What’s important for relationship management? The mediating roles of relational trust and satisfaction for loyalty of cooperative banks’ customers

Building on the corporate reputation model, this study investigates the drivers of customer-based corporate reputation. We consider two corporate reputation dimensions (i.e., the cognitive dimension competence and the affective dimension likeability, and their effects on customer satisfaction and loyalty). Adapting the model to the banking sector, we theoretically extend this model by reasoning that customer satisfaction and relational trust are mediators of the relationship between the two corporate reputation dimensions and loyalty. Studying a sample of 675 customers and members of cooperative banks in Germany, we find perceived attractiveness to be the most important driver of corporate reputation. Furthermore, we confirm a positive relationship between corporate reputation and loyalty, and a mediating effect of both satisfaction and relational trust. With our study, we give support for the proposition of customer satisfaction's as well as relational trust’s role as mediators of the relationship between corporate reputation and loyalty. With this research, we expand our knowledge on the well-known corporate reputation model, which has high relevance and important implications for marketing research and relationship management practice.


Introduction
The worldwide financial crisis had significant consequences for the trust in the banking sector (Skvarciany and Jureviciene 2013) as well as for the reputation of financial institutions (Hurley et al. 2014) and the overall confidence in financial services (Gritten 2011). Corporate reputation plays an important role in banks and entails an increase in the banks' economic success (Otto et al. 2020) as well as perceived attractiveness for private investors (Schütz and Schwaiger 2007). Corporate reputation also has the proven potential of affording a competitive advantage (Deephouse 2000;Gray and Balmer 1998), in addition to being linked to higher levels of customer satisfaction (Walsh and Beatty 2007) and loyalty (Ali et al. 2015).
Banks not only offer a product or service to their customers but also-more than other companies-rely on the trust of customers to safely and securely store and invest their money. Matute-Vallejo et al. (2011) argue that in recent years customer confidence in the banking industry has been disrupted due to various crises, and therefore finding solutions to rebuild relational trust and maintain customer loyalty is critical, not only for managers in the banking sector, but also for strategic management and marketing research. The (relational) trust in banks in relation to corporate reputation as well as customer satisfaction remains, however, an under-researched topic (Bugandwa et al. 2021). Consequently, there is an urgent need for research on relational trust in the context of bank reputation research and its implications for marketing theory and practice, including banks' relationship management.
Corporate reputation management is immensely important for cooperative banks. As one of the three pillars of the German banking system, consisting of commercial banks, savings banks, and cooperative banks, the latter find themselves in a highly competitive banking market. While commercial banks are solely profit-oriented, cooperative banks are committed to maximizing member value and, by law, to incorporate a strong member and local focus (GenG 2006) based on the principles of identity, self-help, autonomy, self-responsibility, and member funding. The German cooperative banking group currently has around 18.5 million members (Stappel 2020). Although the future viability of the cooperative business model has been questioned in the past, especially cooperative banks have proven to be stable in times of crisis, in that they reported declines in earnings for the year 2020 (Stappel 2020). In this respect, however, cooperative banks differ from many commercial banks that reported losses (Bundesbank 2020). Moreover, according to the International Cooperative Banking Association (ICBA 2020), cooperative banks play a key role with regard to tackling and reaching the Sustainable Development Goals (SDGs) that the United Nations adopted in 2015.
For researching the perceived corporate reputation of cooperative banks, we adapted a well-known two-dimensional corporate reputation model by Schwaiger (2004) to the banking context, extending the model by including customer satisfaction and relational trust as mediators of the relationship between corporate reputation and loyalty. Based on a sample of customers and members of cooperative banks, the model was then evaluated by means of component-based structural equation modeling (SEM). The results show that perceived attractiveness is overall the most important driver of corporate reputation, whereas perceived quality is the second most important driver for the affective dimension of corporate reputation (perceived likeability), followed by corporate social responsibility. Perceived performance is mostly important in driving the cognitive dimension of corporate reputation (perceived competence). We furthermore find that both relational trust and customer satisfaction are important mediators of the relationship between corporate reputation and loyalty. Relational trust and customer satisfaction mediate levels of customer loyalty.
This study addresses a key research gap and makes several contributions to both marketing theory and practice: First, we replicate and adapt an established theoretical model. Second, we transfer it to a specific business context, namely the banking industry, thereby strengthening the validity of prior results. Third, we deepen and enhance the theoretical understanding of customer satisfaction and customer loyalty by showing that customer satisfaction is a mediator of the relationship between corporate reputation and loyalty. Fourth, we extend an existing model by adapting a scale of relational trust to the banking context; furthermore, we show this variable's mediating effect on the relationship between corporate reputation and loyalty, which is more important than satisfaction in the context of cooperative banks. Moreover, by applying the latest advances in component-based SEM, our study offers several methodological insights for marketing researchers. Last but not least, our results have implications for marketing practice (i.e., for the marketing managers of banks) by emphasizing the relevance of customer-perceived attractiveness and quality as well as relational trust.
We structure this research article as follows: In Sect. 2, we explore the marketing research stream relating to the corporate reputation of financial institutions, such as banks, as well as develop and explain our theoretical model's components. In Sect. 3, we shortly explain our constructs' operationalizations as well as our data and methodology. We then analyze the data, assess our results (Sect. 4), and discuss our main findings with respect to implications for theory and practice as well as avenues for future research in Sect. 5.

Corporate reputation and its outcomes
Customer-based corporate reputation is defined as "the customer's overall evaluation of a firm based on his or her reactions to the firm's goods, services, communication activities, interactions with the firm and/or its representatives or constituencies (such as employees, management, or other customers) and/or known corporate activities" (Walsh and Beatty 2007, p. 129). In comparison to image and prestige, corporate reputation is a more long-term phenomenon. The image of banks is largely dependent on its communication strategies, whereas its corporate identity is the reality and uniqueness of the organization (Gray and Balmer 1998). According to Gray and Balmer (1998), the organizational identity is shaped by the organization's image and reputation. Englert et al (2020) emphasize the importance of the organizational reputation concept's multidimensionality and find, in the German banking context, "that the organizational features of financial performance and familiarity become more important determinants of the organizational reputational dimensions of visibility and favorability in times of crisis" (p. 1545). Another study conducted during the financial crisis found a company's affective reputation component to be particularly important for future firm value . Various authors have tried to operationalize a company's reputation, especially since (corporate) reputation is a latent variable that is not directly observable. Three relevant measurement models are particularly widespread in the academic literature: the reputation quotient (Fombrun et al. 2000), the customer-based reputation (Walsh et al. 2009), and the two-dimensional corporate reputation model by Schwaiger (2004). In the last decade, the latter model in particular has become established in reputation research, has been used in a large number of studies (Abimbola et al. 2010;Radomir and Moisescu 2019;Radomir and Wilson 2018;Raithel and Schwaiger 2015;Sarstedt and Schloderer 2010;Schloderer et al. 2014;Schwaiger et al. 2009;Yun et al. 2020), and has been validated in different countries (Eberl 2010;Zhang and Schwaiger 2012). The two-dimensional Schwaiger model for measuring long-term corporate reputation is therefore applied in this study and adapted to the context of cooperative banks. According to Schwaiger (2004), corporate reputation is measured on the basis of an affective dimension (likeability = LIKE) and a cognitive dimension (competence = COMP), which together depict reputation. In the Schwaiger (2004) model, four factors influence both dimensions of corporate reputation: (1) the perceived quality (QUAL) of a product or service, (2) the perceived performance (PERF) relative to competitors, (3) the perceived corporate social responsibility (CSOR), and (4) the perceived attractiveness (ATTR) of the investigated company as an employer. Corporate reputation also influences customer satisfaction and customer loyalty. Customer loyalty is a more long-term aspect than customer satisfaction and is often modeled as being a result of customer satisfaction. We therefore view customer loyalty as the target construct in this study.
Based on the above, we pose the following hypotheses: H1 Perceived quality has a positive effect on perceived competence (H1a) and likeability (H1b).
H2 Perceived performance has a positive effect on perceived competence (H2a) and likeability (H2b).
H3 Perceived corporate social responsibility has a positive effect on perceived competence (H3a) and likeability (H3b).

H5
Higher levels of perceived competence (H5a) and likeability (H5b) lead to higher levels of customer satisfaction.
H6 Higher levels of perceived competence (H6a) and likeability (H6b) lead to higher levels of customer loyalty.
The theoretical model's relationships are depicted in Fig. 1 and are further explained in the following section.

The role of relational trust
In any type of business-to-consumer (B2C) relationship, it is important to gain a customer's trust. As Swift (2001) argues, "organizations or stakeholders cannot command trust; rather it must be earned on the basis of trustworthy behavior" (p. 22). The integration of the relational trust construct into a (corporate) reputation model is, therefore, appropriate to generate a greater understanding of customers' relationships with their cooperative bank. For bank managers seeking real-world practical advice, the customers' perception of long-term corporate reputation is important, as it is linked to customer satisfaction and loyalty. Over the past decades, (relational) trust has been an important element of satisfaction studies, especially with regard to gaining customer loyalty. Reichheld and Schefter (2000) emphasized the importance of trust in gaining the loyalty of customers. However, a variety of approaches and understandings of trust exist in the marketing literature. Until now, there is no distinct conceptualization of trust. Sirdeshmukh et al. (2002) emphasized the importance of differentiating between trust and trustworthiness, defining the first as "the expectations held by the consumer that the service provider is dependable and can be relied on to deliver on its promises" (p. 17). They view trustworthiness as a means to operationalize trust in their theoretical framework, including competence, benevolence, and problemsolving orientation. Other studies have adopted and further developed this distinction, such as the study by Colquitt and Rodell (2011) who conceptually understand trustworthiness as the ability, benevolence and integrity of a trustee, and trust as the willingness to rely on others. Due to this broad definitional landscape, Kantsperger and Kunz (2010) emphasize the importance of the clarification of trust, which, although widely accepted as a key mediating variable in the service relationship (Ganesan 1994;Palmatier et al. 2006), is not consistently measured. According to Kantsperger and Kunz's model, the trust in the external circumstances and the trust in the relationship represent two major trust dimensions.
Applying a meta-analytic approach, Geyskens et al (1998) find that trust contributes to satisfaction and longterm orientation over and beyond the effects of the relationship's economic outcomes. They therefore identify trust as "a critical concept in marketing channel relationships" (p. 245). However, few studies have focused on the antecedents of relational trust, and have rather investigated its outcomes instead. In this study, we therefore integrated this construct into a more complex model to identify the link between the two dimensions of corporate reputation (i.e., competence and likeability) and relational trust as well as relational trust's influence on the key outcomes of the model (i.e., customer satisfaction and loyalty). Grayson et al. (2008) distinguish between various dimensions of relational trust and their results imply that relational trust in firms and their representatives is a necessary mediator of trust in the broader context (i.e., the trust in the social context where the relationship takes place). For the purpose of this study, we rely on a narrow definition of relational trust in the context of the customer-bank-relationship, as we focus solely on the direct relationship between relational trust, customer satisfaction, and loyalty outcomes.
Various studies relating to corporate reputation and relational trust have been conducted in the banking sector. Perrien et al. (1993) discuss the importance of implementing relationship marketing to commercial banking as a strategic decision. Crosby et al. (1990) find that relationship quality, which consists of relational trust and satisfaction, is a central element in their model and has a significant influence on the customer's anticipation of future interaction with the salesperson (i.e., loyalty). In their latest study, Bugandwa et al. (2021) find a positive link between corporate social responsibility (CSR) and trust in banks from a customer perspective. However, the authors argue that trust relationships should be analyzed in a more enriched framework including variables such as service quality and reputation. Aramburu and Pescador (2019) studied the mediating effect of corporate reputation on the relationship between perceived CSR (conceptualized as a formative second order construct) and customer loyalty and show that corporate reputation partially mediates the relationship between CSR and customer loyalty. Past studies have already shown that a positive reputation has a positive impact on trust in the banking context (Casaló et al. 2007). Similarly, a mediating role of trust in the relationship between CSR and reputation (Fatma et al. 2015) has been found. There is also proof that trust plays a moderating role in the intention to leave the bank (Kabadayi 2016), which we perceive as the counterpart to loyalty. Furthermore, studies found evidence for perceived quality on trust, reputation, and customer satisfaction (Hamzah et al. 2017). Other studies with a focus on perceived quality again found a direct and indirect positive relationship between quality on customer satisfaction on trust and loyalty (Boonlertvanich 2019).
In contrast, studies also showed a positive relationship between perceived performance, trust, and reputation on customer satisfaction (Eren 2021). In another study, reputation and customer satisfaction led to overriding trust in the bank, with reputation playing a more important role in the banking sector in Asia than in Europe (Nienaber et al. 2014). For the German banking sector, Ebert found a positive relationship between reputation and trust in the banking context (Ebert 2009). In a study on the mediating effect of relational trust on the relationship between customer orientation and willingness to switch, Saparito et al (2004) showed that higher levels of relational trust have a negative impact on the willingness of switching banks.
Overall, research shows that the consumer-company relationship needs to be based on trust (Kang and Hustvedt 2014). Trust between consumers and a company contributes significantly to positive outcomes for the company, such as loyalty toward the company, customer retention, product choice, purchase intention, willingness to act, and overall market performance (Chaudhuri and Holbrook 2001;Erdem and Swait 2004;Matzler et al. 2008;Munuera-Aleman et al. 2003;Willmott 2003). Greenwood and Van Buren III (2010) explain the importance of trust in the organization-stakeholder relationship, and that the trustworthiness of the organization is fundamental to the moral treatment of stakeholders. Cooperative banks' customers and members are key stakeholders in this relationship and we, therefore, find this to be another argument in favor of a focus on the establishment of relational trust.
In this research, we focus on the relationship dimension (i.e., relational trust). We follow the definition of relational trust that Saparito et al (2004) used in their study on banks, supporting the argument that "we explicate the role of (relational) trust in bank-small firm exchanges. Our interest is in tracing relational trust's role (beyond self-interest) in these exchanges" (p. 402). Based on the commitment-trust theory, Morgan and Hunt (1994) explain that relationship commitment and trust are key constructs in their model, which is why they position commitment and trust as mediators between their antecedents and outcome constructs. Similar to their approach, in this study we position customer satisfaction and relational trust as mediators between our corporate reputation dimensions and customer loyalty. Previous research has found a positive relationship between reputation and on trust in the banking sector (Casaló et al. 2007;Ebert 2009), and also between trust and satisfaction via reputation (Hamzah et al. 2017). Moreover, previous research found relational trust to act as a moderator on the intention to switch banks. However, for the banking context, we argue that relational trust is important for both customer satisfaction and loyalty, in that relational trust, which increases satisfaction, leads to even higher levels of customer loyalty, thereby stressing the role of relational trust. Based on the theoretical background, we pose the following hypotheses on the role of relational trust and customer satisfaction in our model ( Fig. 1): H7 Higher levels of perceived competence (H7a) and likeability (H7b) lead to higher levels of relational trust.
H8 Relational trust acts as a mediator in the relationship between corporate reputation and customer loyalty.
H9 Customer satisfaction acts as a mediator in the relationship between corporate reputation and customer loyalty.
H10 Relational trust acts as a mediator in the relationship between corporate reputation, customer satisfaction, and customer loyalty.

Construct operationalization
The two-dimensional corporate reputation model by Schwaiger (2004) is used as the base model and is adapted to the banking context. Furthermore, instead of adding satisfaction as a construct to the process of corporate reputation and loyalty building, we evaluate customer satisfaction and relational trust as potential mediators in this relationship. The theoretical model tested in this study is built upon the relationships described in Schwaiger (2004) and Saparito et al (2004), which were introduced in the theoretical background discussion. We study both dimensions of corporate reputation, namely competence and likeability, as this approach allows identifying the overall (higherorder) concept's specific effects on the lower-order dimensions . The resulting research model, together with the associated research hypotheses, is shown in Fig. 1. The four determinants of corporate reputation (QUAL, PERF, CSOR, ATTR) affect both dimensions of corporate reputation (COMP, LIKE), which, in turn, affect relational trust (TRUST), customer satisfaction (SAT), and loyalty (LOY). The latter is mapped as the target construct.
All our constructs' conceptualizations have already been tested in previous studies ( Table 1). The items of the four antecedents were measured formatively, whereas all remaining items were measured reflectively. The measurement operationalization is based on previous studies by Schwaiger (2004) and Saparito et al (2004), and has been adapted to fit the banking context. Basing measurement scales on already tested scales offers a good basis for validity and reliability assessment. We used multi-item scales, as we dealt with complex psychological constructs that cannot easily be represented by single items (Petrescu 2013). We adjusted the relational trust scale according to Saparito et al (2004)'s conceptualization, as this type of measurement is appropriate for the banking context. Table 1 shows each construct and the corresponding items used in this study. All items were measured on a 7-point Likert scale from 1 "Do not agree at all" to 7 "Do completely agree".

Data and model estimation
Items were translated and back-translated from English into German, ensuring that the translation did not lead to misunderstandings of the formulations and, therefore, response bias. The established path model was tested in two pre-test rounds. The data for the main study were collected via a commercial German market research institute using quota sampling to ensure that the sample was representative of the German population. Counterintuitive or straight-line answers were removed. All questions in our survey were mandatory, for which reason we did not have to deal with any missing values. The final sample comprised 675 cooperative bank customers. The descriptive statistics (Appendix Table A1) indicate that females are slightly overrepresented with 58.2% female compared to 41.8% male respondents. As expected for the cooperative banking sector, nearly two thirds (65.3%) of the respondents are 45 years old or older and almost a quarter are retired (25.9%). Almost half of the respondents are married (46.1%), whereas almost a quarter live alone without a partner (23.4%). Slightly more than half of the respondents had completed vocational training or had obtained a university degree (54.5%). The average household income of respondents in the sample is below  (2015) the German average of approximately EUR 3661 after taxes (Statista 2018). The likely cause of this is the relatively high number of retired people in the sample, as well as the number of single households relying on a single income. The sample is representative of the target group of customers and members of German cooperative banks. We test the relationships in our model using componentbased SEM (i.e., partial least squares structural equation modeling, PLS-SEM). This method allows for analyzing the strength of the constructs' influence on the target construct in a path model (Hair et al. 2022). PLS-SEM supports both explanatory and predictive goals when analyzing the model's causal-predictive relationships (Wold 1982). In keeping with earlier developed theory, the model should support causal explanations and provide predictive accuracy (Chin et al. 2020). This type of research methodology is particularly suited to the development of new theories as well as the extension of existing theories (Richter et al. 2016). Furthermore, PLS-SEM supports the estimation not only of both reflective and formative measurement models (Hair et al. 2022) but also of complex structural models Wold 1982). Researchers across different social sciences disciplines-for instance, human resource management , higher education research (Ghasemy et al. 2020), and information systems research (Chin et al. 2020), and especially in marketing (Liu et al. 2021;Chaouali et al. 2021;Damberg 2021a), particularly corporate and organizational reputation studies (Damberg 2021b;Schloderer et al. 2014)-used the PLS-SEM method in their empirical analyses to support their research goal. We use the software SmartPLS 3  for the estimation and results assessment of our model.

Results
The evaluation of the measurement and structural model follows Hair et al (2019) and Hair et al (2022). To avoid potential common method bias (CMB) in the findings, we included introductory information and descriptions for respondents with the goal of minimizing uncertainty. Moreover, we set all respondents' answers to anonymous in the online survey and explained beforehand that answers are perceptional (= no right or wrong answers). We therefore consider our model free of CMB. We further tested the model fit of the model using the SRMR estimation model statistics. 1 The resulting value of 0.066 is clearly below the threshold value of 0.8, on the basis of which a suitable model fit of the path model can be further confirmed (Hair et al. 2012).
Reflective measurement models are assessed by examining the indicator reliability, internal consistency, convergence validity, and discriminant validity. The indicator loadings of the reflective measurement models are shown in Appendix Table A2. All values exceed the recommended conservative threshold of 0.708, except for LOY_1, which is slightly below the threshold, but still acceptable. Afterward, we assess the internal consistency reliability of the reflective constructs using ρ A (Appendix, Table A2). The values of all constructs are satisfactory (i.e., ρ A > 0.7). The high ρ A values between 0.9 and 0.95 of satisfaction and relational trust are acceptable since the items used per construct are content-wise sufficiently distinctive. The convergence validity of the constructs is checked using the average variance extracted (AVE), which exceeds the threshold of 0.5 for all reflective constructs. The discriminant validity assessment uses the heterotrait-monotrait (HTMT) criterion (Henseler et al. 2015). With two exceptions, all HTMT results are below the critical value of 0.9 (Appendix, Table A3). The two exceptions are HTMT results of 0.900 for likability and satisfaction and 0.902 for satisfaction and trust. Since these constructs are conceptually similar and so close to the critical value, we find these results acceptable to establish discriminant validity.
For the assessment of the formative constructs perceived quality, performance, social responsibility, and attractiveness, the redundancy analysis showed that convergence validity is established for all four antecedents, the formatively measured constructs. All outer variance inflation factor (VIF) values are below the more liberal threshold of 5 with the highest value being 3.878. Furthermore, the outer weights of all formative constructs are significant (Appendix Table A4). To test for significance, we apply the percentile bootstrapping procedure to 10,000 subsamples and the two-sided test based on a 95% confidence level. The outer weights range from 0.183 (QUAL_1) to 0.356 (ATTR_1), which gives each formative indicator a relatively high importance.
The structural model assessment also involves examining potential collinearity problems. The inner VIFs are all below the more liberal threshold of 5 (i.e., the highest VIF is 4.912). Collinearity is thus at a level, which allows to compare and interpret the size of the structural model coefficients. The bootstrapping results indicate that the path coefficients are significant ( Fig. 2; Appendix, Table A5). The only exceptions are the COMP to LIKE and the LIKE to COMP to LOY relationships. We find that ATTR (0.374) and PERF (0.316) have a particularly strong effect on the cognitive corporate reputation construct COMP, while ATTR (0.360) and QUAL (0.294) are the most important explanators for the affective corporate reputation dimension LIKE. For TRUST, we find that both COMP (0.501) and LIKE (0.393) have pronounced effects. Moreover, for SAT, TRUST (0.443) and LIKE (0.360) have relatively strong effects, while that of COMP (0.161) is somewhat lower. Finally, we find that TRUST (0.398), SAT (0.220), and LIKE (0.161) have different relevance for the explanation of the key target construct LOY, while the effect of COMP is not significant.
The mediation analysis (Nitzl et al. 2016, Cepeda Carrión et al. 2017 reveals that COMP has a total indirect effect on LOY via both TRUST and SAT (0.049) and that the effect is significant (Table 2). Since the direct effect of COMP on LOY is not significant, this relationship is fully mediated by TRUST and SAT (Zhao et al. 2010;Hair et al. 2022). The total indirect effect of LIKE via TRUST and SAT on COMP (0.038) is also significant. Since COMP's direct effect on LOY is also significant, we reveal a partial (complementary) mediation. Similarly, the significant indirect effect from of TRUST via SAT on LOY is significant (0.271), which also represents a partial (complementary), mediation, considering the significant direct effect of TRUST on LOY.
The extended corporate reputation model entails relatively high levels of the endogenous constructs' amount of explained variance (i.e., R 2 values; Fig. 2). For the key target construct LOY, the model explains more than 50% of the construct's variance (R 2 = 0.551). More important for drawing conclusions and making managerial recommendations is the model's out-of-sample predictive power. For this assessment, we run the PLS predict procedure on LOY (Shmueli et al. 2016(Shmueli et al. , 2019. The positive Q 2 predict indicate that the PLS-SEM predictions are superior to the naïve mean value prediction benchmark outcomes (Table 3). Moreover, the root mean square error (RMSE) value of the PLS-SEM predictions is one of three cases smaller than the RMSE value of the linear model (LM) prediction benchmark. These results substantiate the predictive power of the model, even though it is on a low level (Table 3).
Finally, an importance-performance analysis (IPMA) allows us to combine the importance (i.e., total effects) of constructs that explain LOY and their performance (i.e., average value on a scale from 0 to 100), as shown in Table 4, to further substantiate managerial recommendations (Ringle and Sarstedt 2016;Hair et al. 2018). In terms of the total effects, TRUST (0.496) has the highest total effect on LOY, followed by LIKE (0.406), COMP (0.352), and SAT (0.221). Regarding the performance values, SAT (77.0) displays the highest values, whereas TRUST (69.8) shows the lowest values. Consequently, TRUST has the highest importance for LOY but at the same time the lowest performance. Bank managers should therefore prioritize their activities to improve the performance of relational trust among customers and staff to affect the performance of customer loyalty positively.

Findings and conclusions
In this study, the importance of corporate reputation, relational trust, and customer satisfaction as well as their influence on loyalty from the perspective of customers of German cooperative banks were empirically tested based on a complex theoretical research model. In addition to evaluating the key determinants of cooperative banks' reputation, which found CSR to be an important determinant compared to other types of banks, the influence of corporate reputation on relational trust and customer satisfaction was examined and   implications for cooperative banks' practice were derived.
McDonald and Rundle-Thiele (2008) explored the relationship between CSR and customer outcomes, such as trust, customer satisfaction, and loyalty, and found that funding directed toward customer-centric initiatives could create better customer satisfaction outcomes than CSR initiatives. Investing in relational trust is a form of investing in the relationship (i.e., a customer-centric initiative). Our primary research objective was to investigate the relationships between corporate reputation, relational trust, customer satisfaction, and loyalty of cooperative banks' customers and members. The results and findings of this quantitative study show that relational trust is an important mediator of the relationship not only between corporate reputation and customer satisfaction but also between corporate reputation and customer loyalty. Our model explains and predicts cooperative banks' reputation, relational trust, satisfaction, and loyalty as perceived by their customers. In addition to perceived quality and attractiveness, CSR is found to be an especially important driver of cooperative bank reputation. This is an important finding, as this element is likely to be less important for other types of banks, such as online banks. It is therefore important to consider customer segments in future studies. Furthermore, we confirm the findings of previous studies of the German cooperative banking sector that there is a positive relationship between corporate reputation, satisfaction, and loyalty. For cooperative banks, the affective dimension of corporate reputation is more important than the cognitive dimension. Moreover, in the proposed mediation model, relational trust is an important element of loyalty, especially in cooperative banks. Interestingly, relational trust is more important for explaining loyalty than customer satisfaction. We therefore conclude that for cooperative banks investing in customer relationship management and its communication is not only beneficial to the customers, but also essential to ensure the long-term survival of the cooperative bank in the highly competitive German banking market.
For a long time, research on customer satisfaction and loyalty in the banking context had not considered the relevance and role of relational trust. With our model, we explain and predict bank customers' loyalty by introducing relational trust and customer satisfaction as mediators of the relationship between corporate reputation and customer loyalty. We find that customer-perceived corporate reputation increases customer loyalty both directly and also via our mediators. We conclude that relational trust plays an important role in studies on corporate reputation, satisfaction, and loyalty in the cooperative banking context, and that cooperative banks should use it as part of their differentiation strategy from other banks. In comparison to, for instance, online banks with naturally less customer proximity, building relational trust should be perceived as a capability and potential for cooperative banks.
The main theoretical contributions of this study are summarized as follows: First, we operationalize and tested a complex theoretical model that links existing constructs from the marketing literature. Second, we address an existing research gap by testing and finding empirical evidence for both customer satisfaction and relational trust in a corporate reputation-loyalty model in the banking context. We integrate the constructs into an established model from the marketing literature, specifically to test relational trust as a mediator of the relationship between corporate reputation, Fig. 2 Path coefficients and R 2 Values. ***p < 0.01. QUAL = perceived quality, PERF = perceived performance, CSOR = perceived corporate social responsibility, ATTR = perceived attractiveness, LIKE = perceived likeability, COMP = perceived competence, SAT = customer satisfaction, LOY = customer loyalty, TRUST = relational trust customer satisfaction, and loyalty. Third, we derive several implications for the cooperative banking practice.
The model that we present and its empirical results have practical implications for both marketing and management of cooperative banks. We find that perceived quality, attractiveness and CSR are important for cooperative banks' corporate reputation, and that relational trust plays a key role in the establishment of customer satisfaction and loyalty. These elements are especially important from the practitioner's perspective, as customer satisfaction is positively related to a firm's performance (Otto et al. 2020). For cooperative bank managers, this study's findings emphasize the need to deal with customers on a more profound level than simply offering them good value for money and high-quality services. The mediation analyses show that from the customers' perspective, establishing relational trust is highly important with regard to establishing long-term loyalty. As Kramer (2009) suggests, a strategy to build trust in customer-company relationships would be for banks to provide signals of trusting acts to their customers to build and maintain long-lasting relationships. In the banking sector, where product differentiation is rather difficult to achieve while, at the same time, competition for new customers is high, relationship management is especially important. With regard to a rather old customer segment of cooperative banks' customers, cooperative banks should focus on building and maintaining relational trust. Especially cooperative banks have the potential to differentiate themselves from purely profit-oriented commercial banks by concentrating on their specific characteristics, such as their democratic structure and their proximity to customers (Jungmeister et al. 2015).
Future studies should further examine potential differences between bank types, such as the role of relational trust in commercial banks versus cooperative banks to derive even more profound implications. Furthermore, several limitations and corresponding points of departure for future research remain. The empirical study was tested with a representative sample of German customers of cooperative banks. Since cultural differences in perception may well exist, it is important to compare the results with analyses in other countries. Multigroup analyses (Sarstedt et al. 2011) between different types of banks could define differences not only in the perception of corporate reputation drivers more clearly but also in the needs of these customer segments to put them into practice. While long-term customers most likely perceive a stronger relational trust, which is also more difficult to destroy, it could be more difficult to retain new or short-term customers or heavy versus light bank users, from a company perspective. Moreover, there could be differences between age, gender, and income. These three groups could be tested as moderators in future analyses. Longitudinal studies would also be useful to test the model further and examine potential differences over time (e.g., the influence of financial crises). Complementary approaches, such as generalized structured component analysis (GSCA), could be used to further validate the results (Cho et al. 2020;Hwang et al. 2020). Moreover, we suggest to test the current model in the banking context across countries and cultures to substantiate the findings. Järvinen (2014) reveals differences in consumer trust between European countries, and identifies countries with low, medium, and high trust in banking and in distinct banking services. We also suggest to test the model in other customer-centric and service-centric industries, such as the insurance industry or the hotel business.
In our model, we modeled customer-perceived corporate reputation as a two-dimensional construct consisting of likeability and competence, and we measured trust in the form of a relational trust. One could argue that it is too close to similar constructs, such as customer satisfaction. However, we did not find any collinearity issues. Nevertheless, other operationalizations of trust could be used to test trust on other target levels. This was done by Pasiouras et al (2020) who examined the impact of trust and a national culture of secretiveness on the number of bank relationships per firm. This could also be further tested on the level of the individual consumer in the future.
In conclusion, this study provides the groundwork for indicating the importance of relational trust as a second, more emotional dimension of the customer-bank relationship, next to the more service-oriented customer satisfaction dimension. This offers research potential and practical implications for all banking sectors. From an overall sustainable banking perspective, future research is required on the antecedents and outcomes of corporate reputation and relational trust, which could be further complemented by qualitative research design focusing on specific target segment markets. Although previous studies have found a link between corporate reputation, (relational) trust, and loyalty, our model can be used as baseline to investigate the specific and detailed factors influencing all the constructs and their interrelationships.