Journal of International Trade, Logistics and Law, Vol. 7, Num. 2, 2021, 30-43 30 THE EFFECTS OF E-SATISFACTION, E-BRAND LOYALTY AND E-TRUST LEVELS ON CONSUMER BEHAVIORAL INTENTIONS: A STUDY ON ONLINE SHOPPERS IN TURKEY Mehmet SAĞLAM Istanbul Commerce University, Turkey Maram JARRAR Istanbul Commerce University, Turkey Received: Augt 14, 2021 Accepted: Oct 25, 2021 Published: Dec 01, 2021 Abstract: Buying product or services online has become a common practice among millions of people around the world. In this process, it is a matter of curiosity what factors affect the behavioral intentions of consumers. The purpose of this study to examine the effect of E-Trust, E- Satisfaction and E-Loyalty on Consumer Behavioral Intentions dimensions (purchase intentions, word of mouth communication and complaining intention). The study is a quantitative study and the online questionnaire form was used as data collecting tool. In the study, convenience sampling methods were used and the data were collected via an online questionnaire on Google Forms. The study was carried out on 239 participants. SPSS 21 was used to analyze the data. The regression analys was applied to examine to our research hypotheses. According to the results of the research, it was determined that e-trust, e-satisfaction and e-loyalty had a positive effect on word of mouth communicaiton, e-loyalty had a positive effect on purchase intention, e-satisfaction and e-loyalty had a negative effect on complaining intention. Keywords: E-satisfaction, E- trust, E-loyalty, Word of Mouth Communications, Purchase Intention, Complaining Intention. 1. Introduction We are moving gradually but steadily towards everything online. Buying things online has become a common practice among millions of people around the world. Recently, the number of people buying goods and services online has grown more than ever. Around 2.14 billion people worldwide purchased goods online in 2021. That same year, global e-retail sales amounted to $ 2.8 trillion. If that's not enough to blow you away, projections show that global e-retail sales will rise to $ 4.8 trillion by 2021. The resulting figures show how the online commerce volume is increasing day by day. 63% of shopping opportunities start online (Statista, 2021). This means that no matter where customers finally buy (online or at a brick and mortar store), the customer journey begins online. The way of purchasing is not exactly the same for two online shoppers. That's why if you own an online store, it's essential to understand what you can do to customize the online journey for customers. With the advancement in digital technology, it's no surprise that today's consumers are in control of the way they buy. They are open to discover as many categories, brands and products as they want. Gaining online customers can be achieved with satisfaction and trust based on quality service. As a result, the importance of customer loyalty is once again emerging in the online commerce environment, which is increasingly decreasing the share of traditional marketing. In this year one of the biggest changes in shopping behavior is undoubtedly due to the impact of the coronavirus, the number of online shoppers. Moreover, the period of the pandemic has made our future clearer about the transition to the online age. In this context, the purpose of this study is to examine the effect of e-satisfaction, e-trust and e-loyalty on consumer behavioral intentions (word of mouth communication, purchase intention and complaining intention). The article is organized as following: First we explain problem statement and research objectives; second we review relevant The Effects of E-Satisfaction, E-Brand Loyalty and E-Trust Levels on Consumer Behavioral Intentions: A Study on Online Shoppers in Turkey 31 literature and place conceptual framework; third review hypothesis development process and the research model is developed and the hypotheses are proposed and fourth we specify research methology and; fifth we present research findings and finally we present significant research results and discuss implications of those findings. 1.1. Problem Statement Undoubtedly, consumer behavior consists of many features that constitute a barrier to direct access to customer loyalty (Odabaşı and Barış, 2002). As we can say, consumer behavior is a constantly changing factor. Especially nowadays, due to the Internet age, which further complicates consumer behavior. Therefore, being loyal online requires a deep understanding of consumer behavior. Based on previous studies that show that the concept of trust is related to consumers' intentions, and is relatively complex as it differs from one individual to another and from one community to another, especially online trust issues. In addition to the issues of trust that are increasing with the increasing relative ease that characterizes the online market at the present time. On the other hand, with the increasing competition in the online market, it becomes more difficult to achieve online customer satisfaction because the ceiling of expectations is relatively escalating. Therefore, this study was conducted taking into consideration the online consumer behavior with the satisfaction and trust factors for loyalty in the online shopping market. 1.2. Research Objective  To determine the main effect of E-brand loyalty levels on consumer behavioral Intentions (word of mouth communication, purchase intention, complaining intention) on online shopping sites.  To examine the effect of E-satisfaction on consumer behavioral Intentions (word of mouth communication, purchase intention, complaining intention) on online shopping sites.  To examine the effect of E-trust on consumer behavioral Intentions (word of mouth communication, purchase intention, complaining intention) on online shopping sites. 2. Conceptual Framework The conceptual framework of thıs study is as shown in Figure 1. Figure 1: Research Model Mehmet SAĞLAM & Maram JARRAR 32 2.1.E-Brand loyalty E-brand loyalty is a concept resulting from the Internet era, researchers have described it from various angles. In general, brand loyalty is a positive response to a particular brand due to the satisfaction and trust usually associated with the desire to repeat the purchase (Eid, 2011). When moving to the electronic concept, what applies to brand loyalty in the traditional situation it also applies in the case of loyalty to the E-brand. In this context, it means the response associated with the desire to repeat purchasing from the related website not from the traditional store (Liu, 2012). Online loyalty is also associated with many dependencies, such as good reputation, influence of word of mouth online and gaining many followers on social media who are waiting for any new products / services (Pratminingsih et al., 2013: 105). Based on previous studies, it is difficult to change a customer's loyalty to a website they feel satisfied and trusted, so it will remain a constant priority for them in their search lists (Audrain-Pontevia et al., 2013). A customer's experience plays a fundamental role in shaping their loyalty or belief in the brand (Chen and Barnes, 2007). 2.2.E-trust Trust in the seller is the main key in the shopping process in general (Jarvenpaa et al., 1999; Urban et al., 2000; Gefen, 2002; Kim and Kim, 2005), and in the case of online shopping, it becomes more difficult to build trust relatively due to what it requires to share financial information and personal data (Egger, 2006). Moreover, electronic trust is not limited to providing customers' financial information in the purchasing process, but rather goes beyond anything that causes the customer to establish a new intention to replicate the purchasing experience from the same website (Jarvenpaa et. al., 1999; Gefen and Straub, 2004). In contrast, the process of building online trust is associated with several risks, including: financial risks, product risks, privacy and security threats, and risks associated with the delivery process. Therefore, the customer prefers to make online purchases from reliable sites. Which means working on building a triangle whose angles are trust first, satisfaction, then loyalty (Jin and Park, 2006; Kim, Jin and Swinney., 2009; Ghane et al., 2011). As a result, the customer's trust that the value to be obtained is greater than the risk concerns they would face if they made an online purchase from this site, this means that this enables the intention to repurchase from the same site (Chen, 2012). 2.3 E- satisfaction The first purchasing experience of any website makes a huge impact. However, Oliver (1999) explained that online satisfaction is fundamentally related to the customer experience that is based on the repetition of a series of purchases from the website. In general, satisfaction increases profits (Bansal, et al., 2004), therefore having a negative customer experience means that they have an opportunity to quickly switch to a new service provider (Gummerus et al., 2004; Tversky and Kahneman, 1974). As a result, the customer service department plays an important role, especially in the current situation. Moreover, prompt customer response provides satisfied and loyal customers, and that means building electronic trust (Wu, 2013). 2.4. Consumer Behavioral Intentions The online consumer can be thought of as an individual or institution that uses the Internet to meet its consumption needs (Tiryaki, 2008). Consumer behavioral intentions are related to what kind of behavior the consumer can engage in during shopping. In generally behavioral intention includes four dimensions (Zeithaml, Berry and Parasuraman 1996). In this research, on the online platforms, three consumer behavioral intentions are highlighted, including: Online Consumer Purchase Intention: Any human behavior is related to its intention to occur. For example, the desire to cry precedes the occurrence of crying behavior. What applies to crying behavior also applies to buying behavior. As a result, the purchasing intention of the consumer is seen as the desire of the consumer to purchase a certain product or service. The existence of the consumer's purchase intention does not guarantee the occurrence of purchasing behavior. On the other hand, it is accepted as the basic basis for the formation of consumer purchasing behavior. For example, a consumer may want to purchase a product or a good, but does not buy it due to the surrounding factors that prevent it from performing the buying behavior (Sağlam, 2014). Today, building loyalty-based behavior is based on trust and satisfaction that push customers to seek the product / service they want from their favorite brand, and all this works to push the customer, as well as follow this brand on all social media sites and maintain this close relationship. Also their involvement in repetitive buying behavior on this The Effects of E-Satisfaction, E-Brand Loyalty and E-Trust Levels on Consumer Behavioral Intentions: A Study on Online Shoppers in Turkey 33 site will be the answer to many when asked "because they trust the site, they feel satisfied and connected to the site," and why they bought this site from one website instead of others. Online Word-of-Mouth Communication: It is assumed that word-of-mouth communication often plays an important role in influencing consumer attitudes and purchasing intentions (Xia and Bechwati, 2008; Sen and Lerman, 2007; Chevalier and Mayzlin, 2006). The literature shows that word-of-mouth communication is more effective than editorial suggestions or advertisements (Trusov et al., 2009; Smith, Satya and Sivakumar, 2005) because of its credibility and persuasion (Mayzlin, 2006; Godes and Mayzlin, 2004; Gruen, Summers and Acito, 2000). We live in an age of speed and expansion, each of us transferring their experiences to the other, and there are easy technical tools for these ideas to spread very quickly. For this reason, companies are working on the most popular marketing techniques in this period, that means working with the ambassadors and influencers who can transfer their experiences to their followers through social media, thus giving users reliable promises from the people they trust and feel loyal to them. Online Consumer Complaining Intention: Based on the literature, the intention to make a complaint is based on three main factors:  Customer dissatisfaction: Although there are many studies confirming that customer dissatisfaction does not generally lead to complaint behavior, it is accepted as a key component associated with complaint behavior (Jacoby and Jaccard, 1981; Singh, 1988).  Response: This response includes a behavioral response or a non-behavioral response (Jacoby and Jaccard, 1981; Singh, 1988; Maute and Forrester, 1993).  A negative experience that results in different reactions from non-behavior to negative complaints and reviews (Day, 1980; Day et al., 1981; Jacoby and Jaccard, 1981). Today, the customer's intention to complain is not just related to their experience with the product / service. It may also include trying it out on the online shopping site. Therefore, corporate governance should provide sufficient areas of expression for customers. In other words, the customer can add comments expressing the shopping experience to the website, as well as presenting the option to complain at any stage before and after the purchase (Yılmaz, 2004: 14). 3. Hypothesis Development The process of developing study hypotheses requires a deep analysis of the relationships of these factors among them. According to previous literature, a positive relationship was observed as a behavioral intentions between purchasing volume and customer loyalty. In other words, the greater the purchasing power of a customer, the more they will be associated with their preferred product/service and thus less likely to convert them from another seller. Trust is a mediating variable between customers and companies in e-commerce. In other words, if customers trust the site where they buy their products and services, this increases the power of electronic loyalty and thus electronic satisfaction (Singh and Sirdeshmukh, 2000). In addition, customer loyalty is related to behavioral intention to repurchase (Garland and Gendall, 2004). In this context, having an element of trust and loyalty improves behavioral intentions. Moreover, the effect of satisfaction on the previously mentioned repetitive behavioral intentions, there are several studies confirming its positive role in positive word-of-mouth communication within the realm of online shopping (Duarte, Silva and Ferreira, 2018; Fang, Shao and Wen, 2016; Gounaris, Dimitriadis and Stathakopoulos, 2010; Kassim and Abdullah, 2010). Based on the term electronic loyalty in previous literature has been associated with three determinant outcomes: repeated purchase intentions, positive word of mouth, and willingness to pay more (Kumar, Pozza, and Ganesh, 2013; Liao, Wang, and Yeh, 2014; Srinivasan, Anderson, and Bonavolo, 2002). Finally, the study by Erçetin and Arıkan (2020) showed that designing websites that guarantee the advantage of providing complete trust to customers creates thereby improving positive word-of-mouth communication. The complaint is about the attitude of the public for three main reasons: dissatisfaction, unhappiness or disappointment in expectations (Rutter, 2007: 28). Therefore, it is related to the inverse state of satisfaction. If the complaint is also answered satisfactorily, it will increase customer loyalty, otherwise the result will be separated and transferred to another brand (Barış, 2008: 25). For this reason, it can be said that disappointment in expectations Mehmet SAĞLAM & Maram JARRAR 34 causes the trust barrier between the company and its customers to be broken. Therefore, companies in their customer service departments must work to restore customer trust by responding positively to customer complaints. 3.1.Research Hypothesis Within the scope of the information obtained from the literature, 9 main hypotheses have been proposed. H1: E-satisfaction has an effect on word of mouth communication. H2: E-satisfaction has an effect on purchase intention. H3: E-satisfaction has an effect on complaining intention. H4: E-brand loyalty has an effect on word of mouth communication. H5: E-brand loyalty has an effect on purchase intention. H6: E-brand loyalty has an effect on complaining intention. H7: E-trust has an effect on word of mouth communication. H8: E-trust has an effect on purchase intention. H9: E-trust has an effect on complaining intention. 4. Research Methodology 4.1. Sample Method This survey is aimed at online shoppers. It was conducted based on convenience sample method within Turkey and is mainly centered on Istanbul. Where the questionnaire was distributed via social media tools (Facebook - WhatsApp - Instagram) and data were collected from 240 participiants and analyzes were conducted on 239 of them. Before starting the data collection process, necessary approval form was obtained from the Istanbul Commerce University Ethics Committee (Issue: E-65836846-044-202523). 4.2 Research Instruments This study is considered a quantitative study, as a questionnaire was used to test the conceptual framework of the research and the hypotheses placed on it. The questionnaire was designed by Google Form and distributed to online shoppers. It must be noted that this questionnaire was mainly based on the five-point Likert type scale, which ranges from a "strongly disagree" with a value of 1 to a rating of "strongly agree" with a value of 5.  E-satisfaction and E-trust scales were adapted from Safa and Solms'(2016) and Ting et al. (2016) and includes 7 items  E-loyalty scale was adapted from Zeithaml, Berry and Parasuraman, (1996) and Gremler (1995) and includes 7 items  Consumer Behavioral Intentions scales were adapted from Zeithaml, Berry and Parasuraman (1996) and it includes four items and 13 items. 4.3. Data Analysis The data collected from the questionnaire were analyzed using IBM SPSS 21 software. Descriptive statistics, factor analysis were performed over the data obtained. Reliability test was carried out to test the research scales. Followed by correlation and regression tests were used to reveal the effects of variables on each other and to reveal the degree of correlation. 5. Findings 5.1. Demographic Analysis Table 1: Sample Demographic Characteristics Variables f % Income Level f % Gender Under 1000 TL 50 20,9 Female 152 63,6 1000-3000 TL 65 27,2 Male 87 36,4 3001-5000 TL 44 18,4 Total 239 100 5001-7000 TL 35 14,6 The Effects of E-Satisfaction, E-Brand Loyalty and E-Trust Levels on Consumer Behavioral Intentions: A Study on Online Shoppers in Turkey 35 Age 7001-9000 TL 16 6,7 0-17 1 ,4 9001 TL and above 24 10,0 18-24 77 32,2 Missing 5 2,1 25-34 109 45,6 Total 239 100 35-44 39 16,3 Do you shop online 45-54 6 2,5 Yes 235 98,3 55-64 6 2,5 No 0 0 Missing 1 ,4 Missing 4 1,7 Total 239 100 Total 239 100 Educational Level Primary Level 3 1,3 High Level 12 5,0 Associate's Degree 9 3,8 Bachelor 103 43,1 Master 85 35,6 PhD 25 10,5 Missing 2 ,8 Total 239 100 Table 1 shows the demographic characteristics of the participants (See Table 1 for details.) The final part of the demographic section includes that it is found that more than 98% of the respondents use online shopping, which confirms the fundamental importance of this type of research at the present time. 5.2. Factor Analysis Results Table 2. Factor Analysis Total Explained Variance Amounts Results No. Variables Disclosed variance ratio of variables 1 E-Satisfaction 62,00 2 E-Trust 55,11 3 E-Brand Loyalty 54,34 4 Consumer Behavioral Intention 59,24 From Table 2, the total variance amounts of the factors belonging to the data collection tools used in the study are e- satisfaction 62.0%; e-trust 55.11%; e-loyalty is calculated as 54.34% and consumer behavioral intention is 59.24%. Other references that should be checked for factor analysis are Kaiser-Meyer-Olkin (KMO) and Bartlett Test. The KMO test tests whether the partial correlations are small and whether the distribution is sufficient for factor analysis. Before starting the factor analysis, it is necessary to check whether Bartlett's test of sphericity is also significant. The Barlett test reveals whether the correlation matrix provides significant correlations between at least some variables in a data set, which is a prerequisite for the study of factor analysis (Bartlett, 1951). Mehmet SAĞLAM & Maram JARRAR 36 Table 3: KMO and Bartlett's Test values Variables Kaiser-Meyer-Olkin Test Bartlett's Test of Sphericity E-Satisfaction ,902 870,084* E-Trust ,839 880,550* E-Loyalty ,848 714,069* Consumer Behavioral Intention ,758 1145,192* *(p<,001) KMO test value is perfect as the value found approaches to 1, unacceptable below 0.50 (excellent at 0.90, very good at 0.80, average at 0.70 and 0.60 and bad at 0.50 (Tavşancıl, 2010). In this study, KMO values were found to be within acceptable ranges, usually by fitting very good levels. Barlett's test result is 870,084 (Sig <,001) for e- satisfaction; 880,550 (Sig <,001) for e-trust; It was calculated as 714,069 (Sig <,001) for the e-loyalty and 1145,192 (Sig <,001) for the consumer behavioral intention. The significance of Bartlett's values also supports the hypothesis that the data come from multivariate normal distribution (Revelle, 2016). Table 4: Factor Analysis Component Matrix Results Scale Items E-satisfaction E-trust E-loyalty Behavioral Intentions 1 2 3 4 SS1 ,724 SS2 ,839 SS3 ,699 SS4 ,827 SS5 ,821 SS6 ,791 SS7 ,800 ST1 ,615 ST2 ,554 ST3 ,702 ST4 ,787 ST5 ,809 ST6 ,816 The Effects of E-Satisfaction, E-Brand Loyalty and E-Trust Levels on Consumer Behavioral Intentions: A Study on Online Shoppers in Turkey 37 ST7 ,860 SL1 ,823 SL2 ,716 SL3 ,834 SL4 ,766 SL5 ,729 SL6 ,795 SL7 ,827 SI1 ,791 SI2 ,828 SI3 ,770 SI4 ,753 SI5 ,657 SI6 ,598 SI7 -,541 ,579 SI8 ,588 ,605 SI9 ,509 ,583 SI10 ,709 SI11 ,812 SI12 ,712 SI13 ,442 In Table 4 shows the component matrix chart for all variables where the dependent variable consists of behavioral intentions that include word of mouth communication, purchase intention, price sensitivity and complaining intention. On the other hand, the independent variables are e-satisfaction, e-trust, and e-loyalty in principal component analysis, the high factor loading values in the factor in which the items are grouped mean that they measure the desired phenomenon or structure together. For this, the factor loading values are expected to be higher than the ,40 score. In addition, items are expected to give a high factor loading value in a single factor, and there Mehmet SAĞLAM & Maram JARRAR 38 should be a difference of at least .10 level between the factor loading values received from other factors (Büyüköztürk, 2003: 118). In this study, Price Sensitivity, as the third factor of the Behavioral Intention Level scale, was excluded because it did not meet this requirement. As seen in the Table 4, when the Price Sensitivity factor, which is the 3rd factor, is removed, the 1st factor of the Behavioral Intention Scale is word of mouth Communication, the 2nd factor is in the Purchase Intention and the 4th factor is in the Complaining Intention factors, and in other factors, it carries a factor loading value higher than 0.10 difference. Therefore, it was deemed appropriate to continue the analysis with the 3-factors structure of the Behavioral Intention Scale. 5.3. Reliability Analysis Reliability analysis refers to the consistency or stability between successive measurements. Cronbach Alpha is one of the most preferred reliability analysis. The range 0<α<,40 is unreliable; The range of ,40< α <,60 is interpreted as low reliability, the interval of ,60< α <,90 is interpreted as highly reliable, and the range of ,90< α is interpreted as highly reliable (Tavşancıl, 2010). Table 5. Cronbach Alpha Coefficients For The Scales Variables Items Cronbach’s Alpha E-Satisfaction 7 ,894 E-Trust 7 ,861 E-Brand Loyalty 7 ,839 Word to Mouth Communication 3 ,865 Purchase Intention 3 ,782 Complaining Intention 4 ,702 Based on Cronbach's Alpha values, reveal that the internal consistency of the scales is sufficient. Therefore, the values obtained as a result of validity and reliability studies show that the scale is usable and reliable. 5.4. Correlation Analysis Correlation analysis is an analysis that determines the strength and direction of the relationship between the study factor structures. Pearson correlation coefficient is used because the data conforms to the normal distribution. Therefore, the Pearson correlation coefficient is used to analyze whether the relationship between factors is statistically significant (Çokluk, Şekercioğlu and Büyüköztürk, 2012: 35). Table 6: Pearson Correlation Analysis Results Pearson Correlation Coefficients 1 2 3 4 5 6 (1) E-Satisfaction 1 ,571* ,491* ,613* ,355* ,179* (2) E-Trust 1 ,467* ,565* ,369* ,015 (3) E-Loyalty 1 ,658* ,586* -,067 The Effects of E-Satisfaction, E-Brand Loyalty and E-Trust Levels on Consumer Behavioral Intentions: A Study on Online Shoppers in Turkey 39 (4) Word of mouth communication 1 ,546* ,029 (5) Purchase Intention 1 -,006 (6) Complaining Intention 1 *p< ,05 According to the results of the correlation analysis, we find that electronic satisfaction have a moderate positive relationship with all variables except complaining intention factor, which have a relatively weak positive relationship. Looking at the electronic trust factor and the electronic loyalty factor, it appears from the Table 6 that there is a moderately significant positive relationship with all variables, except for the complaining intention variable, which was found to have no relationship with both E-trust and E-loyalty. It means that an increase in some or all of the independent variables leads to an improvement in the dependent variables. When the relationships between the dependent variable sub-dimensions were examined, it was determined that only word of mouth was positively related to purchase intention. 5.5.Regression Analysis Regression analysis was performed to determine the effect of the independent variable (e-satisfaction, e-trust, and e- loyalty) on the dependent variable (word of mouth communication, purchase intention and complaining intention). A p (sig) value less than .05 in the regression analysis indicates that the regression model is significant, while the square of the correlation coefficient (R2) indicates the rate of explaining the change on the dependent variable. In the regression analysis, the significance of the model is tested firstly. If the p value of the ANOVA test is less than 0.05, the model is assumed to be significant. The R2 value is used to determine how much of the change in the dependent variable is explained by the independent variables (Büyüköztürk, Çokluk and Köklü, 2013: 122). The Standardized Beta Coefficients in the regression equation show how much effect two or more independent variables have on the dependent variable. In other words, a standard deviation change in the independent variable indicates how much the dependent variable will change with the unit of standard deviation (Bryman and Cramer, 1999 cited in Cevahir, 2020: 132). Table 7: Multiple Regression Analysis Results of Word of Mouth Communication Factor Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -.482 .270 -1.781 .076 E-Satisfaction .390 .074 .290 5.289 .000 E-Trust .246 .065 .203 3.763 .000 E-Loyalty .488 .059 .420 8.248 .000 R=,75 R2=,56 F=103,416 Sig=,000 5.5.1. Dependent Variable: Word of Mouth Communication According to the standardized regression coefficients, it was determined that e-loyalty (β=.488, Sig<,05); e- satisfaction (β=.390, Sig<,05) and e-trust (β=.246, Sig<,05) had an effect on the word of mouth communication. Mehmet SAĞLAM & Maram JARRAR 40 Table 8: Multiple Regression Analysis Results of Purchase Intention Factor Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) .900 .289 3.110 .002 E-Satisfaction .049 .079 .042 .619 .536 E-Trust .109 .070 .103 1.561 .120 E-Loyalty .527 .063 .518 8.319 .000 R=,59 R2=,35 F=43,355 Sig=,000 5.5.2. Dependent Variable: Purchase Intention According to the results of the regression analysis, it was found that only e-loyalty has an effect on purchase intention (β=,518; Sig<,05). Table 9: Multiple Regression Analysis Results of Complaining Intention Factor Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 3.101 .402 7.707 .000 E-Satisfaction .427 .110 -.314 3.893 .000 E-Trust -.095 .097 -.078 -.982 .327 E-Loyalty -.217 .088 -.185 -2.471 .014 R=,26 R2=,06 F=5,663 Sig=,000 5.5.3. Dependent Variable: Complaining intention According to standard regression coefficients, it was determined that e-satisfaction (β= -.314, Sig<,05) and e-loyalty (β = - .185, Sig<,05) had an effect on complaining intention. 6. Conclusion and Recommendation According to the results of the research, behavioral intentions in online shopping is based on e-trust, e-satisfaction and e-loyalty levels. The arrangement of these dimensions according to their impact on achieving behavioral loyalty is as follows: electronic loyalty, electronic satisfaction, followed by electronic trust. In this study, the price sensitivity factor was excluded as a behavioral intention measure. Thus, based on the regression results of this research, the following results can be summarized as follows:  Electronic loyalty has a positive effect on purchase intention.  Electronic loyalty has a positive effect on word of mouth communication.  Electronic loyalty has a negative effect on complaining intention.  Electronic satisfaction has a positive effect on word of mouth communication. The Effects of E-Satisfaction, E-Brand Loyalty and E-Trust Levels on Consumer Behavioral Intentions: A Study on Online Shoppers in Turkey 41  Electronic satisfaction has a negative effect on complaining intention.  Electronic trust has a positive effect on word of mouth communication. According to the results, we find a relatively moderate positive effect of electronic loyalty, electronic satisfaction and electronic trust on word of mouth communication. In addition, the effective positive impact revolves around electronic loyalty and purchase intention. On the other hand, the results show an inverse relationship between electronic loyalty and complaining intention. Based on previous results, it can be said that the interpretation of the results of this study is consistent and logical with those stated in the previous literature. Therefore, electronic trust, electronic satisfaction and electronic loyalty is generally influencing consumer behavioral intentions. Initially, electronic loyalty is associated with outcomes that encourage positive word of mouth and lead to a solid brand reputation. It also directly relates to future repetitive buying behavior. On the other hand, the inability to respond positively to online users' complaints results in their electronic disloyalty. For this reason, companies should develop the concept of e-loyalty and pay more attention to this concept when we move to the choice of online shopping. Secondly, the electronic satisfaction factor can be said to serve as a measurement tool for both word of mouth communication and complaining intention. Finally, electronic trust is also positively associated with positive oral communication resulting in brand reputation. Therefore, it is imperative to work on achieving the three dimensions in any online shopping process. It should be noted that there are many previous studies looking at achieving one or more of these dimensions (trust, satisfaction or loyalty) on consumer behaviors. In previous literature, the term electronic loyalty has been associated with three determinant outcomes: repeated purchase intentions, positive word of mouth, and willingness to pay more (Kumar, Pozza and Ganesh, 2013; Liao, Wang and Yeh, 2014; Srinivasan, Anderson and Bonavolo, 2002). Therefore, the protection of confidentiality is expected to have a positive, albeit indirect, role in positive word of mouth. This study focused mainly on Turkish online shoppers from different online shopping websites. Therefore, it must be noted that such a study be conducted on a larger sample, which will have more accurate results and will have the largest impact on the development of the online shopping sector. References Audrain-Pontevia, A.F., N’Goala, G. & Poncin, I., (2013), “A Good Deal Online: The Impacts of Acquisition and Transaction Value on E-Satisfaction and E-Loyalty”, Journal of Retailing and Consumer Services, 20, 445-452. 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