Determinants of Customers' Behavior for the Adoption of Green Banking Products and Services: UTAUT Model-Based Explanation

Increasing environmental concerns worldwide have spurred individuals and firms to emphasize eco-friendly practices in every walk of life. This study investigates the implication of the UTAUT model in the adoption of green banking products and services by customers in Pakistan. This study also explores behavioral intention as a mediator between explanatory variables and customers' behavior for the adoption of green banking practices. This quantitative study used convenience sampling and structured self-administered questionnaires for data collection. Structural Equation Modelling (SEM) was employed on the data collected from 305 customers of commercial banks and the results revealed a significant impact of performance expectancy, effort expectancy, enabling situation, conditional values, and emotional values on customers’ intention and behavior toward green banking practices. Moreover, facilitating conditions and social influence positively affect customers’ adoption of green banking products and services. Behavioral intention also fully mediates the link between performance expectancy, conditional values, emotional values, and usage behavior. Thus, bank management should consider these factors when creating green banking products and services. The current study focused on the customer perspective only, undoubtedly, it is very pivotal to understand the behavior of customers about green banking. But for a holistic coverage of the factors influencing the adoption of green banking, future research might focus on varied perspectives including bank management, bank employees, and SBP by using both quantitative and qualitative methods.


Introduction
The core of "go green" is preserving nature and its resources to maintain ecological balance.The financial sector as part of the economy may safeguard the environment by adopting green practices and developing green financing goods and services (Bahl, 2012).Central banks in numerous countries are developing green banking regulations and standards (Iqbal et al., 2018;Rifat et al., 2018).To protect the environment, financial institutions should encourage ecologically friendly activities (Hossen et al., 2014).According to Shakil et al. (2014), financial institution employees and clients prioritize sustainable energy technology to reduce carbon emissions.Innovative green banking practices save resources and operational costs (Ahmad et al., 2013;Aslam et al., 2023).The US government established the first green bank in 2011, the UK government established the UK Green Investment Bank in 2012 to preserve the environment, and the Australian government formed the Clean Energy Finance Corporation in 2012 to finance energy-efficient and low-emission technologies.Japan, Malaysia, and Canada are also investing heavily in green banking (Elahi et al., 2023).However, public ignorance and budget constraints have hindered green banking prioritization in developing nations like Pakistan (Nisha et al., 2020).A few private banks have shown interest in green banking and have begun researching eco-friendly goods and services in developing countries.According to the World Global Climate Risk Index 2019, climate change damaged Pakistan from 1988 to 2017.
The SBP announced the Green Banking Guidelines (GBG) in October 2017.The GBGs help banks to achieve their environmental obligations to address environmental issues.The GBGs also require bank management to educate employees and customers on environmental issues and eco-friendly financial products and services (Khan et al., 2023).These recommendations require green banking (GB) offices with a full-time GB manager, GB liaison officers, and GB-savvy staff.Top management must report on bank ecological measures.Green banking will mature in Pakistan's banking industry in a few years.This means banks will need time to modify their processes and develop new products that comply with SBP GBGs (Bukhari et al., 2020).
In developing countries like Pakistan, the implementation of green banking practices is challenging and requires a strong commitment from banks and the Government (Niazi et al., 2023).Further, there is a need to highlight the factors that are important for green banking adoption and success (Aslam et al., 2023).A review of existing literature reveals a few studies on green banking.For instance, Islam et al. (2015), Rifat et al. (2016), and Hoque et al. (2019) focused on commercial banks and their green banking practice; Bhardwaj and Malhotra (2013) focused on commercial banks' green banking strategies and sustainability; Bose et al. (2018) highlighted the influencing factors on green banking disclosure; Nisha et al. (2020) examined customers' perception of green banking; Alalwan (2018) emphasized on the use of internet banking; Boonsiritomachai (2019) examined the use of mobile banking; Dikau, and Vltz (2018) studied the role of central banking, climate change, and green finance in Singapore; and Fashli, et al., (2019) studied the application of green banking on financing infrastructure projects.This study is set in a unique context by Pakistan's environmental threats (pollution, depletion, and dearth of natural resources, improper disposal of industrial and medical waste, etc.), Federal Government initiatives (Ministry of Climate Change (MOCC)), SBP green banking guidelines, NGO awareness campaigns, and financial institutions' eco-friendly financial products and services.Eco-friendly activities by Pakistani banks demonstrate their dedication to environmental dangers.Green technology is the foundation of green banking, so research on client acceptance and uptake is essential.Thus, this study aims to investigate the significant factors that should be focused on to motivate bank clients to adopt green banking practices.
Previous research has used several theoretical models to study technology and system adoption.Davis (1989) used the Technology acceptability Model to evaluate computer technology acceptability and use.Venkatesh et al. (2003) examined various technology models.They developed a comprehensive and enhanced UTAUT model through research.This was done by comparing eight models using longitudinal research.The UTAUT model (Venkatesh et al., 2012) predicted user behavior and intention using four elements.The goal of this research is to replicate the UTAUT model to identify the elements that influence Pakistani clients' green banking preferences.This research builds on the UTAUT model by adding environmental, conditional, and emotional components to provide a research model and theoretical framework.Limited research has examined Pakistan's green banking implementation aspects.The current study examines Pakistan's green banking adoption behavior of customers to fill the research gap.
This study has two goals: First, to investigate commercial bank clients' green banking uptake.The unified theory of acceptance and use of technology (UTAUT) was used in this study.The UTAUT model provides a complete set of multivariate characteristics for analyzing customers' intentions and behavior in the researched situation (Malik et al., 2020).Second, to assess bank employees' green banking practices by investigating how consumers' environmental, conditional, and emotional values affect their green banking product and service adoption.The current study makes two major contributions: First, it provides a comprehensive set of multi-variate / determinants for commercial bank customers to adopt green banking products and services.In the study, researchers examined and demonstrated the relationship between the UTAUT model and customers' environmental and emotional values.Second, the study also explored behavioral intention as a mediator in exogenous-endogenous variable interactions to promote sustainable usage of green banking products and services in emerging economies for environmental preservation.This study's empirical findings will increase policymakers' and commercial banks' understanding of green banking clients and their shifting dynamics.
After describing the motivation for this study in the introduction, a brief account of existing literature and hypotheses are provided in the second section.Methods used for data collection and analysis are briefed in the third section.In the fourth part, the results of the analysis are portrayed.In the last section, the results are discussed and concluded.

Literature Review and Hypotheses Development 2.1. Performance Expectancy
Performance expectancy is a person's conceptual framework or technological adoption that improves outcomes (Algharibi & Arvanitis, 2011). Ventakesh et al. (2003) define this as consumers' perceptions of technology adoption's utility.Nisha et al. (2015), Afshan and Sharif (2016), Chaouali et al. (2016), and Malaquias and Hwang (2016) found that clients are more willing to adopt green banking when they see its benefits.Performance expectancy is the most powerful component of behavioral intention to adopt because technology services improve individual performance.Ho and Ko (2008) also found that perceived usefulness increases customer value of self-service banking technology.Nisha, Iqbal, and Riffat (2020) examined performance expectancy and green banking use in Bangladesh.The following hypotheses are proposed: H1: Performance expectancy significantly affects customers' intention to use green banking products and services.
H2: Performance expectancy significantly affects customers' behavior to use green banking products and services.

Effort Expectancy
Another key aspect influencing user behavior is effort expectancy.Venkatesh et al. (2003) defined effort expectancy as how easily a user adopts a system or technology.Researchers have shown that effort expectancy affects system adoption (Riffai et al., 2012).User effort expectancy shows that they will adapt or learn new technology/systems readily (Alalwan et al., 2016;Kesharwani & Bisht, 2012).Similarly, Nisha et al. (2015) found that effort expectancy influenced green banking uptake.Afshan and Sharif (2016) found that effort expectation strongly affects Internet banking usage.In their investigations, Alwahaishi and Snasel (2013), Chaouali et al. (2016), and Malaquias and Hwang (2016) found that effort expectancy strongly influences green banking uptake and use.Venkatesh et al. (2012) found that effort expectancy affects behavioral intention and usage behavior.The study demonstrated a link between effort expectancy and green banking use (Nisha et al., 2020).From these researchers' findings, this paper hypothesizes: H3: Effort expectancy significantly affects the customers' intention to use green banking products and services.
H4: Effort expectancy significantly affects customers' behavior for using green banking products and services.

Social Influence
Social influence is a user's sense of how important it is for connected people to adopt that technology (Venkatesh et al., 2003).Zhou (2011) defined social influence as the impact of related persons on user behavior.Soroa-Koury and Yang (2010) found a positive association between social influence and intention to utilize mobile apps for hotel booking tables.Yu (2012), Slade et al. (2015), and others have found a link between social effects and green banking.Steininger and Stiglbauer (2015) found that social influence strongly affects behavioral intention.An empirical study on green banking uptake among bankers by Rifat et al. (2016) found no significant association between social impact and behavioral intention.Thus, this study proposes the following hypotheses: H5: Social influence significantly affects customers' intention to use green banking products and services.
H6: Social influence significantly affects customers' adoption behavior of green banking products and services.

Facilitating Conditions
Enabling conditions include technological infrastructure and organizational assistance to help staff and users accept technology or configuration, according to Venkatesh et al. (2003).According to Zhou (2011), implementing a new technical process without proper guidance and intervention can fail.Yu (2012) found a positive link between enabling conditions and the use of new technology.The Alwahaishi and Snasel (2013) and Shaikh and Karjaluoto (2015) studies show a link between favorable conditions and green banking.In their model, Venkatesh et al. (2012) linked enabling factors to behavioral intention.According to Nisha et al. (2015), conducive factors determine green banking practitioners' purpose and conduct.These hypotheses are proposed: H7: Facilitating conditions significantly affect customers' intention to use green banking products and services.
H8: Facilitating conditions significantly affect customers' adopting behavior of green banking products and services.

Environmental Values
Environmental values are how much an individual values environmental protection and safety.Environmental values are linked to nature psychology, according to Kranz and Picot (2011).Scholtz, Govender, and Gomez (2016) observed a positive correlation between both factors.Rifat (2016) found that most consumers favor sustainable business practices and expect businesses to prioritize the environment.The growing literature on green banking in diverse nations reveals that environmental values influence green banking adoption (Iqbal et al., 2018).People who value price more often have lower environmental values (Maheshwari & Malhotra, 2011).Hypotheses from this investigation were: H9: Environmental values significantly affect customers' intention to use green banking products and services.
H10: Environmental values significantly affect customers' adopting behavior of green banking products and services.

Conditional Values
Qasim et al. ( 2019) state that government subsidies and other perks may be given to people when buying goods or services.Green banks offer conditional values as green products and services.Green items are discounted and some governments subsidize green initiatives and products to promote green banking.Portuguese researchers Gonçalves et al. (2016) related conditional values to usage behavior.It is empirically shown that cost and discounts do not affect green banking uptake in Bangladesh.This research suggests the following hypotheses: H11: Conditional values significantly affect customers' intention to use green banking products and services.
H12: Conditional values significantly affect customers' adopting behavior of green banking products and services.

Emotional Values
Emotional values refer to the subjective experiences, sentiments, and attachments that users associate with perceived usefulness, as determined by various evaluative criteria (Sheth et al., 1991).Recently, some researchers explain four types of emotional values: proud emotional values, admiring emotional values, guilty emotional values, and disdainful emotional values (Wang, Bao, Wang, & Wu, 2017).Nowadays, shareholder tries to invest in an organization that is involved in environmental activities, so they emotionally attach, and that leads to buying.Hence, this study that advised the next hypotheses: H13: Emotional values significantly affect customers' intention to use green banking products and services.
H14: Emotional values significantly affect customers' adopting behavior of green banking products and services.

Mediating Effects of Behavioral Intention
Ajzen (1991) defined intention as a factor that motivates and influences user behavior.Sheppard et al. (1988) argued intention is most important in tech adoption.Other theories use this idea: TPA and TRA.Other information technology research showed a strong correlation between these two traits (Venkatesh et al., 2012).In green mobile banking, researchers show how behavioral intention affects technology use (Gu, Lee, & Suh, 2009).Other research link green banking service consumption to behavioral intention (Nisha et al., 2020).This study analyses how behavioral intention mediates the relationship between exogenous variables (performance expectancy, effort expectancy, social influence, enabling conditions, environmental values, conditional values, and emotional values) and usage behavior.The study hypothesized the following: H14-H21: Mediating effects of behavioral intention between the associations of exogenous variables and endogenous variable relating to customers' adopting behavior of green banking products and services.

Theoretical Framework
This study uses Venkatesh et al. (2003)'s unified theory of technology acceptance and application along with environmental values, conditional values, and emotional values.In the UTAUT model the constructs are added as per the context of technology, therefore three variables, which are already used in the banking industry, are linked to the UTAUT model to capture the right picture of the adoption of green banking by clients.Rifat et al., (2016) found that today's public is more aware of globalization and they care about environmental concerns.It is a significant predictor to examine the behavior of clients.Another construct is conditional values which is also is significant predictor as the client always influenced by the discounts and promotion activities.The last construct is emotional values, which also have a positive role in the acceptance of technology as the person feels morally good by using eco-friendly products (Mohd Suki, 2016).
In this model, the first four exogenous variables are adopted from the UTAUT model suggested by Venkatesh et al., (2003).To extend the explanatory power of the theoretical framework, three more exogenous variables (environmental values, conditional values, and emotional values) are added to the UTAUT model, which affects the sustainable behavior of customers (Mohd Suki 2016; Khan and Mohsin 2017;Kadic-Maglajlic et al., 2019 ).In addition, the aforementioned context variables are hypothesized and their relationship with each other is discussed in the next part.Hence, the scholars proposed the following theoretical framework.The study targets commercial banks customers located in Lahore, district of Punjab, Pakistan.For data collection, a convenience sampling technique and a standardized self-administered questionnaire was used.To collect data, respondents were intercepted after completing their transactions at different commercial banks.Most customers filled out the hard copy of the questionnaire, however some did not do due to time constraints and were asked to provide their contact information to send the soft copy (google form).Researchers contacted them individually and offered a link via email and WhatsApp to complete the questionnaire from March to April 2023.According to Hair et al. (2010), and Ahmad and Halim (2017), and (Roscoe 1975), an appropriate sample size is 30-500.Thus, 400 copies of questionnaire were distributed to customers by visiting different branches of commercial banks and google form link of questionnaire was shared to 200 bank customers via WhatsApp and email.Total 456 responses were received from customers.After screening, 305 questionnaires were found appropriately filled and were utilized for analysis.Thus, response rate was remained approximately 50 %.Natural environment research makes the study setting non-contrived.This study is crosssectional since data was obtained once.

Questionnaire Development and Measures
A thorough research of the relevant scholarly literature selected the study's measures.A pre-test with 55 commercial bank clients analyzed the questionnaire's understandability.Certain changes were made to improve clarity.The questionnaire has two parts.The first section includes gender, age, education, occupation, and average monthly income.Green banking perception characteristics are measured in the second half of the questionnaire with 33 items.A multi-item scale on UTAUT construct measured performance expectancy, effort expectancy, social influence, enabling condition, and behavioral intention (Venkatesh et al., 2003).Four items from Ahmed et al. ( 2013), Nisha et al. (2015), and Rifat (2016) measured environmental values.
Previous research by Mohd Suki (2016) measured conditional and emotional values.For measuring the behavior of customers for adopting green banking products and services, three measures proposed by Yadav and Pathak (2017) were analyzed.All measurements were evaluated using five-point Likert scales, as established by Rensis Likert in 1932.To assess variables, participants are given five options to indicate their degree of agreement with the assertions: 1 for considerable disagreement, 2 for disagreement, 3 for neutrality, 4 for agreement and 5 for strong agreement.

Analytical Method
This study analyzes data using SPSS 25 and AMOS 22. Model measurement and fitness (outer model) were assessed by using Confirmatory Factor Analysis (CFA).The researchers consistently used the two-stage structural equation modelling (SEM) method recommended by Anderson and Gerbing 1988 to test the theoretical model (inner model) and postulated context variable correlations.The present study also analyzes how behavioral intention mediates the relationship between exogenous factors and endogenous variable in the specific situation under examination.This study uses bootstrapping method suggested by Horowitz (2019), andMacKinnon (2009).To establish model consistency, accurately address measurement error, and analyze exogenous constructions' simultaneous impact on outcome variables, the researchers performed multiple regression analyses.

Descriptive and Correlation Analysis
Study factors have skewness values between 0.105 and 0.198, ideally within ±2, and kurtosis values between 0.532 and 1.560, ideally within ±10 (refer to Table 2).The results show that data were regularly distributed and bell-shaped.All contextual variables had mean values from 3.758 to 4.373 on a five-point Likert scale, where 1 means "strongly disagree" and 5 means "strongly agree."Most participants were to use green banking.

Reliability and Validity Analysis
The study analyzed context measure reliability using composite reliability (CR) and Cronbach's Alpha.Table 3 shows that Composite Reliability and Cronbach's Alpha are over 0.70 (Hair et al., 2010).Next, the study met all three Fornell and Larcker (1981) convergent validity requirements.A) The standardized factor loadings should be above 0.60 (Bagozzi and Yi, 2012), It is between 0.811 and 0.953 (Table 3).B).The context variables' AVE values, 0.740 to 0.808 (Table 3), should be more than 0.5 (Fornell & Larcker, 1981;Hair et al., 2010).C) Values top out at 0.70.This shows convergent validity is adequate because the data met all three criteria.
According to Fornell and Larcker (1981) and Sussman and Siegal (2003), discriminant validity of studied measures is acceptable.The square root of AVE (bold diagonal elements) exceeds the study measures' average shared variance.Discriminant validity is further shown by factor correlations < 0.70 (Table 2).Model fitness was assessed using confirmatory factors analysis.

Mediating Effect of Behavioral Intention
This study examined how behavior intention (BI) mediates the relationship between UTAUT idea, consumer values, and adoption behavior.The mediation study used MacKinnon (2009)'s bootstrapping technique in AMOS version 22.The study found that business intelligence (BI) fully mediates the relationship between performance expectancy, conditional values, emotional values, and adoption behavior.Additionally, BI partially mediates the link between effort expectancy, social influence, enabling condition, and adoption behavior.As seen in Table 6, BI does not moderate the environmental values-adoption behavior link.Mediating variable statistical results were most reliable and robust (Williams & MacKinnon, 2008).Hypothesis 16: PE's direct effect on AB does not cause BI.The indirect effect of PE on AB is significant with BI (H16: β=.279, p<0.05).Thus, BI fully mediates PE-AB.Hypothesis 21: CV has no direct effect on AB with BI.The indirect effect of CV on AB is significant with BI (H21: β=.329, p<0.05).Thus, BI fully mediates CV-AB.Hypothesis 22: EMOV has no direct effect on AB through BI.In the presence of BI, EMOV has a substantial indirect influence on AB (H22: β =.295, p<0.05).Therefore, BI fully mediates the EMOV-AB relationship.Consistently, the direct effect of EE on AB is significant in causing BI (H17: β =.234, p<0.05).The indirect effect of EE on AB is significant with BI (H17: β =.233, p<0.05).Thus, BI partially mediates EE-AB.Hypothesis 18: SI significantly affects AB in the presence of BI (H18: β =.348, p<0.05).Significant indirect influence of SI on AB is mediated by BI (H18: β =.162, p<0.05).Thus, BI partially mediates SI-AB.Hypothesis 19: FC's direct impact on AB is substantial for BI (H19: β =.388, p<0.05).In the presence of BI, FC has a substantial indirect influence on AB (H19: β =.172, p<0.05).Thus, BI partially mediates FC-AB.

Discussion
The results of the study reveal that performance anticipation is the main predictor of behavioral intention of customers to use green banking.It implies that customers are more likely to adopt green banking if they expect higher performance.This study agrees with Nisha et al. (2015), Chaouali et al. (2016), Malaquias and Hwang (2016) and Rifat et al. (2018).Effort expectancy predicts behavioral intention but not customer green banking adoption.This study finds that clients who expect less effort to use green banking services are more inclined to do so.Since mobile online banking requires few taps, clients have to work less.This study agrees with Alwahaishi and Snasel (2013), and Malaquias and Hwang (2016).Thus, banks should prioritize fast, convenient services.Good IT structure helps make digital services more user-friendly.It saves time and makes green banking like mobile banking easy.Alwahaishi and Snasel (2013) and Malaquias and Hwang (2016) found similar results.This is reinforced by recent investigations by Nisha et al. (2019).
The study indicated that social influence had a considerable impact on consumers' adoption of green banking, but had no significant impact on behavioral intention.Yu (2012), Slade et al. (2015), and Malaquias and Hwang (2016) reported similar results.So, banks must construct their marketing so that customers influence others to use green banking.Rifat et al. (2018) support the results, however Iqbal et al. (2018) does not.Facilitating conditions can speed up green banking service uptake because any new technology or service needs support.The findings of this study match with the findings of Iqbal et al. (2018) and Afshan and Sharif (2016) but contradict the Rifat et al. (2016).
According to this study, environmental considerations do not influence green banking.Environmental values are also essential, but green banking knowledge is still low, and most users consider the banking industry is not vulnerable to the environment, so it does not need green efforts.This differs from Hossain et al. (2015) and Scholtz and Gomez (2016).The results showed that conditional values positively influence behavioral intention and negatively influence the green banking adoption.Conditional values are important today because firm promotions and government subsidies have increased service and product use.Banks offer green loans and other environmentally friendly financial products at lower interest rates.Most banks provide food and clothes discounts to attract digital consumers.Khan & Mohsin. (2017) found conditional values affect customer green product behavior.Qasim et al. (2019) also found that conditional values affect Pakistani organic food consumption.
The study indicated that emotional values positively affect behavioral intention but not green banking adoption.Sustainable/green consumption is often linked to consumer emotions, attachment, or values.Users' sentiments and emotions affect their green product usage (Holbrook, 2006).Positive, joyful, responsive emotions contribute to green banking, while negative, unpleasant, deceptive emotions negatively influence consumer buying behavior (Rex & Baumann, 2007).Thus, if banks help customers feel more connected to the environment, they may attract more customers.These findings match Qasim et al. (2019).The study found that behavioral intention is the strongest predictor of green banking adoption.Intentions determine behavior and often impact user decisions.To encourage people to use green banking services, budgetary allocation and increased investment for green banking projects are needed.This is backed by many studies, including Iqbal et al. (2019) and Rifat (2018).However, the findings of current study contradict with the findings of some earlier studies (e.g., Suki & Suki, 2015;Suki &Suki, 2017 in Malaysia, andKhan, &Mohsin, 2017 in Pakistan).These studies found no significant relationship between emotional values and green product use.
This study evaluated seven mediation connections related to behavioral intention.Performance expectancy affects behavior intention but not usage behavior, hence, there is full mediation because the client wants to utilize green banking to improve their performance.Social impact partially mediates usage behavior.Social influence on behavioral intention includes friends, family, and coworkers influencing green banking services like internet banking.Users use trending services others use.Due to partial mediation between facilitating conditions and usage behavior, facilitating conditions affect usage behavior through behavioral intention.Facilitating conditions including supporting mobile apps, enhanced internet access, and green banking seminars help users use green banking services.Environment parameters do not affect behavioral intention or usage.Despite using some green banking services, Pakistani people are unaware of green banking but assume it is safe for the environment.Conditional values fully mediate usage behavior.Discounts and promotions from banks encourage people to utilize green banking services.Telenor Microfinance and other banks attract online customers with food and clothing discounts.Due to full mediation between emotional values and usage behavior.Emotional values do not affect user behavior, but behavioral purpose does.Users' emotions usually determine their aim.Pakistan is heavily affected by global warming, so users are more aware.Emotions about green banking led to user intention and green banking services.

Conclusion and Implication
The findings of the study conclude that client behavioral intentions are crucial because intentions become behaviors.As this study illustrates the elements that motivate customers to utilize green banking, the majority of factors are relevant.All considerations except environmental values drove green banking adoption.The practitioners must design green bank goods and services considering these important considerations.Banks should offer easy-to-use mobile banking apps with no security issues to improve user convenience.Government and regulatory agencies should fund and rebate green products.Financial organizations can promote green banking by offering discounts and promotions.Public policy makers and financial organizations should launch environmental awareness campaigns to evoke emotional responses and urge active participation in nature and environment preservation.

Future Research Direction
Current study is cross sectional and ignores the temporal developments like regulatory changes, learning behavior of customers, and various incentives offered by banks over time.Thus, there is need of a longitudinal study on green banking adoption to compare findings and get a clearer picture.Future study can include SBP recommendations, cost, and modifiers including age, education, and income.This study evaluates financial institution clients and suggests investigating Pakistani bank