International Journal of Technology Management and Information System eISSN: 2710-6268 | Vol. 5 No. 2 [June 2023]
Journal website: http://myjms.mohe.gov.my/index.php/ijtmis
AN EXPLORATORY STUDY OF HUMAN BEHAVIOR TOWARDS INTENTION TO USE FACIAL BIOMETRIC
PAYMENT AMONG MALAYSIAN CONSUMERS
Sarerusaenye Ismail1* and Nor Adnan Yahaya2
1 Custom Media Sdn Bhd, Puchong, MALAYSIA
2 Faculty of Information Technology, Malaysia University of Science and Technology, Petaling Jaya, MALAYSIA
*Corresponding author: [email protected]
Article Information:
Article history:
Received date : 18 April 2023 Revised date : 2 May 2023 Accepted date : 20 May 2023 Published date : 7 June 2023
To cite this document:
Ismail, S., & Yahaya, N. A. (2023).
AN EXPLORATORY STUDY OF HUMAN BEHAVIOR TOWARDS INTENTION TO USE FACIAL BIOMETRIC PAYMENT AMONG MALAYSIAN CONSUMERS.
International Journal of Social Science Research, 5(2), 16-29.
Abstract: As we move towards cashless society, the entire payment framework is also changing in Malaysia. All payment methods such as cash, cards and online banking require valuable materials such as physical notes, smartphones or cards where users need to be carried along to complete any payment. For banking or credit card users, it is necessary to remember their user ID and passwords also. Worse still, confusion may arise in recalling these credentials when they have more than 2 cards. In addition, it is quite common that there is a “no change” issue with the merchant or seller which causes the buyer to find ways to pay the exact amount to make a payment. All these scenarios create inconveniences among those using cash payment. The aim of this study is two-fold: (1) to identify the perception of Malaysian consumers towards the use of facial as a payment method in their daily transactions, and (2) to investigate the important quality characteristics of face recognition applications which in turn, shall be used to develop a mechanism for controlling and monitoring the quality requirements for the deployment of face recognition cashless payment (FRCP) in Malaysia. A total number of 385 respondents were randomly selected from high density state capitals and major cities, using face to face structured questionnaire. The results of this study are expected to be useful to potential vendors in developing or customizing prototypes as early as possible to make them ready for the full-blown nationwide deployment of face recognition cashless payment systems, in line with the Malaysia Plan 12th (Rancangan Malaysia Ke-12).
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1. Introduction
This research study divided into five sections: (1) literature review regarding on UTAUT model, the readiness of digital payment in Malaysia and the deployment of face recognition, (2) statement of the problem, (3) method and procedure of investigation, (4) analysis phases including general descriptive analysis, test of the correlations and structural equation modelling analysis, lastly (5) discussion and conclusion. Three out of eight quality characteristics from the product quality model defined in ISO/IEC 25010 (functionality suitability, usability, and security) and 5 of constructs from UTAUT model (trust, performance expectation, effort expectancy, social influence, and hedonic motivation) have been selected for this study. The ISO/IEC 25010 is a set of standards in evaluating the properties of a software product and UTAUT2 is a baseline in predicting the behavioural intention to use a technology primarily in organizational contexts. It replicates and integrates theoretical boundaries of the theory toward user acceptance in using facial recognition.
2. Literature Review
Resistance to new IT applications is viewed as the opposition of individuals to change, which is associated with the new technology implementation (Sharma, 2013; Kim and Kankanhalli, 2009). Therefore, user acceptance is an important factor to consider in IT adoption and usage within the organisation because its usage will be determined by the level of user acceptance of the newly introduced IT (Lippert & Davis, 2006; Agarwal & Karahanna, 2000). In 2020, Mlekus, L. and others introduced the User Experience Technology Acceptance Model, and provide a first test of this model. What is lacking in previous user acceptance model, are characteristics of the technology itself that can be used as starting points to design better technologies. Understanding individual acceptance and use of information technology is one of the most mature streams of information systems research. UTAUT becomes an essential model in aspects of research, fields, and main research journals especially in human behaviour, sustainability, and international information management journals (Wang, J. et al, 2022).
UTAUT model extremely being use until now and has been improved to UTAUT2 that provides a refined view of how consumers' behavioural factors affect the adoption of cashless payments (Venkatesh et al., 2012). The factors consist of performance expectancy (PE), effort expectancy (EE), social influence (SI), facility condition (FC), hedonic motivation (HM), price value (PV) and habit (H) which can be replicated by age, gender, and experience. These factors in information technology are primary sources of the adoption of cashless payments.
Venkatesh and others (2012) found hedonic motivation and price value are critical effect to behavioural intention. Both are stronger than performance expectancy because the complicity from moderating variables of age, gender & experience. They found a high-status group of people creates the novelty towards use of technologies. On the other hand, the study proven by Palas and other (2022) that hedonic motivation, price value, social influence, habit and service quality had significant impact on the elderlies’ behavioural intention. It also proven by the study in Australia that revealed the construct of facility condition significant influenced the public intention to use new technology (Khatun, et al., 2017). However, the study by Rahman, M. and others (2021) found the factors influencing the adoption of cashless payment in Malaysia that not only facility condition has the impact, but performance expectancy also Keywords: Software quality requirements, UTAUT2, face recognition, IoT technology, cashless payment system, digital payment, User Acceptance Model.
the most significant influenced. In another case, Shairil, T. and Remali, M. (2017) introduced the Technology Assessment (TA) model where they applied into UTAUT constructs in their study to analyse e-banking perceptions in the context of supply and demand in Malaysia. They found the increasing cashless payment in the Malaysian’s influenced context is by facility condition and transaction costs.
Parasuraman and Colby (2015) emphasized that technology readiness is a measurement of perceptions towards technology and not as a measure of someone's capability or capacity to use technology. In 1998, the Malaysia Digital Economy Development (MDEC) and Malaysian Communications and Multimedia Commission (MCMC) were established to regulate the industry and articulate digital economy initiatives both from public and private sectors (Edrak, B et al., 2022). Digital transformation (DT) in Malaysia is supported by the government through the Shared Prosperity Vision 2030, which postulated the digital economy’s role in driving economic growth as indicated by MEA (2019). The launch of the Malaysia Digital Economic Blueprint or MyDigital, on 19 February 2021 provides a road map to the digital economy's role (HumanResource,2022). The Transformation Nasional 2050 is a 30-year plan prepared for the 12th Malaysia Plan and presented in the 2017 Budget and the government blueprint. The PH government has introduced Shared Prosperity Vision 2030 (SPV2030-Summary-En, n. d.) as a policy framework and many activities have been initiated by government since 2021 such as e-Government, Smart School, Tele-medicine, R&D cluster in technology and innovation, national multipurpose card, borderless marketing centre and worldwide manufacturing web marketing (The Start,2022). The citizens will continue to experience a phenomenon in which they have no choice but to utilise the digital ‘enablers. A report issued by PWC in 2018 shows that Malaysia has 86% of Internet penetration and 64%
of Mobile Penetration rate. In essence, Malaysia is indeed ready for this digital economy as part of its economic ecosystem (PWC,2018). The types and methods of cashless payments vary widely market and there is no single leader with QR codes, near-field technology for bank transfers, and pre-paid cards. In Malaysia, the adoption of cashless transactions has become more prevalent as the pick-up usage of e-wallet is increasing compared to 2021 involved online banking (36%), debit card (32%), e-wallet (27%) and credit card (8%) (The Star,2022). Ipsos Malaysia Public Affair reported e-wallet payment becomes viable across channels, there is an indication that consumers will find their favourite and stick with it. According to Visa Consumer Payment Attitudes (2018) report, Malaysian consumers are increasingly favouring digital payments and Penang state is targeting 100 per cent cashless transactions in 2024 (New Straits Times, 2023). Statisca (2018) showed that total transaction value in the digital payments for Malaysia amounted to US$5,018m in 2018.
Biometrics used in parallel with existing access methods have the level of security that could be greatly increased (Indrawal, D et al., 2019). Two classes of biometrics are ‘behavioural’
and ‘physiological’. Keystroke, signature, and voice are examples of behavioural and in the other hand, fingerprint, hand, iris, DNA, and face are examples of physiological. In our context of face as validation method, it can be used for crime prevention, video surveillance, person verification, and similar security activities. It is the most difficult area in computer vision and pattern recognition (Trivadi, E. D.,2021). Many applications or systems of facial detection or facial recognition have been developed using various techniques such as knowledge-based, features-based, template matching and appearance matching (Popoola, J.J.
et. al, 2017). Knowledge-based recognizes the face following by a set of rules. For example, an image must capture segment of nose, eyes, and mouth at certain distances and positions from one another. Feature-based finds a targeted area in face template by extracting some
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predictive structural characteristics. It is trained as a classifier first, then it is used to distinguish between facial and non-facial parts of the body. For template matching, AI developers make use of pre-defined or parameterized face templates to locate or detect faces by comparing them to the input photos. Furthermore, they identify the skin colour or the face’s texture using this template matching. The appearance-based searches the face for relevant qualities in face photos and the extraction process depends on techniques either from statistical analysis or machine learning to uncover important characteristics of face photos.
There are algorithms can be used in features extraction and recognition. For example, Viola- Jones technique is one of the most popular technique for detecting objects but mainly use for feature extraction (Mehta, P. & Tomar, P., 2016). Otherwise, developers may use Local Binary Pattern Histogram (LBPH) which it uses a technique by describe texture characteristics of the surface. Each pixel value in LBP is binary digits either 0 or 1(Esa, P.,2016). By applying LBPH, texture pattern probability can be summarised into a histogram.
LBP values need to be determined for all the image pixels and the texture regularity might be based on the distribution shape of the LBP histogram (Esa, P.,2016). Aside from that, developers have another option, Principal Component Analysis (PCA) and Support Vector Machines (SVM). PCA is a method of extraction based on statistical characteristics of global graylings of the entire image. PCA brings out strong patterns in a dataset by supressing variations and it is used to clean data sets to make it easier during analysis (Mareano, C.J.A.
et.al,2019). For example, face and non-face images are described from a wavelet feature in AdaBoost method, then PCA generates the feature vector of face and non-face image in Eigen Face (Dave, P. et al., 2014). Also, PCA is used to compress the given information vector. Face detection algorithm gives location of face or face area with facial features (eye, nose, lips etc).
Besides, it is unquestionable that Support Vector machines (SVMs) are formulated to solve a classical two class pattern recognition problem. This algorithm modifies the interpretation of SVM classifier outputs’ and devise a representation of facial images that is concordant with a two-class problem (Philip, P.J,1998). To do so, binary tree recognition strategy is incorporated into SVM to handle the multi-class facial recognition problem (Mareano, C.J.A.
et al.,2019). The performance of SVM can be measured by comparing the standard eigenface approach, and the closest characteristic line (Guo et al., 2001, Peng, 2010, Pradhan, 2013), (Z. Qi, Y. Tian, 2013). For example, as facial recognition, it helps to identify skin colour by combination pattern of the image such as G-B-R, B-R-G, and R-G-B colour spaces (Swasthika Jain, T. J. et al.,2020). Yadav, A. and Mahajan, S. (2017) reported how significant of using face recognition in current trends of technology where it is widely used in security, general identity verification, surveillance, image investigation and smart card and video indexing such as access control in building, ATM machine, border checkpoint, email authentication, witness reconstruction and many more.
The use of facial biometrics as validation in payment method takes less than 10 seconds and user not required any devices. In China, users only need AliPay and wechat account to order and pay their purchasing. This new method has been adopted in many physical stores such as clothing stores, grocery stores, vending machines, and checkpoints of subway stations (Liu, F.,2020). It seems cool and convenient, because it can reduce checkout time and the hassle of pulling out mobile phones (Liu, F.,2020). A Chinese high school in Hangzhou implementing facial recognition technology where the system can do classrooms monitoring in term of students’ emotions, actions, replacing ID cards or wallets at the library and canteen; and records students’ attendance in every 30 seconds (Chan, T.F.,2022). In India, facial recognition has been introduced for marking the classroom attendance. The work has been developed as a touch-free system to prevent the students getting affected from contagious
diseases, especially COVID’19 (Satpute, N. et al.,2022). Next, is the enhancement of facial recognition under mask wearing using FaceNet framework. It shows how this rapid technology growth year by year, FaceNet framework is advance study by modify the existing facial recognition model to improve the performance of both scenarios; mask wearing and without mask-wearing. The result produced 99.2%, an outstanding accuracy which highly noteworthy (Moungsouy, W. et al., 2022). Then, the model was operated on the masked faces, with additional fine-tuning step based cropping the occluded part. It supervised domain adaptation to the resulting model from pre-trained Res-Net50 model (Mandal, B. et al.,2022).
2. Problem Statement
The relationship between user involvement and system success being a multi-faceted which has introduced many problems and challenges for practitioners. For example, systems do not provide adequate insight into individuals’ perspectives of novel systems, neglecting its indicators such as perceived usefulness, and ignoring the relationship between usage attitude and usage intention (Cheng-Min, C.,2019). The most prominent problems caused by user expectancy are related to communications and misunderstanding between users and system developers that led to all kinds of conflicts (Baker-Eveleth, L. & Stone, R.W.,2008). To our knowledge, less research studies have investigated the possible effects of facial recognition self-efficacy on perceived behaviour intention, and theoretical foundations for such a study have not been established in Malaysia. In the case of numerous research results, there are inevitably omissions that mainly takes a particular field as the research area, lacks international vision, and cannot accurately grasp the development of user technology acceptance in different field (Wang, J. et al.,2022). The examination of factors related to behavioural intention of end-users toward new technology adoption and deployment has resulted in the emergence of several adoption theories and models. Several researchers recommend some integrating factors from different theoretical perspectives and provide a holistic understanding of the potential influential factors of IT adoption and deployment (Oliveira & Martins, 2011, Venkatesh et al., 2003). Regardless, quality requirement in develop new technology has 8 components: (1) functional suitability, (2) reliability, (3) operability, (4) performance efficiency, (5) security, (6) compatibility, (7) maintainability, (8) transferability as referred to ISO25010 standard. For any organization or businesses that introduce new technologies inside their companies, it is not a straightforward task, and companies often face a lot of resistance during the adoption of new systems. These challenges usage of IT in organizations have led to the investigation of how different individuals interact with the new technology in their work environment (Skoumpopoulou, D.,2018). IT projects can be deemed to fail because they are treated purely as an IT project and the human involvement aspect is completely overlooked. This is a fatal mistake in the development of an information system, if the user requirements are overlooked then the technology will never match its planned goals (Skoumpopoulou, D.,2018).
3. Method and Procedures
The face-to-face questionnaire has been distributed in the streets via Google form and collection of data was captured by Microsoft Excel. To ensure all respondents know what to do, a short demo video briefly explaining the use of how facial recognition service as a payment method was included in the survey. This video showcased a consumer completing the payment by scanning her face in front of equipped camera at point of sales (POS) system.
Next, SPSS version 29.0.0.0 has been used for analysis purposes because it is popular, fast and a powerful tool for quantitative data analysis. The analysis and activities including
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measure validity and reliability from Cronbach’s Alpha (Anderson and Gerbing, 1988), find mean values from descriptive statistics of Kurtosis and Skewness, analyze correlation for each construct using Pearson correlation and lastly, applied a linear regression analysis to get the values of R2, F-stat,β and T-stat. R2 values show us how much the variation in dependent variables because of the independent variable in a model. Furthermore,we need F-stat values to predict the model fitness where the level of sign or p-value is important in fit confirmation.
β values indicate either 1 unit of change in each independent variable may influence change of β unit in dependent variable or not, and T-stat values should be tested in T-test to determine either we need to accept the null hypotheses or reject the null hypotheses (not significant).
The collinearity and chi square test are required in checking the significant impact of moderate variables. Age, gender and experience using digital payments are used for investigating moderation..
3.1 Materials
Questionnaire items creation based on UTAUT2 (Venkatesh,V. et al,2012) and quality model standard ISO 25010 (ISO25000, n.d.) respectively. The original questionnaire items were appropriately revised to reflect the theme of facial method as payment validation.We have dropped a few constructs as they are considered to be not relevant based on theoretical consideration. For example, this exploratory research does not require ‘facilitating condition’,
‘price value’ and ‘habit’ as constructs. They are already captured in many researches which can be used as complementary to ours. Nevertheless, we believe that these 3 constructs will be useful later when the facial recognition of cashless payment (FRCP) is ready in Malaysia market. In other words, Malaysia has not implemented this use case yet. We interpret ‘trust’
as the level to which consumers believe that FRCP service providers will perform certain activities for meeting consumer’s expectations (Alalwan et al.,2017). ‘effort expectancy’ is the level to which consumers think that using FRCP does not need much effort (Venkatesh et al., 2003). Al-Saedi, Al-Emran, Ramayah and Abusham (2020) reported that performance expectancy is the main determinant of cashless payment adoption. This is about a level to which consumers think that using FRCP does not need much effort (Venkatesh et al., 2003).
A part of it, ISO 25010 standard is a set of quality characteristics of products or software against which the specified quality requirements can be compared for completeness. This set specify, measure and evaluate system and software product quality. Figure 1 presents our conceptual model where each item in constructs has three selected quality characteristics: (1) functionality suitability, (2) usability, and (3) security. Functionality suitability set the function covered all specific task, correct and appropriate system. Usability related to appropriateness, learnability which user has freedom from risk and specified context of use, can be used by people wide range capabilities, and has user error protection. Security measured the degree of data access, prevent unauthorized access to and non repudiation (ISO25000, n.d.). We only covered these three out of eight characteristics because the rest of characteristics can be only analysed after the deployment of FRCP.
Figure 1: Structural mode.Note Trust, Effort Expectancy(Eff_Exp), Performance Expectancy(Per_Exp, Social Influence(Soc_Inf), Henodic Motivation(Hed_Mot), Facial Recognition Cashless Payment(FRCP)
3.2 Samples
The usable data of 385 Malaysian respondents (149 men and 236 women) were gathered.
Table 1 shows three categories of age variation of participants;(1) below 24 years, (2) 25 to 40 years, and (3) 41 to 60 years. Total of 96.4% participants have experience with online banking, 77.1% participants have experience with QR Code and lastly 54.7% participants have experience using E-wallet. According to NetEase (2019), there are about 243 million users in China used facial recognition cashless payment and mostly the range of age was 18 to 40 years. Thus, our study reported the sample composition does not deviate from current proposition of users among the whole Malaysia population at aged 18 years above.
Table 1: Noted. Experience with Digital Method (ExpOn9), Experience with QR Code (ExpQR), Experience with E-Wallet (ExpEw) Sampling Respondents by Gender and Experience with Technology
Transaction
3.3 Site
As of 2021, the average income in Malaysia is MYR3,037(Department of Statistic Malaysia,2023). Therefore, as shown in Table 1, our study classified the group into:(1) lower income with household is below MYR2000, (2) middle income with household between MYR2100 to MYR5000, and (3) upper class income with household income exceeds MYR5000. The sampling has been taken randomly from all states in Peninsular Malaysia and discard Sabah and Sarawak regardless because the difficulty to get there and reach face-to- face respondents. We have chosen Peninsular Malaysia as it has the best available access to various payment services, dynamic and scattered business activities.
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4. Data Analysis
Here, we determine the extent to which the items in our questionnaire are related to each other.
Table 2 shows the result that ensured the reliability of the instrument items for all constructs is at ‘good’ condition with overall were 0.80 α-value using Cronbach’s Alpha.
Table 2: ‘Good’ Condition of Reliability Analysis in Each Construct
The reliability analysis procedure calculates several commonly used measures of scale reliability and provides information about the relationships between construct. We had minimized the total number of questions into three items for each construct with categories which suit the 3 characteristics from product quality model ISO25010 model. These categories were designed intentionally to measure some sub-class of characteristics of Quality Model as shown in Table 2.
4.1 Validity and Reliability
Average mean values are less than 5 with the highest mean is 3.9485 and the lowest mean is 3.4061 as shows in Table 3. It indicates each construct are accepted and reliable as independent variables. On the other hand, values of skewness have been used to check whether the construct has positive or negative values. Any value that close to 0 represent symmetry; positive values mean that there are some high valued outliers and a negative value means some low valued outliers. Statistical the average value of skewness in these 6 constructs is in between -3.16 to -6.99, somehow it is acceptable among most practitioners and dictate has low valued outliers. Aside of that, we found Kurtosis distribution values show no flat and have been clustered in the centre. The distribution of descriptive statistics as stated in Table 3 as per discussion and has been identical as normal distributions. To get a better result, we recommend to validate the reliability of items in small size of sampling such as below 100 sampling before to analyse a bigger size of sampling.
Table 3: Descriptive Statistic Results
Pearson’s r varies between +1 and -1, where according to Table 4, there were a perfect positive correlation (figures 0.679, 0.624, 0.558, 0.718 and 0.7) in this study. We are also interested in the 2-tailed significance value which in this case α is < .001 for all. The standard α-value is .05, which means that our correlation is highly significant, not just a function of random sampling error and others. It is possible to say this correlation to be confident and has a strong correlation. It means we has enough statistical and significant.
Table 4: Correlations and coefficients
Typically, a regression analysis is done in order to predict the value of the dependent variable for individuals for whom some information especially in product quality concerning the explanatory variables is available, or in order to estimate the effect of some explanatory variables on the dependent variable. ANOVA has been used in investigating the relationship between variables. Total of 5 correlations have been discovered perfectly as shows in Figure 2, the results of coefficients among trust, effort expectancy, performance expectancy, social influence and hedonic motivation as the independent variables.
Figure 2: Linear Regression Between 5 Independent Variables and Dependent Variable
4. Results and Discussion
Overall, each of constructs has significant correlation to intention to use FRCP whereby
‘Performance expectancy’ has 71% variation which selected to be the strongest independent variable among the others that significant influence intention to use FRCP. This is aligned to the previous research Al-Saedi, Al-Emran, Ramayah and Abusham (2020). This is followed by ‘Hedonic motivation’ with 70% variation significant influence intention to use FRCP. Both produced the highest reliable data return that can be classified as giving the highest impact of human behaviour towards the proposed investigation of user acceptance theory related to FRCP. 1 unit of ‘Performance expectancy’ increased 84% of intention to use FRCP and 1 unit of ‘Hedonic motivation’ increased 82% of intention to use FRCP. However, fortunately the other constructs have also been identified as significantly correlated to intention to use at least more than 50% variance and in each of them increased the percentage of intention to use such as; 1 unit of ‘Trust’ increased to 74%, ‘Effort expectancy’ increased to 70% and lastly, ‘social influence’ increased to 63%. Therefore, referring to Figure 2, all No 1 to No 5 dependency relationships can be accepted as hypotheses. Meanwhile, the moderator variables such as
‘Age’, ‘Gender’ and ‘Experience’ have changed some relationships. A moderator analysis is used to determine whether the relationship between two variables depends on (is moderated by) the value of a third variable. To analyse the impact of adding the moderator variable towards these relationships, the indicators from coefficients result of collinearity tolerance and variable inflation factor (VIF) are required.
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Chi-Square helps to measure the association that appeared in column of Pearson Chi-Square Pearson Chi-Square” of moderator analysis. In Table 4, the value of the chi square statistics appears in the same row in “Asymptotic Significance (2-sided) as p-value. Experience in using online banking QR code and E-wallet have been shown to have strongly significant effect between all constructs, i.e (1) trust, (2) performance expectancy, (3) effort expectancy, (4) social influence and (5) hedonic motivation to intention to use. On the other hand, there was positive significant moderate between age and the 2 constructs; (1) trust and (2) social influence. However, gender is not significant affect to any relationship between all constructs and intention to use FRCP.
Table 4: Chi-Square Value After Cross Tabulation
First theoretical implication advantage relatively was found to be the most important factors in user acceptance to use FRCP. It is due to the pervasive nature of Fintech in Malaysia. As stated in literature review, Malaysia is ready for digital world since 2019 and to enhance the way of buying products and services included in current Malaysia Plan. Thus, consumers trust and system performance expectation are the advantages of FRCP over existing payment services as the major factors strongly influence their intentions to use it. If service providers can increase consumer’s trust, the face recognition accuracy and security need to be continuously improved. This also can aid in reducing the concern toward new technology adoption. Second, performance expectancy was found to strongly affect consumer’s intention to use FRCP. Therefore, to satisfy the consumers’ performance expectations, service providers must improve the system in term of security, usability, and functionality suitability.
Due to the FRCP service that does not deploy yet in this country, companies should focus on increasing the number of offline stores with capability of digital payment via current available online payments. This reasonable action needs to be taken as soon as possible to make the consumers feel the convenience and usefulness of advance services such as FRCP. As the result, the readiness of FRCP system can be increased. Third, trust notable affects consumers’
intention to use FRCP in a positive direction, implying that how the security and data protection as important items in our questionnaire. Higher functionality suitability, the more consumers trust to use the system. Higher security poses in the system, the stronger trust consumers put in their decision. Third, effort expectancy notably affects consumers’
intentions to use FRCP too. The strategic elements to promote the use of FRCP in society are the companies produce a logic system with simplicity and provide information or demonstrate how to use this system. This allowed consumers to feel that using FRCP does not require much effort. Forth, hedonic motivation can inversely predict consumers’ intentions to use, indicating the importance of reducing or even eliminating the use of cash and increased the experience of not using mobile phone in any payment transaction. Finally, social influence can enhance consumers’ intentions to use FRCP. Companies should start facilitate the consumers’ recommendation behaviours and viral marketing for example, social media platforms such as TikTok is one of the most popular social network services in recent years, can be used by companies for this purpose. Government support such as DT is required in activate the adaptation and encourage the users share their experience on the platform effectively by diligent promotions and be industrious. Based on observation, 62% of
Malaysians is ready to use facial recognition as an alternative payment method among the existing technology because the advantages of no need to bring cash in hands, no card required and not required mobile phone device. Not to forget that we also found gender does not reflect to intentions of use FRCP. However, there is positive impact factor of consumers to intention to use FRCP based on age and experience of using digital payment. As predicted, an adult with experience using digital payment put his or her trust on upcoming technology main stream. Total number of 47.8% Malaysians in a range of below 24 years to 40 years reported they will used FRCP in any potential stores if available.
5. Conclusion
There are a few suggestions can be taken into consideration of extension of this research study in next future. This paper primary. Our original strategy is to develop a user acceptance model based on UTAUT2 and Product Quality Requirement Model. Thus, practically this research paper is our preliminary investigation of the current human behaviours perspectives on accepting the upcoming new technology of facial recognition as a payment method in Malaysia. Some implications were observed that relative advantage plays the most important roles in user acceptance of FRCP. However, the further improvement can be made in term of reducing the complexity to measure moderator variables and elucidating the relation between each product quality model and UTAUT2 model. Firstly, a key goal of regression analysis is to isolate the relationship between each independent variable and the dependent variable. Observed that this study has 3 of independent variables which have big number of collinearity values. The coefficient estimates can swing wildly based on which other independent variables are in the model. The coefficients become very sensitive to small changes in the model. Therefore, we are suggested if you have only moderate single collinearity, you may not need to resolve it. The collinearity affects only the specific independent variables that are correlated. Thus, if it is not present for the independent variables that you are particularly interested in, you may not need to resolve it because suppose your model contains the experimental variables of interest and some control variables only. Other than that, use the Fisher's exact test of independence when you have two nominal variables and, in our case, we want to see whether the proportions of one variable are different depending on the value of the other variable. Fisher’s exact test of independence is more accurate and extreme way than Chi-Square test of independence. This study discovered that in Malaysia technology readiness in term of intention to use, positively affect the deployment of FRCP. However, the limitation of this field of study, the assumption can be concluded only by including the sampling of respondents in rural area which has inconsistency issues in term of internet access and telecommunication coverage. Furthermore, there is no reflection of high-income users that only tend to be loyal in using digital payment as reported by Venkatesh and others (2012) because majority of Malaysians started using advance online payment as early as 2010. In addition, our study also discovered social influence significant influence intention to use FRCP but it is not a dominant variable in human behaviour as per claimed by Palas and other (2022) due to 59.5% of respondents strongly agreed that they are the first person who are always try any new technology among people around them. At the end, we can conclude the current expectation of results are useful for the next research which involved with any new technology or innovation. However, the extensive research analysis after the deployment of FRCP can be used a combination of UTAUT2 with all constructs, User Experience Technology Acceptance Model by Mlekus, L. and other (2020) and systematic survey results from Product Quality Model ISO25010.
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