Abstract
Purpose
This study aims to determine the factors that influence ethical banking behavior among millennials and Gen-Z in Malaysia.
Design/methodology/approach
A stratified sample of 525 millennials and Gen-Z of Malaysian banking customers was used. Extended ethical decision-making (EDM) model was tested using partial least square-structural equation model for the analysis.
Findings
The findings indicated that the engagement of millennials and Gen-Z in ethical banking is influenced by factors such as intention, judgment and awareness, which shaped both generations’ ethical banking behavior.
Practical implications
This study could be a central reference point and assist banking institutions in understanding the preferences of millennials and Gen-Z.
Originality/value
This study extends the previous EDM model that focused solely on consumer's belief systems. Three aspects differentiate this paper and contribute to its originality, namely, the uniqueness of millennials and Gen-Z behavior, incorporating new variables along with the EDM models and study in Malaysian context.
Keywords
Citation
Zahari, S.A., Shahimi, S., Alma'amun, S. and Arshad, M.M. (2024), "Ethical banking behavior among millennials and Gen-Z in Malaysia", International Journal of Islamic and Middle Eastern Finance and Management, Vol. 17 No. 2, pp. 252-273. https://doi.org/10.1108/IMEFM-04-2023-0152
Publisher
:Emerald Publishing Limited
Copyright © 2023, Siti Aisyah Zahari, Shahida Shahimi, Suhaili Alma'amun and Mohd Mursyid Arshad.
License
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 & 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
Ethical banking is a new form of financial business that extends beyond the economic return of traditional banking behavior. Ethical banking refers to the financial institutions that provide products and services that can contribute to economic development, environmental quality and the well-being of society (Martínez-Campillo et al., 2021). In this perspective, ethical banking is a type of financial intermediation that develops new economic ties beyond profit making.
The emergence of ethical bank had started with the establishment of Triodos Bank in Netherlands in 1980. Nowadays, various banks have adopted ethical banks in their practices, such as Triodos Bank, GLS Bank, Cooperative Bank and Charity Bank (Barigozzi and Tedeschi, 2015) to deliver the sustainability agenda as highlighted in Sustainable Development Goals (SDGs). In Malaysia, several banks have started offering ethical banking products and services. Maybank, for instance, has integrated sustainable criteria into its credit risk management process and provide socially responsible investment to their customers (Tan et al., 2017), while CIMB Bank offers products like EcoSave Savings Account-i (CIMB Group Holding Berhad, 2023).
The proliferation of sustainable banking products and services also reflects the growing awareness and demand for ethical banking behavior. Bank customers, particularly millennials and Gen-Z, have increasingly prioritized ethical considerations by putting more concern on social and environmental issues in their decision-making (Puiu, 2016), interested in making sustainable investing decisions (Formánková et al., 2019) and adopted green products based on intrinsic pro-environmental values (Lee, 2020).
The consideration of these generations towards ethical financing is understandable as they are tackling their concerns by taking socially conscious actions (Forbes, 2020) to build a better world for future generations. Unfortunately, many banks have made a mistake by presuming that what worked for past generations will work for the millennials and Gen-Z. Adopting green approach alone can pose challenges for the banks to implement effective ethical banking practices (Ibe-enwo et al., 2019) that can suit the preferences of both generations. Gaining consumers' perspective of ethical banks is crucial (Taneja and Ali, 2021) because anomalous and inefficient products and services can lead to bank failures. In worse situations, the entire economy may suffer huge losses if the banking industry does not cope with financing effectively [Bank Negara Malaysia (BNM), 2019].
Therefore, banking institutions need to respond to the incremental demand of ethical banking products and services by incorporating sustainability criteria into their offerings. The emergence of ethical banking practices not only influences customer preferences but also catalyzes the expansion and diversification of sustainable banking options, creating a dynamic interplay between responsible banking and financial behavior, and the products designed to meet these evolving ethical standards. However, the millennial and Gen-Z ethical banking behavior have been insufficiently addressed in past study. Thus, this study aimed to determine the factors influencing ethical banking behavior among millennials and Gen-Z in Malaysia.
Our findings underscore the pivotal role of concern, skepticism, information, ethical banking awareness, ethical banking judgment, service quality, convenience, religious values, social context, ethical obligation and ethical banking intention in different stages of the ethical decision-making (EDM) process toward ethical banking engagement. This finding provides a comprehensive framework for financial institutions to adapt their marketing strategies, and formulate holistic directions for the development of ethical banking in Malaysia.
2. Literature review
2.1 Ethical bank
Ethical bank is a concept within sustainable finance that emphasizes beyond economic profitability of traditional banking in their banking practices. Social, ethical, green, alternative, sustainable development and solidarity banking are among the terms used to refer to ethical financing and banking (DeClerck, 2009; Chew et al., 2016; Park and Kim, 2020) which refers to bank that take ethical, social and environmental concern into account. The key principles of EB include fair and equal banking operation, good cooperation with the community, committed toward customers, employee well-being, environmentally responsible practices, corporate governance, maintaining a good reputation and transparency (Ferreira et al., 2016).
The primary distinction between ethical banks and conventional banks is that the latter focus solely on increasing their profits, whereas ethical banks operate under the three guiding principles of profit, people, and planet (Martínez et al., 2021). There is no accreditation that identifies a bank as ethical, and membership in the ethical banking movement is entirely voluntary (Martínez et al., 2020). However, most of them are members of the Global Alliance for Banking on Values (GABV), an independent network created in 2009 (Martínez et al., 2020). This ideological link distinguishes ethical banks from conventional banks, where the ideology is more economic than social (San-Jose et al., 2011).
2.2 Ethical banking behavior among millennials and Gen-Z
Globally, millennials and Gen-Z have a strong and potential impact on current and future business practices (Arli et al., 2014). Millennials prefer ethical banks as their preferred banking model, as the products and services are according to their preferences and value (Jayasekera and Pushpakumari, 2021). Millennials tend to invest their money and wealth to bring people out of poverty and protect the environment (Ed Grattan, 2019). Money management is crucial to ensure they live within their means (Alma’amun et al., 2018). Additionally, Gen-Z is also interested in practicing socially responsible investment (SRI) because they want to promote sustainable practices and values through a choice of financial instruments (Chen et al., 2019). Bayer et al. (2019) further stated that the younger generation, consider the choice of ethical banking in their new phase of life after university.
Millennials and Gen-Z approximately account for 18.1 million people, representing almost 55% of the total population in Malaysia [Department of Statistics Malaysia (DOSM), 2020]. As most of the Malaysian workforce is from these generations, financial management is undeniably crucial in managing their wealth and money. Both generations have shifted their concern toward sustainable and ethical practices, which promotes good causes in the future (ICMR, 2021). Customers from these generations constantly consider the EDM process in their final decision to reflect their desire to boost the 3Ps (planet, people, profit). Connecting with these customers, who possess different banking behavior and preference, is critical for the banks to understand factors influencing ethical banking behavior among millennials and Gen-Z.
Past studies regularly debate on factors determining customer intention and behavior toward banking services. One prominent theory used to measure customer intention is Theory of Planned Behavior (TPB) developed by Ajzen in the late 1980s. For instance, Asyari et al. (2022) mentioned that the TPB is very much relevant to Islamic banking as customers' attitudes regarding Islamic bank have a big impact on their purchasing decisions. In the context of ethical banking, Taneja and Ali (2021) emphasized the effectiveness of the TPB in predicting customer behavioral intention toward sustainable banking in India. Attitude is the most significant factor influencing customers to adopt sustainable banking (Taneja and Ali, 2021).
Nonetheless, measuring the customer's ethical banking beyond their intention by examining their actual behavior is crucial. Although the intention is a good predictor of moral behavior (Rifat et al., 2016), it could create an intention-behavior gap. To address the matter, the Rest’s model of EDM has been applied in various studies contexts. Rest’s model of EDM consists of four (4) main parts, namely, awareness, judgment, intention and behavior, had stand as guiding principle in EDM process (Lehnert et al., 2014). For instance, Bayer et al. (2019) extend the EDM model by include additional variable to examine the younger generation's ethical banking intention. In addition, Valentine and Hollingworth (2012) highlighted the positive association between moral intensity and Rest’s EDM model for a Midwestern financial services organization. However, considering only Bayer's EDM model in measuring millennials and Gen-Z ethical banking behavior in Malaysia would not provide these cohorts' actual behavior in engaging in the practices. Thus, this study extends the model by incorporating technology-related factors, ethical obligation, religious values and ethical banking behavior in the model.
2.3 Hypothesis development
Moral awareness is the first stage in making an ethical decision, which requires the consumer to interpret a situation, including the ethical aspect (Rest, 1986). The researcher of the present study interpreted moral awareness as ethical banking awareness to suit the context of the study. Ethical banking awareness is achieved when millennials and Gen-Z realize the ethical banking component that banking institutions practice. The second stage of Rest's model is a moral judgment, which can be referred to as an individual assessment of ethical or unethical actions or behaviors (Culiberg and Bajde, 2013). A few studies have reported a positive link between awareness and judgment (Rest, 1986; Barnett and Valentine, 2004; Valentine and Bateman, 2011; Valentine and Hollingworth, 2012). Hence, the hypothesis on ethical banking awareness and judgment is proposed as follows:
Ethical banking awareness positively impacts ethical banking judgment.
After establishing an ethical banking judgment, this reasoning generated ethical banking intentions (Rest, 1986). Ethical banking intention can be referred to as the intention of millennials and Gen-Z to engage with ethical banking in banking institutions. Several authors (Rest, 1986; Valentine and Hollingworth, 2012; Culiberg and Bajde, 2013; Bayer et al., 2019) identified a positive relationship between judgment and intention. Hence, the second hypothesis is posited as follows:
Ethical banking judgment positively impacts ethical banking intent.
Finally, the last step of Rest's model is the ethical banking behavior. Nonetheless, Bayer's EDM model did not measure the respondents' ethical banking behavior. Thus, this study measures the ethical banking behavior of the millennials and Gen-Z generated by the ethical banking intention to fill in the practical knowledge gap. The individual's ethical banking behavior is demonstrated if they become a customer of an ethical bank (Bayer et al., 2019). Various studies have emphasized the impact of intention on actual behavior. A prior study on banking evaluated the favorable link between intention and behavior (Rifat et al., 2016; Iqbal et al., 2017; Iqbal et al., 2018) and ethics (Rest, 1986; Culiberg and Bajde, 2013; Yadav and Pathak, 2017; Agag, 2019). The following hypothesis has been proposed:
Ethical banking intent positively impacts ethical banking behavior.
The general attitude in Bayer's EDM model is divided into perceived consumer effectiveness (PCE), concern, and skepticism. PCE refers to the individual evaluation of how much their activities may help solve problems or issues (Bryson et al., 2016). Bayer et al. (2019) further explained PCE as the perceived power of customers in deciding their actions. Bayer et al. (2019) discovered that PCE positively influences ethical banking awareness and judgment.
Concern in this study refers to the level of interest (De Pelsmacker and Janssens, 2007: Bayer et al., 2019) and ethical, sustainable, social or green consciousness (Deng, 2015; Bayer et al., 2019). Bayer et al. (2019) highlighted the positive effect between concern and moral awareness and judgment. Meanwhile, skepticism is often described as inviolate of moral principles (Forsyth and O’Boyle, 2011). In the present study, skepticism refers to the customer's distrust of the ethical value delivered by the ethical bank (Bayer et al., 2019). There is a significant negative influence between skepticism and moral awareness and judgment (Bayer et al., 2019). Hammad et al. (2014) stressed the negative impact of skepticism on moral judgment. Therefore, the hypotheses suggested for PCE, concern and skepticism are recommended as follows:
PCE positively impacts ethical banking awareness.
PCE positively impacts ethical banking judgment.
Concern positively impacts ethical banking awareness.
Concern positively impacts ethical banking judgment.
Skepticism negatively impacts ethical banking awareness.
Skepticism negatively impacts ethical banking judgment.
As Bayer et al. (2019) mentioned, bank selection criteria (such as reputation, service quality, economic benefit and convenience) must be included in the EDM model to suit the context of the study. Technology-related factors and religious value were included as additional variables for bank selection criteria to suit the characteristic of millennials and Gen-Z in Malaysian financial landscape. Reputation has been recognized as one of the most effective influences on customers' intention toward banking institutions (Zakiah and Al-Aidaros, 2017; Bayer et al., 2019; Pujianti et al., 2021). Reputation could refer to the bank's prestigious image (Bayer et al., 2019). Second, service quality is the most prominent aspect of the bank selection criteria for any service industry's success (Abduh et al., 2018; Khan et al., 2020). The degree of responsiveness of their services, the competence of employees, and the reliability of the services are among the aspects included in the service quality (Bayer et al., 2019). Most of the studies had stated a positive link between service quality and intention (Bayer et al., 2019; Raza et al., 2020; Suhartanto et al., 2021).
Economic benefit refers mainly to the product's fees and profit, consisting of the cost of products, rate-of-return, lower service charge, and lower monthly payment (Aida and Musa, 2016). In contrast, convenience comprises the aspect that makes customers comfortable and easier to access banking institutions (Andaleeb et al., 2016). As millennials and Gen-Z are born in the technological diversity era, they tend to adopt technology banking more than traditional banking services (Salleh et al., 2017). Thus, the technology-related factor is an important criterion that must be elaborated further concerning millennials and Gen-Z ethical banking selection criteria. The last bank selection criteria in this model is religious values. Previous studies highlighted the importance of religious value for both generations' bank selection criteria (Amin, 2016; Pujianti et al., 2021). The present study will focus on the relationship between millennials and Gen-Z bank selection criteria and ethical banking intention, as proposed by Papaoikonomou et al. (2011) and Bayer et al. (2019). Hence, the suggested hypotheses are as follows:
Reputation positively impacts ethical banking intent.
Service quality positively impacts ethical banking intent.
Economic benefit positively impacts ethical banking intent.
Convenience positively impacts ethical banking intent.
Technology-related factor impacts ethical banking intent.
Religious value impacts ethical banking intent.
The moral intensity construct summarizes a set of issue-contingencies that impact ethical reasoning at the situational level (Barnett and Valentine, 2004; Valentine and Bateman, 2011). According to Jones (1991), moral intensity refers to the extent of imperative moral issues related to a situation with six main factors: magnitude of consequences, social consensus, probability of effect, temporal immediacy, proximity and concentration of effect. There is a positive correlation between moral intensity and moral awareness, judgment and intent (Valentine and Hollingworth, 2012; Talha et al., 2013; Bayer et al., 2019). Consequently, the following hypotheses are presented:
Moral intensity positively impacts ethical banking awareness.
Moral intensity positively impacts ethical banking judgment.
Moral intensity positively impacts ethical banking intent.
Information in the present study comprises the category of knowledge (Papaoikonomou et al., 2011; Bayer et al., 2019), quantity and quality of the information or knowledge (De Pelsmacker and Janssens, 2007; Bayer et al., 2019), and ethical cognitive effort (Deng, 2015; Bayer et al., 2019). Bayer et al. (2019) discovered a significant positive impact between information, moral awareness and intent. Thus, the hypotheses for the present study are suggested as follows:
Information positively impacts ethical banking awareness.
Information positively impacts ethical banking judgment.
Information positively impacts ethical banking intent.
Social context can be defined as perceived social pressure to perform a specific action (Ajzen, 1985). Social context could also be referred to as subjective norm, social pressure, and peer group influence (Bayer et al., 2019). Most of the studies found social context to have a positive correlation with behavioral intention to engage in ethical practices (Oseni et al., 2018; Bayer et al., 2019; Taneja and Ali, 2021). Consequently, the following hypothesis is presented:
Social context positively impacts ethical banking intent.
Denial of responsibility, denial of injury, denial of victim, condemning the condemner and appeal to higher loyalty are among the five cognitive techniques of neutralization stated by Chatzidakis et al. (2007). D’Astous and Legendre (2009) included the government-dependency argument as a criterion of neutralization in their study. Bayer et al. (2019) discovered that neutralizatio n is negatively linked with moral intention. Thus, the following hypothesis is presented:
Neutralization negatively impacts ethical banking intent.
Finally, ethical obligation is included as additional variable for the EDM model. Shaw and Shiu (2002) revealed that ethical obligation is better represented by predicting individual intention. In many instances, ethical obligation has improved intention prediction (Shaw et al., 2000; Shaw and Shiu, 2002; Shaw et al., 2015). In ethical bank consumerism, in which behavior is centered around a concern for others, the exploration of a measure that reflects ethical concerns is imperative in the applications of the model. Therefore, the suggested hypothesis is as follows:
Ethical obligation positively impacts ethical banking intent.
All hypotheses will be tested according to the research framework in Figure 1 with a total of 18 constructs included in the model.
3. Methodology
3.1 Measurement construct
This study is empirical and tends to identify the ethical banking behavior among millennial and Gen-Z in Malaysia. Following a quantitative research approach, this study had analyzed primary data collected via an online survey. The instrument designed had four primary sections: ethical banking awareness and judgment; ethical banking intention; ethical banking behavior; and demography. The section regarding ethical banking awareness, intention, and behavior was measured using a five-point Likert scale (1-Strongly Disagree to 5-Strongly Agree). In contrast, ethical banking judgments were measured using a five-point scale for the respective options. The measurement items were based on previous studies related to ethical, sustainable, green behavior and adapted according to the banking context (Bayer et al., 2019; Taneja and Ali, 2021; Farooq and Yahya, 2021).
The questionnaire was sent to two experts for validation. Then, the validated questionnaire was pretested on 30 millennials and Gen-Z for the reliability and validity of the instrument. The respondents provided several recommendations throughout this pilot test which led to changes in the readability, and clarity of the instrument.
3.2 Sampling and data collection
This study adopted a stratified random sampling technique to recruit the target respondents to ensure the survey sample was representative of Malaysia's millennials and Gen-Z population. Selangor, Putrajaya and Kuala Lumpur were chosen as the sampling location as a large population of millennials and Gen-Z are in these states.
By using the equation,
3.3 Data analysis
The data were analyzed using descriptive statistics using Statistical Package for the Social Sciences (SPSS), while the hypotheses were test using partial least square-structural equation model (PLS-SEM) using SmartPLS. The PLS-SEM is an effective tool to explain causal links among variables in hypotheses (Ting et al., 2019) and a good method in analyzing complex models (Hair et al., 2018).
4. Finding
4.1 Respondents' profile
The sample of 525 respondents comprising 182 males and 343 females were used for the final analysis. Most respondents were identified as Malay (59.4%), followed by Chinese (24.8%) and Indian (13.5%). Regarding age or the generation category, 284 respondents were categorized as Gen-Z and 241 respondents were millennials. In the case of respondent academic qualification, most respondents were found to have a bachelor's degree and above (76.4%). The respondents comprised those located in Selangor (53.7%), Kuala Lumpur (31.0%) and Putrajaya (15.2%). The results of the demographic are presented in Figure 2.
In terms of their engagement with banking services, most of the respondents engage in more than two banks. For conventional banks, most respondents engage with CIMB Bank (22%) and Malayan Banking Berhad (20.5%) (Figure 3). In terms of Islamic banks, most of the respondents engage with BIMB (25.8%) and Maybank Islamic Berhad (16.7%) (Figure 4).
4.2 Measurement model
Indicator reliability, internal consistency, convergent validity, and discriminant validity have been conducted to measure the reliability and validity of the reflective measurement model. The first step is to examine the factor loadings of each item. As suggested by Hair et al. (2011), the factor loading in this study was above 0.5, which indicates a good indicator. This study retained factor loading between 0.4 and 0.7, leading to increased composite reliability (CR), as Hair et al. (2014) stated. A total of 15 items were deleted. In the reflective model, dropping one indicator may not be highly impactful since other indicators are still representative of the construct (Garson, 2016).
The second step is assessing the internal consistency reliability through CR of the constructs. Compared to Cronbach's alpha (CA), CR is the preferred alternative as CA assumes all indicator loading is in the same population. Thus, it may overestimate or underestimate the reliability scale for a reflective model (Garson, 2016). After deleting a few items, all constructs exhibit a CR value between 0.7 and 0.9 (Hair et al., 2018), suggesting internal consistency reliability. The third step is assessing the average variance extracted (AVE). All the AVEs are greater than 0.5, indicating that the construct explains at least 50% of the variance of the items (Hair et al., 2018). The values also indicate the existence of convergent validity in the model. The results of the factor loading, CR and AVE are presented in Table 1.
The fourth step is to assess the Fornell–Lacker Criterion and heterotrait-monotrait (HTMT) ratio to measure the discriminant validity. The discriminant validity was measured to compare the inter-construct relation or the mean value of item correlation of each construct (Hair et al., 2018). Henseler et al. (2015) proposed that the HTMT value above 0.9 indicates a high discriminant validity problem. The findings of the present study (Table 2) fulfilled discriminant validity. Hence, all variables are independent and explain different concepts.
4.3 Structural model
After the measurement model was satisfactory, the structural model was examined. Firstly, collinearity issues must be examined to ensure the issues do not bias the regression results before assessing the structural model. The assessment was undertaken through variance inflation factor (VIF), and the VIF values above five indicate collinearity issues (Hair et al., 2018). Each VIF value in the model of the present study is lower than five (<5), indicating that this study does not include common method bias.
The results of the path coefficient and significant level of the structural model are presented in Figure 5. The outcome of the first chain in EDM model revealed that concern (β = 0.225, p < 0.001), skepticism (β = 0.151, p < 0.01) and information (β = 0.102, p < 0.05) were significant and positively influence awareness. On the other hand, PCE (β = −0.032, p > 0.05) and moral intensity (β = −0.089, p > 0.05) negatively correlate with awareness. In the second chain of the EDM model, awareness (β = 0.242, p < 0.001) and concern (β = 0.176, p < 0.001) significantly and positively correlate with ethical banking judgment, whereas PCE (β = –0.012, p > 0.05) and information (β = −0.044, p > 0.05) negatively influence ethical banking judgment. At the same time, skepticism (β = 0.044, p > 0.05) and moral intensity (β = 0.064, p > 0.05) were found to have insignificant positive impact on ethical banking judgment.
Judgment (β = 0.130, p < 0.05), service quality (β = 0.144, p < 0.01), convenience (β = 0.128, p < 0.01), religious value (β = 0.114, p < 0.05), social context (β = −0.193, p < 0.001), and ethical obligation (β = 0.120, p < 0.05) significant and positively influence ethical banking intent. The result also revealed that the reputation, (β = 0.079, p > 0.05), economic benefit (β = 0.027, p > 0.05), technology related factors (β = 0.012, p > 0.05), and moral intensity (β = 0.071, p > 0.05) have positive impact on ethical banking intent, whereas neutralization (β = −0.134, p > 0.05) and information (β = −0.065, p > 0.05) was found to have negative impact. The ethical banking intent was also found to have a significant and positive impact on ethical banking behavior (β = 0.258, p < 0.001).
The R2 values for the construct in the study were found to be in the range of 0.35 – 0.05. The R2 of ethical banking awareness (0.110) indicates that the model explains 11% of the variance of ethical banking awareness. In contrast, R2 of ethical banking judgment (0.093) indicates that the model explains 9.3% of the variance of ethical banking judgment. In contrast, R2 of ethical banking judgment (0.093) indicates that the model explains 9.3% of the variance of ethical banking judgment. Besides, the R2 of ethical banking intent (0.341) implies that the model explains 34.1% of the variance of ethical banking intent. Hair et al. (2018) confirm that the model has low explanatory power for ethical banking awareness, judgment, and behavior and moderate explanatory power for ethical banking intent.
Blindfolding was subsequently used to assess the path model's predictive accuracy. The Q2 value for ethical banking awareness was 0.089, 0.027 for ethical banking judgment, 0.177 for ethical banking intent and 0.051 for ethical banking behavior, representing the structural model's predictive accuracy as the Q2 > 0. Table 3 summarizes the analysis of the structural model assessment.
5. Discussion
The result of the structural model assessment discovered that half of the hypotheses were supported [H1, H2, H3, H4C, H4D, H5B, H5D, H5F, H7A, H8 and H10] and positively influence different stages of the EDM process when deciding to engage in ethical banking. Nevertheless, the technology-related factor, considered an important characteristic of millennials and Gen-Z, revealed an insignificant positive relationship with ethical banking intent. According to Bayer et al. (2019), the possible explanation for the insignificant relationship is that the EDM model was drawn from publications in different contexts. Thus, the model may not represent the engagement of millennials and Gen-Z EDM in ethical banking.
A significant positive relationship between concern and awareness has been reported in De Pelsmacker and Janssens (2007), Yadav and Pathak (2017) and Ye et al. (2020). According to Taneja and Ali (2021), the concern will enhance customers' awareness and judgment regarding the consequences of their attitude toward ethical banking behavior. Moreover, the present study found that information significantly influences ethical banking awareness. This result further supports the idea of Papaoikonomou et al. (2011) and Bayer et al. (2019). Knowledge regarding the topic helps customers to understand the products better, leading to a high level of awareness (Sulaiman et al., 2022). Customers among millennials and Gen-Z who receive sufficient and reliable information will be highly concerned regarding the impact of their decision to participate in ethical banking. Thus, ethical banking awareness among both generations will be enhanced.
Surprisingly, skepticism was found to have a significant and positive impact on ethical banking awareness among millennials and Gen-Z. This finding was unexpected as prior studies had highlighted the negative relationship between skepticism and awareness (De Pelsmacker and Janssens, 2007; Bayer et al., 2019). This might occur due to a high level of curiosity (Goldgehn, 2004) and their love to discover new things (Vieira et al., 2020). A high level of skepticism toward ethical banking among these generations tends to increase their awareness when they have started to understand the business model of ethical banking.
In the second step of the EDM process, only awareness and concern significantly affect the ethical banking judgment. This finding is consistent with prior studies on ethics and banking (Rest, 1986; Deng, 2015; Martínez and Jaeger, 2016). The positive correlation between concern and ethical banking judgment is consistent with De Pelsmacker and Janssens (2007) and Bayer et al. (2019). The findings could have emerged since recognizing ethical issues among both generations nowadays improves their ability to judge the consequences of their EDM.
The significant and positive relationship between ethical banking judgment and ethical banking intent is in line with Rest (1986), Agag (2019), and Bayer et al. (2019). The good correlation between judgment and intention were due to the level of relativism and idealism (Arli et al., 2014) developed from the awareness of millennials and Gen-Z. In addition, the significant association with service quality was confirmed by prior studies on ethic and banking literature (Lymperopoulos et al., 2012; Nisha, 2016). Customers will be influenced to adopt ethical banking behavior due to the provision of efficient (Ringim, 2014). As for convenience, a significant positive relationship was discovered between ethical banking intent among millennials and Gen-Z. This finding suggests that various ethical banking products and services, time of service and the incorporation of technology in the ethical banking business model can attract customers to adopt ethical banking behavior in the future. The finding has been confirmed by Patterson and McEachern (2018) and Iqbal et al. (2018).
The findings for the religious value are consistent with past studies, which found a significant relationship with engagement in ethical banking (Bukhari et al., 2019; Alsaad et al., 2020; Janah et al., 2020). In Malaysia, Islamic banking conforms to the ethical banking criteria, which solely focus on profit-maximization (Musa et al., 2020). Indeed, ethical banking was in line with value-based intermediation introduced by BNM (Ismail et al., 2020). This relationship indicates that customers of both generations with strong religious values were highly consider practicing it in the future.
The significant influence of social context was supported by Bayer et al. (2019) and Taneja and Ali (2021). The suggestions and opinions of the reference group, especially family members, close friends, co-workers, neighbors, colleagues, promotion by banks and the government, influence their decision-making (Awang et al., 2019; Bayer et al., 2019). Moreover, the results indicate a significant positive influence of ethical obligation with ethical banking intent. A strong ethical obligation from customers' social and environmental values guides them in their decision to be involved in ethical banking, which enhances the ethical banking intention (Tullani et al., 2018).
The ethical banking intent was found to influence ethical banking behavior among millennials and Gen-Z significantly. These results support the finding of Iqbal et al. (2018) and Djafarova and Foots (2022). Djafarova and Foots (2022) reported that Gen-Z turns from ethical intention to ethical behavior when they feel pride in using the products, and the products bring positive outcomes in the future. This study has confirmed that ethical banking intention has been marked as a highly significant antecedent of ethical banking behavior among millennials and Gen-Z in Malaysia.
This study contributes and enriches the literature on ethical banking. To the best of the researchers’ knowledge, this study is the first research undertaken to determine the factors influencing ethical banking behavior among millennials and Gen-Z in Malaysia. Therefore, this study provides valuable insights for future research on sustainable finance and responsible banking practices that help researchers to identify patterns, motivations, and factors influencing ethical banking choices. Researchers can build upon these findings to further investigate the specific drivers and barriers to ethical banking adoption among the both generations.
Second, this study also extended the Bayer’s EDM model by incorporating relevant variables to suit the study context. Hence, the present study confirmed that the extended EDM model are applicable in explaining factors influencing customers' ethical banking behavior. Moreover, this study highlighted the general attitude of concern and skepticism in enhancing the awareness and judgment of millennials and Gen-Z on ethical banking. Motivated by this consideration, a new relationship was identified between skepticism and awareness, thus providing a new basis for future study in the ethical behavior context. Based on the bank selection criteria, the rationality of service quality, convenience and religious value in influencing ethical banking behavior among both generations is emphasized in this study. Besides information, social context and ethical obligation influence the EDM process of engaging in ethical banking.
This study enhances bankers' understanding of the factors influencing ethical banking behavior among Malaysian millennials and Gen-Z. The findings indicate how much ethical banking contributes to social, environmental and economic sustainability and may influence customers' intention to engage in it. Therefore, banks must focus on increasing information and marketing campaigns to promote ethical banking. In the present era of technological diversity, banks and policymakers must use social media to broaden ethical banking awareness. Providing clear, adequate, and trustworthy information on ethical banking will address the high level of skepticism on ethical banking. In turn, social influence aided by word-of-mouth will also increase customers' intention to engage in ethical banking in the future.
Banks must manage their target customers through their service quality and convenience of services to build a strong intention of ethical banking behavior among customers. Besides, religious value makes millennials and Gen-Z confident in being involved in ethical banking. Ethical banking has constantly been linked to Islamic banking. The ethical identity within Islam can be explained by the application of a net fair value approach, which involves a comparative analysis of haram and halal matters (Hassan and Rashid, 2010). Thus, the ethical banking business model must comply with Shariah and provide tools and services that reinforce religious values.
In addition, this study will assist the banks in developing a good ethical banking practices that meet both generations’ preferences, which in turn help them to increase their market share. From a global perspective, this study could create opportunities and thrive the regional financial institutions and international bank understanding on the importance of millennials and Gen-Z toward sustainable financing. In a nutshell, this study has significant implications for research, practice and society which potentially driven the positive changes in the global banking sector. Therefore, in the Malaysian context, a national regulatory framework and policy by BNM are crucial to drive the implementation of ethical financing standards to promote the ethical banking benefits.
6. Conclusion
The present study applied extended EDM model in explaining the behavior of millennials and Gen-Z toward ethical banking in Malaysia. Among the determining factors influencing ethical banking behavior are concern, skepticism, information, service quality, convenience, religious value, social context, ethical obligation, ethical banking awareness, ethical banking judgment and ethical banking intent. By understanding a broader spectrum of the behavior of millennials and Gen-Z, banks can adjust their marketing approaches and practices to keep pace with the preference of these generations. In a wider context, this finding will enhance bankers understanding of customers behavior in providing a good ethical banking practices to develop a prosperous, inclusive and sustainable banking system.
Notwithstanding, the generalizability of these results is subjected to certain limitations. Firstly, the study focused on millennials and Gen-Z customer behavior, yet ethical banking attitudes among other generations behavior may differ from these generations. Additionally, using a self-administered survey may lead to challenges in monitoring, as respondents might not fully grasp the author's intent, potentially causing variations in survey interpretation among participants. Therefore, future study may address the limitation to provide a more comprehensive result.
Figures
Figure 1.
Research framework
Figure 2.
Respondents’ profile
Figure 3.
Number of respondents involved in conventional bank
Figure 4.
Number of respondents involved in Islamic bank
Figure 5.
Results of the structural model
Summary of the measurement model
| Constructs | Items [1] | Loadings | CR | AVE |
|---|---|---|---|---|
| AW | AW | 1.000 | 1.000 | 1.000 |
| CN | CN1 | 0.802 | 0.763 | 0.520 |
| CN2 | 0.703 | |||
| CN3 | 0.650 | |||
| CV | CV3 | 1.000 | 1.000 | 1.000 |
| EB | EB1 | 0.909 | 0.743 | 0.600 |
| EB2 | 0.611 | |||
| EBB | EBB2 | 0.675 | 0.801 | 0.503 |
| EBB3 | 0.644 | |||
| EBB5 | 0.710 | |||
| EBB6 | 0.798 | |||
| EO | EO2 | 0.739 | 0.757 | 0.510 |
| EO3 | 0.691 | |||
| EO4 | 0.711 | |||
| INF | INF2 | 0.941 | 0.796 | 0.667 |
| INF3 | 0.670 | |||
| INT | INT1 | 0.762 | 0.761 | 0.614 |
| INT2 | 0.805 | |||
| JG | JG1 | 0.710 | 0.746 | 0.596 |
| JG2 | 0.829 | |||
| MI | MI2 | 0.628 | 0.779 | 0.546 |
| MI3 | 0.889 | |||
| MI4 | 0.674 | |||
| NR | NR1 | 0.811 | 0.802 | 0.588 |
| NR2 | 0.920 | |||
| NR3 | 0.510 | |||
| PCE | PCE1 | 0.579 | 0.766 | 0.635 |
| PCE3 | 0.966 | |||
| RP | RP1 | 0.762 | 0.808 | 0.679 |
| RP2 | 0.881 | |||
| RV | RV1 | 0.785 | 0.820 | 0.695 |
| RV2 | 0.879 | |||
| SC | SC1 | 0.630 | 0.785 | 0.552 |
| SC2 | 0.818 | |||
| SC3 | 0.768 | |||
| SK | SK1 | 0.692 | 0.757 | 0.510 |
| SK2 | 0.722 | |||
| SK3 | 0.727 | |||
| SQ | SQ2 | 1.000 | 1.000 | 1.000 |
| TR | TR1 | 0.893 | 0.796 | 0.664 |
| TR2 | 0.728 |
The items are part of the survey instrument which has been approved as an intellectual property (IP) right (protected under the Copyright Act 1987) and in the process of registration with MyIPO.
PCE = perceive consumer effectiveness; AW = awareness; CN = concern; SK = skepticism; MI = oral intensity; INF = information; JG = judgment; RP = reputation; SQ = service quality; EB = economic benefit; CV = convenience; TR = technology-related factor; RV = religious value; SC = social context; NR = neutralization; EO = ethical obligation; INT = intent; EBB = ethical banking behavior
Source: Data analysis
Heterotrait-monotrait (HTMT) ratio of the correlation
| Constructs | AW | CN | CV | EB | EBB | EO | INF | INT | JG | MI | NR | PCE | RP | RV | SC | SK | SQ | TR |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AW | ||||||||||||||||||
| CN | 0.329 | |||||||||||||||||
| CV | 0.093 | 0.220 | ||||||||||||||||
| EB | 0.233 | 0.655 | 0.389 | |||||||||||||||
| EBB | 0.249 | 0.287 | 0.165 | 0.343 | ||||||||||||||
| EO | 0.206 | 0.476 | 0.279 | 0.722 | 0.213 | |||||||||||||
| INF | 0.024 | 0.339 | 0.263 | 0.186 | 0.108 | 0.286 | ||||||||||||
| INT | 0.272 | 0.759 | 0.528 | 0.666 | 0.476 | 0.815 | 0.318 | |||||||||||
| JG | 0.492 | 0.592 | 0.032 | 0.858 | 0.437 | 0.729 | 0.192 | 0.846 | ||||||||||
| MI | 0.207 | 0.225 | 0.084 | 0.341 | 0.312 | 0.121 | 0.194 | 0.107 | 0.166 | |||||||||
| NR | 0.062 | 0.081 | 0.062 | 0.307 | 0.270 | 0.083 | 0.073 | 0.173 | 0.156 | 0.537 | ||||||||
| PCE | 0.159 | 0.315 | 0.065 | 0.160 | 0.363 | 0.151 | 0.278 | 0.149 | 0.191 | 0.506 | 0.533 | |||||||
| RP | 0.333 | 0.496 | 0.244 | 0.759 | 0.218 | 0.543 | 0.079 | 0.671 | 0.601 | 0.275 | 0.087 | 0.161 | ||||||
| RV | 0.333 | 0.413 | 0.236 | 0.465 | 0.347 | 0.584 | 0.205 | 0.663 | 0.623 | 0.189 | 0.093 | 0.146 | 0.451 | |||||
| SC | 0.241 | 0.359 | 0.255 | 0.754 | 0.332 | 0.714 | 0.141 | 0.745 | 0.423 | 0.300 | 0.192 | 0.124 | 0.502 | 0.416 | ||||
| SK | 0.303 | 0.297 | 0.127 | 0.381 | 0.325 | 0.1214 | 0.344 | 0.225 | 0.314 | 0.800 | 0.536 | 0.675 | 0.221 | 0.265 | 0.344 | |||
| SQ | 0.150 | 0.291 | 0.314 | 0.389 | 0.233 | 0.234 | 0.135 | 0.529 | 0.302 | 0.082 | 0.103 | 0.151 | 0.453 | 0.188 | 0.232 | 0.118 | ||
| TR | 0.241 | 0.446 | 0.487 | 0.561 | 0.342 | 0.602 | 0.207 | 0.617 | 0.724 | 0.066 | 0.120 | 0.143 | 0.460 | 0.688 | 0.313 | 0.198 | 0.407 |
Source: Data analysis
Summary of the structural model
| Hypothesis | β | t value | p-value | R2 | Q2 | Result |
|---|---|---|---|---|---|---|
| Ethical banking awareness | ||||||
| H4A: PCE → AW | −0.032 | 0.611 | 0.541 | 0.110 | 0.089 | Not supported |
| H4C: CN → AW | 0.225 | 4.702 | 0.000*** | Supported | ||
| H4E: SK → AW | 0.151 | 2.825 | 0.005** | Not supported | ||
| H6A: MI → AW | −0.089 | 1.882 | 0.060 | Not supported | ||
| H7A: INF → AW | 0.102 | 2.142 | 0.032* | Supported | ||
| Ethical banking judgment | ||||||
| H1: AW → JG | 0.242 | 4.706 | 0.000*** | 0.067 | 0.027 | Supported |
| H4B: PCE → JG | −0.012 | 0.160 | 0.873 | Not supported | ||
| H4D: CN → JG | 0.176 | 3.415 | 0.001*** | Supported | ||
| H4F: SK→ JG | 0.044 | 0.716 | 0.474 | Not supported | ||
| H6B: MI → JG | 0.064 | 1.007 | 0.314 | Not supported | ||
| H7B: INF → JG | −0.044 | 0.851 | 0.395 | Not supported | ||
| Ethical banking intent | ||||||
| H2: JG → INT | 0.130 | 2.496 | 0.013* | 0.341 | 0.177 | Supported |
| H5A: RP → INT | 0.079 | 1.697 | 0.090 | Not supported | ||
| H5B: SQ → INT | 0.144 | 3.167 | 0.002** | Supported | ||
| H5C: EB → INT | 0.027 | 0.587 | 0.557 | Not supported | ||
| H5D: CV → INT | 0.128 | 2.575 | 0.010** | Supported | ||
| H5E: TR → INT | 0.012 | 0.258 | 0.796 | Not supported | ||
| H5F: RV → INT | 0.120 | 2.568 | 0.010* | Supported | ||
| H6C: MI → INT | 0.071 | 1.632 | 0.103 | Not supported | ||
| H7C: INF → INT | −0.065 | 1.553 | 0.120 | Not supported | ||
| H8: SC → INT | 0.193 | 4.193 | 0.000*** | Supported | ||
| H9: NR → INT | −0.134 | 1.838 | 0.066 | Not supported | ||
| H10: EO → INT | 0.120 | 2.406 | 0.016* | Supported | ||
| Ethical banking behavior | ||||||
| H3: INT → EBB | 0.258 | 6.195 | 0.000*** | 0.119 | 0.051 | Supported |
***p < 0.001; **p < 0.01; *p < 0.05
Source: Data analysis
Note
Available upon request.
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Further reading
Hofmann, E., Meier-Pesti, K. and Kirchler, E. (2007), “The decision process for ethical investment”, Journal of Financial Services Marketing, Vol. 12 No. 1, pp. 4-16.
Iqbal, M., Rifat, A. and Nisha, N. (2021), “Evaluating attractiveness and perceived risks”, International Journal of Asian Business and Information Management, Vol. 12 No. 1, pp. 1-23.
Mansour, I.H.F. and Diab, D.M.E. (2016), “The relationship between celebrities’ credibility and advertising effectiveness: the mediation role of religiosity”, Journal of Islamic Marketing, Vol. 7 No. 2, pp. 148-166.
Nisha, N. (2020), “Can Central bank survive the green banking revolution? A case of Bangladesh bank”, International Journal of Asian Business and Information Management, Vol. 11 No. 3, pp. 45-64.
Acknowledgements
Statements and declarations:
Funding: The authors express their gratitude to the Faculty of Economics and Management (FEP), Universiti Kebangsaan Malaysia (UKM): EP-2021-009 (Conceptualizing and Profiling Ethical Banking Behavior among Millennials and Gen-Z in Malaysia) for funding this study. This paper was also presented at Islamic Finance, Banking, Business Ethic Global Conference 2022 (IFBBE 2022) and the 10th Islamic Banking, Accounting and Finance International Conference 2022 (IBAF 2022) and has been excluded from the conference e-proceeding/proceedings. Thus, authors would like to thank the participants of these conferences for their helpful comments, which helped improved the quality of the papers.
Authors’ contributions: All authors contributed to the study conception and design. Instrument developments were administered by Shahida Shahimi and Suhaili Alma’amun. Material preparation, data collection and analysis were performed by Siti Aisyah Zahari. The first draft of the manuscript was written by Siti Aisyah Zahari and Mohd Mursyid Arshad. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Competing interests: All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
Ethics statement: This study does not involve direct face-to-face human participants as the survey was electronically administered. Informed consent was obtained from all individual participants involved in the study. Authors are committed to protecting the privacy and confidentiality of all participants.
Corrigendum: It has come to the attention of the publisher that the article Binti Zahari, S.A., Shahimi, S., Alma’amun, S. and Arshad, M.M. (2024), “Ethical banking behavior among millennials and Gen-Z in Malaysia”, International Journal of Islamic and Middle Eastern Finance and Management, Vol. 17 No. 2, pp. 252-273. https://doi.org/10.1108/IMEFM-04-2023-0152, displays Siti Aisyah Zahari’s affiliation incorrectly. This error was introduced during the submission process. Binti Zahari, S.A., Shahimi, S., Alma’amun, S. and Arshad, M.M. (2024) has been corrected to Zahari, S.A., Shahimi, S., Alma’amun, S. and Arshad, M.M. (2024). The authors sincerely apologise for this error and for any misunderstanding.