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Business, Management and Economics » "E-Business - State of the Art of ICT Based Challenges and Solutions", book edited by Dragan Perakovic, ISBN 978-953-51-2884-7, Print ISBN 978-953-51-2883-0, Published: January 18, 2017 under CC BY 3.0 license. © The Author(s).

Chapter 5

Customers’ Online Interaction Experiences with Fashion Brands: E-Information and E-Buying

By Sandra Maria Correia Loureiro and Marlene Amorim
DOI: 10.5772/66619

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Customers’ Online Interaction Experiences with Fashion Brands: E-Information and E-Buying

Sandra Maria Correia Loureiro1 and Marlene Amorim2
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Online platforms (such as websites, blogs, social networks, crowdsourcing) enable consumers to interact with companies and brands in new ways. This chapter is the first attempt to go further and analyse how perceived fashion website quality, social influence and recommendation, credibility, and experience influence fashion consumer behaviour, considering performance expectancy as the core element of online trust, satisfaction and word-of-mouth. The proposed model is tested in the context of the fashion industry. Data comprises a sample of generation Y users of fashion websites to get information and buy clothes. In order to collect data, convenience mall-intercept sampling (Lisbon city centre area) served to draw a broad cross-section of consumers. Researchers used tablets to be used by consumers to answer the online survey. The final sample consisted of 312 participants. The instruments employed were adapted from previous studies and pilot-tested with a group of master’s students to verify the clarity of meaning and comprehension. Findings reveal the stronger influence of perceived quality and experience on the performance expectancy. Performance expectancy, in turn, exercises a positive effect on satisfaction and word-of-mouth.

Keywords: perceived fashion website quality, social influence and recommendation, experience, sources of credibility, performance expectancy, customer satisfaction, trust, word-of-mouth

1. Introduction

Firms are increasingly investing in customer-interacting online technologies (such as websites, blogs, social networks, crowdsourcing) in an effort to increase the connection with consumers. They are improving the website design, interaction experience (e.g. Refs. [13]) and credibility (e.g. Refs. [46]) to enhance consumer behaviour. Actually, [6] stress exponential growth of the Internet penetration in Western Europe and the importance of clothing and sporting goods act as one of the most common online purchases.

Previous studies examine factors that could help consumers perform certain online activities (performance expectancy), such as social influence (e.g. Ref. [7]), past experience (e.g. Ref. [2]) or content quality and website design (e.g. Refs. [8, 9]). Other studies explored the online shopping behaviour and consequences of higher performance expectancy (e.g. Refs. [1012]).

Online trust has also been identified as a critical element of consumer intention in the online context (e.g. Refs. [1315]). Yet, as far as we know, this is the first attempt to go further and analyse how perceived fashion website quality, social influence and recommendation, credibility and experience influence fashion consumer behaviour, considering performance expectancy as the core element of online trust, satisfaction and word-of-mouth.

Following this introduction, this chapter is composed of a theoretical background and hypotheses development, data collection and analysis, as well as conclusions with a discussion, the theoretical and managerial implications, the limitations of the study and the suggestions for further research.

2. Theoretical background and hypotheses development

The current chapter is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) [16]. The UTAUT comprises four main constructs that influence the behavioural intention, such as performance expectancy, facilitation conditions, social influence and effort expectancy. Based on Refs. [16, 17], performance expectancy (PE) means that using online technology helps consumers perform certain activities (e.g. get information and buy process). Facilitation conditions (FC) reflect a consumer’s perception of his/her control over the behaviour [18]. Social influence (SI) is the consumer’s belief in the influence of others that think that he/she should use an online platform, such as blogs or websites [16]. Effort expectancy (EE) represents the degree of ease associated with the use of online platforms [16]. These constructs have exerted effects with different strengths on behavioural intentions. For instance, Alawadhi and Morris [19] found that performance expectancy, effort expectancy and peer influence determine students’ behavioural intentions. Other study reveals that performance expectancy and effort expectation are high predictors of behavioural intention, but social influence prediction power seems to be low in the case of medical staff context [20, 21], as the study considers facilitating condition to be significant in predicting intentions.

In the case of internet banking, Al-Qeisi et al. [22] show that the direct effect of effort expectancy on internet banking usage is non-significant, when performance expectancy is included as an intervening variable, highlighting the importance of performance expectancy above effort expectancy. In the current study, the antecedents of performance expectancy into the context of the online fashion industry and participants in the study having experience in using fashion websites are explored. Hence, effort expectancy is not considered.

2.1. Drivers of performance expectancy

Website elements or web atmospherics or even intrinsic cues are known to influence online behaviour and satisfaction (e.g. Ref. [9, 23, 24]). Here, we follow the definition of [8] for perceived website quality, meaning the users’ evaluation on a website’s features that meet their needs and reflect the overall excellence of the website. We also consider three components of perceived quality of the fashion websites: website content quality, technical quality and information quality. The content quality comprises the content usefulness, completeness, clarity, currency, conciseness and accuracy. Technical quality refers to security, ease of navigation, search facilities, site availability, valid links, customization, speed of page loading, interactivity and ease of access. Finally, information quality reflects relevancy, sufficiency and currency of the information [25, 26]. Thus, website quality is connected to the meaning of facilitating conditions and previous studies stress the relationship between perceived website quality and performance expectations (e.g. Refs. [27, 28]). If fashion customers believe in the quality of the fashion websites, they will also raise their perception of website usefulness and performance. Thus (see Figure 1):


Figure 1.

Proposed model.

H1: Perceived fashion website quality is positively related to performance expectancy.

The social context (peers or family), the way others recommend or not to use an online platform or to buy an online item have been regarded as an important factor to influence the behaviour (e.g. Refs. [17, 29]). If a fashion consumer is enrolled and motivated by his/her peers, family and friends to use a fashion online platform and buy items from there, then the fashion consumer will tend to perceive the technology as more useful (higher performance expectancy), resulting in stronger usage intentions [16, 29]. Therefore:

H2: Social influence and recommendation by other fashion consumers are positively related to performance expectancy.

Past experience using online platforms to buy clothes and accessories contribute favourably to behavioural intentions [29]. Actually, expertise and proficiency influence the use of technology [3, 30]. Past experience is also related to a better performance expectancy (e.g. [31, 32]). Thus:

H3: Experience with online fashion websites is positively related to performance expectancy.

Credibility is regarded as “the believability of the product position information contained in a brand, which depends on the willingness and ability of firms to deliver what they promise” ([33], p. 34). Consumers tend to have more difficulty in evaluating the credibility of an online context, based on the reviews/comments, because they are anonymous sources who have no prior relationship with the receiver [4, 34]. Past studies have explored the influence of source credibility on perceived information usefulness [25, 35, 36]. Therefore, we expect that:

H4: Sources of credibility is positively related to performance expectancy.

2.2. Outcomes of performance expectancy

As previously mentioned, performance expectancy represents how the use of online platforms helps consumers perform certain activities (such as the search for fashion information and buy clothes and accessories). Performance expectancy leads to stronger usage intentions because consumers’ satisfaction with a service or a technology depends on their expectation of the performance of the service or the technology [37]. If fashion consumers are able to use the technology implemented in websites to perform what they intend to, then they will tend to feel satisfied and confident and it will be easier to communicate the positive experience to others. Therefore:

H5: Performance expectancy is positively related to satisfaction.

H6: Performance expectancy is positively related to trust.

H7: Performance expectancy is positively related to word-of-mouth behaviour.

Trust may act as a mediator between satisfaction and recommendation of a lodging [38]. In supermarket context, it is also possible to find the mediator effect of trust between customer satisfaction and behavioural intentions [39]. So, it is expected that online fashion consumers satisfied with the fashion website platforms will say positive things and recommend the websites to others and such will be reinforced by the confidence they have with the brand and the website platforms. Thus:

H8: Online trust mediates the relationships between satisfaction and word-of-mouth behaviour.

3. Method

3.1. Data collection

In order to collect data, convenience mall-intercept sampling (Lisbon city centre area) served to draw a broad cross-section of consumers. So, the sample was a portion of the general population who have experiences of purchasing fashion items from the online website stores (in approaching consumers, we confirmed if such criterion is confirmed). Researchers used tablets to be used by consumers to answer the online survey. The final sample consisted of 312 participants (see Table 1). The factorial analysis helped to analyse the dimensionality of the constructs, followed by SmartPLS2.0 to test the hypotheses.

GenderAge (years)Employment status
Female: 80.4%≤20: 9.9%Employed: 37.2%
Male: 19.6%21–30: 69.9%Self-employed: 8.7%
31–40: 9.9%Student: 46.5%
41–50: 5.8%Unemployed: 3.8%
51–60: 3.5%Other: 3.8%
>60: 0.9%

Table 1.

Sample profile.

3.2. Measures

The questionnaire for the present study was adopted from the previous studies and validated with a pilot test. In the pilot study, 22 graduate students, who have experiences of online fashion shopping in the last 3 months, were asked to verify the content validity and psychometric properties of the measures used in the present study. Based on the comments made by participants in the pilot study, several questionnaire items were revised to include more precise meanings. All responses to questions related to perceived website quality, receiver past experience, social influence and recommendation, credibility, performance expectancy, trust and satisfaction were recorded using a five-point degree scale ranging from 1 (strongly disagree) to 5 (strongly agree).Only Internet experience employed a five-point degree scale ranging from 1 (very bad) to 5 (very good) and word-of-mouth behaviour used a five-point degree scale ranging from 1 (never) to 5 (always). Regarding the website quality, the items employed are based on Yang et al. [40], but we excluded the items measuring the adequacy of information and usefulness of content because past research has demonstrated that the website quality and information quality are two different dimensions (see Table 2). In the first part of the questionnaire, we asked participants: Which is the fashion website (online store) that you frequently browse to follow fashion trends and ideas and eventually buy? Then, we asked to think about that fashion website in order to answer the questions (some of the most mentioned website brands are: Zalando, Zara, H&M, ASOS, Amazon).

Performance expectancy (PE)I find fashion website useful.
Using fashion website enables me to get fashion information more quickly.
Using fashion website increases the effective use of my time in handling my fashion information tasks and purchase.
Using fashion website increases the quality of my fashion information at minimal efforts.
Website quality (WQ)This website…
Is easy to use.
Has well-organized hyperlinks.
Provided opportunities to interact with other customers.
Has high speed of page loading.
Is easily accessible from different media.
Guarantees users’ privacy.
Information quality (IQ)The information in online reviews is…
Relevant to my needs.
Complete for my needs.
[26, 36, 41]
Technical quality (TQ)The website…
Looks secure for carrying out transactions.
Looks easy to navigate.
Has adequate search facilities.
Has valid links (hyperlinks).
Has many interactive features (e.g. online application for fashion services).
Pages load quickly.
Source credibility (SC)The reviewers on this fashion website are…
Are experienced.
Are trustworthy.
Are reliable.
Internet experience (IE)How would you describe your:
Internet knowledge (1-very bad to 5-very good).
General computer knowledge (1-very bad to 5-very good).
Receiver experience (RE)Prior to your participation in this study, how would you rate your level of experience in terms …
Of using (name brand)?
Of browsing (name brand)?
Of online recommendations?
Social influence (SI)People who are important to me think that I should use fashion websites.
People who influence my behaviour think I should use fashion websites.
Recommendation adoption (RA)Online reviews and comments made it easier for me to make purchase decision (e.g. purchase or not purchase).
Online reviews have motivated me to make a purchase decision (purchase or not purchase).
The last time I read online fashion reviews I adopted consumers’ recommendations.
Information from review contributed to my knowledge of fashion product and trends.
Customer satisfaction (CS)I am satisfied with the information I have received from this fashion reviews website.
I am satisfied with my previous experiences with this website.
Trust (T)I think that the information offered by this fashion website is sincere and honest.
I think that the advice and recommendations given by this customer reviews are trustworthy.
I trust the online customer reviews on this website.
I trust this fashion website.
[14, 46]
Word-of-mouth behaviour (WB)Think about the website you chose. How often did you mention this fashion website to others? (1-never to 5-always)
I mentioned to others that I seek fashion information from this website.
I made sure that others know that I rely on this website to purchase fashion products.
I spoke positively about this fashion website to others.
I recommended this website to close friends.

Table 2.

The construct, items and sources.

4. Results

4.1. Measurement results

We employ the PLS approach to treat data, using the software SmartPLS2.0. The measurement model or the adequacy of the measures is assessed by evaluating the reliability of the individual measures, the convergent validity and the discriminant validity of the constructs.

Regarding the adequacy of the measures at the first-order construct level, item reliability is assessed by examining the loadings of the measures on their corresponding construct. Item loadings of scales measuring reflective constructs should be ≥0.707, which indicates that over 50% of the variance in the observed variable is explained by the constructs [48]. In this study, the item loading of each item exceeds the value of 0.707 (see Table 3).

Latent variablesMean LVItem loading (reflective measure)Cronbach’s alphaComposite reliabilityAVE
Website quality (WQ)4.0(0.745–0.826)0.8380.8850.607
Information quality (IQ)3.1(0.852–0.913)0.9450.9560.784
Technical quality (TQ)4.0(0.721–0.808)0.8550.8920.580
Social influence (SI)2.7(0.945–0.959)0.8970.9510.906
Recommendation adoption (RA)2.8(0.799–0.881)0.9180.9420.804
Internet experience (IE)4.0(0.955–0.857)0.9050.9550.913
Receiver experience (RE)3.4(0.765–0.928)0.8390.9050.761
Source credibility (SC)3.0(0.866–0.956)0.9350.9540.838
Performance expectancy (PE)3.9(0.841–0.895)0.8850.9210.744
Customer satisfaction (CS)4.1(0.841–0.895)0.8870.9460.898
Trust (T)3.7(0.767–0.863)0.8330.8860.660
Word-of-mouth behaviour (WB)3.4(0.790–0.911)0.8710.9130.724
Second-order formative constructsFirst-order constructs/dimensionsWeightt-ValueVIF
Perceived fashion website qualityWebsite quality0.378***9.6371.769
Information quality0.443***6.2701.255
Technical quality0.449***9.1091.539
Social influence and recommendationSocial influence0.319***3.9341.020
Recommendation adoption0.877***14.9581.020
ExperienceInternet experience0.453***23.4121.595
Receiver experience0.658***23.6651.595

Table 3.

Measurement results.


***p < 0.001.

All Cronbach’s alpha values are >0.7 and all composite reliability values are >0.8 in Table 3. Therefore, all constructs are reliable since the composite reliability values exceed >0.7. The measures demonstrate convergent validity as the average variance of manifest variables extracted (AVE) by constructs is >0.5, indicating that most of the variance of each indicator is explained by its own construct.

At the second-order construct level, we have the parameter estimates of indicator weights, significance of weight (t-value) and multicollinearity of indicators. Weight measures the contribution of each formative indicator to the variance of the latent variable [49]. A significance level of 0.001 suggests that an indicator is relevant to the construction of the formative index (perceived website quality, social recommendation and experience) and thus demonstrates a sufficient level of validity. The recommended indicator weight is >0.2 [50]. Table 3 shows that all indicators have a positive beta weight >0.2. The degree of multicollinearity among the formative indicators should be assessed by the variance inflation factor (VIF) [51]. The VIF indicates how much an indicator’s variance is explained by other indicators of the same construct. The commonly acceptable threshold for VIF is <3.33 [52]. Table 3 shows VIF values are <3.33 and so the results did not seem to pose a multicollinearity problem.

Regarding discriminant validity, the square root of AVE should be greater than the correlation between the construct and other constructs in the model [53]. Table 4 shows that this criterion has been met. The last part of Table 4 shows that the correlations between each first-order construct and the second-order construct are above 0.6 [54].

Perceived website qualityWQIQTQ
Social recommendationSIRA

Table 4.

Discriminant validity.

4.2. Structural results

In this study, a non-parametric approach, known as Bootstrap (500 re-sampling), was used to estimate the precision of the PLS estimates and support the hypotheses. All path coefficients are found to be significant at 0.001, 0.01 or 0.05 levels, except hypotheses H2, H4, H6 (see Table 5). In the case of hypotheses H8, in addition to the bootstrapping approach, the Sobel test [55, 56] was used for the mediating effect.

PathStandardized coefficient direct effect (t-value)Standardized coefficient total effect (t-value)Test result
Perceived fashion website quality → performance expectancy0.415** (3.059)0.415** (3.059)H1 supported
Social influence and recommendation → performance expectancy0.089 ns (0.816)0.089 ns (0.816)H2 not supported
Experience → performance expectancy0.393*** (4.157)0.393*** (4.157)H3 supported
Source credibility → performance expectancy−0.144 ns (1.209)−0.144 ns (1.209)H4 not supported
Performance expectancy → customer satisfaction0.560*** (6.141)0.560*** (6.141)H5 supported
Performance expectancy → trust0.151 ns (1.878)0.521*** (6.427)H6 not supported (only total effect)
Performance expectancy → W-o-m0.257* (2.127)0.500*** (5.495)H7 supported
Mediation effects
PathStandardized coefficient direct effect (t-value)Standardized coefficient total effect (t-value)Result
Customer satisfaction → trust0.660*** (10.299)0.660*** (10.299)Supported
Trust → W-o-m0.263* (1.936)0.263* (1.936)Supported
Customer satisfaction → W-o-m0.189ns (1.376)0.362*** (3.529)Not supported (only total effect)
Path mediationStandardized coefficientz-test (p-value)Result
Customer satisfaction → trust → W-o-m0.1741.899 (0.06)H8: supported at p < 0.10
R20.453Q2 performance expectancy0.334
R2 trust0.570Q2 trust0.351
R2 customer satisfaction0.314Q2 customer satisfaction0.278
R2 W-o-m0.370Q2 W-o-m0.260
GoF (overall goodness of fit)0.75

Table 5.

Structural results.

Notes: ns: not significant. Mediation was tested via a z-test, which was calculated using Sobel’s (1982)approach [56].

*p < 0.05.

**p < 0.01.

***p < 0.001.

As models yielding significant bootstrap statistics can still be invalid in a predictive sense [57], measures of predictive validity (such as R2 and Q2) for focal endogenous constructs should be employed. All values of Q2 (chi-squared of the Stone–Geisser criterion) are positive, so the relations in the model have predictive relevance [58]. The model also demonstrated a good level of predictive power (R2), as the modelled constructs explained 45.3% of the variance in performance expectancy and 57.0% in trust. The good value of GoF (0.75) and the good level of predictive power (R2) reveal a good overall fit of the structural model (see Table 5). As proposed by Wetzels et al. [48], a GoF greater than 0.35 in the social science field indicates a very good fit.

5. Conclusions and implications

This study examines how perceived fashion website quality, social influence, and recommendation, credibility and experience influence fashion consumer behaviour, considering performance expectancy as the core element of online trust, satisfaction and word-of-mouth.

In the current study, perceived fashion website quality is represented by three dimensions, having a multi-dimensional second-order structure as suggested by Dickinger and Stangl [9]. The information and technical quality dimensions emerge as the most relevant in shaping the overall perceived fashion website quality.

Social influence and recommendation comprises both social influence construct and recommendation adaptation, where the last one has the highest weight in shaping the second-order constructs. Yet, the influence of social influence and recommendation on performance expectancy was non-significant. These results may be explained based on the profile of participants. Participants voluntarily use the fashion websites (they are users) and they rely more on their beliefs and perceptions about the websites than on others’ recommendations. This is aligned with other studies, such as Refs. [6, 16].

Regarding experience, this was also measured as a second-order formative construct aggregating Internet experience and receiver experience. Receiver experience contributes more for shaping experience than Internet experience, expressing the importance of using a particular website, who have experience in browsing or reading online recommendations. In the current study, experience with fashion websites reveals a significant relationship with performance expectancy. Participants with experience in fashion websites tend to perceive them as easy to use.

Like social influence and recommendation, source of credibility does not exercise a significant effect on performance expectancy. Experienced participants are not so connected to the credibility of the information provided by the anonymous reviews. In this situation, website participants are more independent and self-confident, the reason why the source of credibility may not have a significant correlation with performance expectancy.

In what concern to outcomes of performance expectancy, it is interesting to find a significant effect on satisfaction, as expected based on Choi et al. [37], followed by the effect on word-of-mouth. Although we did not find a significant direct relationship between performance expectancy and online trust, the role of trust as a mediator between satisfaction and worth-of-mouth should not be neglected. Satisfaction with the information received from the fashion reviews and with the previous experience seems to enhance the confidence on online information, customer reviews, advice and recommendations given by fashion websites and this, in turn, contributes to advocate in favour of the fashion website to others.

It is noticeable that performance expectancy has an indirect effect on trust, which is reinforced by customer satisfaction (β = 0.521; p < 0.001). When fashion customers are satisfied with the information and previous experiences using the fashion website, these features help to generate trust on fashion websites along with the usefulness and the minimal effort that fashion customers recognize using the website.

Finally, customer satisfaction is not a good predictor of word-of-mouth; online fashion customers need to be confident in the fashion website to have the disposition to engage others to use and purchase in a certain fashion website (β = 0.362; p < 0.001). These findings express, once more, the important role of online trust in order to have fashion customers advocating in favour of the brand and the website.

5.1. Theoretical implications

The current study considers for the first time three second-order formative constructs as drivers of performance expectancy: perceived fashion website quality, experience and social influence and recommendation.

The study also extends the UTAUT model by incorporating the second-order construct and re-structured the inter-relationships among variables. Findings claim that perceived fashion website quality and experience are the important drivers to performance expectancy and those who consider fashion websites ease of use tend to be also satisfied with those online fashion platforms and recommend it to others. Trust in the information offered and in the purchase process contributes to reinforce the recommendation to others.

5.2. Limitations and suggestions for further research

Although the study was conducted with caution, several limitations should be pointed out, which may also be suggestions for further research. First, the questionnaire could be spread in other countries and we may compare the results in order to extend the findings properly. Second, the model should be analysed considering different fashion brands, categorized by fashion product categories. Third, it will be interesting to undertake a longitudinal study in order to be able to prove causality.

5.3. Practical implications

Managers and designers of fashion websites should always be committed with the quality, information and technology of fashion websites. It will be important to keep improving the information provided, up-to-date, the layout and the hyperlinks on the website, as well as, the process of purchase (easy but safe). Managers should also promote the experience of online fashion brands, creating positive surprises and motivating users to be devoted to fashion news. Managers should be able to attract non-users, exposing the potentialities and performance of the fashion websites, through the speed, time and efficiency of the website.

The scales employed to evaluate the three dimensions of website quality, experience, credibility and social influence and recommendation may be used by website managers to prepare surveys to be evaluated by fashion website users.


1 - Moss, G., Gunn, R., & Heller, J. (2006). Some men like it black, some women like it pink: Consumer implications of differences in male and female website design. Journal of Consumer Behaviour, 5(4), 328–341.
2 - Andrews, L., & Bianchi, C. (2013). Consumer internet purchasing behavior in Chile. Journal of Business Research, 66(10), 1791–1799.
3 - Toufaily, E., Ricard, L., & Perrien, J. (2013). Customer loyalty to a commercial website. Descriptive meta-analysis of the empirical literature and proposal of an integrative model. Journal of Business Research, 66(9), 1436–1447.
4 - Park, D.-H., & Lee, J. (2008). E-WOM overload and its effect on consumer behavioural intention depending on consumer involvement. Electronic Commerce Research and Applications, 7(4), 386–398.
5 - Sparks, B.A., & Browning, V. (2011). The impact of online reviews on hotel booking intentions and perception of trust. Tourism Management, 32(6), 1310–1323.
6 - Loureiro, S.M.C., & Breazeale, M. (2016). Pressing the buy button: Generation Y’s online clothing shopping orientation and its impact on purchase. Clothing and Textiles Research Journal, Online 21 February 2016 doi: 10.1177/0887302X16633530
7 - Kim, S.H., & Park, H.J. (2011). Effects of social influence on consumers’ voluntary adoption of innovations prompted by others. Journal of Business Research, 64(11), 1190–1194.
8 - Aladwani, A., & Palvia, P. (2002). Developing and validating an instrument for measuring user-perceived web quality. Information & Management, 39(6), 467–476.
9 - Dickinger, A., & Stangl, B. (2013). Website performance and behavioral consequences: A formative measurement approach. Journal of Business Research, 66(6), 771–777.
10 - McKechnie, S., Winklhofer, H., & Ennew, C. (2006). Applying the technology acceptance model to online retailing of financial services. International Journal of Retail and Distribution Management, 34(4/5), 388–410.
11 - Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research, 62(5), 565–571
12 - Smith, R., Deitz, G., Royne, M.B., Hansen, J.D., Grünhagen, M., & Witte, C. (2013). Cross cultural examination of online shopping behavior: A comparison of Norway, Germany, and the United States. Journal of Business Research, 66(3), 328–335.
13 - Flavián, C., Guinalíu, M., & Gurrea, R. (2006). The role played by perceived usability, satisfaction and consumer trust on website loyalty. Information & Management, 43(1), 1–14.
14 - Jarvenpaa, S.L., Tractinsky, N., & Vitale, M. (2000). Consumer trust in an internet store. Information Technology and Management, 1(1/2), 45–71.
15 - Yoon, S.-J. (2002). The antecedents and consequences of trust in online-purchase decisions. Journal of Interactive Marketing, 16(2), 47–63.
16 - Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
17 - Brown, S.A., & Venkatesh, V. (2005). Model of adoption of technology in the household: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 29(4), 399–426.
18 - Venkatesh, V., Brown, S., Maruping, L., & Bala, H. (2008). Predicting different conceptualizations of systemuse: The competing roles of behavioral intention, facilitating conditions, and behavioral expectations. MIS Quarterly, 32(3), 483–502.
19 - Alawadhi, A., & Morris, A. (2008). The use of the UTAUT model in the adoption of e-government services in Kuwait. Proceedings of the 41st Hawaii International Conference on System Sciences, Hawaii.
20 - Biemans, M., Swaak, J., Hettinga, M., & Schuurman, J.G. (2005). Involvement matters: The proper involvement of users and behavioural theories in the design of a medical teleconferencing application. Proceedings of GROUP’05, November 6–9, 2005, Sanibel Island, Florida, USA.
21 - Foon, Y.S., & Fah, B.C.Y. (2011). Internet banking adoption in Kuala Lumpur: An application of UTAUT model. International Journal of Business and Management, 6(4), 161–167.
22 - Al-Qeisi, K., Dennis, C., Alamanos, E., & Jayawardhena, C. (2014). Website design quality and usage behavior: Unified theory of acceptance and use of technology. Journal of Business Research, 67(11), 2282–2290
23 - Dennis, C., Merrilees, B., Jayawardhena, C., & Wright, L. (2009). E-consumer behavior. European Journal of Marketing, 43(9/10), 1121–1139.
24 - Loureiro, S.M.C. (2015). The role of website quality on PAD, attitude and intentions to visit and recommend island destination. International Journal of Tourism Research, 17(6), 545–554.
25 - Cheung, C.M.K., Lee, M.K.O., & Rabjhon, N. (2008). The impact of electronic word-of-mouth: The adoption of online opinions in online customer communities. Internet Research, 18(3), 229–247.
26 - Park, D.-H., Lee, J., & Han, I. (2007). The effect of online consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125–148.
27 - Lee, Y.K., Park, J.H., Chung, N., & Blakeney, A. (2012). A unified perspective on the factors influencing usage intention toward mobile financial services. Journal of Business Research, 65(11), 1590–1599.
28 - Alsajjan, B., & Dennis, C. (2010). Internet banking acceptance model: A cross-market examination. Journal of Business Research, 63(9), 957–963.
29 - Loureiro, S.M.C., & de Araújo, C.M.B. (2014). Luxury values and experience as drivers for consumers to recommend and pay more. Journal of Retailing and Consumer Services, 21(3) 394–400.
30 - Lassar, W., Manolis, C., & Lassar, S. (2005). The relationship between consumer innovativeness, personal characteristics, and online banking adoption. International Journal of Bank Marketing, 23(2), 176–199.
31 - Dishaw, M.T., Brent, B., & Strong, D.M. (2002). Extending the task-technology fit model with self-efficacy constructs. Proceedings of the 8th AMCIS — Americas Conference on Information Systems (pp. 1021–1027). Illinois: Association for Information Systems.
32 - Johnson, R., & Marakas, G. (2000). Research report: The role of behavioral modeling in computer skills acquisition: Toward refinement of the model. Information Systems Research, 11(4), 402–417.
33 - Erdem, T., Swait, J., & Valenzuela, A. (2006). Brands as signals: Across-country validation study. Journal of Marketing, 70(1), 34–49.
34 - Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407–1424.
35 - Willemsen, L.M., Neijens, P.C., Bronner, F., & De Ridder, J.A. (2011). Highly recommended! The content characteristics and perceived usefulness of online consumer reviews. Journal of Computer-Mediated Communication, 17(1), 19–38.
36 - Filieri, R., & McLeay, F. (2014). E-WOM and accommodation: An analysis of the factors that influence travelers’ adoption of information from online reviews. Journal of Travel Research, 53(1), 44–57
37 - Choi, H., Kim, Y., & Kim, J. (2011). Driving factors of post adoption behavior in mobile data services. Journal of Business Research, 64(11), 1212–1217.
38 - Loureiro, S.M.C., & Gonzales, F.J.M (2008). The importance of quality, satisfaction, trust, and image in relation to rural tourist loyalty. Journal of Travel and Tourism Marketing, 25(2), 117–136.
39 - Loureiro, S.M.C., Miranda, F.J., & Breazeale, M. (2014). Who needs delight? The greater impact of value, trust and satisfaction in utilitarian, frequent-use retail. Journal of Service Management, 25(1), 101–124.
40 - Yang, Z., Cai, S., Zhou, Z., & Zhou, N. (2005). Development and validation of an instrument to measure user perceived service quality of information presenting web portals. Information & Management, 42(4), 575–589.
41 - Cheung, M.Y., Luo, C., Sia, C.L., & Chen, H. (2009). Credibility of electronic word-of-mouth: Informational and normative determinants of on-line consumer recommendations. International Journal of Electronic Commerce, 13(4), 9–38.
42 - Aladwani, A. (2006). An empirical test of the link between web site quality and forward enterprise integration with web customers. Business Process Management Journal, 12(2), 178–190.
43 - Senecal, S., & Nantel, J. (2004). The influence of online product recommendations on consumers’ online choices. Journal of Retailing, 80(2), 159–169.
44 - Smith, D., Menon, S., & Sivakumar, K. (2005). Online peer and editorial recommendations, trust and choice in virtual markets. Journal of Interactive Marketing, 19(3), 15–37.
45 - Pavlou, P.A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134.
46 - Kim, D.J., Ferrin, D.L., & Rao, H.R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544–564.
47 - Brown, T.J., Barry, T.E., Dacin, P.A., & Gunst, R.F. (2005). Spreading the word: Investigating antecedents of consumers’ positive word-of-mouth intentions and behaviors in a retailing context. Journal of the Academy of Marketing Science, 33(2), 123–138.
48 - Wetzels, M., Odekerken-Schröder, G., & van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33(1), 177–195.
49 - Robert, N., & Thatcher, J. (2009). Conceptualizing and testing formative constructs: Tutorial and annotated example. Database for Advances in Information Systems, 40(3), 9–39.
50 - Chin, W.W. (1998). The partial least squares approach to structural equation modeling. In G.A. Marcoulides (Ed.), Modern methods for business (pp. 295–336). Mahwah, NJ: Lawrence Erlbaum Associates Publisher.
51 - Fornell, C., & Bookstein, F.L. (1982). A comparative analysis of two structural equation models: LISREL and PLS applied to market data. In C. Fornell (Ed.), A second generation of multivariate analysis (pp. 289–324). New York: Praeger.
52 - Diamantopoulos, A., & Siguaw, J. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17(4), 263–282.
53 - Fornell, C., & Larcker, D.F. (1981). Evaluating structural models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50
54 - MacKenzie, S.B., Podsakoff, P.M., & Podsakoff, N.P. (2011). Construct measurement and validity assessment in behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293–334.
55 - MacKinnon, D.P.,Warsi, G., & Dwyer, J.H. (1995). A simulation study of mediated effect measures. Multivariate Behavioral Research, 30(1), 41–62.
56 - Sobel, M.E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13(1982), 290–312.
57 - Chin, W.W., Marcolin, B.L., & Newsted, P.R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic mail adoption study. Information Systems Research, 14(2), 189–217.
58 - Fornell, C., & Cha, J. (1994). Partial least squares. In R.P. Bagozzi (Ed.), Advanced methods of marketing research (pp. 52–78). Cambridge: Blackwell.