A Proposed KMS Model for e-Banking at the National Bank of Elbonia

  • Category:
  • Document type:
  • Level:
  • Page:
  • Words:

City and state where it is located

A Proposed KMS Model for e-Banking at the National Bank of Elbonia



3Significance of KMS to an organization

4Knowledge needs analysis

6Basic functions and processes of knowledge management systems

8The middleware layer architecture

9The proposed knowledge management system (ML-KMS)

11Components of ML-KMS architecture

12Knowledge flow in ML-KMS

13System management

14System implementation

15Knowledge forms

15System evaluation




In this knowledge society (Lytras, 2009), it is not enough for organizations to invest in tangible assets. Knowledge management (KM) has become a central element in competitive strategies of business enterprises and organizations (Rasoulinezhad, 2011). Even in the domain of KM, traditional systems are no longer tenable and businesses innovatively upgrade their knowledge systems to meet the ever-changing market demands. A case in point is the e-Banking initiative at the National bank of Elbonia (NBE) in which self-service digitalized solutions are to replacing traditional customer services. This paper proposes an appropriate knowledge management system (KMS) for this endeavor. Benefits of a working KMS include business globalization, leaner and more efficient organizations, corporate memory and technological flexibility.

After stating the importance of this project, the paper analyses knowledge needs for the NBE initiative. This is followed by a discussion on the basic functions and features of KMS. The paper then proposes a KMS architecture for the bank’s initiative. Finally, a brief critique of knowledge forms identified in proposed design is presented.

Significance of KMS to an organization

Stewart (2000) suggests that to retain valuable knowledge, intellectual assets should be identified and legacy materials produced and stored in a manner that makes retrieval and reuse possible and useful. When tangible byproducts of knowledge flow from an individual to another, and among community members, and get back to the organization, they constitute learnt lessons, corporate memory and best practices. KMS has been seen as a systematic strategy for managing and using information so as to institute a continuous knowledge flow to the right person at the right time as this enables efficiency and effectiveness in decision making in everyday business (Goff and Jones, 2003). However, the most important aim of KM is to ensure that knowledge is leveraged to the advantage of an organization (Niclos, 2000).

• Builds organizational memory
• Enables organizations to better stay ahead of the competition
• Cross-fertilizes ideas and increases opportunities for innovation
• Improves knowledge embedded in products and services
• Diffuses best practices
• Solves problems quickly
• Helps drive strategy
Goff and Jones (2003) view KMS as strategies, techniques or tools for retaining, analyzing, improving, and sharing business expertise. From this perspective, a system of knowledge management works if it does most of the following:

Knowledge needs analysis

By assessing the current knowledge needs at NBE, the study identifies KM gaps in the institutional, physical, human, and technological infrastructure with an aim of proposing a responsive strategy. In a strategy developed by Robertson (2004), needs analysis is the second step. This model identifies key groups of people in the system and comprehensively analyse typical needs for each group..

For the case of NBE, a number of issues affecting KM are considered: key business needs, inconsistency in practices, duplicity of effort, inefficiency in business processes, major business risks, and opportunities for improving procedures or policies. The major business need for NBE is to transform into a fully virtual bank by embracing e-Banking. The bank management acknowledges that for this to be realized, organizational and structural changes need to be put in place. Reported inconsistency with the bank’s intention to roll out virtual banking platform comes from the employees who complain of unpreparedness in terms of information, training, and tools. This implies that the senior management plans are inconsistent with staff expectations. Staff unpreparedness may lead to other KMS implementation bottlenecks like inefficiency and duplicity of effort. In case the process of KMS implementation fails to work efficiently, NBE is at risk of losing business in terms of profits, customer base and brand reputation. Adjusting operation procedures and policies will be important in accommodating migration from traditional customer care practices to a fully automated banking system.

It is emerging from the needs analysis that requisite changes are needed to facilitate e-banking and a KMS that supports it. The study identifies physical infrastructure, organizational structure, human resource, technological infrastructure, and customer self-efficacy as the key areas that should be considered in capacity building. Each of these needs will be addressed later in the paper.

When these knowledge needs are not addressed, knowledge gaps emerge. One common gap is the difficulty in accessing key corporate information; for example, bank products, rates, tariffs, branches, and related services would be hard to find. Secondly, management of information would be inconsistent, haphazard and unstructured. Thirdly, corporate and regional banking news would be ineffectively disseminated. Knowledge sharing among interrelated business units would also be chaotic. The bank will be forced to over-depend on long-serving staff members as sole repositories of knowledge. Front office and call centre staff would give inconsistent advice. Cases of cultural and physical barriers between regional staff or customers and the head office would be difficult to resolve. Eventually, there would be disparity between policy development and implementation.

Basic functions and processes of knowledge management systems

To guide in selecting and designing a workable KMS, basic KMS functions and processes are identified and described. According to Jennex (2009) a knowledge management system should be able to perform the following basic functions:

  • Storing and diffusing knowledge resources

  • Describing work practices in terms of business units

  • Enabling communication as well as collaboration

  • Contextualizing knowledge resources to actor’s environments

  • Accommodating user’s comments or feedback

  • Tracing information interchange between actors to the business element that initiated it

Patrick and Choi (2009) outline KMS components that would be important for constructing a KM model. Source, type, management process and management system are the main features emphasized in Patrick’s and Choi’s model as shown in Table 1.0. The model comprises four key KM processes: acquisition, storage, dissemination, and application.

Knowledge acquisition involves six processes namely obtaining customer and industry information, responding to marketplace changes, employing and retaining trained personnel, developing sound financial reporting systems, encouraging skill development, partnering with international customers, and capturing opinions and attitudes(Darroch, 2003). Knowledge storage is the process in which expert, staff, and customer knowledge is converted into corporate memory (Walsh and Ungston, 1991). Corporate memory is stored either formally in tangible knowledge legacies or in intangible knowledge legacies in form of norms, values, beliefs, and other aspects of organizational structure and culture. Turban et al (2004) observe that knowledge dissemination is effective if everyone in the organization can access knowledge in a useful format anytime anywhere.

Table 1.0 Characteristic features of knowledge management systems

Knowledge source and type

KM process

Internal and External knowledge


Corporate repositories

Discovery patterns

Online expert systems

Transactional processing systems

Structured knowledge

Creation of databases using

Digitizing, tagging, indexing

Semi structured Knowledge


Instant messaging

Search engines

Video Conferencing

Knowledge Network


FAQ Repository

ERP (Enterprise resource Planning)

SCM (Supply chain management)

Expert domains

CRM (Customer relationship management)

(Adapted from Patrick and Choi, 2009)

The activities in knowledge dissemination include market information circulation, knowledge-on-the-job sharing, techniques sharing, and use of technology such as video or teleconferencing. Knowledge application occurs when knowledge in the repository is used in practice, decision-making, or problem-solving. It is knowledge application that gives an organization competitive edge, and this is the essence of investing in knowledge management (Niclos, 2000).

Other KM models emphasize knowledge creation (Nonaka and Takeuchi, 1995), knowledge capture (Barchan, 1999), knowledge filtering (De Tienne et al., 2001), knowledge development (Teece, 1998), knowledge strategies (Newell et al., 2002), and knowledge auditing (Gourova et al., 2009).

The middleware layer architecture

One of the integrated systems is the one in which a middleware layer is used to integrate multiple systems and channels allowing different customers to access a range of back-end services from the front-end (Shah and Clarke, 2009). The middleware layer forms a central interface to all existing banking systems, and it allows easy addition of new systems. The head of e-Banking uses the middleware layer architecture as component-based system that enables some tasks such as checking account transactions to be installed as business objects accessible to all channels.

The advantage of the middleware layer architecture is that both management and bank customers are able to visualize the whole financial picture. As such, the clients are provided with information about all the banking services and possibilities summarized and analysed on a single page. The customers are able to know their financial positions at a glance and make informed decisions. The architecture enables account aggregation by synchronizing all relevant information about an account holder and presenting upon a single inquiry. As shown in Figure 1.0 below, middleware layer system enables all branches, ATMs, Internet, wireless application protocol (WAP) and telephone support to access central information on all accounts in terms of all the banking services such as credits, loans, mortgage, insurance, savings, and current accounting. For instance, a customer can access the same information from any branch, ATM or internet-connected gadget.

Figure 1.0 Middleware layer architecture (Adapted from Shah and Clarke, 2009)

A Proposed KMS Model for e-Banking at the National Bank of Elbonia

Another advantage of this technology is its adaptability. A bank using this system can easily adapt to environmental demands of e-Banking without destabilizing the back-end legacy mainframes. This is because the architecture is flexible to accommodate new systems so as to boost the bank’s e-Banking capacity without overhauling the entire system.

The proposed knowledge management system (ML-KMS)

The proposed KMS for e-Banking at the National Bank of Elbonia uses the middleware layer architecture described in the previous section. The architecture presented by Shah and Clarke (2009) can suffice for KMS but it is deficient in one way: it does not make explicit the knowledge processes that underpin tangible knowledge legacies form the backend to the customer. The model concentrates on the customer experience at the front-end but it fails to account for the behind-the-scenes activities that precede knowledge dissemination. However, middleware architecture is very ideal for developing a comprehensive KMS due to its flexibility and ability to add new systems.

Middleware layer architecture basically integrates three systems: Backend, Middleware, and Customer relationship management systems (CRM) (Shah and Clarke, 2009). The backend systems comprise applications that manage data in the custody of the bank. A good example of backend system is data warehousing which is a system for collecting data from diverse sources and building central storage in a manner that data-analysis tools can be used to analyse the data and convert it into meaningful units for decision making. CRM systems are used for gathering, storing, and analyzing customer data to facilitate automated customized services. Managing customer knowledge in this way ensures loyalty and cuts costs. Middleware is the name given to technologies that enable interaction between different systems making it easier for a company to integrate new technology or system with an existing one. A common example of middleware technology is called Service Oriented Architecture (SOA). O’Donnel (2007) notes two advantages of SOA. One, it externalizes functionality into components that can be re-used, and organizes them into a logical framework. Two, it enables a legacy system to communicate with other systems. The proposed model retains the name Middleware for central system but calls the other two systems Back-end repositories and Front-end services respectively.

The architecture as presented by Shah and Clarke adequately reflects the kind of a bank the management at the National Bank of Elbonia envisages: a fully virtual bank. On lower side of the model are the banking products (credit, mortgage, loans, savings, insurance and current account services) while on the upper side of the model are the automated technological systems (ATMs, Internet, WAP, telephone, and iDTV) through which the customers can self-access the banking products anytime, anywhere. In the proposed model, the lower-side elements are named Products/Services while the platforms for self-access on the upper side are collectively referred to as Automated Systems. Figure 2.0 below is a the proposed model built on the middleware architecture. It will be called Middleware Layer Knowledge Management System (ML-KMS)

Figure 2.0 Middleware Layer Knowledge Management Systems

A Proposed KMS Model for e-Banking at the National Bank of Elbonia 1

Components of ML-KMS architecture

The middleware layer knowledge management system (ML-KMS) is a centre-outwards model with the four KM processes – acquisition, storage, dissemination and application (Patrick and Choi, 2009) as the central functions. The Middleware is the knowledge management template upon which systems are added. The Middleware, therefore, acts as a spine, motherboard or processor of the KMS. Each layer or subsystem attached to the middleware will carry out the four basic functions. In terms of knowledge type the middleware comprises knowledge network. This implies that all e-Banking operations of NBE operating on this platform must be defined in terms of the four core knowledge functions.

Attached to the Middleware are two subsystems: back-end and front-end functions. The back-end function is the repository system for NBE heritage handling its history, finances, partners, vision, staff, organizational structure, customer base and other legacies. It is within this function that unstructured and semi-structured knowledge is acquired, stored, disseminated and applied to the competitive advantage of the bank. The front-end function constitutes structured knowledge which is accessible to the customer. Processed knowledge on banking products, branches, customer financial status, tariffs, incentives, financial advice, and customer care is managed at this level.

The external functions of the ML-KMS are knowledge sources on the back-end and customer experience on the front-end respectively. Internal and external knowledge as well as organizational goals, values, and vision form the bulk of the back-end repositories. Customer interaction with the bank’s values, products, staff, technology, and physical infrastructure constitute the front-end knowledge interface.

Knowledge flow in ML-KMS

In this KMS architecture, knowledge flows back and forth from the centre. From the externals at the Back-end, raw and semi-processed data for within (the bank) and without (national and international market) go into the bank’s databank and knowledge repository. Once this raw knowledge is processed and categorized according to knowledge framework provided by the middleware, the processed (structured) knowledge is installed into the Front-end systems. Finally, customized knowledge is made available in the front-service interfaces such as the ATMs, Internet kiosks, brochures, bank statements and so on.

Figure 3.0 Knowledge flow in ML-KMS

Unstructured knowledge

A Proposed KMS Model for e-Banking at the National Bank of Elbonia 2


ML-KMS architecture provides for structuring of knowledge available in the repositories while, at the same time, repackaging customer inputted knowledge from the front-end services as raw knowledge to be used by the bank’s management to improve quality and efficiency.

System management

At the centre of the ML-KMS is the head of e-Banking backed by domain experts. This team runs the middleware and its layers in collaboration with other system players. On the Back-end, knowledge is managed by the senior management who carry the vision of the bank. They make decisions and formulate policies and strategies based on the external and internal knowledge in their possession. Knowledge at the Front-end is mediated by the front-office staffs who package it in the format required by and accessible to the end users (customers) through a process called knowledge customization.

System implementation

The management at NBE visualizes a bank whose services are accessed through various channels such as branches, Internet, digital televisions, telephones, Internet, and mobile phones. The outcome of this process is the improvement of customer service quality and increased productivity. The ML-KMS architecture is, therefore, built to support this initiative, and its implementation requires the bank to heavily invest in information technology as well as reorganize itself structurally. Other changes that would be necessitated by the system’s implementation include, staff training, attitudinal changes, and customer sensitization.

Before rolling out the e-Banking programme, NBE needs to obtain relevant technologies for gathering and analyzing extensive client information. Computer software like QuickStream, Webtrend, and web collaboration tools could be used to accomplish this (Shah and Clarke, 2009). Business structures and processes at NBE should be modified to allow greater organizational flexibility. A new department or team responsible for e-Banking will have to be created and integrated with virtually all departments and business units. This technical team, headed by an IT specialist, will manage the system’s Middleware around which all banking processes and units revolve. Subsequently, many of the banks processes will to be totally automated and integrated making it possible for a customer to get all information at one point or upon a single inquiry. The number of staff need not decrease but new roles will be given to the staff after training that transforms them from knowledge repositories to agents of knowledge management. Customer sensitization will produce corresponding transformation of the customers as knowledge managers who manage their accounts and other banking interests through self-service. The technological, structural and operational changes occasioned by e-Banking would require attitudinal change. NBE’s management, staff and customers would appreciate that personal initiative is required for the bank to realize its virtual banking dream. The outcome will be a universally continuous and rapid service delivery during and after office hours. Finally, the system should also ensure security for private and classified knowledge.

Knowledge forms

Knowledge forms identified by the system are, external or internal, and unstructured/semi-structured or structured. These knowledge dichotomies are important in any KMS architecture. Both external and internal knowledge can either be unstructured or structured. While the former deals with source, the latter deals with form. The only weakness in dichotomizing knowledge in this way arises from the assumption that any information from employees is internal knowledge. This is not always the case as employees can source their knowledge externally. Instead of categorizing knowledge on the basis of the source, content of the knowledge should take precedence.

System evaluation

Success of the KMS can be established at each of the three points: Back-end, Middleware, and Front-end. The extent to which the management’s vision and mission as well as organizational culture are achieved is a measure of the success or failure of the system. Improved sales and brand image are some of the indicators. At the Middleware, the levels of automation and integration of services and products hold the key to the system’s success. Lastly customer feedback, satisfaction, ratings and reviews would indicate if KMS is working for them or not.


The proposed ML-KMS architecture would serve to organize and leverage knowledge in a manner that will ensure efficiency and non-duplicity in NBE’s e-banking initiative. NBE’s advantage in respect to this model is that its e-Banking migration already enjoys top managerial support. Subject Matter Experts from Central Office have been identified to consult on the digital rollout. Other positive indicators include the identification of the pilot branch, the timeline set at six months, and staff motivation for early adoption. However, problems of staff apprehension and resistance will be resolved if the training programme outlined in this paper is undertaken. It is hoped that the proposed KMS not only strengthens the bank’s initiative, but also points out areas that must be addressed if the rollout and operation of programme is to be smooth and successful.


Barchan, M. (1999) Capture knowledge, Executive Excellence, September, p. 11.

Darroch, J. (2003) Developing a measure of knowledge management behaviors and practices, Journal of Knowledge Management, (7) 5, pp. 41-54.

De Tienne et al (2001) Knowledge Management Understanding Theory and Developing Strategy, Competitiveness Review, 10595422,11, 1.

Gourova, E., Todorova, Y. and Antonova, A. (2009). Knowledge audit concepts, processes and practice. WSEAS Transactions on Business and Economics. 6, 605-619. Bottom of Form

Groff, T., and T. Jones. 2003. Introduction to knowledge management: KM in business. Burlington,MA: Butterworth-Heineman .

Jennex, M. E. (2009). Knowledge management, organizational memory, and transfer behavior: global approaches and advancements. Hershey, PA, Information Science Reference.

Lytras, M. D. (2009). Best practices for the knowledge society: knowledge, learning, development and technology for all : Second World Summit on the Knowledge Society, WSKS 2009, Chania, Crete, Greece, September 16-18, 2009 : proceedings. Berlin, Springer. http://public.eblib.com/choice/publicfullrecord.aspx?p=3064550

(accessed July 20, 2016).
Nickols, F. 2000. KM overview, http://home.att.net/~discon/KM/KM_Overview_Context.htm

Nonaka, I. and Takeuchi, H. (1995) The Knowledge Creating Company, Oxford University Press, Oxford.

Newell, S., Robertson, M., Scarborough, H. and Swan, J. (2002) Managing Knowledge Work, Palgrave, Basingstoke.

O, Donnell, T. (2007 March). Addressing SOA’s Vulnerability.Visual Studio Magazine. aspx? editorialsid=2311
Retrieved July 22, 2016 fromhttp://visualstudiomagazine.com/features/article.

Rasoulinezhad, E. (2011) Measuring the Role of Knowledge Management Processes in the Commercial Banks of Iran, The Electronic Journal of Knowledge Management (9)4,.pp353-364

Shah, M., & Clarke, S. (2009). E-banking management: issues, solutions, and strategies. Hershey, PA, Information Science Reference. http://public.eblib.com/choice/publicfullrecord.aspx?p=3309574.

Stewart, T. 2000. Software preserves knowledge, people pass it on. Fortune 142 (5 ): 4 .

Teece, D.J. (1998) Capturing value from knowledge assets: the new economy, markets for know-how, and intangible asset, California Management Review, Vol. 40 No. 3, pp. 55-79.

Turban E, Mclean, E and Wetherbe J. (2004) Information Technology for Management –4th Edition.

Walsh, J. and Ungston, G.R. (1991) Organizational memory, Academy of Management Review, (16) 1, pp. 57-91.