1. Introduction
Complaints are a type of consumer behavior that businesses do not want to encounter, yet they serve as a mechanism to obtain feedback about their problems and gather information to retain existing customers and attract potential customers. The digital age has made it easier for consumers to voice their complaints. Since consumers’ passive complaint behaviors in the online environment are much more damaging, businesses promote active consumer behaviors, enabling customers to express their dissatisfaction directly to them. Cultural factors in relation to consumer complaint behavior are an important matter for consideration, especially for businesses operating in international markets. Recognizing the factors that prevent consumers from engaging in active complaint behavior preferred by businesses in markets with different cultural structures, such as cognitive dissonance theory and brand loyalty, has been an important process. To date, there has been some research on the effect of intercultural differences on complaint behavior [1,2,3,4]; however, the motivation for this research draws from the lack of focus on the impact of cognitive dissonance theory and brand loyalty on consumer complaint behavior in relation to cultural differences. It is important to determine how effective the cognitive dissonance theory and brand loyalty are in influencing consumer behavior in different cultures in terms of consumer satisfaction and, therefore, success in the market in which businesses participate.
In general, cultural differences appear as an essential factor that affects consumer complaint behavior. According to the cognitive dissonance theory, which originated in psychology, if one’s belief, knowledge, or attitude contradicts another belief, knowledge, or attitude, the conflict between the two creates cognitive dissonance [5]. In other words, people’s thoughts and attitudes are dependent on their past experiences and values. Hence, cognitive dissonance appears to inhibit businesses from engaging in meaningful communication with targeted customers, through which the most accurate feedback on their products and services can be obtained. As businesses have failed to predict the consumers’ coping strategies with the instability of consistency, their cognitive and emotional responses to the contradictions, and their impact on the level of consumer satisfaction in the markets in which they operate, cognitive dissonance represents a significant issue for the businesses’ sustainability.
By adopting cognitive dissonance theory and brand loyalty, this research was conducted to help clarify the above-mentioned problems, focusing on how and to what extent consumers achieve a balance of consistency and how this is reflected in their complaint behaviors in Romania and Turkey, which can be considered as two countries with significant differences in cultural structure. In this context, a literature review was conducted primarily on brand loyalty, complaint behavior, cognitive conflict, and culture. As a result of the literature review, previous studies on the subjects are presented, and the infrastructure of the hypotheses is explained. Afterward, the research model and research method are explained, and the findings obtained after the analysis are included. In the conclusion and discussion part, the research findings are interpreted and the differences and similarities with other studies are emphasized. In addition, various suggestions are made for future studies and practitioners by considering the research limitations.
2. Theoretical Background and Literature Review
2.1. Brand Loyalty
Brand loyalty is defined as the interaction between a customer’s relative (positive) attitude toward a brand and their repeat purchase behavior toward that brand [6]. Brand loyalty is initially developed as cognitive loyalty, which later develops into affective loyalty based on the performance and functional characteristics of the brand [7]. Brand loyalty plays a crucial role for companies in the market in terms of “gaining high market share and new customers, supporting brand extensions, reducing marketing costs, and strengthening brand to the competitive threats” [8].
Most research on brand/customer loyalty and consumer behavior has found a significant relationship between the two concepts and concluded that customers with brand loyalty showed increased consumer complaint behavior [9,10,11]. On the other hand, a more recent study on Turkish consumers has found a negative correlation between brand loyalty and consumer complaint behavior [12]. Another recent study found that brand loyalty mediates the effect between consumer hate and negative WOM [13]. In addition, fewer studies focus on brand loyalty and cognitive dissonance compared to other relevant concepts. Mittelstaedt’s [14] study on repeat purchasing behavior showed a relationship between brand loyalty and cognitive dissonance. Sharifi and Esfidani [15] found that cognitive dissonance directly and negatively impacted brand loyalty. Koller and Salzberger [16] found that cognitive dissonance was not directly related to brand loyalty in retail. On the other hand, John and Nair [17] determined that brand loyalty was one of the important reasons why customers experience cognitive dissonance.
2.2. Complaint Behavior
Crie [18] defines consumer complaint behavior as the process that “constitutes a subset of all possible responses to perceived dissatisfaction around a purchase episode, during consumption or during possession of the good (or service).” He also argues that consumer complaint behavior is not an instant response but a process that depends on the evaluation of the situation by the consumer and its evolution over time. Consumer complaint behavior is known to be associated with consumer dissatisfaction [19,20] or unsatisfactory experiences [21]. The reason for this is also shown by the fact that bad experiences can lead to customers’ negative emotions and, thus, consumer complaints [22]. Hawes and Arndt [23] found that a higher level of consumer complaints indicates dissatisfied consumers. Therefore, bad experiences lead to negative emotions, whereas good experiences create positive emotions, and these experiences result in consumers’ positive or negative behavioral responses. If consumers are not provided with the appropriate complaint procedures to express their adverse reactions, it fuels their dissatisfaction, resulting in negative behaviors such as retention, negative word-of-mouth [24,25], doing nothing [26,27], and complaining [28]).
One of the oldest taxonomies of consumer complaint behavior was established by Hirschman [29] with a three-way classification of dissatisfaction responses, including “exit,” “voice,” and “loyalty.” The “exit” response is defined as the customer’s refraining from buying from the firm or leaving the brand, “voice” is the buyer’s direct notification of their disappointment to the firm, and “loyalty” is the consumer’s failure to take any action about the problem. Singh and Pandya [30] listed four types of complaint behavior: exit, adverse WOM, voiced complaints, and third-party responses. The exit refers to leaving the brand, as in Hirschman [29], while the voice refers to communicating directly with the firm, the negative WOM means talking to close friends and in friendship circles, and contacting a third party refers to government authorities or authorized organizations.
A significant number of researchers have focused on the reasons for consumer complaints [18,28,31,32,33,34,35,36,37]. Singh and Wilkes [32] highlighted factors such as previous experiences, attitudes towards the brand or product, high dissatisfaction, expectations about the brand, and alienation as the reasons for consumer complaint behaviors. In their study investigating consumer complaint behaviors in online and offline purchases, Lee and Cude [33] revealed that consumers were more likely to complain about their online shopping than offline ones, which increased with the degree of dissatisfaction.
Another area of research is the consumer’s choice of complaint channel and the types of consumer complaining behavior [28,33,38,39,40]. Robertson [28] revealed that both the consumer complaint behavior (CCB) theory and the media richness theory (MRT) help to explain consumers’ motivation for channel selection. The study suggested that channel choice was more strongly associated with convenience than with task–media fit. Moreira and Silva [39], exploring consumers’ complaints to third parties, pointed out that customers initially complained to the service provider and then turned to the WOM behavior among their social circle and friends, but they did not complain to third parties unless the issue was serious or unresolved. Hart and Coates’ [40] study revealed that university students preferred to submit complaints about their institutions through informal channels and avoided resorting to formal channels.
2.3. Cognitive Dissonance
The concept of cognitive dissonance, introduced by Festinger in 1957, has become a popular construct in psychology and consumer behavior research. Cognitive dissonance is defined as a psychologically uncomfortable state that arises from the existence of mental conflict after making a purchase. In other words, this concept is mostly used to describe the emotional discomfort felt at any stage in the purchasing decision process of consumers [41]. The same concept is used to explain the quandary consumers may be in after making a purchase [35]. This theory emphasizes the notion that individuals who know or think different things that are not psychologically consistent try to reduce the dissonance and achieve consonance in various ways [42].
Cognitive dissonance and dissonance reduction, common subjects of study in the field of marketing, are closely related to consumer complaint behavior. One of the most widely studied topics in consumer cognitive dissonance is the antecedent factors of cognitive dissonance [43,44,45,46]. Yamaguchi and Abe [43] stated that cognitive dissonance was more likely to be prompted when purchase decisions were more difficult to make, for products with a higher price or on sale, and for emotional purchases associated with sensory attributes. In another study, Ranjbarian et al. [45] showed that consumer spiritual intelligence had a negative relationship with cognitive dissonance and emphasized that the level of cognitive dissonance decreased as consumers’ spiritual intelligence increased. In their study, Nosi et al. [47] concluded that as the cognitive efforts of free-riding consumers increase, their tendency to experience cognitive dissonance also increases.
One of the frequently studied topics in relation to cognitive dissonance is the impact of demographic variables on cognitive dissonance [48,49,50]. Jamwal and Pandey [49] demonstrated that, compared to other age groups, young customers were more prone to dissonance, and males displayed a greater tendency to dissonance than females. In another study, Gbadamosi [50] found that low-income women were more sensitive to sale discounts and product comparisons, which led to a high tendency for cognitive dissonance in this demographic group.
Another issue that has been extensively researched is cognitive dissonance’s consequences among consumers [15,35,51,52,53,54,55]. We know that customers’ cognitive dissonance often leads to negative opinions about the product or service, causes a decrease in brand loyalty, and negatively affects repeat purchase intentions [51]. Wilkins et al. [51] drew attention to the relation between pre-purchase consumer expectations and the cognitive dissonance experienced after a purchase and found that, after an experience of cognitive dissonance, consumers showed a high tendency to change brands and were more willing to engage in negative WOM behavior within their social circle. In another recent study dealing with the relationship between cognitive dissonance and consumer behavior, it was found that the cognitive dissonance experienced by consumers had a significant effect on their post-purchase satisfaction levels, and their satisfaction levels had a significant effect on exhibiting EWOM behavior [56]. Demirgüneş and Avcılar’s [52] study showed that consumers who experienced post-purchase emotional and rational inconsistencies exhibited complaint behavior and exit behavior, also known as switching intention. In their study, Sharifi and Esfidani [15] indicated that, thanks to relationship marketing, consumers experienced less cognitive dissonance in the post-purchase stage, which led to more satisfaction and loyalty. In light of the previous research, the following hypotheses were developed:
Customers’ brand loyalty level positively affects the level of cognitive dissonance.
Customers’ brand loyalty level positively affects the level of complaint behavior.
The level of the cognitive dissonance experienced by the customers positively affects the level of complaint behavior.
2.4. Culture
In addition to demographics, motivations, and lifestyles, cultural differences also affect consumer complaint behavior [57]. Culture, which shows significant differences across geographies, is a whole consisting of beliefs, roles, behaviors, value judgments, customs, and traditions of the human community [58]. Culture affects consumer perceptions about service quality and reactions to service failures [59,60].
Many studies on customer behavior in relation to culture appear to have used the model developed by Hofstede [61] in determining the dimensions of cultural differences. The model was developed as a result of studies conducted to identify the differences between national cultures, and six dimensions were identified to explain cultural differences. These dimensions are power distance, avoidance of uncertainty, individualism–collectivism, masculinity–femininity, long-term and short-term orientation, and indulgence–restraint [62]. We also used the Hofstede model to identify cultural differences in our research. According to the model, the cultures of Turkey and Romania have similarities across most dimensions, but the indulgence dimension, which has been added to the model relatively recently and is predicted to have a high impact on complaint behavior, seems to be a distinguishing element between the two cultures [63]. While developing our hypotheses, it was taken into consideration that customer behaviors may show relative differences in the Romanian culture, where the indulgence rate is relatively higher.
There are many studies investigating the relationship between cultural differences and complaint behavior [1,2,3,4,40,64,65,66,67,68,69,70]. In their study, Liu and McLure [1] emphasized that complaint behaviors varied across cultures. Badghish et al. [3], investigating the differences in complaint behaviors between Saudi Arabians and Filipinos, concluded that Saudi Arabians were more aggressive and demanding complainers, both in voicing their complaints and finding solutions.
A large volume of studies aiming to determine the cultural characteristics of customers with regard to complaint behavior has been mainly conducted in the service industry [2,64,65,71,72,73,74,75,76,77]. Most of these studies were conducted in the hotel and restaurant sectors. Heung and Lam [2] highlighted that non-Asian customers in Hong Kong were more prone to complain than Chinese customers, who were passive about communicating dissatisfaction. DeFranco et al. [64] found that the Houston group showed more personal reactions and complaint behaviors toward the management than the Hong Kong group, while Ngai et al.’s [72] study on the consumer complaint behavior of Asian and non-Asian customers about hotel services revealed that Asian customers avoided face-to-face complaints and tended to take private complaint action. Ekiz and Au [73] found that, while Chinese customers spoke to their friends and relatives about their dissatisfaction, American consumers tended to use third parties to disseminate their dissatisfaction. Huang et al. [71] compared the complaint behavior of Japanese and American guests and concluded that Americans were more likely to consider complaining as a responsibility and voice their complaints of unsatisfactory service to receive better service in the future. Jahandideh et al. [74] found that Arab customers tend to inform relatives and friends about their negative hotel experiences, while Chinese customers were more likely to use official complaint channels in the restaurant context. Yüksel et al. [65] found that Israeli tourists express their complaints more than Turkish tourists due to having an individualist culture. Beyond the service industry, Tao and Jin [4] compared Chinese and American customers and found that electronic word of mouth (eWOM) and electronic negative word of mouth (eNWOM) were more important and effective for American customers than complaining to their families.
Research has shown that culture is an effective factor in customers’ experiences of cognitive dissonance [78]. Ko, An, Haan, and Yoon [79] found that customers who experienced cognitive dissonance reported their dissatisfaction directly to the store and participated in negative electronic word of mouth (eNWOM). The cognitive dissonance experience significantly affected customer satisfaction and led to negative post-purchase behavior. There have also been studies on the impact of cultural differences on customers’ brand loyalty [80,81,82,83]. Based on the literature review, the differences between the levels of brand loyalty, cognitive dissonance, and customer complaint behavior in different cultures were examined in this study. Hypotheses in relation to the three variables are as follows.
There is a significant difference between participants’ levels of brand loyalty in different cultures.
There is a significant difference between participants’ levels of cognitive dissonance in different cultures.
There is a significant difference between participants’ levels of complaint behavior in different cultures.
3. Materials and Methods
The study is a quantitative research study and uses the associational research model (Figure 1). The associational research model determines the interactions between two or more variables [84]. The following model was created during the research to determine whether brand loyalty affects cognitive dissonance and customer complaint behavior and whether cognitive dissonance impacts customer complaint behavior.
3.1. Subsection
The sample for this research consists of customers who are citizens of Turkey and Romania and who own mobile phones. Significance levels were taken as a basis for the sample size in the research. Since it is assumed that the population size exceeds 1,000,000, at least 384 scales had to be administered. The questionnaire was administered to the participants via the internet for one month using the snowball method. In order to reach the participants, different social media environments, messaging applications, and emails were used, through which we reached 394 people in Turkey and 396 people in Romania.
3.2. Data Collection Technique, Measurement Tools, and Data Analysis
Quantitative research methods and survey techniques were used to collect data. Three scales were used in the research. We referred to Sharifi and Esfidani [15] for the Brand Loyalty Scale, Sweeney, Hausnecht, and Soutar [85] for the Cognitive Dissonance Scale, and Wilkins, Beckenuyte, and Butt [51] for the Customer Complaint Behavior Scale (Appendix A). The participants were asked to complete the Brand Loyalty Scale considering their favorite smartphone brand. They were asked to answer the questions in the Cognitive Dissonance Scale and the Customer Complaint Behavior Scale based on the scenarios (You learned that a new model of your favorite smartphone brand is on the market. Suppose you buy this latest model phone, even though the price is hard on your budget. Imagine that, shortly after you start using the phone, you learn that a feature of the phone that is important to you has a technical fault in terms of functioning and that the manufacturer has avoided taking the necessary responsibility for eliminating the fault.) created for the scales. The questions regarding the scales in the questionnaire were first translated into Turkish and Romanian and then translated back into English to test cross-language equivalences. A 7-point Likert format (1 = disagree/7 = agree) was used in all scales to determine the participants’ agreement with the statements.
Within the scope of the research, the data were entered into the SPSS 22.0 program. Before analyzing the research data, the data entered into the computer environment were carefully examined and checked for errors. The accuracy of data entry for all variables included in the research was examined by creating frequency tables to observe missing data and the characteristics of the distribution. Thus, the data structure was determined.
After examining the frequency tables, we checked if the variables were normally distributed. Next, SPSS 22.0 and AMOS 20.0 package programs were used to determine the construct validity of the scales. SPSS 22.0 program was used for frequency analysis, descriptive statistics, and reliability analysis, while confirmatory factor analysis (CFA) and path analysis were carried out in AMOS 20.0 program.
In the analysis process, confirmatory factor analysis (CFA) was performed using the structural equation model (SEM) to test the compatibility of the variables in the research model with the data. The results of the analysis are shown in the values for the reliability and validity of the scales), the confirmatory factor analysis diagram of the model), the generally accepted model fit indices and the fit indices of the model The structural equation model was used to test the research model after it was determined that all variables were compatible with the data with this model. For path analysis, the SEM diagram model goodness-of-fit values and scale items and structural equation model results are shown.
3.3. Validity, Reliability, and Confirmatory Analyses of the Scales
The present study used the alpha model for conducting reliability analysis of the data collection tools. The resulting reliability coefficients for the scales are displayed in Table 1. According to Cronbach’s alpha criterion, reliability coefficients above 0.90 indicate excellent reliability, while coefficients ranging from 0.80 to 0.90 indicate high reliability, and those between 0.70 and 0.79 indicate moderate reliability [86]. Based on the coefficients obtained, the scales used in this research exhibited reliable properties.
Additionally, the study employed confirmatory factor analysis (CFA) to assess the convergent validity of the research variables. As Fornell and Lacker [87] recommended, the evaluation of convergent validity was based on three indicators: (a) reliability of each measurement item, (b) composite reliability (CR), and (c) average variance extracted (AVE). Table 1 presents the results of the standardized factor loadings for each item, which ranged from 0.448 to 0.925, indicating an acceptable range. The CR for each construct ranged from 0.814 to 0.890, surpassing the cut-off value of 0.7 [88]. Moreover, although the AVE value for complaint behavior was less than 0.5, it was deemed acceptable given that if the CR value is above 0.6, an AVE below 0.5 is still adequate for ensuring construct validity. Therefore, the results of the present study supported the sufficient convergent validity of the research variables.
In order to evaluate the discriminant validity of the constructs, the analysis used a comparison of the square root of the average variance extracted (AVE) of each construct with its correlation with other constructs. Specifically, if the square root of the AVE of a given construct is greater than its Pearson’s correlation coefficient with other constructs, it can be inferred that this construct exhibits satisfactory discriminant validity [89]. As presented in Table 2, the square root of the AVE for each construct surpassed the Pearson’s correlation coefficients with other constructs, indicating that all constructs in this study exhibited strong discriminant validity.
Confirmatory factor analysis (CFA) aims to test the model that deals with the relationship between observed measures or indicators and a smaller set of latent variables. CFA is used to check whether the proposed model is appropriate using sample data [90]. In confirmatory factor analysis, three basic measurement models are tested depending on the scale used: single-factor model, first-order confirmatory factor model, and second-order confirmatory model. First-order confirmatory factor analysis is the analysis that includes the relationship between the factors created in the model. Considering the variables used in the study, a first-order confirmatory factor analysis model was used in the study.
It is important to use different goodness-of-fit indices to determine the adequacy of the tested model in confirmatory factor analysis. In the study, goodness fit values assessed in accordance with Schreiber et al., [91], Hooper [92] and Kline [93] showed in Table 3 were used to test the accuracy and fit of the model determined via factor analysis.
After the model has been created and tested and the fit indices have been examined, researchers can modify the hypothetical model for a better-fit model. To this end, the AMOS program can suggest some corrections or modifications for the researcher. The researchers of this study followed the proposed modifications to improve the model. If the modifications are insufficient to capture the fit values, the model should be reestablished differently based on existing theoretical considerations [91].
First-order confirmatory factor analysis diagrams for the scales used in the research are given in Figure 2.
According to the CFA, the structural equation model results of the scales were found to be significant at the p = 0.000 level, and the items in the scale and single sub-dimensions were determined to be relevant to the scale structure. Modifications have been made to the models. In the model modification, the variables that reduced the fit were determined, and new covariances were created for those with high covariance between the residual values. Table 4 shows the accepted fit indices as a result of the recalculations of the fit indices.
When Table 4 was examined, the model was validated because the values obtained were within acceptable limits.
4. Findings
This part of the research paper presents the statistics on demographics and hypotheses. In the study, we used the structural equation model using the AMOS 20.0 program to test H1, H2, and H3, and examined the normality of the distributions and homogeneity of variances using the SPSS 22.0 program and used Mann–Whitney U tests to test H4, H5, and H6.
4.1. Findings on Demographic Variables
The demographic characteristics of the participants (gender, age, educational status, occupation, and favorite phone brand) are shown below:
The majority of the participants from Turkey were female (60.2%). When evaluated in terms of the age variable, it was determined that the majority of the participants were between the ages of 18 and 25 (55.1%), and the majority of them were university graduates (52%). Regarding the profession variable, most participants were students (50.3%), followed by civil servants (28.4%). The favorite phone brands of the respondents were Apple/iPhone (43.7%), Samsung (28.4%), and Xiaomi (14.5%).
When the participants from Romania were analyzed, it was observed that the vast majority of the participants were women (67.9%), as in Turkey. Similar to the numbers in Turkey, the participants’ age range was 18–25 (43.9%). When the educational status was analyzed, it was observed that 38.6% were university graduates and 37.6% were high school graduates. The occupational variable showed that the participants were predominantly private sector employees (35.4%) and students (30.1%). The respondents’ favorite phone brands were Samsung (44.9%), Apple/iPhone (33.8%), and Huawei (10.9%).
4.2. Findings on Hypotheses
Structural equation modeling (SEM) was used to test the H1, H2, and H3 models. The structural equation model established to examine the interaction between brand loyalty, cognitive dissonance, and complaint behavior is shown in Figure 3, and the values of the goodness-of-fit indices are shown in Table 5.
When the values were examined, it was determined that the factor loadings and the values of the goodness-of-fit indices for the model were within acceptable limits after modal modification.
The scale items and the outputs of the measurement model are shown in Table 6. We used standardized coefficients according to Anderson and Gerbing [94] to evaluate the measurement model of the study. The standardized factor loadings lie between 0.488 and 0.909 for all the items. The critical ratio values lie between 8.789 and 20.007 for all the items. This means that for all items, the all-path coefficient is significant at the 0.001 level.
Table 7 shows the values of the hypotheses tested through the path analysis to measure the relationship between brand loyalty, cognitive dissonance, and customer complaint behavior.
Table 7 illustrates that the level of brand loyalty had a significant positive effect on the level of cognitive dissonance (H1: β = 0.236, p = 0.001), and the customers’ level of cognitive dissonance showed a significant positive effect on the level of customer complaint behavior (H3: β = 0.553, p = 0.000). In addition, we found that brand loyalty did not have a significant effect on the level of customer complaint behavior (H2: β = −0.052, p > 0.05).
To determine the appropriate statistical methods for testing H4, H5, and H6, we used a normal distribution analysis. According to the test of normality results, the significance value was p = 0.000 in all three scales based on the Kolmogorov–Smirnov and Shapiro–Wilk analyses. This value was less than the significance level of 0.05. We concluded that the data were not normally distributed based on this significance level. Since the data did not show a normal distribution, they failed to fulfill the assumptions of the parametric test. Therefore, non-parametric tests were used to test the hypotheses. The Mann–Whitney U test was used to detect cultural differences in relation to the scales. In the data analysis, the significance value was determined as p < 0.05.
Table 8 shows the results of the Mann–Whitney U test, which was used to test whether the scores of the Brand Loyalty, Cognitive Dissonance, and Complaint Behavior scales showed a significant difference based on the country variable. The p < 0.005 indicated a significant difference in the customers’ levels of brand loyalty, cognitive dissonance, and complaint behavior based on the countries; hence, H4, H5, and H6 were accepted. The findings showed that Romanian customers’ level of brand loyalty was higher than Turkish customers’. In comparison, Turkish customers’ cognitive dissonance and complaint behavior levels were higher than Romanian customers’.
5. Discussions
As a result of the analyses, we found that the data supported all but one of the proposed hypotheses. The results showed that the customers’ brand loyalty level positively affects the level of cognitive dissonance. The level of cognitive dissonance experienced by the customers positively affects the level of complaint behavior. As a result of the previous study, which focused on the effect of cognitive dissonance directly on complaint behaviors, it was determined that there was a positive relationship between the two variables, and in another study, brand loyalty had a positive effect on cognitive dissonance [17,51]. In this respect, our research results support previous research results. However, research showed that the customers’ brand loyalty levels did not positively affect the complaint behavior levels. While in a significant part of the previous research results [9,10,11], it was determined that brand loyalty has a positive effect on complaint behavior, in another study [12] it was found that brand loyalty has a negative effect on consumer behavior. Although these results concurred with existing literature, this study provided information about different cultural contexts and provided illuminating data for new research to clarify the relationship between brand loyalty and complaint behaviors.
In addition, the research fills a significant gap in comparing consumers’ levels of cognitive dissonance in different cultures. This study on cross-cultural differences shows that the differences in the behavior of Romanian and Turkish consumers are consistent with the theoretical background of the study. The dimension of “indulgence” that distinguishes the two cultures may explain why Turkish consumers engage more in complaint behavior. In addition, the higher level of cognitive dissonance experienced by Turkish consumers is another explanatory factor in the high level of complaint behavior, which is also in parallel with the theoretical knowledge.
6. Conclusions
Cognitive conflict is a situation that arises because of inconsistencies between what consumers generally know about brands and their experiences with them. The subject of this study was the determination of the relationship between cognitive dissonance, brand loyalty, and customer complaints, and, in this context, the determination of intercultural differences. As a result of the research, it was found that cognitive dissonance had a positive effect on brand loyalty and customer complaints. However, as a result of the research, cognitive conflict, brand loyalty levels, and customer complaint behavior levels were found to be significantly different between Turkish and Romanian cultures. The results of the research expand the literature by supporting the limited number of studies on the subject.
This research may contribute to the marketing strategies that can be developed concerning management by showing the effect of experienced cognitive dissonance on complaint behavior and the differences in the cognitive dissonance and complaint behavior levels in different cultures. Given the effect of cognitive dissonance on brand loyalty and complaint behavior, it is of great importance that marketing managers aiming for brand loyalty and thus minimal complaints try to eliminate customers’ cognitive dissonance. The service quality offered at the post-purchase stage, where consumers with cognitive dissonance try to rationalize their purchasing decisions, can also help to reduce their cognitive dissonance. In addition, managers operating in international markets should be aware that consumer complaint behavior may emerge at different levels in different cultures, and while preparing business plans for customer satisfaction, they should consider that the effort spent on complaint management may function at different levels in different cultures.
7. Limitations of Future Directions
In addition to important theoretical contributions, our research has several limitations. Most importantly, the typical constraints of quantifying data when measuring consumer behavior are acknowledged in the studies of consumer behavior. Since it helps to more quickly collect samples, considering time and cost constraints, the snowball sampling method was used for the research. This may also cause sampling bias because the participants are more likely to direct the study toward other participants who are similar to them. This may prevent the generalization of research results. We did not have the opportunity to conduct a supporting study in this context, but a similar study can be conducted in a laboratory environment using different research methods (fMRI, EEG, etc.). In addition, the scope of the research remained narrow, as our resources allowed us to make a comparison only between two cultures. In future studies, the results could be more widely generalized by researching different cultures.
Conceptualization, V.Y., H.G., E.G., F.O.V. and S.D.; Methodology, V.Y., H.G., E.T.A.G. and N.B.-M.; Validation, V.Y. and H.G.; Formal analysis, N.B.-M., S.D. and F.O.V.; Investigation, N.B.-M., S.D. and F.O.V.; Resources, V.Y., H.G., E.G. and E.T.A.G. Writing—original draft preparation, V.Y., H.G., F.O.V. and S.D. All authors have read and agreed to the published version of the manuscript.
Ethical review and approval were waived for this study due to the nature of this research (voluntary internet questionnaire).
Informed consent was obtained from all subjects involved in the study.
Data will be available on request.
The authors declare no conflict of interest.
Footnotes
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Reliability and validity factor analysis.
| Component | Items | Std. | SMC | T İstatistic | p | Cronbach’s Alpha | CR | AVE |
|---|---|---|---|---|---|---|---|---|
| Brand Loyalty | Loyalty1 | 0.510 | 0.260 | 0.861 | 0.857 | 0.514 | ||
| Loyalty2 | 0.651 | 0.424 | 16.222 | *** | ||||
| Loyalty3 | 0.461 | 0.212 | 12.380 | *** | ||||
| Loyalty4 | 0.774 | 0.599 | 14.230 | *** | ||||
| Loyalty5 | 0.855 | 0.731 | 14.847 | *** | ||||
| Loyalty6 | 0.925 | 0.855 | 15.165 | *** | ||||
| Cognitive Dissonance | Dissonance1 | 0.584 | 0.342 | 0.899 | 0.890 | 0.582 | ||
| Dissonance2 | 0.820 | 0.673 | 19.744 | *** | ||||
| Dissonance3 | 0.910 | 0.829 | 18.118 | *** | ||||
| Dissonance4 | 0.894 | 0.799 | 17.979 | *** | ||||
| Dissonance5 | 0.710 | 0.505 | 16.479 | *** | ||||
| Dissonance6 | 0.590 | 0.348 | 13.730 | *** | ||||
| Complaint Behavior | Complaint1 | 0.533 | 0.284 | 0.774 | 0.814 | 0.434 | ||
| Complaint2 | 0.505 | 0.255 | 15.038 | *** | ||||
| Complaint3 | 0.749 | 0.561 | 14.087 | *** | ||||
| Complaint4 | 0.780 | 0.704 | 14.317 | *** | ||||
| Complaint5 | 0.836 | 0.698 | 14.735 | *** | ||||
| Complaint6 | 0.448 | 0.192 | 11.276 | *** |
Note: Std: standardized factor loading; SMC: square multiple correlations; CR: composite reliability; AVE: average variance extracted; ***: p < 0.001.
Discriminant validity analysis.
| Brand Loyalty | Cognitive Dissonance | Complaint Behavior | |
|---|---|---|---|
| Brand Loyalty | 0.716 | ||
| Cognitive Dissonance | 0.238 | 0.762 | |
| Complaint Behavior | −0.04 | 0.543 | 0.659 |
Note: The on-diagonal entries in bold are square roots of AVE; off-diagonal entries represent Pearson’s correlation estimates.
Accepted measures of goodness-of-fit used in the study.
| X2/sd | GFI | CFI | AGFI | NFI | RMSEA | |
|---|---|---|---|---|---|---|
| Criterion | ≤5 | ≥0.90 | ≥0.90 | ≥0.90 | ≥0.90 | ≤0.08 |
Fit indices of the scales in the single-factor confirmatory factor analysis.
| X2/sd | GFI | CFI | AGFI | NFI | RMSEA | |
|---|---|---|---|---|---|---|
| Criterion | ≤5 | ≥0.90 | ≥0.90 | ≥0.90 | ≥0.90 | ≤0.08 |
| Model goodness-of-fit | 3.342 | 0.946 | 0.962 | 0.924 | 0.946 | 0.054 |
Accepted measures of goodness-of-fit used in the study.
| X2/sd | GFI | CFI | AGFI | NFI | RMSEA | |
|---|---|---|---|---|---|---|
| Criterion | ≤5 | ≥0.90 | ≥0.90 | ≥0.90 | ≥0.90 | ≤0.08 |
| Model goodness-of-fit | 4.359 | 0.926 | 0.943 | 0.927 | 0.950 | 0.065 |
Scale items and confirmatory analyses.
| Construct | Item No. | SRW | S.E. | CR | p-Value |
|---|---|---|---|---|---|
| Loyalty → | Loyalty 1 | 0.525 | * | * | |
| Loyalty → | Loyalty 2 | 0.664 | 0.071 | 16.658 | *** |
| Loyalty → | Loyalty 3 | 0.488 | 0.089 | 11.086 | *** |
| Loyalty → | Loyalty 4 | 0.776 | 0.081 | 14.694 | *** |
| Loyalty → | Loyalty 5 | 0.853 | 0.097 | 15.350 | *** |
| Loyalty → | Loyalty 6 | 0.919 | 0.092 | 15.713 | *** |
| Dissonance → | Dissonance 1 | 0.589 | * | * | |
| Dissonance → | Dissonance 2 | 0.821 | 0.066 | 20.007 | *** |
| Dissonance → | Dissonance 3 | 0.909 | 0.076 | 18.331 | *** |
| Dissonance → | Dissonance 4 | 0.894 | 0.083 | 18.200 | *** |
| Dissonance → | Dissonance 5 | 0.711 | 0.070 | 15.818 | *** |
| Dissonance → | Dissonance 6 | 0.590 | 0.063 | 13.831 | *** |
| Complaint → | Complaint 1 | 0.545 | * | * | |
| Complaint → | Complaint 2 | 0.548 | 0.062 | 16.270 | *** |
| Complaint → | Complaint 3 | 0.739 | 0.094 | 14.227 | *** |
| Complaint → | Complaint 4 | 0.787 | 0.104 | 14.663 | *** |
| Complaint → | Complaint 5 | 0.830 | 0.106 | 14.967 | *** |
| Complaint → | Complaint 6 | 0.489 | 0.076 | 8.789 | *** |
Note: SRW: standardized regression weight; CR: critical ratio; S.E.: standard error. * Unstandardized regression weights anticipated as 1. *** Significant level at p < 0.001.
The results of the path analysis indicating the relationship between variables.
| Hypotheses | Path | Standardized Values (Beta) | S.H | T-Value (Cr) | p | Decision |
|---|---|---|---|---|---|---|
| H1 | BL → CD | 0.236 *** | 0.033 | 4.789 | 0.001 | Accept |
| H2 | BL → CB | −0.052 | 0.054 | −1.459 | 0.145 | Reject |
| H3 | CD → CB | 0.553 *** | 0.029 | 10.056 | 0.000 | Accept |
Note: BL: brand loyalty, CD: cognitive dissonance, CB: complaint behavior, *** p ≤ 0.001.
Mann–Whitney U test results by “Country”.
| N | Rank Average | U | Z | p | ||
|---|---|---|---|---|---|---|
| Brand Loyalty | Turkey | 394 | 339.06 | 55774.000 | −6.960 | 0.000 * |
| Romania | 396 | 451.66 | ||||
| Cognitive Dissonance | Turkey | 394 | 499.33 | 37102.000 | −12.773 | 0.000 * |
| Romania | 396 | 292.19 | ||||
| Complaint Behavior | Turkey | 394 | 463.80 | 51101.500 | −8.399 | 0.000 * |
| Romania | 396 | 327.54 |
* Significant level at p < 0.001.
Appendix A. Measurement Scales
| Construct | Items |
| Brand Loyalty | Loyalty1. As long as products from this brand are available, I doubt that I will change it |
| Cognitive Dissonance | Dissonance1. I felt disappointed with myself |
| Complaint Behavior | Complaint1. I would contact the shop to complain |
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; Güven, Hüseyin 2
; Sofia, David 3
; Güven, Esra 4 ; Bărbuță-Mișu, Nicoleta 3
; Emine Türkan Ayvaz Güven 5 ; Virlanuta, Florina Oana 6
1 Department of Public Relations and Publicity, Communication Faculty, Manisa Celal Bayar University, Manisa 45140, Turkey
2 Karabaglar Guidance and Research Center, İzmir 35150, Turkey
3 Department of Business Administration, Faculty of Economics and Business Administration, “Dunarea de Jos” University of Galati, 800008 Galaţi, Romania
4 Department of Office Services and Secretary, Gördes Vocational School, Manisa Celal Bayar University, Manisa 45760, Turkey
5 Department of Human Resources Management, Ahmetli Vocational School, Manisa Celal Bayar University, Manisa 45540, Turkey
6 Department of Economics, Faculty of Economics and Business Administration, “Dunarea de Jos” University of Galati, 800008 Galaţi, Romania