Moderating effects of onlineshopping experience on customer

satisfaction and repurchaseintentions

Ilias O. Pappas and Adamantia G. PateliDepartment of Informatics, Ionian University, Corfu, Greece

Michail N. GiannakosDepartment of Computer and Information Science,

Norwegian University of Science and Technology, Trondheim, Norway, and

Vassilios ChrissikopoulosDepartment of Informatics, Ionian University, Corfu, Greece

Abstract

Purpose – Satisfaction and experience are essential ingredients for successful customer retention.This study aims to verify the moderating effect of experience on two types of relationships: therelationship of certain antecedents with satisfaction, and the relationship of satisfaction with intentionto repurchase.

Design/methodology/approach – This paper applies structural equation modelling (SEM) andmulti-group analysis to examine the moderating role of experience in a conceptual model estimatingthe intention to repurchase. Responses from 393 people were used to examine the differences betweenhigh- and low-experienced users of online shopping.

Findings – The research shows that experience has moderating effects on the relationships betweenperformance expectancy and satisfaction and satisfaction and intention to repurchase. This studyempirically demonstrates that prior customer experience strengthens the relationship betweenperformance expectancy and satisfaction, while it weakens the relationship of satisfaction withintention to repurchase.

Practical implications – Practitioners should differentiate the way they treat their customers basedon their level of experience. Specifically, the empirical research demonstrates that the expectedperformance of the online shopping experience (performance expectancy) affects satisfaction only onhigh-experienced customers. Instead, the effort needed to use online shopping (effort expectancy) andthe user’s belief in own abilities to use online shopping (self-efficacy) influence satisfaction only onlow-experienced customers. The effect of trust and satisfaction is significant on online shoppingbehaviour on both high- and low-experienced customers.

Originality/value – This paper investigates how different levels of experience affect customers’satisfaction and online shopping behaviour. It is proved that experience moderates the effect ofperformance expectancy on satisfaction and the effect of satisfaction on intention to repurchase. It alsodemonstrates that certain effects (effort expectancy and performance expectancy) are valid for onlyone of the two examined groups, while only one effect (trust) is valid for both (high- andlow-experienced).

Keywords Online shopping, experience, Satisfaction, Multi-group analysis, Intention to repurchase

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0959-0552.htm

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Received 27 March 2012Revised 21 October 2012

15 November 201231 May 2013

Accepted 25 June 2013

International Journal of Retail& Distribution Management

Vol. 42 No. 3, 2014pp. 187-204

q Emerald Group Publishing Limited0959-0552

DOI 10.1108/IJRDM-03-2012-0034

IntroductionOnline shopping, based on the internet’s growth, is emerging in a fast manner. Agreat number of opportunities for the e-vendors have risen in order to create andpreserve interaction with customers. Although not all customers are the same for acompany, those who spend more money and buy more often are the most importantones. Thus, customer retention includes a major concern for firms wishing to obtaina competitive advantage. Prior studies show that increase in customer retentionleads to higher profits for the companies (Lee et al., 2009). Consequently, it is criticalto identify important factors that affect customers’ intention to repurchase. Such afactor, identified from previous studies, is satisfaction. A satisfied customer is morelikely to return (Lee et al., 2009), making satisfaction not only a key factor thataffects customers’ online purchase behaviour but also an important factor ofcustomer loyalty.

Of particular interest to this study is the role of online shopping experience, whichrefers to the number of purchases that customers made in the past. Experience is likelyto affect customers’ future purchase behaviour. Kim et al. (2012) identify theimportance of online experience and suggest that it is interesting to examine the effectof different levels of experience on key factors that affect online shopping behaviour.Saprikis et al. (2010) state that adopters and non-adopters have different perceptionstowards online shopping, which may lead to different behaviour. Finally, Khalifa andLiu (2007) posit that the influence of satisfaction on repurchase intention is moderatedby experience.

This paper offers evidence on the role of online shopping experience in thegeneration of customer satisfaction and intention to repurchase. Previous research hasindicated that experience influences positively intention to purchase (Zhou et al., 2007),although customers satisfied with previous experiences might not always return to thesame provider (Sanchez-Garcıa et al., 2012). There are numerous studies revealingcontingency factors that influence satisfaction and intention to repurchase.Nevertheless, most of them assume the same level of experience. This paper aims tocontribute in this research by examining the moderating role of experience in a rathertypical online shopping model. Hence, the following research questions are raised:

RQ1. To what extent does online shopping experience act as a moderator on theonline shopping behaviour model?

RQ2. How the typical model is differentiated between high and low-experiencedcustomers?

To address the above questions, an empirical model is proposed and tested with theuse of the structural equations modelling (SEM) and the multi-group analysis methods.

The paper is organised as follows. In the next section, the source theories of theprime factors included in our online shopping research model are briefly discussed andrelated work is presented. Section 3 presents the hypothesised research model. Thenext section presents the methodology and the measures adopted for collecting data,while section 4 discusses the empirical results derived. The paper concludes with adiscussion of the findings, contributions for research and practice, andrecommendations for future research.

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Literature reviewTheoretical backgroundIntention to adopt has been explained in the past by many theories. Specifically,several models have been used to explain more than 40 per cent of the variance inindividual’s intention to adopt (Venkatesh and Morris, 2000). The most commonly usedtheories that explain intention to adopt are:

. Theory of reasoned action (TRA).

. Theory of planned behaviour (TPB).

. Expectation confirmation theory (ECT).

. Social cognitive theory (SCT).

In 2003, Venkatesh et al. proposed the unified theory of acceptance and use oftechnology (UTAUT), which explains as much as 70 per cent of the variance inintention to adopt. Intention to repurchase is considered as application of the “intentionto adopt” concept in online shopping.

Online shopping and its marketing depends highly on customer’s experience.Although, as with traditional offline shopping, not all customers have the sameexperience online and many differences are seen among them. Different theoriesstudying user behaviour have been proposed. Venkatesh et al. (2003), in their unifiedtheory of acceptance and use of technology (UTAUT), identify three factors thatmoderate the effect of effort expectancy on behavioural intention, with one of thembeing user experience. The latter was found to increase the effect of effort expectancyon behavioural intention for less experienced users. Consequently, effort expectancy isadopted to examine how experience moderates its relationship with satisfaction.Likewise, other studies have found that experience affects the relationship betweenperformance expectancy and future intention specifically for experienced users(Castaneda et al., 2007). Both factors are significantly correlated (Venkatesh et al.,2003). Nevertheless, Venkatesh et al. (2003) have not examined the moderating effect ofexperience on the relationship between performance expectancy and intention. Thus,performance expectancy is adopted to examine how its relationship with satisfaction isinfluenced by experience.

Social cognitive theory (SCT), a theory drawn from the psychology literature,explains how people acquire and maintain certain behavioural patterns, while itprovides the basis for intervention strategies (Bandura, 1986). In general terms, it maybe used in acceptance and information technology studies (Venkatesh et al., 2003).Self-efficacy, a factor sourced from SCT, is used as a form of self-evaluation, andinfluences the decision and effort needed to undertake certain behaviours. According toGravill and Compeau (2008), self-efficacy is the user’s belief in his own abilities toperform a task, which in turn promotes the sharing of knowledge. Experience isconsidered as the strongest generator of self-efficacy (Bandura, 1986).

Expectation confirmation theory (ECT), a theory applied in marketing contexts, hasbeen used to investigate repeating decisions in the customer behaviour literature(Oliver, 1993). ECT considers satisfaction as a key variable for customers’ continuanceintention. Customers’ loyalty to an online or offline vendor is highly affected from theiroverall satisfaction. Altogether, satisfaction explains a high amount of variance ofintention to repurchase (Giannakos et al., 2011). Ittner and Larcker (1998) posited that

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increased customer retention might result from higher levels of customer satisfaction.Adding to the above, Anderson and Srinivasan (2003) demonstrated that the lesssatisfied a customer feels the harder is for the company to retain him and enhance aclose relationship with him.

Online shopping related workExperience is considered important in forming customers’ perceptions, regarding theirexpectations from online retailers. Liang and Huang (1998) have found thathigh-experienced customers are more likely to continue shopping. Liu et al. (2008) haveidentified the significance of customer satisfaction in online shopping. Successfulprevious purchases and satisfaction that derives from them may increase customers’effort expectancy and performance expectancy. Similarly, Tong (2010) have conducteda cross-national study and examined, among others, the direct effects of previousonline shopping experience on perceived usefulness and ease of use. According toBandura (1986) and Dabhokar and Sheng (2009), experience is the strongest generatorof self-efficacy. A good experience with online shopping creates positive attitudes,increases customers’ self-efficacy and influences future intentions, while a badexperience might have exactly the opposite effects. Furthermore, Giannakos et al.(2011) have found that customers who are satisfied with previous experiences haveincreased self-efficacy.

Previous studies have identified the importance of brand trust on customers’shopping intention (Hahn and Kim, 2009). Positive experiences with online shoppingare likely to affect customers’ sense of trust (Chiu et al., 2009), making the seller reliablefor the customer. Chen and Chou (2012) have demonstrated that trust has a positiveeffect on customers’ satisfaction, but it has no effect on their intention to continueshopping online. However, trust is important for all customers, regardless their level ofexperience (Gefen et al., 2008).

As to experience, Zhou et al. (2007) have showed that it affects positively customers’intention to purchase online. Specifically, higher experience leads to increasedsatisfaction with online shopping and to an increased number of completed purchases.Nevertheless, Dholakia and Zhao (2010) found that it is more difficult to satisfyexperienced customers, because during the process they have acquired moreinformation.

There are several previous studies investigating the moderating effect of experienceon satisfaction and intention to repurchase. Regarding the prediction of onlineshopping acceptance, Liang and Huang (1998) have found that customers havedifferent concerns according to their level of experience. Online shopping experiencemoderates the effect of perceived usefulness on behavioural intentions (Hsieh and Liao,2011). In the context of online banking, experience moderates the effect of ease of useon customer satisfaction (Dagger and O’Brien, 2010). Experienced customers valueself-efficacy more than potential customers, because they feel more confident when itcomes to completing an online purchase (Hernandez et al., 2010). Khalifa and Liu (2007)found that experience moderates the relationship between online shopping satisfactionand intention to repurchase, although they do not examine the differences for low- andhigh-experienced customers. Another critical issue is that customers’ high experiencewith online shopping leads to lower perceived risk (Miyazaki and Fernandez, 2001),hence the levels of trust are likely to increase.

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The levels of perceived risk influence the effect of experience on trust. Ganesan(1994) found no significant effect of experience on trust, while Giannakos et al. (2011)posit that the effect of experience on trust is marginal when comparing high- andlow-experienced customers. Jin et al. (2008) suggest that experience should beexamined as a moderator of the effect of trust on satisfaction and customer loyalty,because trust seems to be more important when the customer has less informationabout the online vendor or has not made many online purchases in the past. Hsieh andLiao (2011) add to this suggestion by verifying the moderating effect of experienceamong trust and customers’ behavioural intentions.

As the related work has demonstrated, experience is of crucial importance inpredicting online shopping behaviour. The majority of previous studies examine thedirect or moderating effect of experience on specific antecedents of satisfaction, onsatisfaction itself as well as on intention to repurchase. This paper presents anexamination of the moderating role of experience on a well-established set ofsatisfaction antecedents and on the relationship of satisfaction with intention torepurchase. The ultimate purpose is to reveal differences in online shopping behaviourbetween high- and low-experienced customers.

Research hypotheses and model developmentFigure 1 presents a research model, which integrates factors from UTAUT with SCT,and ECT. Based on the aforementioned theoretical and empirical research, a set ofhypotheses has been formulated and examined in this paper regarding the moderatingeffect of experience on the relationships of effort expectancy, performance expectancy,self-efficacy, trust with satisfaction, and that of satisfaction with intention to repurchase.

Effort expectancy (EE)Effort expectancy refers to customers’ perspective that online shopping is free of effort(Venkatesh et al., 2003). Consequently, when customers use a web site to search forinformation and to make purchases, the amount of effort they have to put in, affectstheir intention to adopt the online shopping method. For instance, too many securityfeatures, used to increase customers’ trust to the online vendor, make the web site

Figure 1.

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harder to use affecting their satisfaction (Shen and Chiou, 2010). Experience moderatesthe relationship of effort expectancy with behavioural intentions (Venkatesh et al.,2003), and the relationship of ease of use with satisfaction in the context of onlinebanking (Dagger and O’Brien, 2010). However, the effect of different levels of customerexperience on the relationship between effort expectancy and satisfaction in onlineshopping needs further study. Therefore, it is hypothesised that:

H1(a,b). Effort expectancy has a positive effect on satisfaction of (a)low-experienced, (b) high-experienced customers.

H1c. The strength of the relationship between Effort Expectancy andSatisfaction differs between high- and low-experienced customers.

Performance expectancy (PE)Performance expectancy refers to the degree to which customers believe that onlineshopping improves their transaction experience and, like effort expectancy, affectstheir future intentions (Venkatesh et al., 2003). Whenever customers receive pleasurefrom online shopping, their satisfaction increases, which is likely to lead them tocontinued use. However, Deng et al. (2010) have found that when the performance of asystem provides experiential and emotional values it decreases users’ satisfaction,while when it provides useful functionalities it increases users’ satisfaction. Accordingto Bhattacherjee (2001), when a user believes that a system is useful to him, he feelssatisfied and wants to continue using it. Castaneda et al. (2007) have found that highlevels of internet and web site experience moderate the impact of perceived usefulnesson users’ future intentions. Similar to effort expectancy, the effects of different levels ofexperience on the relationship of performance expectancy with satisfaction needfurther investigation. In consequence, the following set of hypotheses is formulated:

H2(a,b). Performance expectancy has a positive effect on satisfaction of (a)low-experienced, (b) high-experienced customers.

H2c. The strength of the relationship between effort expectancy and satisfactiondiffers between high- and low-experienced customers.

Self-efficacy (SEF)Taylor and Todd (1995) argue that the capability to perform a task (self-efficacy) isgreater for rather experienced users. Self-efficacy is more important for experiencedusers (Hernandez et al., 2010) and influences their final behaviour (Dabhokar andSheng, 2009). Likewise, online customers that buy often are likely to have moreconfidence in their ability to shop online, and consequently get more satisfied with theprocedure (Yoon et al., 2002). Increased self-efficacy leads to increased enjoyment andpositive attitude towards online shopping (Yang, 2012). According to Hsu et al. (2006),self-efficacy has a positive effect on customers’ satisfaction, while customers’ futureintentions are directly affected by their satisfaction. Hence, it is hypothesised that:

H3(a,b). Self-efficacy has a positive effect on satisfaction of (a) low-experienced, (b)high-experienced customers.

H3c. The strength of the relationship between Self-Efficacy and Satisfactiondiffers between high- and low-experienced customers.

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Trust (TR)In online shopping, trust is defined as the buyer’s belief that the e-vendor is behavingethically (Pavlou and Fygenson, 2006). Although trust is not of high importance whenthe customer is only looking for information, it is imperative if a transaction is going totake place. Customers must feel safe and secure if they are to shop online, while it is theseller’s duty to develop and retain customers’ trust (Palvia, 2009). Previous studieshave showed that customers who develop trust present higher level of satisfaction withthe online shopping process (Chiou, 2004), and thus may act as a predictor ofsatisfaction (Chiu et al., 2009). Trust and satisfaction need to be maintained in onlineshopping, regardless of customer experience, because they are important factors ofcustomer retention when meeting negative situations such as service failure (Liao andKeng, 2013). Trust is important for all customers, either they are new or experienced(Gefen et al., 2008). Although the importance of trust when using a web site decreaseswith experience (Gefen et al., 2008), high-experienced customers are more likely to besatisfied because they have more realistic expectations from online vendors.Consequently, the following set of hypotheses is formulated:

H4(a,b). Trust has a positive effect on satisfaction of (a) low-experienced, (b)high-experienced customers.

H4c. The strength of the relationship between Trust and Satisfaction differsbetween high- and low-experienced customers.

Satisfaction (STF)By increasing customers’ satisfaction, firms achieve higher retention rate, positiveword of mouth and increased profits (Zeithaml, 2000). Based on ECT and previousstudies, satisfaction affects positively customers’ intention to repeat online purchases(Lee and Lin, 2005). Hsu et al. (2006) posit that satisfaction has a positive influence oncustomers’ intention to repeat their online purchases. Additionally, experience affectspositively the users’ attitude and consequently their satisfaction. Experiencedcustomers, in contrast to inexperienced ones, enjoy online shopping and have afavourable attitude towards it (Yang, 2012). Prior studies have provided empiricalsupport for the effects of experience on the relationship among customers’ satisfactionand intention to repurchase (Khalifa and Liu, 2007). Additionally, Dholakia and Zhao(2010) suggest that high-experienced customers are more difficult to be satisfied.Nevertheless, how these effects vary for low- and high-experienced customers has notbeen examined. Accordingly, it is hypothesised that:

H5(a,b). Satisfaction has a positive effect on intention to repurchase of (a)low-experienced, (b) high-experienced customers.

H5c. The strength of the relationship between Satisfaction and Intention toRepurchase differs between high- and low-experienced customers.

MethodologySamplingThe research methodology included a survey conducted through the delivery andcollection of individual questionnaires. A number of different methods wererecruited for attracting respondents; questionnaires were distributed in various

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places (universities, public areas) and e-mails were sent to different mailing lists. Itwas made clear that there was no reward for the respondents and the participationwas voluntary. In order to collect data from active online shoppers, only thosewho had made at least one purchase the past six months were targeted. Thesurvey took place during the last four weeks of April 2010, aiming at about 1,000Greek users of online shopping, 413 of which finally responded. Out of them, 20respondents did not make any purchases in the past six months and wereremoved from the sample. The remaining 393 questionnaires were used foranalysis.

As Table I shows, the sample of respondents was composed of more men (78.1 percent) than women (21.9 per cent). In terms of age, the majority of the respondents (46.1per cent) were between 25 and 34, while the second more frequent age group (30.5 percent) involved people between 19 and 24. Finally, the great majority of the respondents(76.7 per cent) included graduates or post-graduate students.

The data was divided into two groups based on the respondents’ level of experienceby performing a median split (Chang and Chen, 2008). The median of the sample was 5.Hence, one group contained the respondents who had made at least 1 online purchasewithin the past six months and no more than 5 (low-experienced users), while the othergroup contained those who had made more than 5 online purchases in the past sixmonths. The low-experienced group consisted of 214 respondents, from which the vastmajority of which (71.5 per cent) were men. Also, most of the respondents were single(84.6 per cent), between the ages of 19-34 (79.5 per cent), and were graduates (54.7 percent). The high-experienced group consisted of 179 respondents, out of which 86 percent were males. Also, 74.9 per cent were singles, 73.2 per cent were between 19-34, and45.8 per cent were graduates.

Demographic profile No. Percentage

GenderMale 307 78.1Female 86 21.9

Marital statusSingle 315 80.2Married 71 18.1Divorced 8 1.7

Age0-18 27 6.919-24 120 30.525-34 181 46.135-44 52 13.245 þ 13 3.3

EducationGymnasium 9 2.3High School 80 20.4University 199 50.6Postgraduate 105 26.7

Table I.Users’ demographicprofile

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MeasuresThe questionnaire was divided into two parts. The first part included questions on thedemographics of the sample (age, gender, education), as discussed above. The secondpart included measures of the various constructs identified in the literature reviewsection. The Appendix (Table AI) lists the questionnaire items used to measure eachconstruct. In almost all cases, with an exception standing for the “online shoppingexperience” variable, seven-point Likert scales were used to measure the model’svariables.

Data analysisReliability and validity. The constructs used in this research were evaluated in terms ofreliability and validity. Reliability was tested with the use of the Cronbach alphaindicator, which required to be higher than 0.7 for every factor (Ref). Validationanalysis consists of convergent and discriminant validity. Establishing validityrequires that average variance extracted (AVE) is greater than 0.50, the correlationbetween the different variables in the confirmatory models does not exceed 0.8 points,as this suggests low discrimination and that the square root of each factor’s averagevariance extracted (AVE) is larger than its correlations with other factors (Lee et al.,2009).

Goodness of fit describes how well the model fits its data. Here, several fit indiceswere used to assess model-data fit. Root mean square error of approximation (RMSEA),comparative fit index (CFI) value and x2/df ratio were all used to evaluate model-datafit (Byrne, 2009). RMSEA less than 0.05 suggests good model-data fit; between 0.05 and0.08 it suggests reasonable model-data fit and between 0.08 and 0.1 suggestsacceptable model data fit. CFI indices greater than 0.90 suggest good model-data fitand greater than 0.80 suggest adequate model-data fit. A x2/df ratio less than 3 isacceptable.

Multi-group analysis (invariance analysis). When analysis involves more than onesample, the model needs to be tested for invariance across groups (Ref). In other words,it must be examined if components of the measurement and structural model areequivalent across particular groups of interest. Prior to invariance testing, each groupwas assessed using a goodness of fit test. Invariance of the components is highlyimportant. Unless it is proved, the examination of the structural model has no value.Also, if invariance cannot be proved for the structural model, path differences shouldbe examined in order to find which ones differ among the groups (Byrne, 2009). Thenext step was to estimate the effect of the four antecedents of satisfaction, the effect ofsatisfaction on intention to repurchase and the moderating effect of experience bymeans of a multi-group SEM analysis. Multi-group analysis was performed with theuse of the standard SEM software AMOS Version 18.0 software.

Research findingsBefore conducting a multi-group analysis, reliability and validity of the constructs wasexamined. Reliability testing provided acceptable indices of internal consistency, sincethe Cronbach alpha indicator exceeded the cut-off level of 0.70 for all constructs(Table II). Validation testing results are also provided in Table II. AVE for allconstructs was higher from the level of 0.50, all correlations were lower than 0.80 andsquare root AVEs for all constructs were larger from their correlations.

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Before testing for invariance, goodness of fit was examined for each group.Specifically, the indices for the low-experienced group were X2ðdfÞ ¼ 2:4, CFI ¼ 0:9,RMSEA ¼ 0:08; all acceptable. Similarly, the indices for the high-experienced groupwere X2ðdfÞ ¼ 1:8, CFI ¼ 0:9, RMSEA ¼ 0:06; all acceptable. Comparing themeasurement model with the unconstrained one, group equivalence was proved.Specifically, as Tables III and IV demonstrates, the p-value was non-significant withDX2ð16Þ ¼ 20:125 (Byrne, 2009). Hence, the examination of the equivalence among thestructural weights followed. Unlike the measurement model, the structural model had asignificant p-value with a DX2ð43Þ ¼ 105:921 (Tables III and IV). Consequently, testingfor path differences in the model was possible.

The moderating effect of experience in the proposed model was estimated through amulti-group analysis. Results are presented in Tables V and VI. Testing for differencesbetween the two groups was achieved by doing a pairwise comparison of the coefficients,using the critical ratios for differences on Amos. Significant group differences werefound for the effect of performance expectancy on satisfaction and for the effect ofsatisfaction on intention to repurchase. Also, as Tables V and VI shows, only one out ofthe four hypothesised factors (trust) had a significant effect on both groups of customers.Specifically, the empirical research demonstrated that the expected performance of theonline shopping experience (performance expectancy) affects satisfaction only on

ConstructConstruct Mean SD Cr. Alpha AVE EE PE SEF TR STF IR

EE 5.48 0.92 0.837 0.54 0.73PE 6.00 0.91 0.860 0.56 0.43 0.75SEF 6.43 0.76 0.759 0.50 0.40 0.50 0.71TR 5.09 1.01 0.851 0.62 0.40 0.35 0.29 0.79STF 5.94 0.85 0.843 0.50 0.49 0.51 0.50 0.56 0.71IR 6.32 0.88 0.804 0.55 0.32 0.47 0.58 0.34 0.64 0.74

Notes: Diagonal elements (in italic) are the square root of the average variance extracted (AVE); Off-diagonal elements are the correlations among constructs (all correlations are significant, p , 0:01); Fordiscriminant validity, diagonal elements should be larger than off-diagonal elements; EE, effortexpectancy; PE, performance expectancy; SEF, self-efficacy; TR, trust; STF, satisfaction; IR, intentionto repurchase

Table II.Descriptive statistics andcorrelations of latentvariables

Model df x2 RMSEA CFI

Unconstrained model 396 825.595 0.53 0.903Measurement weights 412 845.721 0.52 0.902Structural weights 439 931.516 0.54 0.889

Table III.Invariance – summary ofgoodness of fit indices

Model comparisons df x2 diff (DX2) P-value

Measurement weights 16 20.125 Non-significantStructural weights 43 105.921 0.000

Table IV.Invariance – differentialgoodness of fit indices

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high-experienced customers. Instead, the effort needed to use online shopping (effortexpectancy) and the user’s belief in own abilities to use online shopping (self-efficacy)influence satisfaction only on low-experienced customers.

In a similar way, satisfaction has a significant effect on intention to repurchase forboth types of customers, although it is notable that the effect is smaller for thehigh-experienced group.

Discussion and conclusionsIn an attempt to understand customers’ intention to repeat their online purchases, thepaper sheds light on the effect of experience on a rather complete and commonlyapplied online shopping behaviour model. Specifically, this study explores themoderating effect of experience on the relationship of effort expectancy, performanceexpectancy, self-efficacy, and trust with satisfaction, as well as on the relationship ofsatisfaction with intention to repurchase. The findings indicate that experiencemoderates the relationship of performance expectancy with satisfaction (H2c), as wellas, the relationship between customers’ satisfaction with intention to repurchase (H5c),while the effect of trust remains constant and significant regardless of the customers’level of experience (H4c). Specifically, experience strengthens the effect of performanceexpectancy on satisfaction, keeps constant the effect of trust on satisfaction, anddownsizes the effect of satisfaction on intention to repurchase.

Regarding performance expectancy, the findings suggest that the greater the users’experience, the more satisfied they are with their online purchases. However,satisfaction of low-experienced customers does not seem to be affected by performance

Low-experienced

High-experienced

Differences betweengroups

Proposedhypotheses

Effort expectancy 0.154 * 0.155 Non-significant Accepted (H1a)Rejected (H1b,c)

Performance expectancy 20.039 0.348 * * p , 0:05 Rejected (H2a)Accepted (H2b,c)

Self-efficacy 0.514 * * 0.082 Non-significant Accepted (H3a)Rejected (H3b, c)

Trust 0.374 * * 0.398 * * Non-significant Accepted (H4a,b)Rejected (H4c)

R 2 0.69 0.55

Notes: * p , 0:1; * * p , 0:001; Goodness of fit: x2ðdfÞ ¼ 2:1; CFI ¼ 0:9; RMSEA ¼ 0:053

Table V.Multi-group analysis –

satisfaction

Low-experienced

High-experienced Differences between groups

Proposedhypotheses

Satisfaction 0.792 * 0.668 * p , 0:01 Accepted (H5a, b, c)R 2 0.63 0.45

Notes: * p , 0:001; Goodness of fit: x2ðdfÞ ¼ 2:1; CFI ¼ 0:9; RMSEA ¼ 0:053

Table VI.Multi-group analysis –intention to repurchase

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expectancy. Although the results are partially consistent with other studies showingthat performance expectancy has a positive effect on satisfaction (Khalifa and Liu,2007), they are opposed to the results of UTAUT, based on which experience has nomoderating effect on performance expectancy.

This study has found no differences between high- and low-experienced customersregarding the effect of trust on satisfaction. In any case, trust has important impact onsatisfaction for both high- and low-experienced customers. This finding is consistentwith Gefen et al. (2008) arguing that trust is important for all customers, no matter theirlevel of experience. These results contradict those of Saprikis et al. (2010), who found thattrust is more important for users without previous experience. This might be explainedby the fact that Saprikis et al. (2010) studied totally inexperienced users, while the entiresample used in this study had previous experience (even low) with online shopping.

Contrary to H1c, no significant difference was found between low- andhigh-experienced users for the impact of effort expectancy (H1c) on satisfaction.Effort expectancy has a significant effect on customers’ satisfaction but only for low-experienced users. Based on the fact that effort expectancy becomes less important tocustomers as they acquire more experience, a moderating effect was expected amonglow- and high-experienced customers. However, this was not verified by the results,possibly because effort expectancy was not important at all for the high-experiencedgroup. This is consistent with the UTAUT model, where effort expectancy is importantfor those with limited experience. Nonetheless, the analysis provides only weaksupport for this finding.

Similarly, no significant difference was found between low- and high-experiencedusers for the impact of self-efficacy (H3c) on satisfaction. The study demonstrates thatself-efficacy influences only low-experienced users. It is somewhat surprising that nosignificant difference between the two groups exists, although the effects of self-efficacyon satisfaction for low- and high-experienced customers are so different. A possibleexplanation may be that the process of online shopping for high-experienced customersis not that complex, hence self-efficacy is not important for them. Another possibleexplanation for this is the learning effect in electronic shopping. Customers get used tothe process as they acquire more experience. It can, thus, be suggested that self-efficacyis important only on new online customers. This result is partially consistent withHernandez et al. (2010), who found that, although self-efficacy is important for customersin online shopping, it has no significant relationship with satisfaction.

This study indicates that the effect of satisfaction remains significant for bothgroups of customers. However, the relationship between satisfaction and repurchaseintention is greater when experience is low than when experience is high. Findingssuggest that low- experienced customers give more importance on the impact ofincreasing satisfaction on their future repurchase behaviour, consistent with Seiderset al. (2005). Instead, high- experienced users are mostly affected by other factors(i.e. performance of the online shopping experience), consistent with Evanschitzky andWunderlich’s (2006) findings. This may be explained by the fact that high-experiencedcustomers base their choices on a wide range of information acquired from differentsources, while low-experienced customers rely more on their previous experiences.Consequently, satisfaction has a stronger effect on low- than high-experiencedcustomers. This finding contradicts Khalifa and Liu (2007) arguing that onlineshopping experience strengthens the effect of satisfaction on intention to repurchase.

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Theoretical implicationsCustomers’ perceptions play an important role in enhancing their purchase decisions.This study provides empirical results that prior customer experience influences therelationship between performance expectancy and satisfaction, weakens therelationship of satisfaction with intention to repurchase, while it keeps a significantconstant effect on the relationship of trust on satisfaction. Additionally, the resultsshow that effort expectancy and self-efficacy differentiate their effects for low- andhigh-experienced customers. Hence, research on e-commerce should distinguishbetween customers with low and high experience on online shopping. The two groupshave different perceptions based on their experience, which in turn affects theirbehavioural patterns. Specifically, performance of the online shopping medium affectssatisfaction of high-experienced customers. Instead, the degree of ease associated withonline shopping and the confidence given to customers that they are able to completethe online purchase without help affect their satisfaction when they arelow-experienced. This work also demonstrates that trust is essential for allcustomers regardless their experience.

These results address a few shortcomings of previous studies in the area.Specifically, Khalifa and Liu (2007) investigate the moderating effect of onlineshopping experience on intention to repurchase. Although they include performanceexpectancy in their model by examining its effect on intention to repurchase, they donot test for a moderating effect of experience on that relationship. Likewise, theyexamine moderating effects of experience on the relationship between satisfaction andintention to repurchase, but they do not investigate the differences between low- andhigh-experienced customers. Lee et al. (2009) study the effect of satisfaction onrepurchase intention. However, they do not consider the effect of customer experience,which this study identified as a moderator of that relationship. Chiu et al. (2009) haveincluded experience in their model, which examines satisfaction, intention andperceived usefulness among others. Eventually, they examined the direct effect ofexperience on customers’ loyalty intention and found no significant effect.

Practical implicationsThis study implies that online retailers should consider different marketing strategieswhen planning to launch a product or service, depending on the experience level oftheir targeted market. For experienced online customers, firms should focus onproviding appropriate mechanisms increasing the performance of the online shoppingmedium. High-experienced customers are more rational on their decisions thanlow-experienced customers (Cheema and Papatla, 2010). When addressinglow-experienced customers, firms should keep in mind that effort expectancy andself-efficacy are more effective on them. Thus, firms should invest in increasing ease ofuse and friendliness of the online shopping medium. Finally, trust is a critical factorand e-vendors should always invest on it no matter their target market.

The findings suggest that experience weakens the effect of satisfaction on intentionto repurchase. While interacting with their loyal online customers, companies shouldfocus on understanding them and complying with their performance requirements,since the more experience and knowledge an online customer acquires, the moredifficult it is to be satisfied from a vendor (Dholakia and Zhao, 2010). Any onlinevendor should be aware of the effect these factors not only on satisfaction but also on

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intention to repurchase. Consequently, in order to attract low-experienced customers,companies should focus on making the online shopping process as easy as possible andincrease their self-efficacy, as this should increase their satisfaction and ultimatelytheir intention to repurchase.

Limitations and future researchAlthough the findings provide meaningful implications for online shopping research,this study has several limitations. First, the study was carried out in Greece, hence theresults may not be fully generalised for other countries, as beliefs and perceptions maydiffer among countries. Also, a self-report scale based on users’ perceptions was usedto measure research variables, so the results might suffer from common method bias.Various methods, such as customer interviews via think-aloud protocols and directobservation and analysis of actual behaviour via log files, could be employed toprovide richer understanding of online shopping behaviour and to overcome potentialbias from common method variance. The measure used for experience consisted of asingle item. Future empirical research, which models experience as a latent variableconstruct and assesses rest of the constructs with the use of objective measures ratherthan subjective perceptions, is planned.

Last but not least, future research is encouraged towards incorporating the group ofnon-experienced online customers, with the ultimate research goal of identifyingappropriate mechanisms for attracting new online customers.

References

Anderson, R.E. and Srinivasan, S.S. (2003), “E-satisfaction and e-loyalty: a contingencyframework”, Psychology & Marketing, Vol. 20 No. 2, pp. 123-138.

Bandura, A. (1986), Social Foundations of Thought and Action: A Social Cognitive Theory,Prentice-Hall, Englewood Cliffs, NJ.

Bhattacherjee, A. (2001), “Understanding information systems continuance.An expectation-confirmation model”, MIS Quarterly, Vol. 25 No. 3, pp. 351-370.

Byrne, B. (2009), Structural Equation Modelling with AMOS, 2nd ed., Multivariate ApplicationsSeries, Routledge Academic, New York, NY.

Castaneda, J., Munozleiva, F. and Luque, T. (2007), “Web acceptance model (WAM): Moderatingeffects of user experience”, Information & Management, Vol. 44 No. 4, pp. 384-396.

Chang, H.H. and Chen, S.W. (2008), “The impact of customer interface quality, satisfaction andswitching costs on e-loyalty: internet experience as a moderator”, Computers in HumanBehavior, Vol. 24 No. 6, pp. 2927-2944.

Cheema, A. and Papatla, P. (2010), “Relative importance of online versus offline information forinternet purchases: product category and Internet experience effects”, Journal of BusinessResearch, Vol. 63 No. 9, pp. 979-985.

Chen, Y.T. and Chou, T.Y. (2012), “Exploring the continuance intentions of consumers for B2Conline shopping: perspectives of fairness and trust”, Online Information Review, Vol. 36No. 1, pp. 104-125.

Chiou, J.S. (2004), “The antecedents of consumers’ loyalty toward internet service providers”,Information and Management, Vol. 41 No. 6, pp. 685-695.

Chiu, C.M., Lin, H.Y., Sun, S.Y. and Hsu, M.H. (2009), “Understanding customers’ loyaltyintentions towards online shopping: an integration of technology acceptance model andfairness theory”, Behaviour & Information Technology, Vol. 28 No. 4, pp. 347-360.

IJRDM42,3

200

Dagger, T.S. and O’Brien, T.K. (2010), “Does experience matter?: Differences in relationshipbenefits, satisfaction, trust, commitment and loyalty for novice and experienced serviceusers”, European Journal of Marketing, Vol. 44 Nos 9/10, pp. 1528-1552.

Dabhokar, P.A. and Sheng, X. (2009), “The role of perceived control and gender in consumerreactions to download delays”, Journal of Business Research, Vol. 62 No. 7, pp. 756-760.

Deng, L., Turner, D.E., Gehling, R. and Prince, B. (2010), “User experience, satisfaction, andcontinual usage intention of IT”, European Journal of Information Systems, Vol. 19 No. 1,pp. 60-75.

Dholakia, R.R. and Zhao, M. (2010), “Effects of online store attributes on customer satisfactionand repurchase intentions”, International Journal of Retail & Distribution Management,Vol. 38 No. 7, pp. 482-496.

Evanschitzky, H. and Wunderlich, M. (2006), “An examination of moderator effects in thefour-stage loyalty model”, Journal of Service Research, Vol. 8 No. 4, pp. 330-345.

Ganesan, S. (1994), “Determinants of long-term orientation in buyer-seller relationships”,The Journal of Marketing, Vol. 58 No. 2, pp. 1-19.

Gefen, D., Benbasat, I. and Pavlou, P. (2008), “A research agenda for trust in onlineenvironments”, Journal of Management Information Systems, Vol. 24 No. 4, pp. 275-286.

Gravill, J. and Compeau, D. (2008), “Self-regulated learning strategies and software training”,Information & Management, Vol. 45 No. 5, pp. 288-296.

Giannakos, M.N., Pateli, A.G. and Pappas, I.O. (2011), “Identifying the direct effect of experienceand the moderating effect of satisfaction in the Greek online market”, International Journalof E-Services and Mobile Applications, Vol. 3 No. 2, pp. 39-58.

Hahn, K.H. and Kim, J. (2009), “The effect of offline brand trust and perceived internet confidenceon online shopping intentionin the integrated multi-channel context”, International Journalof Retail & Distribution Management, Vol. 37 No. 2, pp. 126-141.

Hernandez, B., Jimenez, J. and Martın, M.J. (2010), “Customer behavior in electronic commerce:the moderating effect of e-purchasing experience”, Journal of Business Research, Vol. 63Nos 9-10, pp. 964-971.

Hsieh, J.Y. and Liao, P.W. (2011), “Antecedents and moderators of online shopping behavior inundergraduate students”, Social Behavior and Personality: An International Journal,Vol. 39 No. 9, pp. 1271-1280.

Hsu, M., Yen, C., Chiu, C. and Chang, C. (2006), “A longitudinal investigation of continued onlineshopping behaviour: an extension of the theory of planned behaviour”, InternationalJournal of Human-Computer Studies, Vol. 64 No. 9, pp. 889-904.

Ittner, C.D. and Larcker, D.F. (1998), “Are nonfinancial measures leading indicators of financialperformance? An analysis of customer satisfaction”, Journal of Accounting Research,Vol. 36, pp. 1-35.

Jin, B., Park, J.Y. and Kim, J. (2008), “Cross-cultural examination of the relationships among firmreputation, e-satisfaction, e-trust, and e-loyalty”, International Marketing Review, Vol. 25No. 3, pp. 324-337.

Khalifa, M. and Liu, V. (2007), “Online consumer retention: contingent effects of online shoppinghabit and online shopping experience”, European Journal of Information Systems, Vol. 16No. 6, pp. 780-792.

Kim, J., Spielmann, N. and McMillan, S.J. (2012), “Experience effects on interactivity: functions,processes and perceptions”, Journal of Business Reasearch, Vol. 65 No. 11, pp. 1543-1550.

Lee, G.G. and Lin, H.F. (2005), “Customer perceptions of e-service quality in online shopping”,International Journal of Retail & Distribution Management, Vol. 33 No. 2, pp. 161-175.

Satisfaction andrepurchaseintentions

201

Lee, H., Choi, S.-Y. and Kang, Y.-S. (2009), “Formation of e-satisfaction and repurchase intention:moderating roles of computer self-efficacy and computer anxiety”, Expert Systems withApplications: An International Journal, Vol. 36 No. 4, pp. 7848-7859.

Liang, T.-P. and Huang, J.-S. (1998), “An empirical study on consumer acceptance of products inelectronic markets: a transaction cost model”, Decision Support Systems, Vol. 24 No. 1,pp. 29-43.

Liao, T.H. and Keng, C.J. (2013), “Online shopping delivery delay: finding a psychologicalrecovery strategy by online consumer experiences”, Computers in Human Behavior, Vol. 29No. 4, pp. 1849-1861.

Liu, X., He, M., Gao, F. and Xie, P. (2008), “An empirical study of online shopping customersatisfaction in China: a holistic perspective”, International Journal of Retail & DistributionManagement, Vol. 36 No. 11, pp. 919-940.

Miyazaki, A.D. and Fernandez, A. (2001), “Consumer perceptions of privacy and security risksfor online shopping”, The Journal of Consumer Affairs, Vol. 35 No. 1, pp. 27-44.

Oliver, R.L. (1993), “Cognitive, affective, and attribute bases of the satisfaction response”, Journalof Consumer Research, Vol. 20 No. 3, pp. 418-430.

Palvia, P. (2009), “The role of trust in e-commerce relational exchange: a unified model”,Information & Management, Vol. 46 No. 4, pp. 213-220.

Pavlou, P.A. and Fygenson, M. (2006), “Understanding and predicting electronic commerceadoption: an extension of the theory of planned behaviour”, MIS Quarterly, Vol. 30 No. 1,pp. 115-143.

Sanchez-Garcıa, I., Pieters, R., Zeelenberg, M. and Bigne, E. (2012), “When satisfied consumers donot return: variety seeking’s effect on short- and long-term intentions”, Psychology& Marketing, Vol. 29 No. 1, pp. 15-24.

Saprikis, V., Chouliara, A. and Vlachopoulou, M. (2010), “Perceptions towards online shopping:analyzing the Greek university students’ attitude”, Communications of the IBIMA,Vol. 2010, December, pp. 1-13.

Shen, C.C. and Chiou, J.S. (2010), “The impact of perceived ease of use on internet serviceadoption: the moderating effects of temporal distance and perceived risk”, Computers inHuman Behavior, Vol. 26 No. 1, pp. 42-50.

Seiders, K., Voss, G.B., Grewal, D. and Godfrey, A.L. (2005), “Do satisfied customers buy more?Examining moderating influences in a retailing context”, Journal of Marketing, Vol. 69No. 4, pp. 26-43.

Taylor, S. and Todd, P.A. (1995), “Assessing IT usage: the role of prior experience”,MIS Quarterly, Vol. 19 No. 4, pp. 561-570.

Tong, X. (2010), “A cross-national investigation of an extended technology acceptance model inthe online shopping context”, International Journal of Retail & Distribution Management,Vol. 38 No. 10, pp. 742-759.

Venkatesh, V. and Morris, M.G. (2000), “Why don’t men ever stop to ask for directions? Gender,social influence, and their role in technology acceptance and usage behaviour”,MIS Quarterly, Vol. 24 No. 1, pp. 115-139.

Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003), “User acceptance of informationtechnology: toward a unified view”, MIS Quarterly, Vol. 27 No. 3, pp. 425-478.

Yang, K. (2012), “Consumer technology traits in determining mobile shopping adoption:an application of the extended theory of planned behavior”, Journal of Retailing andConsumer Services, Vol. 19 No. 5, pp. 484-491.

Yoon, D., Cropp, F. and Cameron, G. (2002), “Building relationships with portal users: theinterplay of motivation and relational factors”, Journal of Interactive Advertising, Vol. 3No. 1, pp. 1-18.

IJRDM42,3

202

Zeithaml, V.A. (2000), “Service quality, profitability and the economic worth of customers: whatwe know and what we need to learn”, Journal of the Academy of Marketing Science, Vol. 28No. 1, pp. 67-85.

Zhou, L., Dai, L. and Zhang, D. (2007), “Online Shopping Acceptance Model – a critical survey ofconsumer factors in online shopping”, Journal of Electronic Commerce Research, Vol. 8No. 1, pp. 41-62.

Further reading

Yoon, C. (2010), “Antecedents of customer satisfaction with online banking in China: the effectsof experience”, Computers in Human Behavior, Vol. 26 No. 6, pp. 1296-1304.

About the authorsIlias O. Pappas received a B.Sc Degree in Informatics (specialisation in Humanities Informatics)from the Ionian University, Corfu, Greece and a MSc Degree in Informatics (specialisation inInformation Systems) from the same university. He is currently working on a PhD degree atIonian University. His current research interests are in the areas of electronic commerce, mobilecommerce, information technology adoption usage and strategy, innovation adoption, consumerbehaviour in online environments, customer relationship management (CRM), personalisationand recommendation agents and internet marketing. Ilias O. Pappas is the corresponding authorand can be contacted at: ilpappas@ionio.gr

Adamantia G. Pateli is Lecturer of Information Systems at the Department of Informatics ofthe Ionian University, Corfu, Greece. She holds a BSc Degree in Informatics (Specialisation inInformation Systems) from the Athens University of Economics and Business (AUEB), aMaster’s Degree in Electronic Commerce from the University of Manchester, and a PhD degreefrom the Department of Management Science and Technology of the Athens University ofEconomics and Business (AUEB). Since 1997, Dr Pateli has participated in a number of nationaland European-funded research projects in the areas of Electronic Commerce and InformationSystems. She has published more than 30 research articles in leading academic journals, such asthe European Journal of Information Systems, Journal of Organizational Change Management,International Journal of Technology Management, Electronic Markets, and ManagementDecision, as well as in several European and international peer-reviewed conferences. Her currentresearch interests lie in the areas of information systems management, mobile and wirelessservices, e-government, electronic human resource management, technology-induced innovationand strategic technology alliances.

Michail N. Giannakos is an ERCIM “Alain Bensoussan” Fellow in the Department ofComputer and Information Science at Norwegian University of Science and Technology (NTNU)and a Visiting Richard T. Cheng Fellow in the Center for Real-time Computing, Virginia, USA. Heearned a PhD in Educational Technology from Ionian University, Corfu, Greece. Giannakos is themain author of more than 25 peer-reviewed research articles in several international journals andconferences in the area of educational technology and computer science education. Giannakos isinterested in how people learn in the presence of technology and each other. He has worked as aResearch Associate on Marie Curie CULT project and on Microsoft Research VideoPal project.

Vassilios Chrissikopoulos is the Chairman of the Department of Informatics of the IonianUniversity, Greece. He holds a Diploma in Mathematics from Aristoteleio University ofThessaloniki, Greece, an MSc from the University of Warwick, an MSc from the University ofChelsea and a PhD from Royal Holloway College of London. He has several years’ researchexperience through his participation in several national and international-funded projects. Hehas published more than 100 research articles in several European and international journals andconferences. His current research interests include information systems, information security,algorithms, smart cards, electronic money and digital libraries.

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Appendix

Constructs Items Sources

Effort expectancy(EE)

It is easy to become skilful at using online shop (EE1) Chiu et al. (2009)Learning to operate online shop is easy (EE2)Online shops are flexible to interact with (EE3)My interaction with online shop is clear andunderstandable (EE4)Online shops are easy to use (EE5)

Performanceexpectancy (PE)

Online shopping enables me to search and buy goodsfaster (PE1)

Chiu et al. (2009)

Online shopping enhances my effectiveness in goodssearching and buying (PE2)Online shopping makes it easier to search for andpurchase goods (PE3)Online shopping increases my productivity insearching and purchasing goods (PE4)Online shopping is useful for searching and buyinggoods (PE5)

Self-efficacy (SEF) I feel capable of locating shopping sites on the internet(SEF1)

Hernandez et al. (2010)

I feel comfortable searching for information about aproduct on the internet (SEF2)

Trust (TR) Based on my online shopping experience, I know thatonline shops are honest (TR1)

Chiu et al. (2009)

Based on my experience with online shop, I know theyare not opportunistic (TR2)Based on my experience with online shops, I knowthey keep their promises to customers (TR3)Based on my experience with online shops, I knowthey are trustworthy (TR4)

Satisfaction (STF) I am satisfied with the online shopping experience(STF1)Hsu et al. (2006)I am pleased with the online shopping experience(STF2)My feeling with using online shops was good (STF3)

Intention torepurchase (IR)

I intend to continue online shopping in the future (IR1) Hsu et al. (2006)I will continue online shopping in the future (IR2)I will regularly use online shops in the future (IR3)

Online shoppingexperience (EXP)

How many times (approximately) have you purchasedproducts from an online store in the past six months?

Chiu et al. (2009)Table AI.Key factors constructsand items

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