Factors influencing the pace of adoption of biometric authentication technology in South African Automated Teller Machines (ATMs).
Applied Research Project Proposal
submitted by
Terrance L Njikelana
Student number: 1664884
Tel: +27 83 418 8633
Email: [email protected] / [email protected]

Proposed Supervisor:
Dr. Tetyana Loskutova

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Wits Business School
April 2018


Project format: Research article

Nominated journal: IEEE Xplore: IET Biometrics

Supplementary files: https://wits.eu.qualtrics.com/jfe/form/SV_0vvbXlArqlUStRH

Today’s financial institutions rely on Identity Documents (IDs) and/or Smart Identity Cards, Personal Identity Numbers (PINs) and Signature Verification as means of positively verifying the authenticity of their clients’ identities, in order to secure client personal information as well as client transactional data. Financial institutions lose large amounts of money fighting crimes relating to false identification and unsuccessful identity verification processes. Despite the modern secure identity verification methods being used today, criminals continue to find new and ingenious ways to bypass them. They use ID cloning; ID theft, and forging of signatures to bypass these authentication processes.
“Optimising ATMs is the way to offset the decline of bank branches in developed countries. There are 1.1M bank branches worldwide, in comparison to 3.5M ATMs. The expectation is that this ratio will be four ATMs to every branch by 2020”, (Walker, 2017). “ATMs are more and more becoming an important bank channel as bank branches are no longer cost efficient” (Walker, 2017).
This has opened new channels for criminals to take advantage of, because an ATM requires only a two-factor authentication (2FA) which is made up of (a) a PIN and (b) a smart card, and these 2 authentication steps are the only required forms of identification before being able to access an account. Unfortunately, neither of these 2 authentication steps are fully secure, and are obtainable by criminals. For this reason, there is a need to improve identify authentication, with the Payments Association of South Africa (PASA) announcing in 2016 the Countrywide new standardised specification to facilitate biometric authentication on payment cards (PASA, 2016).

However, banks in South Africa still use closed-circuit television (CCTV) systems to monitor activities that take place at their ATMs. CCTV systems have their shortcomings and do not prevent crime; they merely ensure the crime takes occurs outside the view of the security cameras. Criminals do not conduct their crimes at the ATM or in the vicinity of the banks. Once criminals visit the bank vicinity, they have already conducted their crimes.
In 2008, Prakash and Malik stated that almost every banking transaction is conducted online, meaning every transaction involving money is computerized. Though banking transactions are conducted online and a lot of bank customers are owners of some sort of a computer or cell phone, there is still a number of customers who still use ATMs, which is why banks still invest a lot of money in ATMs. This is because not everybody is computer literate, nor has access to the Internet, Prakash et al (2008).
South African banks’ ATM security is based on two-factor authentication (2FA) process, which is “what the client knows”, usually the PIN, and “what the client has”, which is the ATM smart card. Even though Capitec is one of the first banks in South Africa to use biometric technology as part of its authentication process, it was never extended to their ATMs. In 2015, van Zyl reported that banks are still investigating the use of biometric technology. The 2FA security process can be compromised and must be incorporated with another form of authentication security that will authenticate “who the client really is” which is what characterizes the client, and integrates that with the ATM PIN and ATM smart card.
This study is concerned with unearthing the perceived factors that influence the pace of adoption of biometric technology authentication on banks’ ATMs. Biometrics is generally described as the use of unique physiological and behavioural characteristics to identify an individual (Onyema, 2011). The application which most people associate with biometrics is security. Uniqueness is very important in information security systems.
Below is the key problem statement of this study:
• The problem is the lack of knowledge of the factors that influence the pace at which biometric authentication technology is adopted at South African ATMs.
Based on the research problem and within the scope articulated above, the objective of the research is formulated in the section below.
The objective of this study includes the following:
• Identifying the factors that influence the pace at which biometric authentication technology is adopted by South African banks at their ATMs;

The main research question for this study is:
• What are the factors that influence the adoption of ATM biometric technology or biometric technology at ATM?
In an attempt to answer the main research question, the researcher has singled out the following key sub-questions which must be addressed:
• What is the current state of adoption of these biometric technologies by banking industry in South Africa?
• What are some of the technology, organisation and environment factors persuading the tendency of South African banks to adopt biometric technologies?
The research question will guide the literature review process, research design, and data-collection methods, all within the scope defined below.
1.4. SCOPE
The scope of this research will be narrowed to FirstRand group ATMs in South Africa. The biometric technology that will be studied in this research is narrowed to SAGEM biometric devices only. The reason for this is to narrow the scope of the research, for accessibility; to easily measure and observe the data collected from the investigation process, and to have a better understanding of patterns in data collected.
This will be accomplished by using the quantitative approach to collect data from FirstRand Bank stakeholders, using the TOE as the underlying theory. With No-rule sampling technique to be used when collecting data from the Online survey distributed to the Projects Sponsors, System/Business Owners, Architects (Data, Solutions, Technical, and Enterprise), Business Analysts, Developers and Information Security, mainly those who are in ManCO and ExCo, which will be self-administered. This is only concerned with the perceived factors affecting the pace of biometric technology adoption as amplified in the propositions section.
During the research period, participants may not be willing to participate in the investigation process. It must be taken into consideration that the investigation process should not be complicated nor be an inconvenience to possible participants. Only FirstRand group ATMs will be studied, since it is the only technology available and accessible to the researcher. Further limitations will be encountered during the research period and will be dealt with as they arise.
The study is reductionist in approach and assumes the effects of individual factors can be isolated and no complex interactions between factors occur (Fitzgerald, B., & Howcroft, D., 1998). The study also investigates the banks tendency to adopt a new technology. This may not be an accurate reflection of the tendency to adopt individual technologies within the bank’s ATM department, i.e. the bank may have a lower tendency to adopt the entirely new technology, but has a higher tendency of adopting technology in smaller chunks.
The search strategy used by Turner, Kitchenham, Brereton, Charters and Budgen (2010) was adopted and provides the inputs for the literature review. The search strategy used to obtain literature pertaining to the adoption of and propensity to adopt biometric systems included the selection and searching of accessible electronic academic databases which host top ranked Information Systems and biometrics journals. Google Scholar was used as a supplementary academic search engine, as it indexes other databases and journals that may have been omitted during the selection. Search terms and strings, “biometrics technology adoption” or “three-factor authentication” or “multi-factor authentication” or “automated teller machines authentication” or equivalents thereof (i.e. “MFA”, “3FA”, “ATM authentication”) were used to perform database lookups to obtain articles.

To answer research question, the aim of the study Investigating the factors influencing the pace at which biometric authentication technology is adopted by South African banks at their ATMs. This output will serve as a basic list of factors influencing the pace at which biometric authentication technology is adopted by South African banks at their ATMs and address the research question.
The views and suggestions of various authors are analysed and discussed below. The review of the literature in this research is based on the three elements of TOE theory which are Technology, Organisation and lastly Environment.
This study is underpinned by the Technology-Organization-Environment Theory (TOE). A conceptual framework is developed for analysing the pace of adoption of biometric technology authentication on banks ATM in the context of IT innovation, which is developed based on the Technological-Organizational-Environmental (TOE) Framework Theory. This involves investigating and understanding how financial institute adopt new technology.
Since the adoption of biometric technology has not yet occurred, it is possible that the banking environment itself plays a large role in the organizational adoption decision along with the characteristics of the technology. The Technology Threat Avoidance Theory (TTAT) and Technology Acceptance Model (TAM) were also identified for this study, as two frameworks that are concerned with studying why and how individual IT users engage in threat avoidance behaviours.
It was appropriate to ground this study in a framework that considers the influence of the technology, the organization, and the environment to account for broader factors influencing the pace of biometric technology adoption at banks ATMs. Therefore, the use of Tomatzky and Fleischer’s (1990) technology-organization-environment (TOE) framework enables the consideration and proposed investigation of specialized factors likely to influence the pace of adoption of biometric technology adoption at banks ATMs.
According to Lippert (2006), the TOE framework asserts three principle contexts which are technology, organization, and environment. This influences the process by which an organization adopts and accepts a new technology. The technological context entails the ATM system that will be able to incorporate the biometric technology device, and also to understand if this technology is feasible.
With regard to organizational context, Lippert (2006), defines it in terms of resources available to support the acceptance of the innovation. The organizational context includes firm size and scope, interconnectedness, and complexity of the organisational structure; and the quality and availability of the financial institutions’ human resources to support this infrastructure.
According to Lippert (2006), the environmental context of the TOE theory describes the setting in which the financial institutions conducts business, financial institution competitors, and the resource and infrastructure supplied by others, and interactions with the government. The following diagram shows the linkages between all the concepts around TOE theory.

Figure 1: Outline of three elements of TOE theory, (Depietro, R. Wiarda, E. ; Fleischer, M., 1990)
Several studies (Ko 2005; Bolle 1997), suggest that, there is no single biometric system that is expected to effectively satisfy the needs of all identification, verification, and/or authentication applications. A number of biometrics have been proposed, researched, and evaluated for identification, verification (authentication) applications. Each of these biometric technologies has their strengths and limitations (Ko 2005; Bolle 1997); and accordingly, each biometric system is suited to a particular identification (authentication) application.
The limitations of biometrics are the key arguments as to why many Information Systems (IS) applications revert to the tried and tested authentication processes. User name and password are the tried and tested means of authentication, which are the reasons a bank like First National Bank (FNB) still requires their clients to authenticate their identities using ATM smart cards and PINs.
The biometric equipment and matching processes are expensive, time consuming and therefore not yet suitable for large scale biometrics applications for civilian usage, (Jain K.A, Kumar, A ; Babich, A., 2012 ; 2010).
First National Bank has continued to use the smart card and PIN identification combination authentication process, despite PASA announcing the standard to facilitate biometric authentication on payment card, (PASA, 2016). This is because their clients are liable for safe guarding their smart cards and are penalised if they are mishandled.
In support of Pooe ‘s (2009) findings, most of the South African banking institutions are still using the applications that were developed years ago. This can be credited to the beliefs that the banks have regarding modern technology. Banks opt for the tried and tested technologies before they can roll them out into their system, says Pooe, (2009). This is because the banks believe that the latest technological applications have defects upon their initial release. Only when the patches files with the required improvements, fixes and defect resolutions has been released will they consider implementing them into their systems.
A biometric or biometric authentication is an objective measurement of a physical characteristic of an individual which, when captured in a database, can be used to check against other entries in the database (Phadke, 2013). All biometric authentication systems work in a similar way and each can be summarized as follows:
(Prakash VS, & Sattiraju YS, 2011), fingerprint authentication is one of many biometric forms of human identification. First, one or more fingerprint image or minutia needs to be registered on the device used to perform the authentication, and a template of the fingerprint is stored on a local database. When users want to authenticate, they must have their registered finger scanned again, and a second template is generated. This is used to match the second fingerprint template against the one registered in the device or local database. Once the two templates match then the authentication is a success.
First, an image of the face needs to be captured. This acquisition can be accomplished by digitally scanning an existing photograph or by using an electro-optical camera to acquire a live picture of the client, Prakash et al. (2011). Once that has been done, software is employed to detect the location of any faces in the acquired image. This often uses patterns of what a face “looks like” (two eyes and a mouth set in an oval shape) are employed to pick out the faces. Similar to the fingerprint, the of the client needs to be captured firstly for the authentication to be successful. Once the image has been stored, later the live image of the face can be used to compare with the one captured initial with the use of nodal points for distinguishing features of the face.
Like with any other biometric authentication system, the client’s templates need to be registered firstly on the local database of the system, if not networked. Prakash et al. (2011), states that, once the image has been captured, “an algorithm is used to filter and map segments of the iris into hundreds of vectors (known here as phasors)”. These algorithms also take into account the changes that can occur within an iris, for example the pupil’s expansion and contraction in response to light will stretch and skew the iris. This information is used to produce what is known as the Iris Code. This record is then stored in a database for future comparison.

According to Prakash et al. (2011), a voice biometric is a numerical representation of the sound, pattern or rhythm of an individual’s voice. Prakash et al. (2011), states that a voice biometric or “voice print,” is as unique to an individual as a finger print. Like with all stated biometric authentication systems, the client’s templates need to be registered firstly on the local database of the system, if not networked. This will enable the evaluation against the live template.
When a comparison is required the same process is followed but instead of storing the record it is compared to all the Iris Codes, numerical representations of the sound, patterns and rhythm minutia records stored in the database. The comparison doesn’t compare the image of the iris, minutia, numerical representation of the sound, pattern or rhythm but rather compares the hexadecimal value produced after the algorithms have been applied.
According to Atmia (2011) report, it explicitly differentiates ATM security into seven security types that need to be satisfied when the bank’s clients perform banking transactions at the ATM. The security types are: end-user security; physical security; account security; software/applications security; card security; authentication security; and transaction security. All these security types have one thing in common, and all of them depend on the bank’s firewall, with the exception of Physical Security. Based on Atmia (2011) , report, a strong, reliable firewall is a critical layer of digital security that is designed to make all possible electronic points of entry invisible to hackers, viruses and worms. In a normal situation, the firewall is coupled with other software that scans and authenticates any source attempting to make contact to the ATM.
Atmia (2011), states that the digital security is very important when it comes to ATM security, this is credited to the technology advancement that has made it easier for criminals to obtain both the card PIN and the Card holders sensitive data held in the debit and or credit card. According to Atmia (2011), it states that the most common ATM theft happens in one of the following formats: lost or stolen cards and PINs, Card Theft (Trapping, Swapping, Distraction); Card Skimming; Phishing; Smishing, ATM Physical attacks; Shoulder Surfing for PIN; Social Engineering / Consumer Negligence and Human attack after a cash withdrawal.
Atmia (2011) also states that the current form of security the banks practice is not enough to solve the ATM theft that costs banks huge amounts of money. With that in mind, the question that surfaces are: what is the best form of identification and verification security that banks can incorporate with their current form of ATM security. (ATMIA, 2011), also states that a good security is the one that is user friendly and ease of use, yet reliable and secured.
With the introduction of a networked ATM, it allowed customers to get cash from any ATM machine in the world, regardless of bank affiliation. It also allowed banks to place ATMs in more areas. Interbank networks like Cirrus and Plus also enables transactions between banks.
Innovation is necessary for many organisations, not necessarily for its own sake ? innovation provides a means of creating new solutions, services or systems. This in turn helps in creating added value for end-users: the people who will actually use the solution, services or systems. The ambition to create innovations with added value for end-users is especially important. In the ICT industry, there is the risk that designers/developers invent solution, services or systems that only a few people need, want to use, or, for that matter, are able to use. There seems to be a gap between the world of the designers, and the world of end-users and customers (Muller, 2002).
Banking is still a surprisingly vertically and horizontally integrated industry. However, to say the banking business has changed considerably is an understatement. The advancement of information technology has changed the financial landscape dramatically. Interbank competition has heated up and banks are now facing increased competition from nonbanking financial institutions and the financial institutes.
According to Vickery (2012), the size, scope, and complexity of large U.S. bank holding companies have grown significantly in recent decades, shaped by consolidation, legislative changes, and growth in the overall size of the financial system.
People facilitate organisational processes, therefore understanding what is required of them and relations amongst them and technology is crucial for the organisation’s benefit. According to (Cockburn, A. & Highsmith, J., 2001), if people executing the tasks are good enough, they can use almost any process and accomplish their assignment. If they are not good enough, no process will repair their inadequacy. “People trump process” is one way to say this. People exchange knowledge while working because of their process flow. This entails how people relate to the technologies assigned to them. (Diaper, 1989) says that one of the most essential reasons for task observation is precisely because what people actually do in a task is often different from what they say they do.
(Diaper, 1989) goes on to say less conservatively, but still common, is to propose that there is also collective knowledge, which can be of many types ranging from widely accepted cultural beliefs to small group knowledge that is specific to a system or part thereof. According to (Diaper, D. & Stanton, N., 2003), the view of goals in task analysis, however, is that when people do articulate goals as the reasons for their behaviour, this is often, if not always, post hoc and is actually an accepted cultural device for solving the task of behaviour explanation and that such articulated goals are often not the individual cognitive, or even group social, mechanism that causes behaviour. According to (Cockburn, A. & Highsmith, J., 2001), a project is built from people having differing personalities and differing skills, working in a physical environment within an organisational culture. The people, environment, and organisational culture all influence one another. When a skilled person leaves, the organisation rearranges itself to compensate; when the team spreads itself across multiple floors, communications change; and so on (Cockburn, 2001).
The South African banking sector is regulated to ensure proper oversight its operations (PWC, 2013). Legislation that affects the banking industry includes, amongst others:
• The Banks Act;
• The Competition Act;
• The Consumer Protection Act;
• The Financial Intelligence Centre Act (FICA);
• The Financial Intermediary and Advisory Services Act (FAIS);
• The Home Loan and Mortgage Disclosure Act;
• The National Credit Act; and
• The National Payment System Act
Furthermore, banks are required to comply with the King Code on Corporate Governance and Basel requirement. There are also a number of ombudsmen tasked to achieve a quick and effective dispute resolution between banks and its customers (PWC, 2013).
The National Treasury also plays a role in proposing a twin-peaks regulatory system to ensure a safer financial sector. Under a regulatory system model, the SA Reserve Bank is responsible for ensuring the safety and soundness of banks, insurers and financial conglomerates and macro-financial stability, while the Financial Services Board will be responsible for market conduct supervision.
With the purpose of the National Credit Act being “to promote and advance the social and economic welfare of South Africans, promote a responsible, efficient, fair, competitive, sustainable, transparent, effective and accessible credit market and industry, and to protect consumers” (NCR, 2005), the act’s objective is to establish a credit market that is accessible for every citizen.
Before a credit provider enters into a credit agreement or increases a loan amount, the credit provider must assess whether the consumer understands the risks and costs of the proposed credit and whether the consumer understands his or her rights and obligations under the credit agreement (NCR, 2005).
According to Pooe, (2009), the table below depicts the factors that impacts the adoption of biometric authentication in the local banking sector.
Number Factors Description TOE Factor
1 Legacy Systems Legacy system are impacting on the organisation’s ability to fully adopt biometric technology for banking applications Technological Factor
2 The South African Banking culture The culture in the South African banking sector is against the use of biometric technology Organisational Factor
3 User Perceptions User perceptions regarding the impact of legislatives concerns on the use of biometrics Environmental Factor
4 Available Biometric Standards Available Standards in South Africa in biometrics negatively affect banks adoption of biometric technology Environmental Factor
5 Human cultural habits Human cultural habits affect South African banks’ adoption of biometric technology Environmental Factor
6 Banks cultural habits Banks cultural habits affect South African banks’ adoption of biometric technology Organisational Factor
Table 1: Factors impacting the adoption of biometric authentication
According to (Welman, C. & Kruger, S.J., 2001), research design provides the plan according to which the researcher selects research participants (subjects) and collect information from them. This section deals with the research design, research instrument, population and sampling, data collection and statistical analysis.
The proposed research will follow an exploratory, descriptive and contextual investigation. A quantitative research will be used during the investigation process. This approach will be used to collect data using questionnaires. These approaches will be used to collect data from FirstRand Bank stakeholders, collecting information on past solution methods for ATM systems and the technique used when adopting a new technology in the bank.
Creswell & Plana Clark (2007) say that the deductive researcher “works from the ‘top down’, from a theory to hypothesis to data to add to or contradict the theory”. Soiferman (2010), states that in research, the two main types of analysis typically used are quantitative and qualitative, and since this study adopted the quantitative research, it will take the deductive reasoning approach.
The data collection technique and method that would be adopted in this study is as follows: questionnaires. This technique and method is considered most appropriate in collecting the relevant data to achieve the said objectives of this study.
Questionnaires will be used widely as the vehicle for extracting the primary research data. Hamlet, (2005), indicated that cost is often an important element when it comes to deciding on the best method of distributing a questionnaire ? this will depend on the size and location of the sample. Anderson, (2004), emphasizes that surveys using questionnaires are perhaps the most widely-used data-gathering techniques in research.
The No-rule sampling technique will be adopted when distributing those questionnaires for data collection. (Barreiro P.L. & Albandoz J. P., 2001) defines No-rule sampling as, taking a sample without any rule, being the sample representative if the population is homogeneous and no selection bias. In addition, the selection of respondents from a population will not be based on any form of a random procedure, other the identifying them using the FRB email distribution list.
All employees of First National Bank (FNB) in the city of Johannesburg and Pretoria will be sampled using No-rule sampling. No-rule sampling will be used on approximately two hundred (200) sample from FNB Johannesburg and FNB Pretoria.
Every profession at FRB has an email distribution list (DL) used by members of that profession as a vehicle to communicate messages relating to their profession. A snapshot of the Projects Sponsors, System/Business Owners, Architects (Data, Solutions, Technical, and Enterprise), Business Analysts, Developers and Information Security, mainly those who are in ManCO and ExCo will be taken using there DLs and a list of responded using the No-rule sampling approach will be compiled, the whole population comprised of approximately 200 from FNB Johannesburg and FNB Pretoria and the analysis will be done on those who have responded. Those employees will be requested to complete the survey/questionnaires will be forwarded to, this is done because of the small population.
It is also important to keep the potential questionnaire participant group in mind, guiding the level of question complexity. The length of a will depends on the type of respondent. Depending on the individual respondent, questionnaires will be scheduled to take up to not more than fifteen minutes to be completed. The researcher will try to steer clear of posing a questionnaire that will take longer than fifteen minutes to complete, to avoid the risk of “putting off” the respondents from completing the questionnaire until they have more time.
The questionnaire will comprise mostly closed-ended questions, since open-ended questions sometimes tend to provide data that is difficult to decode and analyse (O’Leary, 2004). Closed-ended questions will be utilized to retrieve the maximum amount of information without imposing on the time and resources of the respondents.
A pilot study will also be conducted in order to test the research instruments for validity and reliability, by using the researcher’s co-workers before circulating to the wider population. The raw data from the proposed questionnaires will be transformed into numerical format for processing and analysis. That information will be arranged in tables and simple bar charts for an uncomplicated analysis.
Data analysis demands that the analyst break down data into constituent parts to obtain answers to the research question. A descriptive analysis will be undertaken to process the raw data obtained from the questionnaires upon completion of the survey from the earmarked two hundred to be sampled. Using the descriptive analysis technique, the description of what are the factors that influence the pace of adoption of ATM biometric technology for ATM will be provided. The questionnaires will be tailor-made to depict what are those technological, Organizational and Environmental factors.
Data modelling will help improve the quality of data. It will also ensure the definition of high-quality data requirements, reducing the data analysis time, maintenance time, and redundancy, among other benefits. The most conspicuous benefit of data modelling will be its enabling of a reduction in the cost of maintenance and development. These are some of the reasons for modelling the collected data. A list of themes will be extracted from the list of questionnaires that respondents will be required to answer. A meaningful theme (data coding) will be given to those questionnaires.
Data coding will be used to lessen the number of keystrokes used when inputting data. This is also opted for because it will save time, it is easy to work with, while also reducing the data size. The table below depicts the data coding that will be applied.
Description (type of response) Code
Strongly Agree 2
Agree 1
Not Sure 0
Disagree -1
Strongly Disagree -2
Table 2: Type of Response and Data Coding
A quantitative research approach will be used to determine the factors influencing the adoption of biometric technology. Online survey will be distributed to the Projects Sponsors, System/Business Owners, Architects (Data, Solutions, Technical, and Enterprise), Business Analysts, Developers and Information Security, mainly those who are in ManCO and ExCo.
For that reason, the study will have methodological limitations which are discussed below:
Andrews, Nonnecke and Preece (2003) stated that employing the electronic survey methodology attempts to reach a hard to involve population of participants. Since the survey will be online and web-based, access to the survey will be targeting FNB staff who are the Projects Sponsors, System/Business Owners, Architects (Data, Solutions, Technical, and Enterprise), Business Analysts, Developers and Information Security, but can be accessed by anyone other than the predefined.
The data will also be self-reported therefore relying on the honesty of the respondent. Despite promises of confidentiality, the self-reports may be influenced by reluctance of participants to honestly disclose organisational data or their inclination to provide socially desirable responses (Podsakoff, P. M., & Organ, D. W., 1989).
The surveys will be self-administered, and as opposed to interactive interviews, further explanations to misunderstood questions will not be provided. Meaning the risk, of the respondent not providing an accurate response to a misunderstood question, exists.
Below are the factors envisaged to be the factors that influence the pace of adoption of ATM biometric technology for ATM, which are going to the investigated:
• The user perceptions regarding the impact of legislatives concerns on the use of biometrics;
• South African biometrics standard, negatively affect banks adoption of biometric technology
• Human cultural habits affect South African banks’ adoption of biometric technology;
• Cultural habits in the bank, affect them in adopting the biometric technology;
• Banks doesn’t envisage any apparent benefits or competitive advantages rolling out biometric technology;
• Banks perceive biometric technology as being complicated to roll out and support;
• By-In from Banks Senior Management on the roll out of biometric technology;
• There are no penalties for none compliance on the roll out of biometric technology.
The following is the expected contribution of this research to the body of knowledge:
This study seeks to provide a theoretical contribution to information systems literature. By using the TOE framework, it addresses a gap in the literature where this framework has not been extensively used to understand biometric technology adoption.
• The study will provide the basis for further research into factors influencing the pace of adoption of biometric technology authentication on banks ATM.
• This proposed field of study, in essence, contributes to the improvement of banks’ IT systems, IT security and the usability of ATM and biometric authentication processes; and
• The shortcomings or narrowed scope of this research will open doors for future investigation in this field of study; this paper will serve as a reference structure.
Participants will be asked to give their permission for information to be used for the research; confidentiality will be maintained throughout the research. The personal identifies of the participants will not be published in the proposed research. Information collected will be encrypted using RSA cryptosystem and stored in an offline location, to ensure the safety of the participants’ information. The collected information will only be kept until the research has being completed, and will later be safely discarded.
In this section, the problem context is discussed, directing attention to factors that influence the pace of adoption of biometric technology. The problem to be addressed is formulated with the fundamental concern being to identify factors that negatively influence the adoption of biometric technology by banks on their ATM.


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