International Journal of Application on Economics and Business (IJAEB) Volume 3.
Issue 1, 2025.
ISSN: 2987-1972
DOES AGE INFLUENCE AI-ENABLED MOBILE BANKING APP
USAGE? ANALYZING COGNITIVE FACTORS
AND SUSTAINED INTENTIONS
Yeo Chu-May Amy1*.
Lee Ee Chee2.
Ong Yu Yan3 Faculty of Accountancy.
Finance and Business.
Tunku Abdul Rahman University of Management and Technology.
Malaysia Email: yeocm@tarc.
Chin Hin Group Bhd.
Malaysia Asia Pacific University.
Malaysia *Corresponding Author Submitted: 07-12-2024.
Revised: 11-01-2025.
Accepted: 24-02-2025
ABSTRACT
This research investigates the impact of cognitive aspects and AI attributes on the continued adoption of AIpowered mobile banking applications in Malaysia.
It examines the relatively recent incorporation of AI into Malaysian mobile banking and the extent to which age influences user intentions.
A total of 398 participants were surveyed, with data analysis conducted using SPSS.
The results indicate that factors such as perceived usefulness, ease of use, enjoyment, and intelligence significantly contribute to users' continued engagement with AI-enabled banking apps, with age playing a moderating role.
However, perceived anthropomorphism did not have a statistically significant effect on user intention, nor did age significantly moderate the connection between perceived intelligence and continuance intention.
The studyAos findings aim to enhance AI-enabled banking applications, fostering a more user-friendly and satisfying experience across different age groups.
These insights provide valuable direction for software developers and financial institutions aiming to optimize user satisfaction and engagement with AI-powered mobile banking systems.
Keywords: AI-enabled mobile banking, continuance intention to use, perceived usefulness, perceived ease of use, perceived enjoyment, perceived anthropomorphism, perceived intelligence.
INTRODUCTION
The evolution of artificial intelligence (AI) has significantly influenced various sectors, including manufacturing, retail, and financial services.
AIAos capability to simulate human behavior with minimal intervention has positioned it as a crucial tool for business operations (Rahman et al.
, 2.
Concurrently, mobile technology has transformed traditional banking services, leading banks to adopt AI-driven systems to enhance operational efficiency and remain competitive (Lee & Chen, 2.
AI-powered banking solutions streamline processes, extend service availability beyond regular banking hours, and enhance customer experiences.
Financial experts emphasize AIAos potential in personalizing banking services, making them more responsive to user needs (Sheth et al.
, 2022.
Abdulquadri et al.
, 2.
Indeed, electronic banking is now vital in global e-commerce due to the growth of online transactions and its role in facilitating financial transfers, highlighting its essential role in the e-commerce ecosystem.
Furthermore, the rapid advancement of information technology has brought significant changes to banking, including the transformation of money and currency forms and the evolution of transfer systems (Danyali, 2.
Also, electronic banking enables secure access to banking services without physical visits to a bank (Salhieh et al.
, 2.
While, mobile-based banking apps have further revolutionised the industry, offering convenient fund transfers, investment opportunities, and account management (Hassan & Wood, 2020.
Kwateng et al.
, 2.
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International Journal of Application on Economics and Business (IJAEB) Volume 3.
Issue 1, 2025.
ISSN: 2987-1972
In fact, investments of approximately $11 billion in AI by the financial industry in 2020 indicate the sectorAos commitment to AI adoption (Bergur Thormundsson, 2.
It can be observed that the banking sector is aggressively developing AI in its business to adapt to current and future Undoubtedly, usersAo confidence in AIAos integration and application plays a pivotal role in its successful adoption in mobile banking services (Modiba, 2.
Additionally, age significantly influences technology acceptance, particularly in differentiating the behaviours of different generations, despite the increasing rapidity of new technologies (Yang & Shih, 2.
The profound significance of AI in mobile banking lies in its ability to mirror human comprehension and perception, particularly in attributes like anthropomorphism and Despite.
AI-enabled banking services can be complex, the anticipation is that they will enrich the overall customer experience (Lee & Chen, 2022.
Sheth et al.
, 2.
Moreover, existing literature suggests that users of AI-enabled applications often consider cognitive factors such as perceived usefulness (Hidayat-ur-Rehman et al.
, 2021.
Yuan et al.
, perceived ease of use (Zhang et al.
, 2.
, and perceived enjoyment (Ashfaq et al.
Lin et al.
, 2.
, which can influence their intention to continue using these AI Additionally.
AI features related to anthropomorphism and intelligence can impact usersAo perceptions and their intention to continue using AI-enabled applications (Balakrishnan et al.
, 2.
Based on these observations, the research question is proposed: Do perceived usefulness, perceived ease of use, perceived enjoyment, perceived anthropomorphism, perceived intelligence, and age affect continuance intention to use? This exploration draws upon the foundational insights of the Technology Acceptance Model (TAM) and the comprehensive framework of the Extended meta-Unified Theory of Acceptance and Use of Technology Model (Extended Meta-UTAUT), guiding the development of the research framework and associated Research Model Development The research model is presented in Figure 1.
This model leverages the TAM framework to assess the influence of perceived usefulness, ease of use, and enjoyment on continued AIenabled banking app usage.
Furthermore, the Extended Meta-UTAUT framework is employed to examine the roles of perceived anthropomorphism and intelligence.
Age serves as a moderating factor, potentially influencing these relationships.
Perceived usefulness reflects usersAo belief in an applicationAos effectiveness in meeting their financial needs (Bhattacherjee, 2.
It significantly influences user adoption and sustained engagement (Shih, 2.
When users find AI-powered banking apps beneficial, their likelihood of continued usage increases (Hidayat-ur-Rehman et al.
, 2021.
Yuan et al.
, 2.
Therefore, this leads to the formulation of the following hypothesis:
H1: Perceived usefulness is positively related to continuance intention to use.
The importance of user-friendly technology in the adoption of new technology highlights the significant impact of perceived ease of use on customer intention and loyalty to mobile apps (Zhang et al.
, 2.
Likewise, it also stresses the critical role of perceived ease of use in driving customer intent and fostering loyalty to mobile apps (Ashfaq et al.
, 2.
Furthermore, ensuring a comprehensible and transparent service that facilitates seamless task completion is crucial for sustaining user intent (Ashfaq et al.
, 2.
In essence, user-friendliness strongly influences mobile banking adoption, significantly impacting usersAo determination to continue using the service (Jo, 2.
Therefore, based on these observations, we hypothesise that:
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International Journal of Application on Economics and Business (IJAEB) Volume 3.
Issue 1, 2025.
ISSN: 2987-1972
H2: Perceived ease of use is positively related to continuance intention to use.
Extensive literature supports the idea that usersAo intrinsic enjoyment and the overall pleasure derived from using a system significantly influence their intention to continue using it.
In some cases, users engage with technology for entertainment and enjoyment, not just for performance (McLean et al.
, 2020.
Ashfaq et al.
, 2.
Multiple studies confirm that perceived enjoyment is a crucial predictor of usersAo intention to continue using technology, especially in tech-related contexts (Ashfaq et al.
, 2020.
Lin et al.
, 2.
When users interact with AI-enabled apps, a delightful and pleasurable experience can evoke positive sentiments (Ashfaq et al.
, 2020.
Chung et al.
, 2.
In summary, individuals who find technology use enjoyable and fun are more likely to continue using it (To & Trinh, 2.
Consequently, we hypothesise that:
H3: Perceived enjoyment is positively related to continuance intention to use.
Consumers often develop anthropomorphic connections with technology, which can lead to increased brand loyalty over time.
AI-enabled apps use various elements like names, tags, avatars, and emoticons during interactions, which users become accustomed to and form connections with (Balakrishnan et al.
, 2022.
Chandler & Schwarz, 2.
The incorporation of anthropomorphic features boosts usersAo confidence and sense of control, resulting in a positive response to the technology (Pillai & Sivathanu, 2.
Furthermore, anthropomorphism simplifies the adoption and use of technology by leveraging our familiarity with human-like traits to understand the behaviour of non-human agents like AI-powered systems (Grazzini et , 2.
This, in turn, encourages users to continue using AI-enabled apps (Balakrishnan et , 2022.
Chandler & Schwarz, 2.
It is evident that human-like traits are expected to promote the continued use of AI-enabled mobile banking apps, leading to our hypothesis that:
H4: Perceived anthropomorphism is positively related to continuance intention to use.
Intelligent systems, such as AI-enabled apps, offer substantial advantages by adapting to usersAo evolving needs through self-learning characteristics This adaptability is readily perceived by users and motivates them to continue using these apps (Balakrishnan et al.
, 2022.
Shim & Jo.
Intelligent systems also significantly enhance user experiences in various aspects of daily life, touching every facet (Malhotra & Ramalingam, 2023.
Spanaki et al.
, 2.
Moreover, user interactions with these systems play a crucial role in improving AIAos performance and intelligence, making it an essential part of usersAo decision-making processes and daily life (Malhotra & Ramalingam, 2023.
Seeber et al.
, 2.
The personalised experiences offered by AI-enabled apps empower users and encourage continued utilization (Montes & Goertzel, 2.
Based on these insights, we hypothesize that:
H5: Perceived intelligence is positively related to continuance intention to use.
Recent research reveals significant age-related disparities in technology interactions (Chawla & Joshi, 2.
Younger adults exhibit expertise and a strong attraction to new technologies, emphasising practical benefits (Larson, 2.
In contrast, older adults contemplate technology differently, often influenced by familial preferences (Chung et al.
, 2.
, and may not fully grasp advanced innovations (Wong et al.
, 2.
In addition, older individuals encounter challenges with mobile apps compared to tech-savvy younger users (Wong et al.
, 2.
, which is attributed to younger individualsAo interest and adaptability (Wonseok et al.
, 2.
However, younger adults display enthusiasm towards adopting new gadgets and prioritise factors such as price and specifications over ease of use.
On the other hand, older adults prioritise ease of use over other considerations (Iancu & Iancu, 2020.
Naqvi et al.
, 2020.
Chung et al.
, 2.
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International Journal of Application on Economics and Business (IJAEB) Volume 3.
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ISSN: 2987-1972
Besides, younger individuals derive pleasure from using mobile apps, even incorporating them into daily activities (Mehra et al.
, 2.
The COVID-19 pandemic further amplified these agerelated differences, with younger people adapting more readily to e-commerce and new mobility services (Liu et al.
, 2.
Conversely, some older individuals may express frustration with new technology used by younger generations but can still find enjoyment in using mobile apps (Liu et al.
, 2021.
Chawla & Joshi, 2.
Then, in the realm of anthropomorphism, age also plays a role.
Younger individuals tend to be more receptive to feelings, while older individuals exhibit a balance between feelings and thoughts (Letheren et al.
, 2.
Contrarily, older people may have a stronger inclination to anthropomorphise applications (Blut et al.
, 2021.
Kamide et al.
, 2.
In fact, voice assistants offer socioemotional support to the elderly, aiding in alleviating loneliness (Pradhan et al.
Additionally, older individuals perceive AI as an assistant, a tool that mimics human intellect and may have challenges in fully understanding AI-enabled mobile apps due to their limited familiarity with advanced app features (Shandilya & Fan, 2.
These age-related differences impact the adoption and intention to continue using AI-enhanced apps (Kim et al.
Pillai & Sivathanu, 2.
Clearly, different age groups may have diverse perspectives towards AI-enabled apps.
Hence, we hypothesised that.
H6: The influence of perceived usefulness on continuance intention varies across different age H7: Age plays a moderating role in the relationship between perceived ease of use and the intention to continue using AI-enabled mobile banking apps.
H8: The extent to which perceived enjoyment impacts continuance intention is influenced by age differences.
H9: Age moderates the effect of perceived anthropomorphism on usersAo intention to sustain their use of AI-powered mobile banking apps H10: Age Moderates the Relationship Between Perceived Intelligence and Continuance Intention to Use.
The formation of theoretical framework as indicated in Figure 1 below.
Figure 1.
Research Framework https://doi.
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International Journal of Application on Economics and Business (IJAEB) Volume 3.
Issue 1, 2025.
ISSN: 2987-1972
RESEARCH METHOD
Data collection and sample This study uses a single cross-sectional design and survey data collection methods to gather its The target sampling for this research is Malaysian mobile banking users.
The questionnaires have been designed according to the recommendations of Illum et al.
Belanche et al.
, and Lee and Chen .
These suggestions focus on keeping the questionnaires concise and easy to understand, ensuring that the length is controlled.
With the assistance of a professional online survey service provider such as Google Forms, respondents can promptly receive the survey form, allowing us to obtain data from them quickly (Lin & Lee, 2.
This is facilitated by sending the survey formAos URL and QR code via social media Before creating the final questionnaire, a pilot test was conducted to identify and correct any faults or errors.
Our survey ensures complete respondent anonymity and exclusively uses the data for academic research.
This approach encourages honest participant responses and safeguards personal identities and privacy with strict confidentiality measures.
Selecting the right sample size is crucial for study validity and ethics.
Too large or too small a sample can compromise rigour (Andrade, 2.
A well-chosen sample size prevents biases and errors (Taherdoost, 2.
and statistical formulas maintain population representation (Khalilzadeh & Tasci, 2.
Inadequate sample sizes yield weak and inconclusive results.
Larger samples may not always offer better insights and can waste time and resources (Tripathi et al.
, 2.
Azeem et al.
recommend a sample size exceeding 30 but below 500 for robust studies, also maintaining 200 or more participants in research ensures accurate and trustworthy insights into variable relationships, particularly in routine research situations (Hoque & Sorwar, 2017.
De Winter et al.
, 2.
Indeed, this research has successfully gathered a total of 398 responses, meeting the prescribed sample size criteria, and thus, the sample is deemed suitable for comprehensive data analysis.
The demographic information of accepted responses is presented in Table 1.
Table 1.
Table Demographic Demographic of the sample Information of The Sample Demographic Variables Frequency .
Percentage Gender
Male
Female Age
18-24 years old
25-34 years old
35-44 years old
45-54 years old
55 years old and above Average monthly income RM0-RM1000
RM1001-RM2000
RM2001-RM3000
RM3001-RM4000
RM4001-RM5000
RM5001 and above Education background
SPM
Foundation Diploma BachelorAos degree MasterAos degree PhD/ DBA Frequency of using mobile Less than once per year Two or three times per year Two or three times per quarter Two or three times per month https://doi.
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Two or three times per week
Every day
Never
RM5001 and above Education background
SPM
Foundation International JournalDiploma of Application on Economics and80Business (IJAEB) 20.
BachelorAos degree Volume 3.
Issue 1, 2025.
ISSN: 2987-1972
MasterAos degree PhD/ DBA
Frequency of using mobile Less than once per year
Two or three times per year
Two or three times per quarter
Two or three times per month
Two or three times per week
Every day
Never
RESULTS AND DISCUSSIONS
This study utilised Statistical Package for Social Science (SPSS) software to analyse data.
SPSS will facilitate the organisation of the responses into easily interpretable tables, enabling meaningful insights to be extracted from the data.
By conducting these analyses, the research aims to unveil the intricate relationships among the dependent variable, independent variable, and moderating variable within the study.
The analysis of multicollinearity in the study shows that all variables related to tolerance value (TV) have values between 0.
325 and 0.
607, and the variance inflation factor (VIF) for these variables ranges from 1.
647 to 3.
Notably, all TV values exceed the threshold of 0.
1, while all VIF values are below the recommended threshold of 5, as suggested by prior researchers (Shrestha, 2020.
Hair et al.
, 2011.
Pan, 2010.
Chen et al.
, 2.
The study, as indicated in Table 3, does not have any multicollinearity issues.
Table 3.
Table 3.
Multicollinearity Analysis Multicollinearity analysis Model Collinearity Statistics T_CI T_PU Tolerance Value (TV) Variance Inflation Factor (VIF) T_PEU T_PE T_PA T_PI Note: T_CI=Total Continuance Intention to Use.
T_PU=Total Perceived Usefulness.
T_PEU=Total Perceived Ease of Use.
T_PE= Total Perceived Enjoyment.
T_PA=Total Perceived Anthropomorphism.
T_PI=Total Perceived Intelligence Considering the data presented in Table 4, the following correlations were identified.
Rungskunroch et al.
state that an AorAo value of 0.
1 is a small correlation, 0.
3 is a moderate correlation, and 0.
5 is a substantial correlation.
So, all constructs in the study exhibited either strong or moderate correlations with one another.
Table 4.
Correlation Analysis Constructs T_PU T_PEU T_PE T_PU T_PEU T_PE T_PA T_PI T_CI **.
Correlation is significant at the 0.
01 level .
-taile.
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T_PA
T_PI
T_CI
International Journal of Application on Economics and Business (IJAEB) Volume 3.
Issue 1, 2025.
ISSN: 2987-1972
Note: T_CI=Total Continuance Intention to Use.
T_PU=Total Perceived Usefulness.
T_PEU=Total Perceived Ease of Use.
T_PE= Total Perceived Enjoyment.
T_PA=Total Perceived Anthropomorphism.
T_PI=Total Perceived Intelligence Examinations of reliability and validity are essential to ascertain the accuracy of the variables.
CronbachAos Alpha is employed to evaluate the reliability of the measurement scales, and as indicated in Table 5.
CronbachAos Alpha values surpass the threshold of 0.
70, in alignment with the recommendations of Hair et al.
The validity assessment encompasses KaiserMeyer-Olkin (KMO).
BartlettAos Test, and factor loadings.
In Table 6, the KMO value at 0.
signalling data suitability for factor analysis, as endorsed by Bennett et al.
and Nkansah .
Additionally, the significance value of BartlettAos Test is remarkably less than 0.
well below the conventional significance threshold of 0.
05, thus confirming the dataAos suitability for analysis, following the recommendations of Bennett et al.
and Taherdoost et al.
Furthermore, the factor loadings in Table 5 distinctly reveal the presence of six separate factors, each with factor loadings exceeding 0.
Consequently, there are no apparent concerns regarding discriminant validity, further strengthening the validity of these constructs, as corroborated by Napitupulu et al.
and Arifin & Yusoff .
Table 5.
Result CronbachAos and factor loadings Tableof 5.
Result Alpha
of CronbachAos Alpha and Factor Loadings Constructs Items Factor
CronbachAos Alpha
Loadings () Perceived PU1
Usefulness PU2
(PU)
PU3
PU4
PU5
Perceived Ease of PEU1
Use PEU2
(PEU)
PEU3
PEU4
PEU5
Perceived PE1
Enjoyment
PE2
(PE)
PE3
PE4
Perceived PA1
Anthropomorphism PA2
(PA)
PA3
PA4
PA5
Perceived PI1
Intelligence PI2
(PI)
PI3
PI4
PI5
PI6
PI7
Continuance CI1
Intention to Use CI2
(CI)
CI3
CI4
In this study, five direct hypotheses (H1.
H2.
H3.
H4, and H.
were formulated to examine relationships between constructs.
The analysis focuses on evaluating the significance, denoted by p-values (Bennett et al.
, 2.
, and the t-values (Koy & Erkan Can, 2.
According to the findings presented in Table 6, the variation in continuance intention to use (CI) can be largely explained, approximately 44.
7%, by the combined influence of five independent https://doi.
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International Journal of Application on Economics and Business (IJAEB) Volume 3.
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ISSN: 2987-1972
variables: perceived usefulness (PU), perceived ease of use (PEU), perceived enjoyment (PE), perceived anthropomorphism (PA), and perceived intelligence (PI).
It was evident that there were statistically significant, direct, positive relationships between PU and CI.
PEU and CI.
PE and CI, and PI and CI.
These relationships were supported by results showing p-values below 0.
05 and t-values exceeding 1.
96, indicating their statistical robustness and significance.
In contrast, the relationship between PA and CI did not exhibit statistical significance, as indicated by a p-value exceeding 0.
05 and a t-value falling below 1.
Table 6.
Summary for PU PEU PE PA PI Ie CI In this research, age serves as a moderating variable, prompting the division of the sample into two groups: AuyoungAy and Auold.
Ay This classification aligns with Liybana-Cabanillas et al.
Aos .
study, where individuals with a median age of less than 35 were identified as AuyoungAy users .
, while those aged 35 and above were categorized as AuoldAy users .
Moderated hypotheses are deemed valid when the t-value is Ou 1.
645, p-value O 0.
05, and no missing values fall within the confidence interval (LLCI to ULCI) (Sukri et al.
, 2.
RA is commonly used to assess predictive accuracy, but when few exogenous variables influence an endogenous latent variable, relying solely on RA can be problematic.
CohenAos effect size .
A) serves as an alternative to assess the model, representing RA variations when excluding a specific construct (Karimi Mazidi et al.
, 2.
In this research, five hypotheses (H6.
H7.
H8.
H9, and H.
have been formulated to examine the moderating effect of age on relationships.
There are 4 hypotheses supported: H1.
H2.
H3 and H5.
While H4 is not supported.
This research makes a notable theoretical contribution to existing literature.
Grounded in the Technology Acceptance Model (TAM) and the Extended Meta-UTAUT (Unified Theory of Acceptance and Use of Technolog.
framework, the empirical results clearly indicate that users' decisions to continue using AI-driven mobile banking applications are closely linked to their perceptions of usefulness, ease of use, enjoyment, and intelligence.
Notably, this study highlights the crucial role of perceived intelligence in shaping usersAo cognitive responses and their assessment of AI technologyAos influence on their sustained usage intentions (Lee & Chen.
Conversely, the findings suggest that perceived anthropomorphism has a relatively https://doi.
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International Journal of Application on Economics and Business (IJAEB) Volume 3.
Issue 1, 2025.
ISSN: 2987-1972
weaker impact on usersAo perceptions regarding their continued engagement with AI-enabled mobile banking applications.
Furthermore, this study probes the impact of age on responses associated with the ongoing commitment to using AI-powered mobile banking apps across diverse user demographics.
this regard, it expands upon the Technology Acceptance Model (TAM) and the Extended MetaUTAUT framework by elucidating the moderating role played by cognitive factors and AI attributes in determining the continuation of usage.
In summary, this research bridges a crucial gap by providing empirical evidence that uncovers the intricate relationships among cognitive factors.
AI features, usersAo age, and the intent to continue using AI-enabled mobile banking Consequently, it makes substantial theoretical contributions and implications for our understanding of usersAo experiences in terms of cognitive dimensions and functional aspects, ultimately influencing their continued intention to use mobile banking within AI-driven The current study carries significant practical ramifications deserving of thoughtful attention.
Primarily, mobile banking software developers must exercise prudence in the construction of AI-infused mobile banking systems.
These systems should deliver pertinent, reliable, tailored, accurate, and up-to-date information in an easily accessible format (Ashfaq et al.
, 2.
thereby nurturing a favourable disposition among users towards the adoption of AI-driven mobile banking apps (Belanche et al.
, 2.
To excel in delivering top-tier service quality.
AIpowered mobile banking systems must tailor information to the unique needs of mobile banking users while staying attuned to current trends.
This strategic approach aims to broaden the user base for AI-driven mobile banking applications, prompting banks to address customer concerns and elevate perceptions of AI systems (Ashfaq et al.
, 2.
To achieve this goal, it is imperative to educate customers about the advantages and profitability associated with using AI-powered mobile banking apps.
This can be facilitated by enabling user engagement with robo-advisor platforms, even if their investments are minimal or non-existent.
Alternatively, integrating conversational systems such as chatbots or digital assistants can enhance humanmachine interactions and elevate interface interactivity (Belanche et al.
, 2.
The research and development teams should contemplate the integration of AI technology to better harmonise with usersAo needs and objectives, reduce errors, and enhance dependability.
The overarching objective is to imbue mobile banking apps development with heightened intelligence, thereby elevating efficiency and ensuring users can seamlessly accomplish their banking tasks (Lee & Chen, 2.
Simultaneously, within our progressively diverse society, user requirements are becoming increasingly multifaceted, thus underscoring the necessity for tailored services.
CONCLUSIONS AND SUGGESTIONS
This study acknowledges specific limitations that can guide future research efforts.
First, survey samples for this study are exclusively from Malaysia, potentially limiting the broader applicability of the findings.
Future research could replicate this study in different countries or regions to attain results of greater relevance.
Second, several studies have suggested that affluent and highly educated individuals are more inclined to adopt and consistently use Internet banking services (Mattila et al.
, 2003.
Polatoglu & Ekin, 2.
Future research could investigate whether income and education levels act as moderating factors, exploring whether the model behaves differently when applied to users with varying income and educational https://doi.
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Finally, the study did not establish the significance of perceived anthropomorphism in relation to the intention to continue using mobile banking apps.
While some research suggests that highly human-like systems may threaten usersAo human identity, alternative studies emphasise the role of perceived anthropomorphism in shaping usersAo determination to continue using such applications (Pelau et al.
, 2021.
Balakrishnan et al.
, 2.
Conducting longitudinal research to explore the lasting effects of perceived anthropomorphism on usersAo resolve to continue using AI-enhanced mobile banking applications is advisable.
REFERENCES