Abstract
Purpose
Gaining and maintaining trust in e-commerce is crucial for online purchases. Specifically, understanding trust formation and its consequences in a cross-market online shopping context is important, as cross-market studies are scarce. Therefore, this study examines antecedents and consequences of consumer trust in online shopping (TOS) by comparing advanced and emerging markets.
Design/methodology/approach
To test the formulated hypotheses, data including 397 responses from Austria and 205 from Moldova are analysed. Using partial least squares (PLS) path modelling, implications for theory and practice in cross-market e-commerce are obtained.
Findings
Empirical findings show that company reputation, perceived security and website quality positively influence consumer TOS. TOS corresponds directly positively with purchase intentions (PI). Our research confirms the negative relationship between trust and perceived risk (PR) as well as that between PR and PI. Furthermore, a significant difference between Austria and Moldova regarding the influence of experience and perceived website quality (PWQ) on TOS is observed.
Originality/value
Our study fills research gaps concerning TOS within the context of cross-market e-commerce. It contributes theoretically and practically and reveals the importance of customer trust and risk reduction for online retailers within advanced and emerging markets in order to provoke online PI.
Keywords
Citation
Quintus, M., Mayr, K., Hofer, K.M. and Chiu, Y.T. (2024), "Managing consumer trust in e-commerce: evidence from advanced versus emerging markets", International Journal of Retail & Distribution Management, Vol. 52 No. 10/11, pp. 1038-1056. https://doi.org/10.1108/IJRDM-10-2023-0609
Publisher
:Emerald Publishing Limited
Copyright © 2024, Michaela Quintus, Kathrin Mayr, Katharina Maria Hofer and Yen Ting Chiu
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Recently, online shopping has increased strongly, making online channels for firms in numerous industries and markets (Fokina, 2023) more important and technological advancements facilitating this development (Rahman and Hossain, 2022). In a globalized economy, the importance of digital channels is increasing (Orzol and Szopik-Depzynska, 2023), and companies need to adapt their businesses constantly to this change (Kraus et al., 2021). While the economic benefits of selling online are evident, e-retailers increasingly face difficulties in gaining and maintaining customers' trust (Alkhalifah, 2021). Contrary to offline trust, online trust focuses on the internet, the website or the technology (Nosi et al., 2021). Shopper behaviour has changed (Teller et al., 2019), with trust playing a central role in the online shopping process (Nosi et al., 2021), even more than in store based retailing (Jadil et al., 2022). Thus, consumer online trust is vital for the success of digital distribution channels. Particularly, the website raises issues of security and risk, as a direct contact between firm and customer is lacking (Zulauf et al., 2021). Moreover, especially in emerging markets, where online shopping is evolving, understanding trust, its antecedents and its consequences is important. However, results found in advanced markets cannot be transferred to emerging markets without empirical foundation (Hallikainen and Laukkanen, 2018; Wagner Mainardes et al., 2017). While studies of TOS are available for both advanced (Reynolds-McIlnay and Morrin, 2019; Grosso et al., 2020) and emerging markets (Kardes et al., 2021; Raposo Junior et al., 2022) separately, only a few studies employ a combined perspective (Ladwein and Sánchez Romero, 2021). In an increasingly globally integrated world, a cross-market approach to the topic looks promising (Hallikainen and Laukkanen, 2018), especially given the lack of empirical research on consumers’ online behaviour in emerging economies (Jadil et al., 2022). As only scant research is available in this field (Dospinescu et al., 2021; Hallikainen and Laukkanen, 2018), our study closes this gap by providing a cross-market validation of website trust.
To do so, a closer look at social and psychological factors is necessary (Wang et al., 2016). Therefore, we refer to the trust and commitment theory (CTT) (Morgan and Hunt, 1994), which provides a useful theoretical foundation for analysing consumers’ online behaviour (Wang et al., 2016). Besides some antecedents of trust, research has identified trust and PR as predictors of online shopping intentions (Jadil et al., 2022; Ruiz-Herrera et al., 2023). Thus, our research investigates antecedents along with the effect of trust on online PI in an advanced (Austria) and an emerging (Moldova) market based on CTT (Morgan and Hunt, 1994). We chose Austria because it represents a typical European advanced market located in Europe and Moldova because it represents a classic emerging market in Europe (IMF, 2023). Both European countries have received little research attention in this field so far, but the cross-market setting is of central interest in the academic field (Dospinescu et al., 2021; Jadil et al., 2022).
This paper contributes to TOS by contrasting advanced and emerging markets in one study. It proposes a comprehensive conceptual model of antecedents and consequences of consumer trust in cross-market e-commerce and tests this model in two contrasting markets. Therefore, this study closes an existing research gap and provides practical insights which firms can use to create a differentiated strategy in their cross-market online presence.
2. Literature review
Reviewing literature (see Table 1) displays most important research concerning TOS. It reveals that only limited research focuses specifically on online shopping, as the majority of research investigates trust within the service sector or very specific online retailing channels such as mobile shopping. Further, the research variables are either antecedents or consequences, with research falling short to examine both, and multiple dimensions of each variable. Referring to the theoretical foundation, current research circles mainly around trust in commerce and social media. However, it leaves the trust theory and e-commerce aspects aside. Despite online shopping and its importance in emerging markets, only one study compares advanced and emerging markets.
Therefore, this research examines TOS on the basis of CTT (Morgan and Hunt, 1994; Konuk, 2022), investigating multiple antecedents and consequences, taking a cross-market perspective.
3. Theoretical underpinnings
The role of trust has been explored extensively in relation to social commerce (Yahia et al., 2018; Yeon et al., 2019) and mobile shopping (Bashir et al., 2018), where the ability to rely on and confide in a business partner represents a precondition for lasting buyer-seller relationships (Morgan and Hunt, 1994). However, unlike in traditional offline exchanges, trust can be an obstacle due to the virtual shopping environment and its uncontrollable characteristics, along with privacy concerns (Alzaidi and Agag, 2022). As consumers have safety concerns regarding online transactions (Abdulrahman Al Moosa et al., 2022), trust in the online trader is a crucial antecedent to attitude towards technology (Singh, 2019) and use intentions (Gefen and Straub, 2003).
3.1 Commitment-trust theory
CTT implies that trust directly affects the relationship with commitment within an economic exchange process (Morgan and Hunt, 1994). It considers several parameters which mediate the relationship between trust and commitment, such as shared values or cooperation.
Factors representing dimensions of communication in accordance with CCT in online retailing are TOS antecedents such as the experience of a customer (Barari et al., 2020), the perceived reputation of the business (van Tran and Nguyen, 2022), as well as the PWQ (Kalaignanam et al., 2018). These constitute trust factors which are in the control of the retailer. Perceived security, along with risk perceptions, form dimensions of uncertainty. These include trust factors which are in the control of the retailer, such as perceived security, and those which are not in their control, such as risk perception, which is a highly psychological and complex phenomenon (Zulauf et al., 2021). The commitment induced by trust and therefore the consequence of TOS is represented by customers’ responses in the form of their intentions to purchase online (Kazancoglu and Aydin, 2018).
3.2 Antecedents of TOS
With increasing e-commerce and online trust moving into the focus of academic interest, empirical research reveals antecedents and consequences of online trust (Abdulrahman Al Moosa et al., 2022). Several studies have identified the level of experience with the system as a predictor of system trust (Punyatoya, 2019). Similarly, Bhattacherjee (2002) found familiarity, resulting from prior interactions with the system, would lead to higher levels of trust in the online retailer. The decision to engage with an online retailer often depends on his perceived overall reputation.
Another antecedent to TOS is the perceived system quality, defined by reliable functionality and accurate information, which impacts online trust and PI (Punyatoya, 2019). Despite technological advancements, data privacy remains a concern in web-based transactions (Martin et al., 2020) as consumers want to ensure that their personal data are processed according to legal regulations and safeguarded from unauthorized use (Aiello et al., 2020). Thus, perceived security is seen as an antecedent to online trust (van Tran and Nguyen, 2022).
Therefore, we hypothesize:
Experience (H1), perceived reputation (H2), PWQ (H3) and perceived security (H4) influence TOS positively.
3.3 Consequences of TOS
Research confirms the impact of TOS on a variety of cognitive, emotional and behavioural outcomes (Bulsara and Vaghela, 2020). Trust increases online purchase and loyalty intentions, either directly or indirectly through other mediating variables such as perceived usefulness and attitude (Marriott and Williams, 2018; Ladwein and Sánchez Romero, 2021). Further, trust intervenes between online support services and customer satisfaction (Pandey and Chawla, 2018), while online reviews function as an antecedent of trust (Raposo Junior et al., 2022). Others argue that trust influences risk perceptions and, consequently, willingness to buy and pay online (Zhu et al., 2022). The correspondence of PR with trust is documented in several studies (Marriott and Williams, 2018; Zulauf et al., 2021), with most research regarding risk as an antecedent. To conclude, uncertainties will reduce a consumer’s trust in a technology (Ortlinghaus et al., 2019), however some studies apply the reverse logic by arguing that lacking trust corresponds with higher PR (Marriott and Williams, 2018). Accordingly, it is hypothesized that:
The higher levels of TOS, the lower levels of PR (H5). The lower levels of PR, the higher levels of PI (H6). TOS influences PI positively (H7).
3.4 Antecedents and consequences of TOS in advanced and emerging markets
The overall market context influences online shopping behaviour (Chiu and Hofer, 2015; Gupta and Ramachandran, 2021), and the online purchasing process in a cross-market setting is of interest in the academic field (e.g. Frasquet et al., 2017). Accordingly, in this study, we investigate TOS in advanced and emerging markets. In emerging markets, transportation and communication infrastructure is developing (Kardes et al., 2021), with increasing access to high-speed internet, online shopping adoption and the growing middle class contributing to increasing purchasing power (Belbağ et al., 2019). Rapidly transforming economies can also be found in Eastern Europe (Cavusgil, 2021). Sheth (2011) differentiates emerging markets from advanced markets based on market heterogeneity, socio-political governance, chronic shortage of resources, unbranded competition and inadequate infrastructure. We selected Moldova for this study as it represents a classic European emerging market (IMF, 2023) and as it represents an under researched market (Dospinescu et al., 2021). Austria, as a classic advanced market and central European country, offers promising avenues for investigation due to the paucity of current academic research (Moerth-Teo et al., 2021).
In Moldova, the number of online shoppers is constantly rising. Of the 4.033 million inhabitants of Moldova, 3.07 million are internet users (Lupusor, 2021), and 1.4 million are online shoppers (Lone et al., 2021). The value of e-commerce in Moldova is estimated at 131 million Euros in 2021. By 2025, e-commerce in Moldova is expected to hit 210 million Euros, which represents an annual sales growth of 12.4% (Dospinescu et al., 2021). Moldovan consumers shop comparatively less than other European consumers with 272,000 representing regular online shoppers purchasing several times monthly. With respect to demographics, online shoppers tend to be female, living in urban regions and representing millennials (Lupusor, 2021).
The number of Austrian online shoppers has doubled in the last decade, which is of relevance among younger age groups with higher household income (WKO, 2018). In 2021, the total value of the Austrian e-commerce market is estimated at 6.675 million Euros and expected to reach 8.076 million Euros by 2025, representing an annual increase of 5% (International Trade Administration, 2022). Whereas in both markets fashion represents the biggest segment of e-commerce (Gittenberger and Teller, 2021; Lupusor, 2021), the general adoption of online shopping reveals differences between both countries.
As academic research depicts differences in marketing concepts between advanced and emerging markets (Sheth, 2011; Gupta and Ramachandran, 2021), we hypothesize the following differences:
There is a significant difference between advanced and emerging markets regarding the influence of experience (H8a), of perceived reputation (H8b), of PWQ (H8c) and of perceived security (H8d) on TOS and concerning the influence of trust on PR (H8e), and that of PR (H8f) and also of trust (H8g) on PI.
Accordingly, TOS is embedded in a cross-market context as illustrated in the research model (see Figure 1).
4. Methodology
4.1 Data collection and sample
To test our hypotheses, an online survey was conducted in Austria and Moldova, with the data collected primarily via email and social media in German and English languages. As a widely applied method (Wu et al., 2022), online surveys are used due to cost effectiveness, ease of analysis and fewer errors (Saleh and Bista, 2017). Pretests targeting individuals with online shopping experience from both countries and forward/backward translation were imposed, resulting in minor adaptions.
For data collection, the respondents received a survey link with an encouraging message attached and the answers relating to their most recent online purchases. A random sample among consumers in Austria and Moldova yielded 602 usable responses; 397 responses from Austria, and 205 from Moldova. We excluded surveys which failed the attention check (<0.1%) and which were incomplete or significantly below the average the response time (<0.1%). While in Austria the women-men split was balanced, in Moldova more women participated in the survey indicating a bias towards female respondents. Furthermore, we focused on the younger generation with higher educational background due to their strong English skills and social media presence within the Moldovan study. Table 2 shows the sample characteristics by market.
4.2 Measures
For construct measurement, we adopted validated scales from the literature (see Appendix). The measurement items were adapted from Doney and Cannon (1997), Jarvenpaa et al. (1999), McKnight et al. (2002), Wolfinbarger and Gilly (2003). We used five-point Likert scales ranging from 1 = “strongly disagree” to 5 = “strongly agree”.
4.3 Common method bias and measurement invariance assessment
Common method bias (CMB) can appear if exogenous and endogenous variables come from the same data source (Jayachandran et al., 2005). To avoid CMB, various actions were imposed. We randomized the items, used established scales, tested the items for ambiguity, guaranteed anonymity and provided instructions for answering the questionnaire (Podsakoff et al., 2003, 2012). Furthermore, we conducted Harman’s single factor test. Our unrotated exploratory factor analysis carried out in SPSS revealed a value for a single factor of 50.51% of the variance, which is near to the threshold of 50% (Aguirre-Urreta and Hu, 2019). We therefore conclude that there is no CMB. For measurement invariance assessment we followed Henseler et al. (2016) and assessed configural invariance with an inspection of the model setup. For Step 2, we assessed compositional invariance based on permutation-based confidence intervals. Although not all permutation p-values were above the 0.05 cut-off, a low value implies compositional invariance due to the small sample sizes (Henseler et al., 2016). Therefore, we conclude there is partial measurement invariance. To test multicollinearity of variables we also examined the variance inflation factors (VIF). The results show that all values were lower than 5, indicating that collinearity is not an issue (Hair et al., 2019).
4.4 Data analysis
The data were analysed using a variance-based structural equation modelling (SEM) approach (partial least squares (PLS) path modelling) as we predict and test a complex structural model with reflective indicators and due to the sampling distribution. As PLS-SEM is widely used and enables the estimation of research models through the combination of principal components with ordinary least squares regressions, it was a suitable approach for our study. Furthermore, compared to other statistical techniques, PLS path modelling requires a smaller sample size, which is another advantage (Hair et al., 2019; Sarstedt and Cheah, 2019). Our analysis includes the evaluation of the measurement model and the assessment of the structural model (Hair et al., 2019).
5. Findings
5.1 Measurement model assessment
Table 3 shows all latent variables, items and their fit indices. We evaluated the measurement model in terms of item reliability, composite reliability, average variance extracted (AVE) analysis and discriminant validity following Hair et al. (2019). To ensure convergent validity, we kept only items with factor loadings greater than 0.4. All AVE values are at or above 0.5 (Hair et al., 2019), which underlines the confirmation of convergent validity. Next was the assessment of internal consistency measured through composite reliability (CR) and Cronbach’s alpha. The CR of the total sample ranged from 0.85 to 0.97 and all constructs display sufficient Cronbach’s alpha values exceeding 0.7. Next, we employed heterotrait-monotrait (HTMT) ratios for discriminant validity assessment (Henseler et al., 2015). Henseler et al. (2015) recommend a maximum threshold of 0.9 for similar concepts. As five values are above 0.90, we executed bootstrapping to test whether the HTMT values are significantly different from one which shows that no interval includes the value 1.
5.2 Structural model assessment
The structural model assessment includes the coefficients of determination (R2) and paths, and their statistical significance, based on a bootstrapping procedure with 5,000 subsamples. After presenting the results of the total sample (see Table 4), we split the results by country.
The results reveal that perceived company reputation (β = 0.508, p < 0.001), perceived security (β = 0.220, p < 0.001) and PWQ (β = 0.189, p < 0.001) have a positive influence on consumer TOS (R2 = 0.834). Thus, H2, H3 and H4 are supported. H1 could not be supported, as the influence of experience on consumer TOS (β = 0.065) is non-significant. The positive effect of trust on PI became evident (β = 0.824, p < 0.001; R2 = 0.670) in support of H7. Analysing the role of PR displays a negative relationship between trust and PR (β = −0.199, p < 0.001) and also PR and PI (β = −0.054, p < 0.05), supporting H5 and H6.
Finally, we employed PLS Multi-Group Analysis (PLS-MGA), which indicates a significant difference if the p-value is smaller than 0.05 or greater than 0.95 (SmartPLS GmbH, 2022). Thus, a significant difference between Austria and Moldova in terms of the influence of experience (p = 0.0029) and PWQ (p = 0.9963) on TOS is observed. Contrasting the Moldovan sample, in the Austrian subsample, experience has a positive influence on trust. Contrary to the Austrian sample, in Moldova, PWQ has a positive influence on trust. Thus, H8a and H8c are supported. As no other significant differences were detected, H8b and H8d-g could not be supported.
6. Discussion
6.1 Theoretical implications
This study investigates antecedents and consequences of consumer trust from a cross-market e-commerce perspective. The results show differences across markets, thus advancing research on cross-market retailing. This is important as emerging markets require a different strategic approach to advanced markets (Wagner Mainardes et al., 2019). Given the paucity of cross-national studies on TOS (Kardes et al., 2021), this research contributes by identifying specifics regarding advanced vis-a-vis emerging markets.
Consistent with previous research our results suggest that perceived reputation, PWQ and security influence TOS positively (Jadil et al., 2022; Huang and Chang, 2019; Singh, 2019).
Contradicting previous research (Gefen, 2000; Punyatoya, 2019), we could not support the influence of consumers’ experience on TOS. An explanation for this could be that we surveyed people with online experience. Within research, there is a paucity regarding the influence of experience on trust. To et al. (2023) found a positive influence of emotional, service and product experience on consumer trust, but confirmed a negative impact of website experience in the Vietnamese market. While Zimmer et al. (2010) corroborate the trust building effects of website experience, we show additional factors such as perceived reputation and security influencing TOS.
Our results support existing research by confirming the positive influence of trust on PI, but existing research concerning emerging markets, display diverse results (Ventre and Kolbe, 2020). In Kuwait trust influences PI positively (Gibreel et al., 2018) and in Brazil lacking trust impacts online PI negatively (Wagner Mainardes et al., 2019). Adding to previous findings we show that increased trust lowers risk perceptions, which increases PI (Farivar et al., 2017). Contrary to Ventre and Kolbe (2020), we confirm the influence of PR on PI in line with other research (Singh and Srivastava, 2018).
Our study contributes by highlighting the need to challenge marketing concepts from advanced and emerging markets, as we analyse differences in TOS antecedents (Sheth, 2011). In contrast to Moldova, experience has a positive influence on TOS among Austrian consumers as online shopping experience is fairly low in Moldova compared to in advanced markets (Lupusor, 2021).
Higher online trust in advanced markets relates to well-established e-commerce behaviour in such countries (Ventre and Kolbe, 2020; Wagner Mainardes et al., 2019). While website quality is an important influencing factor for Moldovan consumers’ TOS due to early e-commerce adaption, this relationship could not be confirmed for the Austrian sample. Our findings are consistent with existing research and corroborate the need for cross-market investigations (e.g. Chetioui et al., 2021).
6.2 Practical implications
This study provides practical suggestions for online retailers in different markets regarding building and maintaining TOS. Our findings apply for website planning and support e-retailers with building e-commerce strategies for advanced and emerging markets. For e-commerce firms, it is essential to enhance trust in order to increase revenues. To gain consumers` TOS, e-retailers should focus on their reputation through external communication, the improvement of their website’s quality through usability friendliness, the integration of social media and through design attributes such as a positive visual appearance. Moreover, our study shows that TOS can be enhanced through the highlighting of perceived security by ensuring data protection, especially in terms of payment.
Additionally, our study provides practical implications for international marketers creating e-commerce strategies for advanced and emerging markets. In terms of customer experience, managers should take a differentiated view, as the findings vary significantly between markets. Firms should be aware of the positive influence of experience on TOS in advanced markets, while for consumers in emerging markets this factor does not yet matter in online purchase behaviour. Furthermore, while in advanced markets the quality of the website plays a minor role, offering high website quality and supporting services is an important tool for enhancing TOS in emerging markets. This means website developers have to focus on creating a positive user experience, especially in emerging markets by simplifying search methods, improving loading times, integrating user-friendly content and using an attractive responsive design. This study shows that e-commerce retailers should focus on TOS promoting factors like perceived reputation, website quality and security as strengthening trust reduces the PR and increases PI.
6.3 Research limitations and outlook
The empirical setting of this study is limited to Europe. Therefore, future research could include other markets to enhance the generalizability of the results. Concerning our sample, more younger people and in Moldova more women and university graduates participated in the survey which represents a bias. Further, the discriminant validity of our study is limited as five HTMT values are above 0.90. Future research could thus replicate this study by including other markets, bigger sample sizes and also through integration of mobile commerce which is not addressed in our study.
Figures
Figure 1
Conceptual model
Sample characteristics
| Criterion | Austria (N = 397) | Moldova (N = 205) |
|---|---|---|
| Gender (%) | ||
| Male | 46.1 | 28.8 |
| Female | 53.9 | 71.2 |
| Age (%) | ||
| <20 | 2.3 | 17.6 |
| 21–30 | 64.2 | 66.3 |
| 31–40 | 10.8 | 14.1 |
| 41–50 | 13.1 | 1.5 |
| >50 | 9.6 | 0.5 |
| Education (%) | ||
| Primary school | 6.0 | 3.4 |
| Secondary school | 10.6 | 10.7 |
| School leaving examination | 35.5 | 4.4 |
| University | 47.9 | 81.5 |
| Money spent for online shopping (last 12 months) (%) | ||
| Less than € 100 | 10.33 | 30.3 |
| € 101 - € 500 | 46.85 | 46.8 |
| More than € 500 | 37.78 | 22.9 |
| Not applicable | 5.04 | 0.0 |
Source(s): By author
Scale measurement properties
| Constructs | Items | Standardised coefficients | ||
|---|---|---|---|---|
| Total sample | Austria | Moldova | ||
| Experience (pc = 0.854, AVE = 0.676, HTMT = 0.822) | ||||
| Have you made purchases from online stores in the past? | 0.942 | 0.799 | 0.864 | |
| I frequently buy products through online shopping | 0.950 | 0.825 | 0.610 | |
| I frequently buy products from online stores | 0.490 | 0.830 | −0.552 | |
| Perceived reputation (pc = 0.934, AVE = 0.826, HTMT = 0.909) | ||||
| This online store is well known | 0.910 | 0.506 | 0.756 | |
| This online store is known to be concerned about customers | 0.892 | 0.824 | 0.874 | |
| This online store has a reputation for being honest | 0.925 | 0.858 | 0.809 | |
| Perceived website quality (pc = 0.913, AVE = 0.685, HTMT = 0.828) | ||||
| Overall, this website worked very well technically | 0.937 | 0.844 | 0.864 | |
| Visually, this website resembled other sites I think highly of | 0.565 | 0.358 | 0.531 | |
| This website was simple to navigate | 0.944 | 0.849 | 0.882 | |
| On this website, it was easy to find the information I wanted | 0.940 | 0.841 | 0.873 | |
| This website clearly showed how I could contact or communicate with the person in charge | 0.673 | 0.487 | 0.645 | |
| Perceived security (pc = 0.909, AVE = 0.770, HTMT = 0.878) | ||||
| I feel like my privacy is protected at this website | 0.833 | 0.736 | 0.933 | |
| I feel safe in my transactions with this website | 0.916 | 0.888 | 0.922 | |
| The website has adequate security features | 0.882 | 0.533 | 0.913 | |
| Trust in online shopping (pc = 0.947, AVE = 0.748, HTMT = 0.865) | ||||
| The online store keeps promises it has made | 0.907 | 0.713 | 0.837 | |
| I believe the information that this website provides me | 0.895 | 0.794 | 0.831 | |
| I trust the online store keeps consumers’ interests in mind | 0.850 | 0.656 | 0.859 | |
| The online store is trustworthy | 0.899 | 0.794 | 0.833 | |
| The online store has more to lose than to gain by not delivering on its promises | 0.721 | 0.376 | 0.488 | |
| The online store’s behaviour meets my expectations | 0.904 | 0.745 | 0.830 | |
| Risk perception (pc = 0.879, AVE = 0.645, HTMT = 0.803) | ||||
| Entering credit card information over the web is unsafe | 0.790 | 0.866 | 0.654 | |
| I think it is risky to provide one’s credit card information to online stores | 0.793 | 0.893 | 0.791 | |
| I hesitate to enter my credit card information on the web | 0.835 | 0.872 | 0.857 | |
| I would hesitate to enter personal information like my name, address and phone number on the web | 0.794 | 0.638 | 0.842 | |
| Purchase intention (pc = 0.972, AVE = 0.921, HTMT = 0.960) | ||||
| How likely is it that you would return to this online store? | 0.945 | 0.838 | 0.922 | |
| How likely is that you would consider purchasing from this online store in the next three months? | 0.961 | 0.943 | 0.867 | |
| How likely is it that you would consider purchasing from this online store in the next year? | 0.973 | 0.934 | 0.946 | |
Source(s): By author
Standardized path coefficients
| Path | Path coeff | t-values | |
|---|---|---|---|
| Total sample | (Trust: R2 = 0.834, Q2 = 0.595, PI: R2 = 0.670, Q2 = 0.617, RP: R2 = 0.040, Q2 = 0.021) | ||
| Experience → Trust in Online Shopping | 0.065 ns | 1.713 | |
| Perceived Reputation → Trust in Online Shopping | 0.508*** | 11.959 | |
| Perceived Security → Trust in Online Shopping | 0.220*** | 6.284 | |
| Perceived Website Quality → Trust in Online Shopping | 0.189*** | 4.704 | |
| Risk Perception → Purchase Intention | −0.054* | 2.163 | |
| Trust in Online Shopping → Purchase Intention | 0.824*** | 53.411 | |
| Trust in Online Shopping → Risk Perception | −0.199*** | 5.203 | |
| Austria | (Trust: R2 = 0.518, Q2 = 0.233, PI: R2 = 0.286, Q2 = 0.223, RP: R2 = 0.067, Q2 = 0.041) | ||
| Experience → Trust in Online Shopping | 0.236*** | 4.265 | |
| Perceived Reputation → Trust in Online Shopping | 0.406*** | 8.189 | |
| Perceived Security → Trust in Online Shopping | 0.257*** | 5.947 | |
| Perceived Website Quality → Trust in Online Shopping | 0.054 ns | 1.260 | |
| Risk Perception → Purchase Intention | −0.172*** | 3.522 | |
| Trust in Online Shopping → Purchase Intention | 0.464*** | 8.414 | |
| Trust in Online Shopping → Risk Perception | −0.259*** | 5.367 | |
| Moldova | (Trust: R2 = 0.655, Q2 = 0.382, PI: R2 = 0.445, Q2 = 0.340, RP: R2 = 0.130, Q2 = 0.063) | ||
| Experience → Trust in Online Shopping | 0.035 ns | 0.675 | |
| Perceived Reputation → Trust in Online Shopping | 0.357*** | 5.247 | |
| Perceived Security → Trust in Online Shopping | 0.273*** | 4.433 | |
| Perceived Website Quality → Trust in Online Shopping | 0.279*** | 3.906 | |
| Risk Perception → Purchase Intention | −0.131* | 2.002 | |
| Trust in Online Shopping → Purchase Intention | 0.608*** | 8.762 | |
| Trust in Online Shopping → Risk Perception | −0.361*** | 5.367 |
Note(s): Bootstrapping based on = 5,000 subsamples *p < 0.05; **p < 0.01; ***p < 0.001; ns = non-significant
Source: By author
Measures
| Constructs/items | Source |
|---|---|
| Experience | |
| Have you made purchases from online stores in the past? | Doney and Cannon (1997) |
| I frequently buy products through online shopping | Jarvenpaa et al. (1999) |
| I frequently buy products from online stores | |
| Perceived reputation | |
| This online store is well known | Jarvenpaa et al. (1999) |
| This online store is known to be concerned about customers | Doney and Cannon (1997) |
| This online store has a reputation for being honest | Doney and Cannon (1997) |
| Perceived website quality | |
| Overall, this website worked very well technically | McKnight et al. (2002) |
| Visually, this website resembled other sites I think highly of | |
| This website was simple to navigate | |
| On this website, it was easy to find the information I wanted | |
| This website clearly showed how I could contact or communicate with the person in charge | |
| Perceived security | |
| I feel like my privacy is protected at this website | Wolfinbarger and Gilly (2003) |
| I feel safe in my transactions with this website | |
| The website has adequate security features | |
| Trust in online shopping | |
| The online store keeps promises it has made | Doney and Cannon (1997) |
| I believe the information that this website provides me | |
| I trust the online store keeps consumers’ interests in mind | |
| The online store is trustworthy | |
| The online store has more to lose than to gain by not delivering on its promises | Jarvenpaa et al. (1999) |
| The online store’s behaviour meets my expectations | |
| Risk perception | |
| Entering credit card information over the web is unsafe | McKnight et al. (2002) |
| I think it is risky to provide one’s credit card information to online stores | |
| I hesitate to enter my credit card information on the web | |
| I would hesitate to enter personal information like my name, address and phone number on the web | |
| Purchase intention | |
| How likely is it that you would return to this online store? | Jarvenpaa et al. (1999) |
| How likely is that you would consider purchasing from this online store in the next three months? | |
| How likely is it that you would consider purchasing from this online store in the next year? | |
Source(s): By author
Literature review
| Authors | Journal | Context | Market comparison | Setting | Theories | Correspondence with trust | Corresponding research variables |
|---|---|---|---|---|---|---|---|
| Abdulrahman Al Moosa et al. (2022) | IJRDM | NA | no | service | trust theory/theory of affordances | antecedents | multiple |
| Alves and Wagner Mainardes (2017) | IJRDM | offline | no | service | social exchange theory/value co creation | consequence | single |
| Alzaidi and Agag (2022) | JRCS | online | no | service | technology acceptance model | antecedents + consequence | multiple antecedents single consequence |
| Bashir et al. (2018) | JRCS | online | no | retail | perceived financial risk/online buying intention | consequence | single |
| Grosso et al. (2020) | JR | offline + online | no | retail | privacy as contextual integrity | consequence | single |
| Hallikainen and Laukkanen (2018) | IJIM | online | yes | retail | Trust, Hofstede’s culture theory | antecedents + consequences | multiple |
| Irshad et al. (2020) | IJRDM | online | no | retail | social media marketing | antecedents + consequence | multiple antecedents single consequence |
| Jadil et al. (2022) | IJIMDI | online | no | retail | trust in e-commerce | antecedents + consequence | multiple |
| Jiang et al. (2019) | JRCS | online | no | service | social commerce/social presence theory | antecedents + consequence | multiple antecedents single consequence |
| Kalaignanam et al. (2018) | JR | online | no | service | personalization effectiveness | consequences | multiple |
| Konuk (2022) | IJRDM | offline | no | retail | trust transfer theory | consequence | single |
| Mainardes and Cardoso (2019) | IRRDCR | offline | no | retail | customer experience quality | antecedents + consequence | multiple antecedents single consequence |
| Marriott and Williams (2018) | JRCS | online | no | retail (mobile shopping) | risk and trust | antecedents | multiple |
| Rahman and Hossain (2022) | JR | offline | no | retail (omnichannel) | perceived omnichannel customer experience | antecedent | single |
| Raposo Junior et al. (2022) | IRRDCR | online | no | service | product review blog marketing | antecedents + consequences | multiple |
| Reynolds-McIlnay and Morrin (2019) | JR | offline + online | no | retail | retail transaction auditory confirmation | antecedent + consequences | single antecedent multiple consequences |
| Sharma and Klein (2020) | JRCS | online | no | retail (cooperative commerce) | consumer involvement in online group buying websites | antecedent + consequence | single |
| Ventre and Kolbe (2020) | JICM | online | no | retail | risk and trust | antecedent + consequence | single |
| Yahia et al. (2018) | JRCS | online | no | service | trust in online contexts | antecedents + consequence | multiple antecedents single consequence |
| Yeon et al. (2019) | JRCS | online | no | service | trust in commerce | consequence | single |
| Zhani et al. (2022) | JRCS | NA | no | service | unified theory of acceptance and use of technology | NA | NA |
Source(s): By author
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Acknowledgements
We thank Elisabeth Brugger and Ana Slanina for assisting in the data collection.
Corresponding author
About the authors
Michaela Quintus is a doctoral student at the Institute for Retailing, Sales and Marketing (JKU Business School) at the Johannes Kepler University Linz, Austria. Her research focuses on international marketing, corporate social responsibility and e-commerce. Besides her studies, she works as a teacher and consultant in the field of economics and e-commerce.
Kathrin Mayr is a Lecturer at the Institute for Retailing, Sales and Marketing (JKU Business School) at the Johannes Kepler University Linz, Austria. Her research focuses on consumer research, sales psychology and sales management as well as deviating customer behaviour.
Katharina Maria Hofer is an Associate Professor at the Institute for Retailing, Sales and Marketing (JKU Business School) and Head of the Centre for International Marketing at Johannes Kepler University Linz, Austria. Her research focuses on international marketing, services, corporate social responsibility and pricing.
Yen Ting Chiu is an Associate Professor at the Department of Marketing and Retail Management at National Kaohsiung University of Science and Technology, Taiwan. Her research focuses on service marketing, retail innovations, and sustainability marketing.