Abstract
Purpose
Quality of life is a concern of banking customers, but it has received little attention in studies conducted within the banking context. This study aims to investigate the influence of mindfulness on customers’ quality of life and the mediating role of service value, satisfaction and loyalty to the company in this relationship.
Design/methodology/approach
Three hundred banking customers participated in the survey. In this study, partial least square structural equation modeling (PLS-SEM) was used to test the relationship between the variables. Then, complementary methods were used to assess the robustness of the PLS-SEM results.
Findings
In this study, it was found that mindfulness directly influences service value, satisfaction and quality of life. Service value was also found to directly influence satisfaction. Satisfaction directly influences loyalty to the company. In addition, loyalty to the company, but not satisfaction, directly influences quality of life. However, this study did not find any evidence that service value, satisfaction and loyalty to the company mediate the influence of mindfulness on quality of life.
Practical implications
Banking marketing managers need to ensure that their customers have an impressive moment-to-moment experience with the services provided to support improving their quality of life.
Originality/value
The findings help to advance the understanding of how banks can improve their customers’ quality of life while maintaining the well-being of other stakeholders.
Objetivo
La calidad de vida es una preocupación de los clientes de banca, pero ha recibido poca atención en los estudios realizados en el contexto bancario. Este estudio pretende investigar la influencia del mindfulness en la calidad de vida de los clientes y el papel mediador del valor del servicio, la satisfacción y la lealtad a la empresa en esa relación.
Diseño/metodología/enfoque
Trescientos clientes de banca participaron en la encuesta. Este estudio utilizó la modelización de ecuaciones estructurales por mínimos cuadrados parciales (PLS-SEM) para comprobar la relación entre las variables. A continuación, se utilizó métodos complementarios para evaluar la solidez de los resultados del PLS-SEM.
Resultados
Este estudio halló que mindfulness influye directamente en el valor del servicio, la satisfacción y la calidad de vida. El valor del servicio también influye directamente en la satisfacción. La satisfacción influye directamente en la lealtad a la empresa. Además, la lealtad a la empresa, pero no la satisfacción, influye directamente en la calidad de vida. Sin embargo, este estudio no encontró pruebas de que el valor del servicio, la satisfacción y la lealtad a la empresa medien la influencia de mindfulness en la calidad de vida.
Originalidad
Los hallazgos ayudan a avanzar en la comprensión de cómo los bancos pueden mejorar la calidad de vida de sus clientes al tiempo que mantienen el bienestar de otras partes interesadas.
Implicaciones prácticas
Los directores de marketing bancario deben asegurarse de que sus clientes tienen una experiencia impresionante en cada momento con los servicios prestados para apoyar la mejora de la calidad de vida de los clientes.
目的
生活质量是银行业客户关心的问题, 但在银行业范围内的研究中, 它很少得到关注。本研究旨在研究正念对客户生活质量的影响, 以及服务价值、满意度和对公司的忠诚度在这种关系中的中介作用。
设计/方法/途
三百名银行业客户参与了调查。本研究采用偏最小平方结构方程模型（PLS-SEM）来检验各变量之间的关系。然后, 本研究使用补充方法来评估PLS-SEM结果的稳健性。
研究结果
本研究发现, 正念直接影响了服务价值、满意度和生活质量。服务价值也被发现直接影响满意度。满意度直接影响到对公司的忠诚度。此外, 对公司的忠诚度, 但不是满意度, 直接影响了生活质量。然而, 本研究没有发现任何证据表明服务价值、满意度和对公司的忠诚度可以调解心态对生活质量的影响。
原创性/价值
研究结果有助于推进人们对银行如何在保持其他利益相关者福祉的同时提高客户的生活质量的理解。
实践意义
银行营销经理需要确保他们的客户对所提供的服务有令人印象深刻的时刻体验, 以支持改善客户的生活质量。
Keywords
Citation
Burhanudin, B. (2024), "Mindfulness and banking customers’ quality of life", Spanish Journal of Marketing - ESIC, Vol. 28 No. 1, pp. 21-40. https://doi.org/10.1108/SJME-02-2022-0015
Publisher
:Emerald Publishing Limited
Copyright © 2023, Burhanudin Burhanudin.
License
Published in Spanish Journal of Marketing - ESIC. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
The banking experience needs to enhance customers’ quality of life, which is an individual’s perception of living a good life (Diener, 2000). A failure to address banking customers’ quality of life indicates that there is some incongruency between the business of the bank and the customers’ concept of self (Ogunmokun and Timur, 2022). The number of savings accounts potentially decreases dramatically if customers perceive that their bank is not helping to improve their quality of life, not only when they are in a successful condition (D’Agostino et al., 2020) but also when they are facing challenges (Sharif et al., 2020).
Apart from its practical importance, quality of life is theoretically important to address. Quality of life is an underresearched but nonetheless important construct within the banking sector (Ogunmokun and Timur, 2022) and in nonbanking contexts as well (Daniel et al., 2022). Quality of life has rarely been discussed in studies that examine consumer behavior even though this factor has a significant role in tapping into promising future business opportunities and maintaining long-term relationships with customers (Ogunmokun and Timur, 2022).
A relatively new phenomenon that potentially contributes to customers’ quality of life is mindfulness. Mindfulness refers to “the state of being attentive to and aware of what is taking place in the present” (Brown and Ryan, 2003, p. 822). Mindfulness is a rapidly emerging area in the field of business management (Daniel et al., 2022), but it has received relatively little attention within the marketing context (Flavian et al., 2020; Laukkanen et al., 2022). Mindfulness plays an important role within the marketing context, but whether it helps customers easily observe the service value offered by a provider is currently unclear (Loureiro et al., 2019).
This study aims to investigate the influence of mindfulness on customers’ quality of life and the mediating role of service value, satisfaction and loyalty to the company in this relationship. This study offers both academic and practical implications. Academically, the findings of this study explain the relationship between mindfulness and quality of life (Daniel et al., 2022) and the need to identify the mediators of that relationship (My-Quyen et al., 2020). Practically, the findings of this study will help banks penetrate the Indonesian banking sector, which has achieved one of the highest net interest margins of banks worldwide at 4.625% (Statista, 2022).
2. Literature review
2.1 Theoretical background
Both human motivation theory and quality-of-life theory have been regarded as essential theories for better understanding people’s quality of life. Ogunmokun and Timur (2022) suggested using both human motivation theory (Maslow, 1943) and quality-of-life theory (Sirgy, 1986) to advance the understanding of customers’ ideals such that suitable marketing programs can be designed to improve their quality of life. According to the theory of human motivation (Maslow, 1943), individuals develop their full potential gradually by satisfying their lower-order needs prior to satisfying their higher-order needs. According to quality-of-life theory (Sirgy, 1986), people react to the fulfillment of such needs in their satisfaction level statements.
2.2 Mindfulness
Mindfulness is an important concept for better understanding customers’ experiences. Mindfulness suggests that customers focus on the moment-to-moment experiences they face without comparing them with other experiences (Gupta and Verma, 2020) or being evaluative of the experience (Flavian et al., 2020). An example of mindfulness is customers’ awareness of their need for an easy mobile payment system and how technology can provide a solution to that need (Flavian et al., 2020). Mindfulness can allow customers to optimally benefit from the banking services provided (Flavian et al., 2020), as mindfulness can eliminate negative thoughts (Mick, 2017), make customers more motivated to engage in the activity at hand (Laukkanen et al., 2022) and help them build a good relationship with the service provider (Daniel et al., 2022). Studies on consumer mindfulness have only received attention from marketing scholars; thus, the application of consumer mindfulness in marketing has started to appear only recently (Daniel et al., 2022; Flavian et al., 2020; My-Quyen et al., 2020).
2.3 Service value
Service value plays a strategic role in marketing. Service value includes the monetary (e.g. the money spent to receive a service) and nonmonetary sacrifices (e.g. the time spent to benefit from a service) that differentiate it from service quality (Matarazzo et al., 2021). Service value is helpful for auditing customers’ needs and the positioning of a company relative to its competitors (Roy et al., 2018). In particular, a company can focus on service value to evaluate whether its service is innovative, speedy and precise (Matarazzo et al., 2021). Currently, the investigation of service value within the banking context is lacking (Roy et al., 2018) even though the banking sector offers a wide range of channel services, such as stock exchange transactions, that increase the service value (Manohar et al., 2020).
2.4 Satisfaction
The rapid growth of the banking sector has created competition among banks in regard to satisfying their customers. Satisfaction refers to customers’ perceived fulfillment of their needs or expectations (Casidy and Wymer, 2016). Investigating satisfaction allows banks to track the hierarchical needs of their customers as well as shows them on how to update their services to meet their customers’ expectations. In addition, satisfaction must follow the progression of customers’ needs with the goal of maintaining customer loyalty (Sirgy, 1986). The diversity of views on customers’ satisfaction level across various countries and cultures needs to be investigated further (Gong and Yi, 2018; Prakitsuwan and Moschis, 2021).
2.5 Loyalty to the company
It is important for banks to build loyalty to the company among their customers. Loyalty to the company refers to customers’ feelings of devoted attachment to the company (Casidy and Wymer, 2016). Attachment to the company should make customers perceive that switching to another company could be costly. For this reason, companies can exercise a price premium through their innovative services. Sanjiv et al. (2015) advised business organizations to continue trying to determine the best marketing strategy for their customers to enhance their customers' loyalty. This is because keeping existing customers is more cost effective than attracting new customers (Casidy and Wymer, 2016). When a customer is loyal to one company, he or she is not easily swayed by the price or service availability of another company (Casidy and Wymer, 2016; Lai et al., 2009).
2.6 Quality of life
Individuals plan their personal finances to increase their quality of life. They may use banking services to address their concerns regarding sustainability issues to live a good life over the longer term (Burhanudin et al., 2021). Furthermore, customers may plan to use banking services to help manage their financial distress connected to medical treatments (Sharif et al., 2020). Finally, they may plan to use banking services that protect the environment to neutralize any discomfort the customer may feel from acting in an eco-unfriendly way (Burhanudin et al., 2021). Previous studies have regarded quality of life as an important element in customers' decision-making processes (D’Agostino et al., 2020; Sharif et al., 2020), but the extent to which banks help improve customers’ quality of life remains unexplored.
2.7 Mindfulness and service value
It is important to investigate the relationship between mindfulness and service value. Mindfulness is a useful concept for better understanding consumers’ perceived value (Mick, 2017), such as the value of processing marketing information or the value of interpreting information (Daniel et al., 2022). Mindful consumers process information concerning the service attributes in an efficient (Daniel et al., 2022) and objective way (Ndubisi, 2014), which allows customers to easily identify the value of the service provided (Daniel et al., 2022; Ndubisi, 2014). Being attentive to the moment-to-moment experience makes the benefits of the services more visible to customers (Daniel et al., 2022). Thus:
Mindfulness has a significant influence on service value.
2.8 Mindfulness and satisfaction
It is crucial to link satisfaction with mindfulness. An investigation into the relationship between the constructs helps researchers to better understand the quality of the relationship between customers and service providers (Ndubisi, 2014). This investigation also helps to develop a better understanding of the way in which consumers adapt their expectations in relation to the experiences they are facing, which affects their satisfaction level (Mick, 2017). Because satisfaction is the confirmation of the customer's expectation when compared with the service experience (Ndubisi, 2014) and expectations can be more easily managed by mindful customers than by nonmindful customers (Mick, 2017), mindfulness potentially leads to satisfaction (My-Quyen et al., 2020). Thus:
Mindfulness has a significant influence on satisfaction.
2.9 Mindfulness and quality of life
In the literature on mindfulness, it is recommended that quality of life should be investigated as an important outcome of mindfulness. Mindfulness causes customers to have a better relationship with others (Ndubisi, 2014). Consistently practicing mindfulness allows customers to eliminate stress and concurrently perform positive activities such as adopting healthy eating behavior (My-Quyen et al., 2020). Because mindfulness involves an average individual’s tendency (Brown and Ryan, 2003; Daniel et al., 2022; Laukkanen et al., 2022) that potentially leads to the improvement of the individual’s quality of life (My-Quyen et al., 2020), this study proposes the following:
Mindfulness has a significant influence on quality of life.
2.10 Service value and satisfaction
Creating service value that leads to customer satisfaction is important. Service value and satisfaction are different constructs, but both depend on the consumption experience and the customers’ judgment (Sánchez-Fernández and Iniesta-Bonillo, 2009). Banks are aware of the need to deliver superior customer value (Roy et al., 2018), and they are increasing their strategic focus on customer satisfaction levels (Roy et al., 2018). Given the service performance, the customers may make a further judgment on what banking services were provided to fulfill their needs; thus, they may conclude that it is the ideal bank, which is a sign of satisfaction (Beerli et al., 2004). Thus:
Service value has a significant influence on satisfaction.
2.11 Satisfaction and loyalty to the company
It is important to examine the relationship between satisfaction and loyalty to the company. Satisfaction is a necessary condition for loyalty (Kaura et al., 2015). Hence, if they are satisfied, customers are more likely to engage in a long-term relationship with their respective banks (Narteh, 2017) and will recommend that particular bank's services to those asking for their advice and even to other people who do not (Kaura et al., 2015). In addition, customers who are satisfied with the services provided by a particular bank will view that bank as their first choice and do more business with that bank (Kaura et al., 2015). Thus:
Satisfaction has a significant influence on loyalty to the company.
2.12 Satisfaction and quality of life
It is important to examine the relationship between satisfaction and quality of life. There could be satisfaction with a bank's services that customers perceive as improving their quality of life (Prakitsuwan and Moschis, 2021). Satisfaction is associated with consumption, which is one part of the experience of life (Diener, 2000; Gong and Yi, 2018). This part of life's experience could be crucial in an individual’s various life domains, considering how banks can help customers in various ways (e.g. the payment of debts, buying goods and services, and advice regarding funds for retirement). Satisfaction with the consumption experience may lead to improved quality of life for customers (Gong and Yi, 2018). Thus:
Satisfaction has a significant influence on quality of life.
2.13 Loyalty to the company and quality of life
Much of the marketing literature demonstrates that loyalty benefits companies. For example, loyal customers maintain their long-term relationships with companies (Aksoy et al., 2015; Gong and Yi, 2018). Loyalty to the company takes place across life's domains, and a certain domain could be more important to a particular customer (Aksoy et al., 2015). For example, a customer may be loyal to a bank that helps them by automatically making monthly money transfers to their family members. According to Gong and Yi (2018), such experiences make customers feel better, so the following is proposed:
Loyalty to the company has a significant influence on quality of life.
2.14 Mindfulness, satisfaction and quality of life
An important question to investigate is whether satisfaction mediates the relationship between mindfulness and quality of life. Mindfulness takes place in customers’ daily lives (Loureiro et al., 2019) and potentially relates to their satisfaction level with the services received from providers (Ndubisi, 2014) and their quality of life (Daniel et al., 2022; Gupta and Verma, 2020). Mindful customers are open to any service feature offered by the service provider (Loureiro et al., 2019). Thus, it is likely that mindful customers need to perceive that they are satisfied with the services delivered by the provider (Ndubisi, 2014) before they can see an improvement in their quality of life (Daniel et al., 2022; Gupta and Verma, 2020). Thus:
The influence of mindfulness on quality of life is mediated by satisfaction.
2.15 Mindfulness, satisfaction, loyalty to the company and quality of life
Satisfaction and loyalty to the company may subsequently mediate the relationship between mindfulness and quality of life. Mindfulness relates to both satisfaction and loyalty (Ndubisi, 2014). Banking services have the potential to help their customers in many ways, such as completing transactions at their convenience (Flavian et al., 2020) and contributing to a better environmental condition (Burhanudin et al., 2021). Both satisfaction and loyalty to the company affect this relationship. Therefore, being mindful potentially leads to satisfaction, which in turn generates loyalty to the company and, in turn, a perceived improvement in their quality of life. Thus:
The influence of mindfulness on quality of life is subsequently mediated by satisfaction and loyalty to the company.
2.16 Mindfulness, service value, satisfaction, loyalty to the company and quality of life
The banking industry needs to have a greater level of understanding regarding the influence of mindfulness on service value and subsequently on satisfaction, loyalty to the company and quality of life. A perceived improvement in customers’ quality of life may come from the evaluation of many of their life domains, such as home and work. Within this complexity, the influence of mindfulness on quality of life may involve multiple mediators. Mindful customers focus on moment-to-moment experiences (Flavian et al., 2020), which makes it easier to find value in the services (Loureiro et al., 2019), in turn increasing the level of satisfaction with these services (Ndubisi, 2014). Next, the increase in satisfaction generates loyalty to the service provider (Ndubisi, 2014) and customers’ discovery that their quality of life has improved (Daniel et al., 2022). Thus:
The influence of mindfulness on quality of life is subsequently mediated by service value, satisfaction and loyalty to the company.
A summary of the above relationships is shown in Figure 1.
3. Method
3.1 Sampling
Indonesian banking customers were the participants in this study. Because banks do not allow information about their customers to be shared for privacy and security reasons (Alalwan et al., 2016) and due to the similarity of the service of attracting deposits and lending to those with such a need (Burhanudin et al., 2021), convenience sampling was determined to be the most appropriate technique to apply (Reynolds et al., 2003) within the banking sector (Alalwan et al., 2016). To identify respondents with different backgrounds, potential respondents were identified from a number of sources, including banking offices, banking forums, community meetings and public places (malls, minimarkets and public transport stations).
3.2 Data collection
To ensure that the research instrument works as intended, the questionnaire was pretested. In the pretest, the questionnaire was distributed to 40 respondents, which is above the minimum number of 30 respondents recommended (Perneger et al., 2015). The results showed that no issues, such as unclear questions or unfamiliar words, were detected. Then, 350 questionnaires were distributed to a larger group of Indonesian banking customers. From the questionnaires distributed, 300 responses were found to be valid for further analysis, resulting in an 85.71% response rate, well above the minimum threshold of 60% (Fincham, 2008).
3.3 Measures
Five items from previous studies (Brown and Ryan, 2003; My-Quyen et al., 2020) ranging from 1 representing totally disagree to 7 representing totally agree were used to measure mindfulness. Two items from Cronin et al. (2000) ranging from 1 representing completely low to 7 representing completely high were used to measure service value. Three items from Beerli et al. (2004) ranging from 1 representing not at all close to 7 representing completely close (SAT1 and SAT2) and from 1 representing not at all satisfied to 7 completely satisfied (SAT3) were used to measure satisfaction. Five items from Zeithaml et al. (1996) ranging from 1, representing not at all likely to 7 representing very likely were used to measure loyalty to the company. Three items from Sweeney et al. (2015) ranging from 1 representing totally disagree to 7 representing totally agree were used to measure quality of life. A seven-point scale was used in this study because this was the optimal number of response alternatives and legitimately allowed for the adoption of a neutral position (Cox, 1980).
In adapting the above measures to the Indonesian banking context, this study used the committee approach. While back-translation has been widely used within the social sciences (Douglas and Craig, 2007), the committee approach is the method recognized by Brislin (1976) and recommended to maximize the mix of expertise (Harkness et al., 2010, p. 128). To allow the development of translation that is easily understood by the target individuals, two bilingual individuals who best typify the target subjects were selected for this study and worked independently to translate the questionnaire into the target language (Brislin, 1976; Douglas and Craig, 2007; Harkness et al., 2010). A review meeting involving the two translators and the researcher as an adjudicator was then conducted to discuss the various versions and decide on the final version (Douglas and Craig, 2007; Harkness et al., 2010).
4. Data analysis and results
To detect common method variance (CMV), the variance inflation factor (VIF) values of the latent variables were inspected in this study. CMV was not indicated in the study, as the VIF values were below 10 (O’Brien, 2007). To evaluate the psychometric properties of the scales used and to test the hypotheses, the partial least square structural equation modeling (PLS-SEM) procedure was applied (Hair et al., 2017). Smart-PLS software was used to conduct PLS-SEM in this study (Ringle et al., 2015), first by assessing the measurement model and second by assessing the structural model.
The profile of the respondents indicated that 68.3% were female and 31.7% were male. Regarding age, the largest percentage was of those aged 17–22 years (64.3%), followed by 23–28 years (21%), 29–34 years (8%) and both 35–40 years and aged above 40 years (3.3%, respectively). For the finished education category, those with a senior high school degree (81.3%) dominated compared to an associate or bachelor’s degree (14.7%), junior high school graduation or lower (2.3%) and master’s or higher degrees (1.7%). For the category of banking customers, 54.6% were the customers of private banks and 45.4% went to state-owned banks. All the participants are banking customers of funding services. The profile of the respondents was determined to be close to that of the Indonesian population, with more than half of the citizens being young people (Harsono, 2021) and senior high school graduates (Dilas et al., 2019).
4.1 Measurement model assessment
Table 1 shows the results of the measurement model assessment. As Table 1 shows, all of the factor loadings for the constructs are above the minimum threshold of 0.708, confirming that there is item reliability (Hair et al., 2019). Average variance extracted (AVE) values are above the minimum threshold of 0.50, confirming that there is convergent validity (Hair et al., 2019). Table 1 further shows that Cronbach’s alpha and the composite reliability values are above 0.7 (Hair et al., 2019), confirming that there is internal consistency reliability (Hair et al., 2019; Peterson, 1994). Table 2 shows that the square root of the AVEs is greater than the interconstruct correlations, confirming that there is discriminant validity (Fornell and Larcker, 1981).
4.2 Structural model assessment
Table 3 shows the direct effects, and Table 4 shows the indirect effects. For the direct effects, Table 3 shows that the path from mindfulness to service value is significant (0.575, p < 0.001); thus, H1 is supported. The path from mindfulness to satisfaction is significant (0.232, p < 0.001); thus, H2 is supported. The path from mindfulness to quality of life is significant (0.465, p < 0.001); thus, H3 is supported. The path from service value to satisfaction is significant (0.682, p < 0.001); thus, H4 is supported. The path from satisfaction to loyalty to the company is significant (0.849, p < 0.001); thus, H5 is supported. The path from satisfaction to quality of life is not significant (−0.003, p > 0.05); thus, H6 is not supported. The path from loyalty to the company to quality of life is significant (0.229, p < 0.05); thus, H7 is supported. For the indirect effects, Table 4 shows that service value, satisfaction and loyalty to the company do not work as significant mediators in the relationship between mindfulness and quality of life; thus, the results do not support H8–H10. Results of the structural model assessment are shown in Figure 2.
4.3 Robustness checks of the PLS-SEM results
4.3.1 Assessment of the nonlinear effect.
Assessment of the nonlinear effect was conducted via two tests. The first is Ramsey’s RESET test (Ramsey, 1969) to identify the nonlinear relationship (Sarstedt et al., 2019). Ramsey’s RESET test (Ramsey, 1969) was used using SPSS (Sarstedt and Mooi, 2019), and it was found that the partial regression of SVL on MIN [F(2,296) = 0.288, p = 0.750]; LOY on SAT [F(2,296) = 2.574, p = 0.078]; and QOL on MIN, SAT and LOY [F(2, 294) = 0.385, p = 0.681] indicates a linear relationship (Sarstedt et al., 2019; Sarstedt and Mooi, 2019). However, the partial regression of SAT on MIN and SVL [F(2,295) = 7.733, p = 0.001] indicates a nonlinear relationship (Sarstedt et al., 2019; Sarstedt and Mooi, 2019). For that nonlinearity indication, Sarstedt and Mooi (2019) suggested exploring the concrete form of the relationship. To explore this indication, a curve estimation was run and the relationship was specified as quadratic (Sarstedt and Mooi, 2019).
The second is the quadratic effect test. This second test is necessary because the nonlinear effect needs to be checked to determine if it is significant (Sarstedt et al., 2019). This is because implementation of RESET uses the construct scores estimated from the linear effect (Sarstedt et al., 2019). Table 5 shows that one out of seven relationships is significant (p < 0.05). For the significant relationship, Hair et al. (2018) suggested inspecting f2 to assess the strength of the nonlinear effect. This is because significance does not imply relevance (Hair et al., 2018). In particular, f2 values of 0.02, 0.15 and 0.35 constitute small, medium and large effects, respectively (Cohen, 1988). Thus, the f2 values shown in Table 5 are considered small (Cohen, 1988), and the significant nonlinear relationship is not of particular relevance (Cohen, 1988; Hair et al., 2018).
4.3.2 Assessment of endogeneity.
Park and Gupta’s (2012) Gaussian copula approach was used to detect endogeneity in PLS-SEM. For implementation, a nonnormality test was initially conducted because the Gaussian copula approach requires the composite score of the endogenous construct to be nonnormally distributed (Hult et al., 2018). Then, the Kolmogorov–Smirnov test was run with the Lilliefors correction using R statistical software as recommended by Hult et al. (2018) on the latent variable scores of the independent variables in the PLS path model’s partial regressions (i.e. MIN, SVL, SAT and LOY). The results show that none of the construct’s scores are normally distributed, suggesting that the Gaussian copula approach was the most suitable instrument-free method to apply in this study (Eckert and Hohberger, 2023).
Then, Park and Gupta’s (2012) Gaussian copula approach was implemented with R statistical software as recommended by Hult et al. (2018). The results of the approach appear in Table 6. As shown in Table 6, the significant Gaussian copula was found when more than one endogenous variable was considered, but not if one endogenous variable, suggesting that any endogeneity issue may not be substantial (Hult et al., 2018).
4.3.3 Assessment of unobserved heterogeneity.
To detect unobserved heterogeneity, FIMIX-PLS was initiated. The initial analysis was started with a one-segment solution and the stop criterion (10−10 = 1.0 × 10−10), the maximum number of iterations (5,000) and the number of repetitions (10) were specified (Hair et al., 2018; Matthews et al., 2016; Sarstedt et al., 2019). Using G*Power software, a minimum sample size of 43 was needed for this study to create a power of 0.80 for the PLS model with a 1.5 effect size. By dividing the sample size (i.e. 300) by the minimum sample size (i.e. 43), the upper boundary of the range of segment solutions was determined to be 6.97. Considering the complexity of the model (Sarstedt et al., 2019) and that fewer segments are preferred (Hair et al., 2018), a one- to five-segment solution was selected (Matthews et al., 2016). Using the same settings as with the initial analysis, the FIMIX-PLS was then rerun for two to five segments. The results of the analysis are shown in Table 7 for the fit indices and Table 8 for the relative segment sizes.
Then, the fit indices were evaluated to indicate the number of segments to retain from the data. Table 7 shows the value of each fit measure in bold to indicate the number segments to retain (Hair et al., 2018; Sarstedt et al., 2019). A body of literature on unobserved heterogeneity (Hair et al., 2016; Hair et al., 2018; Sarstedt et al., 2019) has suggested using a joint evaluation of AIC3 and CAIC to observe if they indicate the same number of segments as the optimal solution. Alternatively, AIC3 and BIC, or AIC4 and BIC, needed to be jointly evaluated for this study. Table 7 shows that AIC3 and CAIC do not indicate the same number, nor do AIC3 and BIC, or AIC4 and BIC. Because the joint evaluation of the criteria does not point to a specific segmentation solution, it is assumed that unobserved heterogeneity is not at a critical level affecting the data (Sarstedt et al., 2019).
5. Discussion
Mindfulness plays a significant role in banking services. Consistent with the prediction of Daniel et al. (2022), it was found in this study that mindfulness influences service value. Being mindful allows customers to easily notice which banks can provide benefits that exceed the costs incurred to receive the services (Daniel et al., 2022; Flavian et al., 2020). Another finding is the influence of mindfulness on satisfaction, which is consistent with the prediction of My-Quyen et al. (2020). The next finding is the relationship between mindfulness and quality of life, which is in line with Daniel et al. (2022). This relationship exists because mindful customers emphasize positive rather than negative factors when evaluating their experience with banks (Brown and Ryan, 2003), meaning that they can easily perceive any improvement in their quality of life (Bahl et al., 2016) as predicted by quality-of-life theory (Lee and Sirgy, 2004; Sirgy, 1986).
The marketing literature supports the finding indicating the influence of service value on satisfaction. Service value is an important driver of customer satisfaction in relation to dental services, auto services, restaurants, hairstylists (Mcdougall and Levesque, 2000) and telecommunication services (Lai et al., 2009). The finding that satisfaction leads to loyalty to the company is consistent with the work of Narteh (2017), as satisfied customers are more likely to build a long-term relationship with their bank. It was found in this study that loyalty to the company influences quality of life. This is consistent with quality-of-life theory (Lee and Sirgy, 2004; Sirgy, 1986) and the prediction of the marketing literature regarding that relationship (Ogunmokun and Timur, 2022).
In this study, no support was found for the hypothesis that satisfaction influences quality of life. Following Fischer et al. (2014), a possible explanation is that customers perceive that satisfaction with banking services is not an important component of their life domains. This could be because the average interest rate for saving accounts is below the inflation rate (Jiao and Sihombing, 2022), which makes customers perceive that satisfaction with the banking service does not contribute to the achievement of their standard of living (Fischer et al., 2014). In this study, no support for service value, satisfaction and loyalty to the company mediating the influence of mindfulness on quality of life was found. A possible explanation could be that within the Indonesian market (Phua, 2020), technological developments have allowed nonbanking institutions to provide mobile payments and money transfers similar to traditional banking services at a lower cost (Wiradji, 2021).
In conclusion, mindfulness plays an important role in improving banking customers’ quality of life. This improvement is essential to better tap into the promising business of the Indonesian banking sector, which is regarded as one of the most profitable banking sectors in the world due to its high net interest margin (Statista, 2022). This conclusion is aligned with the current practice of Indonesian banks to improve their customers’ quality of life, such as that undertaken by PermataBank (2022). Furthermore, this conclusion provides empirical evidence to answer the prediction of how mindfulness can improve customers’ quality of life (Daniel et al., 2022) and supports the hypothesis that mindfulness has a significant role within the marketing context (Flavian et al., 2020; Loureiro et al., 2019).
6. Implications
6.1 Theoretical implications
The findings of this study offer several theoretical implications. First, this study establishes that mindfulness influences service value, satisfaction and quality of life. These findings are crucial to advancing the understanding of customers within the financial service industry (Burhanudin et al., 2021). Second, this study has established that customer satisfaction is a consequence of service value. Currently, understanding the relationship between service value and satisfaction is lacking within the banking context (Roy et al., 2018; Ruiz et al., 2008). Third, satisfaction drives loyalty to the company within the banking context. Loyalty to the company has received little attention in the marketing literature (Kaura et al., 2015) compared to other loyalty variables such as brand loyalty (Beerli et al., 2004). Fourth, loyalty to the company is a determinant of customers’ quality of life. Extensive studies have focused on identifying people with a high quality of life, but few studies have focused on the determinants of quality of life (Diener, 2000) within the banking context, as well as the relationship between loyalty to the company and quality of life (Aksoy et al., 2015).
The findings of this study regarding how customers may improve their quality of life are very important. Being mindful helps customers improve their quality of life because in that condition, they are being more open-minded (Ndubisi, 2014), have better control over their expectations (Mick, 2017), focus on the present reality (Brown and Ryan, 2003) and process information more economically (Daniel et al., 2022). The current findings theoretically suggest that mindfulness is an important construct to understand within banking studies (Flavian et al., 2020), which is consistent with the recommendation of the recent literature on mindfulness (Daniel et al., 2022). Because customers are concerned with their quality of life (Bahl et al., 2016; Daniel et al., 2022), as are banks (PermataBank, 2022), the findings of this study are essential within the context of banking services (Flavian et al., 2020) and in the Indonesian context in particular (Wiradji, 2021).
6.2 Managerial implications
Managerial implications can be derived from the findings of this study. Bank managers need to ensure that their customers have impressive moment-to-moment experiences with their services. This marketing effort is important because customers have the inclination to be mindful within the banking context (Flavian et al., 2020; Murakami et al., 2012). To do so, managers need to identify the life domains that their customers perceive as important for enhancing their quality of life (Ogunmokun and Timur, 2022). For example, if customers perceive that the family domain is highly important in the evaluation of customers’ quality of life, banking managers need to ensure that their banking services contribute to customers’ standard of living in that domain.
Furthermore, bank managers need to ensure that their services meet the expectations of their customers to create satisfaction. Customers may expect that banks should offer comprehensive digital banking services, and for this reason, digital banking should be provided as the service standard. In addition, loyal customers tend to have more frequent contact with the staff of banks, which suggests that there are opportunities for the staff to help customers improve their quality of life (Ogunmokun and Timur, 2022). For example, the staff may focus on helping customers prepare their pension fund plans at the earliest opportunity. A summary of conclusions, theoretical and managerial implications is shown in Table 9.
7. Limitations and future research directions
Apart from the contribution of this study to the banking marketing literature, there are limitations that should guide future studies. First, this study included the application of nonprobability sampling, thus limiting the generalization of the findings (Reynolds et al., 2003). Probability sampling may be considered for future studies. Second, in this study, mindfulness, service value, satisfaction and loyalty to the company were used to understand quality of life. Considering that the determinants of customers’ quality of life are complex (Lee and Sirgy, 2004), future studies may involve other variables, even in a nonservice context (Gong and Yi, 2018). Last, human needs are hierarchical (Maslow, 1943), and this study has not explored how long customers stay in a particular stage (e.g. esteem need) and how long they need before progressing to a higher level (e.g. self-actualization need). Future studies should be concerned with the time duration that customers generally stay in any particular stage and the usual time they need before moving to a higher level.
Figures
Figure 1.
Proposed framework
Figure 2.
The results of the structural model assessment
Reliability and convergent validity
| Construct/Indicator | Loadings |
|---|---|
| Mindfulness (MIN). Adapted from Brown and Ryan (2003) and My-Quyen et al. (2020) α = 0.895; CR = 0.927; AVE = 0.760 | |
| Whenever i use banking services… | |
| I stay focused | 0.875 |
| I am really attentive | 0.853 |
| I am aware of what i am doing without judgment | 0.891 |
| I find myself with my full attention | 0.868 |
| Service value (SVL). Adapted from Cronin et al. (2000) α = 0.845; CR = 0.928; AVE = 0.866 |
|
| Overall, the value of this facility’s services to me is | 0.924 |
| Compared to what I had to give up, the overall ability of this facility to satisfy my wants and needs is | 0.937 |
| Satisfaction (SAT). Adapted from Beerli et al. (2004) α = 0.864; CR = 0.917; AVE = 0.787 |
|
| To what extent does this bank live up to your general expectations of it? | 0.901 |
| Imagine the perfect bank. How far and/or close does this bank come to your ideal? | 0.849 |
| Given your experience with this bank, how satisfied or dissatisfied are you with it overall? | 0.910 |
| Loyalty to the company (LOY). Adapted from Zeithaml et al. (1996) α=0.934; CR = 0.950; AVE = 0.791 |
|
| Say positive things about the company to other people | 0.894 |
| Recommend the company to someone who seeks my advice | 0.931 |
| Encourage friends and relatives to do business with the company | 0.902 |
| Consider the current company my first choice to buy banking services | 0.834 |
| Do more business with the company in the next few years | 0.884 |
| Quality of life (QOL). Adapted from Sweeney et al. (2015) α = 0.878; CR = 0.925; AVE = 0.805 |
|
| I am satisfied with the quality of my life | 0.926 |
| I am happy with the quality of my life | 0.943 |
| I have a sense of well-being | 0.817 |
Discriminant validity (square root of the AVEs in diagonal)
| Construct | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| 1. Loyalty to the company | 0.890 | ||||
| 2. Mindfulness | 0.591 | 0.872 | |||
| 3. Quality of life | 0.502 | 0.599 | 0.897 | ||
| 4. Satisfaction | 0.849 | 0.624 | 0.482 | 0.887 | |
| 5. Service value | 0.752 | 0.575 | 0.441 | 0.815 | 0.930 |
Structural path results
| Hypothesis | Path coefficient | t-value | p-value | Conclusion |
|---|---|---|---|---|
| H1: Mindfulness → Service value | 0.575 | 12.887 | 0.000 | Supported |
| H2: Mindfulness → Satisfaction | 0.232 | 3.795 | 0.000 | Supported |
| H3: Mindfulness → Quality of life | 0.465 | 7.072 | 0.000 | Supported |
| H4: Service value → Satisfaction | 0.682 | 13.749 | 0.000 | Supported |
| H5: Satisfaction → Loyalty to the company | 0.849 | 38.622 | 0.000 | Supported |
| H6: Satisfaction → Quality of life | −0.003 | 0.022 | 0.983 | Not Supported |
| H7: Loyalty to the company → Quality of life | 0.229 | 2.096 | 0.036 | Supported |
Specific indirect effects
| Mediation path | Indirect effect | LLCI | ULCI | t-value | p-value | Conclusion |
|---|---|---|---|---|---|---|
| H8: MIN → SAT → QOL | −0.034 | −0.226 | 0.096 | 0.133 | 0.894 | Not supported |
| H9: MIN → SAT → LOY → QOL | 0.054 | −0.049 | 0.230 | 0.240 | 0.810 | Not supported |
| H10: MIN → SVL → SAT → LOY → QOL | 0.210 | −0.569 | 0.548 | 0.130 | 0.897 | Not supported |
MIN = Mindfulness; SVL = Service value; SAT = Satisfaction; LOY = Loyalty to the company; QOL = Quality of life; LLCI = Lower-level confidence intervals; ULCI = Upper-level confidence intervals
Assessment of nonlinear effects
| Nonlinear relationship | Coefficient | p-value | f2 | Ramsey’s RESET |
|---|---|---|---|---|
| MIN * MIN → SVL | 0.039 | 0.204 | 0.005 | F (2,296) = 0.288, p = 0.750 |
| MIN * MIN → SAT | −0.037 | 0.352 | 0.008 | F (2,295) = 7.733, p = 0.001 |
| SVL * SVL → SAT | −0.045 | 0.344 | 0.007 | |
| SAT * SAT → LOY | −0.012 | 0.756 | 0.001 | F (2,296) = 2.574, p = 0.078 |
| MIN * MIN → QOL | −0.058 | 0.229 | 0.007 | F (2,294) = 0.385, p = 0.681 |
| SAT * SAT → QOL | 0.147 | 0.012 | 0.001 | |
| LOY * LOY → QOL | −0.054 | 0.362 | 0.005 |
MIN = Mindfulness; SVL = Service value; SAT = Satisfaction; LOY = Loyalty to the company; QOL = Quality of life
Assessment of endogeneity test using the Gaussian copula approach
| Test | Construct | Coefficient | Test | Construct | Coefficient | Test | Construct | Coefficient |
|---|---|---|---|---|---|---|---|---|
| Gaussian copula of model 1 (MIN) |
MIN | 0.381*** | Gaussian copula of model 6 (MIN, SAT) |
MIN | 0.406*** | Gaussian copula of model 11 (MIN, SVL, SAT) | MIN | 0.399*** |
| SVL | 0.012NS | SVL | 0.010NS | SVL | 0.042NS | |||
| SAT | −0.013NS | SAT | −0.054NS | SAT | −0.070NS | |||
| LOY | 0.228** | LOY | 0.233** | LOY | 0.231** | |||
| CMIN | 0.042NS | CMIN | 0.229NS | CMIN | 0.026NS | |||
| Gaussian copula of model 2 (SVL) |
MIN | 0.457*** | CSAT | 0.024NS | CSVL | −0.014NS | ||
| SVL | −0.033NS | Gaussian copula of model 7 (MIN, LOY) |
MIN | 0.323*** | CSAT | 0.032NS | ||
| SAT | −0.009NS | SVL | 0.027NS | Gaussian copula of model 12 (MIN, SVL, LOY) | MIN | 0.342*** | ||
| LOY | 0.231** | SAT | −0.039NS | SVL | −0.029NS | |||
| CSVL | 0.019NS | LOY | 0.353** | SAT | −0.041NS | |||
| Gaussian copula of model 3 (SAT) |
MIN | 0.443*** | CMIN | 0.099** | LOY | 0.377*** | ||
| SVL | 0.009NS | CLOY | −0.076* | CMIN | 0.089* | |||
| SAT | −0.079NS | Gaussian copula of model 8 (SVL, SAT) |
MIN | 0.0.443*** | CSVL | 0.025NS | ||
| LOY | 0.235** | SVL | 0.031NS | CLOY | −0.087* | |||
| CSAT | 0.039NS | SAT | −0.092NS | Gaussian copula of model 13 (SVL, SAT, LOY) | MIN | 0.474*** | ||
| Gaussian copula of model 4 (LOY) |
MIN | 0.475*** | LOY | 0.234** | SVL | −0.005NS | ||
| SVL | 0.013NS | CSVL | −0.009NS | SAT | −0.205NS | |||
| SAT | −0.013NS | CSAT | 0.046NS | LOY | 0.397*** | |||
| LOY | 0.253** | Gaussian copula of model 9 (SVL, LOY) |
MIN | 0.482*** | CSVL | 0.012NS | ||
| CLOY | −0.016NS | SVL | −0.087NS | CSAT | 0.096* | |||
| Gaussian copula of model 5 (MIN, SVL) |
MIN | 0.379*** | SAT | −0.021NS | CLOY | −0.091* | ||
| SVL | 0.017NS | LOY | 0.316** | Gaussian copula of model 14 (SAT, LOY, MIN) | MIN | 0.376*** | ||
| SAT | −0.013NS | CSVL | 0.046NS | SVL | 0.028NS | |||
| LOY | 0.228** | CLOY | −0.048NS | SAT | −0.178NS | |||
| CMIN | 0.043NS | Gaussian copula of model 10 (SAT, LOY) | MIN | 0.472*** | LOY | 0.416*** | ||
| CSVL | −0.002NS | SVL | 0.021NS | CSAT | 0.0.75NS | |||
| SAT | −0.216NS | CLOY | −0.106** | |||||
| LOY | 0.390*** | CMIN | 0.063NS | |||||
| CSAT | 0.102** | Gaussian Copula of model 15 (MIN, SVL, SAT, LOY) | MIN | 0.378NS | ||||
| CLOY | −0.087* | SVL | 0.015* | |||||
| SAT | −0.172NS | |||||||
| LOY | 0.419NS | |||||||
| CMIN | 0.062NS | |||||||
| CSVL | 0.005** | |||||||
| CSAT | 0.072NS | |||||||
| CLOY | −0.107NS |
MIN = Mindfulness; SVL = Service value; SAT = Satisfaction; LOY = Loyalty to the company;
Variables inside the parentheses are endogenous variables. c indicates the copula term in the model. *** Significant at p ≤ 0.001; ** Significant at p ≤ 0.01; * Significant at p ≤ 0.05
NS Not significant at p > 0.05
Fit indices for the one- to five-segment solutions
| Criteria | Number of segments | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| AIC | 2,407.025 | 2,407.025 | 1,551.336 | 1,517.069 | 1,508.124 |
| AIC3 | 2,418.025 | 2,418.025 | 1,586.336 | 1,564.069 | 1,567.124 |
| AIC4 | 2,429.025 | 2,429.025 | 1,621.336 | 1,611.069 | 1,626.124 |
| BIC | 2,447.766 | 2,447.766 | 1,680.968 | 1,691.147 | 1,726.647 |
| CAIC | 2,458.766 | 2,458.766 | 1,715.968 | 1,738.147 | 1,785.647 |
| HQ | 2,423.33 | 2,423.33 | 1,603.215 | 1,586.736 | 1,595.577 |
| MDL5 | 2,698.733 | 2,698.733 | 2,479.498 | 2,763.458 | 3,072.74 |
| LnL | −1,192.512 | −1,192.512 | −740.668 | −711.535 | -695.062 |
| EN | na | Na | 0.789 | 0.72 | 0.73 |
| NFI | na | Na | 0.798 | 0.691 | 0.695 |
| NEC | na | Na | 63.322 | 84.059 | 80.889 |
AIC = Akaike’s information criterion; AIC3 = modified AIC with factor 3; AIC4 = modified AIC with factor 4; BIC = Bayesian information criteria; CAIC = consistent AIC; HQ = Hannan–Quinn criterion; MDL5 = minimum description length with factor 5; LnL = log likelihood; EN = entropy statistic; NFI = non-fuzzy index; NEC = normalized entropy criterion; na = not available; numbers in italic indicate the best outcome per segment retention criterion
Relative segment sizes (N = 300)
| No. of segments | Segment 1 | Segment 2 | Segment 3 | Segment 4 | Segment 5 |
|---|---|---|---|---|---|
| 1 | 1.000 | ||||
| 2 | 0.807 | 0.193 | |||
| 3 | 0.633 | 0.192 | 0.175 | ||
| 4 | 0.454 | 0.233 | 0.191 | 0.122 | |
| 5 | 0.454 | 0.192 | 0.151 | 0.114 | 0.090 |
Conclusions, theoretical and managerial implications
| Conclusions | Theoretical and managerial implications |
|---|---|
|
|
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Acknowledgements
The author thanks the editor and the reviewers for their time and comments that have helped to improve the manuscript. This work was supported by the Indonesian Ministry of Education, Culture, Research, and Technology (grant number 5172/Pk.20300/04/19).