers to believe in the products and decide to buy the products in the website. Based on the Elaboration Likelihood Model Theory (ELM) (Petty & Cacioppo, 1986), researchers believe that sophisticated consumers may use central route to evaluate the quality of information and arguments to trust the counterparts (e.g. Kim & Benbasat, 2009; Zhou, 2012; Zhou, Lu, & Wang, 2014). Cyr (2013) conducted several studies on website quality and suggested that e-retailers should pay attention to (1) quality of information on website, (2) website navigation (easy to find what consumers want) and (3) website interface design (photos, graphics, colours). Findings from Nicolaou and McKnight (2006) study presented that the quality of information has profound effects on perceived risks and trust.

Features of the website contribute to the formation of consumer opinion and trust with online retailers, primarily from the website interface design, information and navigation of the website (Cyr, 2013). As such, the following research hypotheses relate to the relationship from website interface design elements, website information and website navigation to the building of trust in online retailers are developed: 

H1: E-retailer‘s website interface design has positive impacts on consumers‘ trust.

H2: E-retailer‘s website navigation has positive impacts on consumers‘ trust.

H3: E-retailer‘s website information has positive impacts on consumers‘ trust.

2.3. E-retailer’s reputation, consumers’ satisfaction and consumers’ trust

A number of studies presented evidence on the relationship between reputation and consumers‘ trust (Chen & Barnes, 2007; McKnight et al., 2002a). In the first buying decisions, consumers cannot use the prior experience with the e-retailer to assess the website‘s credibility and ability, but rather they have to employ the peripheral route (ELM) in which other signals such as retailer‘s reputation is considered. Reputation can enhance consumers‘ trust and reduce the perceived risks and push customers to buying decision.

Prior researches show that reputation has several impacts on the customers‘ satisfaction (Jin, Park, & Kim, 2008). There are differences among cultural groups in using frame of reference, where countries with collectivism culture tend to use group's advices as source of reference. Reputation is considered a frame of reference which can affect buying decisions and increase consumers‘ satisfaction (Jin et al., 2008).

E-retailer‘s reputation increase consumers‘ trust toward the retailer, on the other hand, it reduces consumers‘ perceived risks and makes them believe in their decisions (Casalo, Flavian, & Guinaliu, 2007). Prior studies show that retailer‘s reputation has certainly provided impacts on trust, as well as satisfaction (e.g. Jin et al., 2008). The measurement scale for reputation in this paper is adapted and modified from previous studies (e.g. Brown, Venkatesh, Kuruzovich, & Massey, 2008; Na & Kim, 2013).

On the basis of findings from empirical evidence in previous studies, the hypotheses about the relationship between e-retailer‘s reputation and trust, as well as risk perception of online consumers are as follows:

H4: E-retailer reputation has provided positively impacts on consumers‘ trust.

H5: E-retailer reputation has provided negatively impacts on consumers‘ risk perception.

H6: E-retailer reputation has positively provided impacts on consumers‘ satisfaction. 

2.4. Consumer’s trust and perceived risks

Trust is important in situations where potential risk may occur. As such, it requires customers to trust their partners (Cheung & Lee, 2006). Due to the complicated social relationship, there is a mechanism to reduce the complicated social relations and anxiety (Wu & Chen, 2005). Risks may also have a role in perceived behavioral control concept in the theory of planned behavior (TPB) from Ajzen (1991). In this paper, we test the relationship between risks and trust as well as between risks and buying behavior. Measurement scales for perceived risks are adapted from Jarvenpaa, Tractinsky, and Vitale (2000). Therefore, the hypothesis of the relationship between risks and trust can be developed as follows:

H7: Risk perception has negatively provided impacts on consumer‘s trust.

H8: Risk perception has negatively provided impacts on consumer‘s buying behaviors.

2.5. Consumer’s trust, trial buying behavior and satisfaction

Oliver (1997) argued that satisfaction is consumer‘s reaction when they compare the experience in consuming the product with that of prior expectation. If results matched or exceeded what the customer expected, customer may be satisfied, or they may be dissatisfied if the expectation is disconfirmed. Wu (2013) developed a model to measure trust, customers satisfaction and intention to complain, and the results showed that the expectation confirmation model should be applied to measure scale for satisfaction.

Häubl and Trifts (2000) defined online shopping as a shopping activity using electronic device such as computer-based interface. Characteristics of online shopping include connectivity, high interaction, information exchange, personalization, customizable content and instant feedback (Alba, Lynch, Weitz, Janiszewski, Lutz, Sawyer, & Wood, 1997; Häubl & Trifts, 2000).

All of these studies confirmed a strong relationship between trust and customers‘ satisfaction. Kim‘s (2012) study on trust lifecycle begins from the time customers first formed the initial trust on the e-retailer, then customers may make the first purchase from trustee (e-retailer). If customer satisfied he/she will form an ongoing trust, thus there is a relationship between satisfaction and trust in e-commerce. Kim, Ferrin, and Raghav Rao (2009) study on consumers‘ behavior before and after shopping, in which trust has provided effects on customers satisfaction. The study of Chiu, Lin, Sun, and Hsu (2009) asserted the direct impact of trust on customers satisfaction, or trust is considered as crucial factor resulting in the satisfaction of online customers (Lin & Wang, 2006; Wu, 2013).

There are evidences about the relationship between trust and intentions (buying intention and repurchase intention) (e.g. Bart, Shankar, Sultan, & Urban, 2005; Hong, 2015). However, few studies have been conducted on the relationship between trust and buying behaviors.

Moreover, when consumers decide to buy, they want the products with good value and fully meet their expectations. But in some circumstances, they may choose a lower cost product to reduce the risks due to lack of trust. On that basis, the following hypotheses are developed and tested:

H9: Consumers‘ trust has positive impacts on consumers‘ satisfaction.

H10: Consumers‘ trust has positive impacts on consumers‘ trial/initial purchase.

H11: Trial buying behavior has positive impacts on consumers‘ satisfaction.

3. Research method

3.1. Research design

This paper combines qualitative and quantitative methods using interviews and survey questionnaires. Experts and online consumers were interviewed to provide their views on the concept of trust, online behaviors and measurement. The questions are developed based on previous studies and theories. These questions are modified to fit the Vietnamese context. Then, a questionnaire with proposed scales was sent to those who have recently made online purchases. Excel, SPSS and AMOS software are used in this study. 

Thirty online consumers were invited to participate in a dialogue on online shopping, buying behaviors, website quality, trust and risks. Half of them were selected to further participate in a discussion on suggested scale based on theories and prior research. Misunderstanding statements (items) were corrected or replaced with sentences that better described the concepts. The results showed that the proposed measurements need to be refined to make a cohesive and comprehensible measurement scale.

The final questionnaire with refined scales, comprehensive statements, clarity and agreement to selected participants was then released and send out on social network, email, students and several companies in Ho Chi Minh City. After data was collected, scales were examined for reliability and validity. For this paper, 594 answers from people who have recently made an online purchase within the last 3 months were collected.

3.2. Measurement scale and data processing

Eight variables are examined in which one is a multi-dimensional (initial trust), seven uni-dimensional (web design, navigation, information, perceived risk, satisfaction and buying behaviors). Initial trust comprised two components including trust beliefs and trust intention. Trust beliefs have three components (competence, integrity and benevolence). Competence was measured using four proxies. Integrity was measured using four proxies. Benevolence was measured by five proxies. In addition, trust intention is measured by 4 four different proxies (McKnight et al., 2002a).

For the website quality, there are three uni-dimensional variables. The web design (website interface design) was measured using 4 proxies. Web navigation was measured by four proxies and information was measured using five proxies. Reputation scales were based on several previous studies (e.g. Brown et al., 2008). Measurement scales for perceived risk were based on Jarvenpaa et al. (2000) with three items. The Satisfaction construct was measured by five items and based on the research of Wu (2013). This study adapted some measurement scales of impulse buying behavior and other purchase behaviors measurement scale (Bearden & Netemeyer, 1999) as well as adopted the concept of buying behaviors from qualitative research and developed scale of buying behaviors with four items.

Factor loading was employed to examine the convergence and discriminant of trust‘s sub-constructs. Measurement scales for trust and other constructs were then tested by the Confirmatory Factor Analysis (CFA). Finally, a structural equation model (SEM) was employed to test the relationships among constructs in the proposed model and confirmed for the validity of the measurement.

4. Results and discussions

4.1. A sample

There are 694 questionnaires returned. After elimination of the unsatisfactory ones (including incomplete answers, invalid answers or answer from people who has no experience in online shopping), 594 answers remained for analysis, of which 35.4 per cent were from male customers, 64.6 per cent from female customers (see Table 1).

Table 1: Characteristics of the sample

OTGHB7_2020_v18n3_5_t0001.png 이미지

Note: 1 million VND ≈ 51717 KRW ≈ 43.2 USD

4.2. Initial trust (third order construct)

The trust construct‘s items are all loaded strongly together into the two factors in the exploratory factor analysis (EFA). A second analysis was performed by specifying four components (factors) based on theory and arguments about trust construct. The study of McKnight et al. (2002a) has also presented the same problem with trust beliefs. The items of trust construct were then assessed using Cronbach alpha reliability and EFA based on the data set (n = 594) collected. The results of factor analysis have shown that trust construct is divided into four components. There are trust intention and three components of trust beliefs including competence, benevolence, and integrity. Several items have been removed due to factor loading score lower than 0.5 (the cut off criteria), or the discriminating score among factors is lower than 0.3. These factors were test for reliability, the results revealed that the composite reliability of trust intention, benevolence, integrity, and competence were all satisfied and greater than 0.7.

Table 1: Characteristics of the sample

OTGHB7_2020_v18n3_5_t0001.png 이미지

The confirmed factor analysis (CFA) show that χ2 is 328.952 with 73 degrees of freedom (p < 0.05). However, the χ2/df is 4.506 (less than 5), other indicators such as CFI (0.960), GFI (0.929) and TLI (0.950) are greater than 0.9 and close to 1. The RMSEA reaching 0.077 (greater than 0.05 but less than 0.08) was acceptable (Hu & Bentler, 1999; Steiger, 1998), so it was possible to conclude that the model with data is fit to the theory model thus the unidimensionality of measurement scales are assured (Steenkamp & Van-Trijp, 1991). The standardized regression weight of all measurement scales are greater than 0.5 and were statistically significant. As such, the research concepts matched convergent validity criteria (Bagozzi & Yi, 1988; Gerbing & Anderson, 1988). The variance extracted is greater than 0.5 and composite reliability of measurement scales is greater than 0.7 which revealed that the measurement scales attained reliability (Bagozzi & Yi, 1988; Hair, Black, Babin, & Anderson, 2014).

4.3. Model specification

The saturated model is the model in which constructs are freely correlated with each other. As such, it has a lowest degree of freedom. In this measurement, modelling trust is treated as a third order construct. The analysis results show that χ2 is 2176.265 with 785 degrees of freedom (p < 0.05). However, the χ2/df is 2.772 (less than 3), other indicators such as CFI (0.927), and TLI (0.920) are greater than 0.9 and close to 1. GFI (0.839) is greater than 0.8 and is acceptable. More importantly, RMSEA reaching 0.055, which is greater than 0.05 but less than 0.08, was acceptable. As such, the model with data is fit to the theory model supporting both convergent and discriminant validity.

Moreover, standardized regression weight of all measurement scales are greater than 0.6 and were statistically significant at p<0.001. As such, constructs matched convergent validity criteria (Bagozzi & Yi, 1988; Gerbing & Anderson, 1988). Table 3 has shown that variance extracted is greater than 0.5 and composite reliability of measurement scales is greater than 0.7 which altogether revealed that the measurement scales attained reliability convergent validity (Bagozzi & Yi, 1988). In addition, the square root of constructs‘ AVE is greater than the correlations among contructs. As a result, discriminant validity of these scales are assured (Hair et al., 2014) (see Table 4).

Table 3: Measurement model results

OTGHB7_2020_v18n3_5_t0003.png 이미지

Table 4: Discriminant validity and cross constructs correlations.

OTGHB7_2020_v18n3_5_t0004.png 이미지

Note: ** Correlation is significant at the 0.01 level, ***Correlation is significant at the 0.001 level. Square root of average variance (AVE) is shown on the diagonal of the matrix Others represent correlation coefficients.

Common method bias (CMB) occurs when there is a variance attributable to the measurement method instead of the constructs that the measures represent (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). There are methods to test CMB including Harman Method, Lindell & Whitney Method (Lindell & Whitney, 2001) and Bagozzi, Yi, and Phillips Method (1991). The Harman single factor method is not a good method. On the other hand, the Bagozzi method has shown that there is no common method bias due to the correlations among constructs which are below 0.9 and the p-values are smaller than 0.05. While zero constraints test was significant meaning that measurable bias was detected. The equal constraints test shows that unevenly distributed bias was detected (unevenly distributed bias) and so the construct should be retained for causal analyses.

4.4. Structural regression model

The results supported most of the hypotheses with the exception of H7 (the relationship between perceived risk and trust). Hypothesis H1 (effects of website interface design on trust) is only supported at p-value 10 per cent. The results in Table 5 show that the hypotheses H2, H3, H4, H5, H6, H8, H9, H10 and H11 are supported with p-value smaller than 0.05. Website quality including website interface design, information and web navigation all have effects over trust in which information has the most impact (0.518) and website interface design has the least (0.10). Reputation of website has impact over trust (0.186), risk perceived (-0.298), and customer satisfaction (0.136) after initial purchase. Reputation of website has positive effects over trust and customer satisfaction but has negative effects over risk perceived. Trust has strong relationship with that of buying behavior (first purchase) (0.686) and satisfaction (0.636). Furthermore, buying behavior has positive affects over satisfaction (0.172).

The relationship between information quality, navigation, design and reputation toward trust in e-retailer is positive which means the more quality of information or easy to find information on website or friendly design or reputation, the higher level of trust consumers will lay on e-retailer. There is significant relationship from reputation toward perceived risk and customer satisfaction. The relationship between reputation and risk is negative where the higher level of reputation e-retailer has, the lower level of risk customer perceived about e-retailer‘s brand. Meanwhile, the relationship between reputation and satisfaction is positive meaning that the higher level of reputation the website has, the higher level of satisfaction the customers experience. The relationship from buying behavior toward satisfaction is supported. However, the size of effect is modest (0.172).

Table 5 shows that risk perceived has negative relationship with trust (H7). However, this relationship is not significant. In addition, the relationship of perceived risk and buying behavior (H8) is significant and this relationship is negative (-0.235). Other research also considered that risk perceived has effects over buying behavior rather than trust (Kim, Ferrin, & Rao, 2008; Pavlou & Gefen, 2004).

Table 5: Key parameters of the structural model

OTGHB7_2020_v18n3_5_t0005.png 이미지

Note: a P ≤ 0.1, * P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001

5. Conclusions

This study is conducted to examine various aspects of trust which is generally defined as complex and multidimensional issue. Findings from this paper have shown that trust is a multi-dimensional and complicated construct in which trust has two components including trust beliefs and trust intention. Within trust belief, three components such as competence, integrity and benevolence are considered. Empirical evidence from his paper confirms that trust has great impact on purchasing behaviors and customers' satisfaction. As such, trust does not only encourage potential customers to take action but also reinforce their satisfaction—a strong foundation for building customers' loyalty and repeated purchase.

The trust in e-retailers is greatly influenced by the information provided by the website. As such, contents provided on the website must be sufficient, clear, accurate and reliable. In addition, these contents must be useful for consumers. Structure of information and website navigation will also assist customers in their process of searching for information and products, as well as developing trust on the retailer‘s website.

In this paper, we established and examine the relationships from risk to trust as well as from risk to buying behaviors. Evidence from this paper shows that the first hypothesis is not significant while the second relationship is significant and consistent with prior studies. Hence, perceived risk can be considered as behavioral control in the TPB theory in which it has impact over the behavior and keep customers from committing a certain behavior.

The findings from this paper help e-retailers understand the vital role of providing effective information on the websites - accurate, clear, reliable and significant - because information has significant impact on trust and eventually buying behavior. Moreover, e-retailers should create a reputation of excellent competence, integrity and benevolence by providing information on their ability, response and reaction toward customers‘ questions, reviews and comments, and the willingness to serve and please customers (excellent customer services). This information and customer reviews on the website may activate initial trust from new customers.

E-retailers should put effort to reduce customers' perceived risk, including risk incurred from online shopping as well as risk from retailers. Providing terms and policies unambiguously promised to keep customers away from financial risk and product risk. For instance, e-retailers may offer free shipping, free return and refund policies as well as cash on delivery which is available in the Vietnamese market in which retailers allow customers to delay payment until they receive product. In addition, e-retailers could partnership with third parties to control the transactions and to make sure that both e-retailers and customers have fulfilled their responsibilities. Moreover, e-retailers should build reputation by raising awareness, and make it more popular so that potential customers could get familiar with website. Because reputation not only impacts on customers‘ trust, perceived risk but also on customers‘ satisfaction, which is a foundation for customers‘ loyalty and repeated purchase.

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