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CHALLENGES FACING THE IMPLEMENTATION OF
DECISION SUPPORT SYSTEMS IN LOAN ALLOCATION
AMONG COMMERCIAL BANKS IN KENYA
By:
Franklin Mutai Rono
A Management research project submitted in partial fulfillment of
the requirements for the award of the Degree of Master of Business
Administration,(MBA) School of Business, University of Nairobi.
October, 2010
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DECLARATION
This management research project is my original work and has never been
presented for the award of a degree in any other university or institution of
learning.
Signed……………………………………date……………………………
Franklin Mutai Rono
D61/70628/2008
This management research project has been submitted for examination with
my approval as the university supervisor.
Signed……………………………………date……………………………
Mr. Peterson O. Magutu
Department of Business Administration
School of Business
University of Nairobi
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DEDICATION
This study is dedicated to my parents Bernard C. Mutai and Florence C. Mutai for their
sacrifice to educate me and their endless support throughout the entire duration of the
course.
Further dedications go to my brothers and sisters Charlotte Chelangat, Rose Chepkirui,
Gilbert C. Rono, Moses Rono, Abraham Chepkwony, Emmanuel Kipngeno and my niece
Hope Chepkorir for their abundant support during the duration of my study. I sure will
reciprocate the kindness.
God Bless You all.
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ACKNOWLEDGEMENTS
To the almighty God for the abundant blessings and for bringing me this far. It is by his
mercies that I have come from initiation to completion of this program.
To my parents for the enormous financial and moral support throughout the entire
course I couldn‟t have done it without you.
To my supervisor Mr. Peterson Magutu for his patience and intellectual guidance
without which this work could not have been completed.
To all the banks that took the trouble out of their busy schedule to respond to my
questionnaire I wouldn‟t have made it without your support.
To my brothers and sisters Charlotte Chelangat, Rose Chepkirui, Gilbert C. Rono,
Moses Rono, Abraham Chepkwony, Emmanuel Kipngeno and my niece Hope
Chepkorir for their abundant support.
To all those who assisted me during data collection , especially Brain Awori and Sammy
Rere and anyone whose name I haven‟t mentioned but did assist.
To all You I say God Bless You.
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ABSTRACT
Computer aided decision support systems came into the lime light in the 1950s and 60s
performing basics tasks as updating accounts payables, analyzing sales etc. Decision
making process in the current corporate world has ceased to be a matter of intuition but
rather an informed process in the sense that many companies employ the use of highly
advanced decision making tools that involve automating the actual decision steps that a
person would take in order to make a good decision. These tools are the decision support
systems. They are management information systems that utilize the use of model bases to
explore and evaluate between decision alternatives. Loan allocation process is a sensitive
and core to the business of any financial institution and needs a highly advanced way of
assessing who to give loans, what amount of loan, a reasonable repayment period among
other considerations.
DSS are becoming the backbone of loan allocation in most financial institutions in Kenya
due to their computational and statistical model of decision aid. This is because of the
corporate investment and reliance on these systems which they have used for competitive
advantage in their loan allocation process as well as reducing the duration of loan
processing. In the long run their use enhances organizational planning in terms of loan
disbursement reduction of operation cost as well as ease of decision making. It is
therefore critical that the implementation of these systems be successful. However,
despite the proliferation of computers, the implementations of these systems remain a
complex issue (Ginzberg, 1981; Lucas, et al.,1990; Tait and Vessey, 1989 )
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Table of Contents
Declaration......................................................................................................................... ii
Dedication ......................................................................................................................... iii
Acknowledgements .......................................................................................................... iv
Abstract .............................................................................................................................. v
List of tables...................................................................................................................... ix
Abbreviations .................................................................................................................... x
CHAPTER ONE: INTRODUCTION ............................................................................. 1
1.1 Background ................................................................................................................... 1
1.1.1 Decision Support Systems ......................................................................................... 2
1.1.2 Loan Allocation ......................................................................................................... 2
1.1.3 Commercial Banks of Kenya ..................................................................................... 3
1.2 Problem Statement ........................................................................................................ 4
1.3Research Questions ........................................................................................................ 5
1.4 Objectives ..................................................................................................................... 5
1.5 Importance of the Study ................................................................................................ 5
CHAPTER TWO: LITERATURE REVIEW ................................................................ 6
2.1 Information Systems Implementation ...................................................................... 6
2.2 Benefits of DSS........................................................................................................ 7
2.2.1 Improving Personal Efficiency............................................................................. 7
2.2.2 Expediting Problem Solving ................................................................................ 7
2.2.3 Facilitating Interpersonal Communication ........................................................... 8
2.2.4 Promoting Learning.............................................................................................. 9
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2.2.5 Increasing Organizational Control ....................................................................... 9
2.3 Challenges of DSS Implementation ....................................................................... 10
2.3.1 Lack of Top Management Support .................................................................... 10
2.3.2 User Resistance .................................................................................................. 10
2.3.3 Lack of User Involvement .................................................................................. 11
2.3.4 Lack of Technological know how ...................................................................... 12
2.3.5 Technical Deficiency ............................................................................................... 12
2.3.6 Communication Barrier ...................................................................................... 13
2.3.7 Turnover among Implementer and Users ........................................................... 13
2.3.8 Improper Change Management Approach ......................................................... 14
2.4 Conclusion ............................................................................................................. 14
2.5 Conceptual framework ........................................................................................... 15
CHAPTER THREE: RESEARCH METHODOLOGY ............................................. 16
3.1 Research Design..................................................................................................... 16
3.2 Population .............................................................................................................. 16
3.4 Data Collection ...................................................................................................... 16
3.5 Data Analysis ......................................................................................................... 17
CHAPTER FOUR: DATA ANALYSIS AND PRESENTATION.............................. 18
4.1 Introduction ................................................................................................................. 18
4.2 General Company information ................................................................................... 18
4.3 Benefits of DSS........................................................................................................... 19
4.4 Challenges of DSS implementation ........................................................................... 22
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CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS ......................... 26
5.1 Introduction ............................................................................................................ 26
5.2 Conclusion .................................................................................................................. 26
5.3 Recommendations ....................................................................................................... 27
5.4 Limitations of the study .............................................................................................. 28
5.5 Suggestions for further study ...................................................................................... 28
References ......................................................................................................................... 29
APPENDIX I .................................................................................................................... 31
QUESIONNAIRE ............................................................................................................. 31
APPENDIX II ................................................................................................................... 37
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List of tables
Table 1: what necessitated the implementation? .............................................................. 19
Table 2: Benefits of DSS implementation in loans allocation .......................................... 20
Table 3: Challenges of DSS implementation .................................................................... 22
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ABBREVIATIONS
DSS : Decision support system
IS : Information Systems
IT : Information Technology
CHAPTER ONE: INTRODUCTION
1.1 Background
Information systems use has evolved over the years to match up with changes in
organizational challenges from being operational tools to being used for strategic
purposes. Organizations both global and local have employed the use of information
systems to achieve a competitive edge against its competitors in the industry. Critical to
most information systems are information technologies, which are typically designed to
enable humans to perform tasks for which the human brain is not well suited, such as:
handling large amounts of information, performing complex calculations, and controlling
many simultaneous processes and aiding in decision making process (Silver et al, 1995).
According to Porter, (1998) the recent rapid technological change in information systems
is having a profound impact on competitive advantage because of the pervasive role in
value chain, scheduling, controlling optimizing, measuring activities of the organization.
Therefore it is important to align IS strategies with the overall business strategy so as to
gain competitive advantage from such system (Clarke, 2001).
Decision making process in the current corporate world has ceased to be a matter of
intuition but rather an informed process in the sense that many companies employ the use
of highly advanced decision making tools that involve automating the actual decision
steps that a person would take in order to make a good decision. “Effective decision
making requires consideration of the criteria influencing the decision. It is often the case
that the facts which initially appear important when working within a semi structured or
unstructured decision situation are not the ones that, after they are explored by the
decision maker, turn out to be the most influential in affecting decision outcome while
efficiency in decision making addresses the means for performing a given defined task in
order to achieve output as well as possible, relative to some predefined performance
criteria. ” (Keen and Scott Morton‟s study (as cited by John l. Bennett (1983) ).
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1.1.1 Decision Support Systems
Decision support systems are interactive, computer based information systems that use
decision models and specialized databases to assist the decision making process of
managerial end user. Obrien (1999). Keen and Scott Morton, 1978; Sprague and Carlson,
1982; Turban, 1995 (as cited by Ting-Peng Liang and Shin-Yuan Hung).stated that a
typical decision support system must meet the following criteria: Support but not replace
decision makers, Tackle semi-structured decision problems; and Focus on decision
effectiveness, not efficiency (Keen and Scott Morton, 1978; Sprague and Carlson, 1982;
Turban, 1995).
According to Raggad (1997) “DSS usefulness may be assessed by studying problem
structure and problem solutions. DSS is useful, for example, for those decision situations
that are semi structured or unstructured, difficult, interdependent, uncertain, and non-
routine”. In order to support these decisions, DSS require data which they acquire from
other systems like transaction processing system and office support systems (Lucey,
2005)
Like any other IS, DSS follow all the development lifecycle to the implementation stage.
It is at this stage that this research wants focus on to explore Challenges that affect DSS
implementation. Some of the renown challenges that affect successful systems
implementation include: Lack of management support, Lack of user involvement, User
resistance, Improper change management approach, Lack of Skills and technological
know how to implement it, Communication barrier between end user and developer,
Security problems.
1.1.2 Loan Allocation
Loans are the largest single source of income for banks providing more than 50% of total
bank revenue today (Thomas, 2006). Bank loans unlike securities often involve a
personal relationship between banker and borrower. Usually the connection is established
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through opening an account with the bank (Bion, 1953). These loans vary in order of
magnitude from real estate, business and consumer loans. Before a client can be given a
loan some details need to be made available these include: Sourcing details like Sales
Persons, Customer, Co-Applicants & Guarantor Details, Customer/Guarantor
Demographic & Financial Details, Asset Details, Property Details and Credit Card
Details, Work/Business Details, Corporate Management Details, Relationship with the
Bank, Exposure details with the bank, Note Pad Facility
Disbursement of loan is the last stage in the loan cycle. After the loan is approved (in the
underwriting stage), details of the application move to LMS for the management of loan.
This process is known as initiating loan disbursement. The payment of loan amount is
released to the customer in LMS .
1.1.3 Commercial Banks of Kenya
Commercial Banks and Mortgage Finance Institutions are licensed and regulated
pursuant to the provisions of the Banking Act and the Regulations and Prudential
Guidelines issued there under. They are the dominant players in the Kenyan Banking
system and closer attention is paid to them while conducting off-site and on-site
surveillance to ensure that they are in compliance with the laws and regulations.
Currently there are 44 licensed commercial banks and 1 mortgage finance company as
listed in the central bank of Kenya register (www. centralbank.go.ke/financialsystem
/banks/Register.aspx). These are the Commercial Banks responsible for conducting
banking services which include disbursing loan to its client. Commercial banks obtain
funds by accepting deposits, borrowing from nondeposit sources and issuing equity
claims (bank capital). Banks use the funds mainly to make loans and purchase securities
(Thomas, 2006).
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1.2 Problem Statement
Loan allocation decisions are majorly unstructured hence would warrant the use of
decision support system in expediting loan allocation to clients. This can be achieved by
using some of the DSS analysis like the “what if analysis”. According to (Lucey2005)), a
user may wish to investigate how great a loan to take out from a bank, the model enables
the user to perform what if analysis by making changes to variables like loan amount,
interest rate and the number of repayment years. By altering any of the variables, the user
will get tabulated report of the repayment rate at various time periods.
Similarly DSS can also perform sensitivity analysis by changing the value of only one
variable and repeatedly displays the changes in the other variables. Goal seeking analysis
enable the user to set targets for one variable the repeatedly changes the values of the
other variables until the target value is achieved (Lucey, 2005))
Implementation - more accurately, the lack thereof - has been a significant problem
throughout the history of computer based systems (alters 1980). Even the most
beautifully designed system can fail due to reasons of insufficient top management
support, user dissatisfaction among others. These reasons are responsible for Information
systems having the highest systems failure rate due to several implementation factors.
The main role of employing the use of information systems in an organization is to
manage operations as well as gain strategic competitive edge over other players in the
industry Obrien(1993 pg 35) . This can only be achieved if the systems are properly
implemented in order to exploit their maximum potential. Apparently this is not the case,
as an example, ERPs are said to have a 70% failure rate according to Vidyaranya B.
Gargeya and Cydnee Brady. Being among the highest revenue earner in the Kenyan
economy, contributions towards future success in DSS implementation in this sector
would mean better service provision for their client as well as improved revenues to both
the commercial banks and the country. In that respect, this study intends to document the
challenges facing commercial banks in Kenya in their implementation of DSS in loan
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allocation. In relation to the commercial bank sector in Kenya, a number of researches
have been done in regards to information systems but I have not come across any
research in relation to DSS implementation.
1.3Research Questions
While trying to achieve the above listed objectives the researcher asks the following
questions;
i. What are the some of the benefits to loan allocation in commercial banks that
arise from implementing a DSS?
ii. How do commercial banks in Kenya handle challenges in the implementation of
DSS?
1.4 Objectives
The main objectives of the study are;
i. To examine the benefits of DSS in loan allocation among commercial banks in
Kenya
ii. To identify challenges that may hamper the successful implementation of
Decision Support Systems in loan Allocation by Commercial banks in Kenya.
1.5 Importance of the Study
This study will be of great importance to any commercial banks intending to implement a
Decision Support System since
a. The institutions under study are a sample of those Commercial Banks that have
already implemented a Decision Support System. Hence the findings will help
any other commercial banks intending to implement a DSS to identify root
challenges and learn how to address these challenges.
b. The findings in this study will provide a platform for future studies in the same
field and provide other scholars with a basis of further research.
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CHAPTER TWO: LITERATURE REVIEW
2.1 Information Systems Implementation
According to Alter (1980), “an appreciation of the kind of things that can go wrong
should help future implementers anticipate and avoid similar pitfalls.” In so doing
understanding the challenges facing general information systems implementation, will
afford us an understanding of the challenges facing DSS implementation in the financial
industry.
This in turn will help other institutions intending to implement a DSS to steer clear of the
already known deterherent factors to success. Implementation of a new system involves a
number of activities as Dawson (2005) tells us some of which if ignored will cause a lot
of problems to the system. These activities include: acceptance testing by the user,
training of the users, setting up data files, documentation etc.
Several DSS have been designed and implemented an example is the clinical decision
support system for medical diagnosis. Other examples include concerns the Canadian
National Railway system, which tests its equipment on a regular basis using a decision
support system. A problem faced by any railroad is worn-out or defective rails, which can
result in hundreds of derailments per year. Under a DSS, CN managed to decrease the
incidence of derailments at the same time other companies were experiencing an
increase. DSS are also prevalent in forest management where the long planning time
frame demands specific requirements. All aspects of Forest management, from log
transportation, harvest scheduling to sustainability and ecosystem protection have been
addressed by modern DSSs. The DSSAT4 package, developed through financial support
of USAID during the 80's and 90's, has allowed rapid assessment of several agricultural
production systems around the world to facilitate decision-making at the farm and policy
levels. There are, however, many constraints to the successful adoption on DSS in
agriculture.
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2.2 Benefits of DSS
Alters, 1980 states that the main benefit of a DSS to an organization is to increase
individual effectiveness through; Improving Personal Efficiency, Expediting Problem
Solving, Facilitating Interpersonal Communication, Promoting Learning, Increasing
Organizational Control,
2.2.1 Improving Personal Efficiency
Tasks that would take time being done by an employee can be accomplished by the
system in a fraction of the time without errors hence giving the employee time to attend
to clients. According to Power (2002) this is achieved by eliminating value chain
activities for example a bank may use a DSS to consolidate the number of steps and
minimize the number of staff hours needed to approve a loan.
DSS can improve communication and collaboration among decision makers. In
appropriate circumstances, communications-driven and group DSS have had this impact.
Model-driven DSS provide a means for sharing facts and assumptions. Data-driven DSS
make "one version of the truth" about company operations available to managers and
hence can encourage fact-based decision making. Improved data accessibility is often a
major motivation for building a data-driven DSS. This advantage has not been adequately
demonstrated for most types of DSS Power (2007).
2.2.2 Expediting Problem Solving
A DSS can expedite problem solving by providing rapid turnaround, increased accuracy,
and detailed examination of different scenarios and provide consistency. sensitivity
analysis and DSS capabilities such as what if and goal seeking are all instrumental in
accelerating problem solving process Bidgoli (1998).With the centralization of
information in a DSS, it is always easy to track how a similar problem was solved
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previously hence giving the user a upper hand in tackling a problem at hand in a
consistent way with the set company standards.
The ability to make business decisions based on tightly focused, fact-based analysis is
emerging as a measurable competitive edge in the global economy." Further Davenport
said "Organizations that fail to invest in the proper analytic technologies will be unable to
compete in a fact-based business environment." Davenport's conclusions are based upon
interviews with 40 C-level executives and directors at 25 globally competitive
organizations. Analytic technologies can provide organizations a competitive edge. Power
(2005)
2.2.3 Facilitating Interpersonal Communication
DSS models enable several departments to put their plans in the system and merge them
into the organizations targets and goals. This happens mainly in things like budgets where
every department can post theirs into the system and see how they fit together in the
master budget. This eases communication across departments and provide them with
tools of persuasion to enable them negotiate across departments and organizational
boundaries Bidgoli (1998). Philips (1992) states that this gives all contributors especially
in a group decision support system a framework for interaction and negotiation in order
to arrive at a decision.
Communications-driven decision support systems can remedy a number of problems
associated with group communication and group decision making. The most basic
systems address the problems of reducing communication barriers and emphasize
improving interpersonal communication, increasing idea generation, facilitating
discussion, and sharing ideas. More sophisticated systems add decision support models
and group decision techniques to enhance the group evaluation and choice process. Power
(2009).
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2.2.4 Promoting Learning
Some DSS are designed such that they foster organizational or personal learning resulting
in a better understanding of the business environment. Through the learning employees
can better perform their duties which in turn boost their work esteem hence achievement
of organizational targets. According to John T Mentzer information systems are valuable
in promoting learning through information acquisition, dissemination and shared
interpretation of information across functions within the firm. This will enable improve
employee effectiveness in the organization by leveling the information platform from
which they operate hence improve their judgment and consequently decision making
ability.
Learning can occur as a by-product of initial and ongoing use of a DSS. Two types of
learning seem to occur: learning of new concepts and the development of a better factual
understanding of the business and decision making environment. Some DSS serve as "de
facto" training tools for new employees. This potential advantage has not been adequately
examined Power (2007).
2.2.5 Increasing Organizational Control
Apart from assisting employees in performing their duties, DSS also provide data for the
purpose of organizational control. In a credit scoring scenario, the purpose is to ensure
that the credit officer doesn‟t accept too many risky loans or rejecting too many marginal
but secure loans. According to Power (2009), data driven DSS often makes business
transaction data available for performance monitoring and ad hoc querying. Such systems
enhances management understanding of business operations hence maintain control of
the organizations operations.
In addition, organizations are confronted with increasing amounts of sensitive data.
Simple encryption solutions just don‟t get the job done. In order to maintain control over
sensitive data without placing additional pressure on your IT infrastructure, you need a
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solution that can secure increasing volumes of data, while ensuring your organization
remains in control.
2.3 Challenges of DSS Implementation
Some of the renown challenges that affect successful systems implementation include:
Lack of management support, Lack of user involvement, User resistance, Improper
change management approach, Lack of Skills and technological know how to implement
it, Communication barrier between end user and developer, Security problems.
2.3.1 Lack of Top Management Support
Top management support is key in any information systems project development and
implementation. In order for any system to be implemented successfully it has to have the
backing of the top management (Kuruppuarachchi, mandal, smith 2002), Since they
provide the financing and other top level support for the system say rallying support from
lower levels of the organization in support of the system .
Cost benefit analysis have majorly become a measure by which top management
determines whether or not to authorize the implementation of the system. In the financial
sector it is not possible to implement one system after the other because of the nature of
their operation which dictates the downtime that the organization can.
2.3.2 User Resistance
User resistances to a system usually occur as a result of fear of the new way of doing
thing Obrien (1993). These fears are confirmed when conceptual design problems arise,
as a result of designers of DSS being over optimistic in regard to the users‟ ability to
figure out how to use the system. According to (Wakefield, 2004), in order for a system
to steer clear of some technical issues that may hinder its success, a parallel people
project should run along the technical project.
This aims at making the impact of the new system on the organization as positive as
possible by ensuring that users and managers understand the objectives of the new system
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and are committed to achieving them, they have a realistic expectation of what the system
do and what is required of them in order for that to be achieved, proper end user
education as well as improved communication between IS professionals and the end users
of the system. By so doing, the risk of users resisting the system after Implementation
will not arise since they participated in part.
2.3.3 Lack of User Involvement
Lack of user involvement can only mean one thing in systems implementation and that is
system requirement may not be correctly taken. According to Sharda (2007), it is
important for user to be involved throughout the development of a system in order to
achieve success at the implantation stage of the system. They further state that this will
encourage users and developer to learn about decision making; the ill structured; complex
problem; and the technologies that can potentially be applied to solve the problem. It also
provides an early opportunity for the IS specialist to start giving the participating users
training on the system hence reducing user resistance to the system.
It also provides an umbrella that ensures that users in other departments get the
information they used to get in the old system hence they are not left out of touch Awad
(1997). Involvement of user gives them a sense of ownership Obrien (1993) of the system
since their ideas are reflected in the system which in turn makes them own the system
even if it may have small problems here and there. A meta-analysis of 144 findings
concluded that user-situational variables (involvement, training, and experience) are more
important than cognitive styles, personality, and demographics Alavi and Joachimsthaler,
(1992) as cited by (Eom)
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2.3.4 Lack of Technological know how
Lack of Skills and technological know how to implement it. The mere nature of DSS
being complex systems is enough to dictate the level of expertise required of the
implementer. According to Alters 1980, gross incompetence by implementers of the
system will tend to lead to bad results. In an analogy he say a general medical practitioner
might not perform particularly well if he is called on to perform brain surgery. Systems
implementation is not a day to day activity hence it is a requirement that it be done by a
specialist or not at all. This is because of the cost implication of implementing a regular
system is by far too harsh to the organization leave alone a DSS. In an event that the DSS
fails, it becomes a double blow to the organization especially if you are dealing with a
commercial banks.
2.3.5 Technical Deficiency
Technical deficiency is having technology that is way out of date for a new system. Alter
1980 states that, In order to understand the technical challenges encountered, it is
important to distinguish between technical “constraints‟ and technical “problems”. The
nature and scope of all Decision support systems are constrained by the available
technology.
When applied to small systems, the technology is not a constraint since the existing
technology is powerful enough to drive the system but when applied to very complex
models and broad databases, the existing technology could become a binding constraint.
In this regard technological assessment needs to be carried before implementing the
system to determine the whether the hardware resource that is in place can support the
new system as well as examine the network resource available to ensure it is of sufficient
capacity and connect all parties involved.
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2.3.6 Communication Barrier
Communication barrier between end user and developer usually results in the “Tower of
Babel” scenarios once the system is ready for implementation. When implementing a
system where the specifications are not clear, the system usually ends up being a
disappointment. Similarly, if the requirements are collected from a large number of users,
it will makes it difficult for the developer to capture all those requirements into a single
system in the limited timeframe usually provided by organization for the project to be
completed.
Obrien (1993) and Awad (1997) both give a pictorial explanation of what actually goes
on in the system procedure when there is communication breakdown in to six sentences
as follows first the requirements as proposed by the sponsor, as specified in the project
request, as designed by the senior analyst, as produced by the programmer, as installed at
the user‟s site and finally what the user actually wanted. These sentences depict a serious
breakdown in that none is even close to what the user wants.
2.3.7 Turnover among Implementer and Users
According to Alter 1980, Turnover among user and implementer of the system is
frequently cited as a serious problem in the development and use of the system. This is
because there is of lack of continuity during implementation. The successor of the
implementer takes over the system but the users who gave the requirements have moved
elsewhere hence no one to corroborate the system specifications. This scenario could lead
to a system stalling at implementation or even stopping.
These calls for proper planning at the initiation stage of the project so that all concerned
parties are committed to the system within the duration that the system is being
development and deployed on site. Tracking changes in a system could ensure success if
you can find someone to back those changes.
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2.3.8 Improper Change Management Approach
Improper change management approach will definitely affect the implementation of a
new system in that it may result in the reengineering or redesign of business processes of
an organization. Some of this new ways of doing things often instill fear in employees
hence build resistance. Obrien (1997) suggests that change management activities aimed
at innovative ways to measure, motivate and reward performance .This is particularly of
great concern to this study since the implementation of a DSS in loan allocation in a
commercial banks means that the culture that used to exist in loan allocation will have to
change. It is upon the implementer to take care of the business culture of the end user if
the success of the project if to be realized.
2.4 Conclusion
Information systems consists of a set of people, procedures and resources that collects
transforms and disseminates information in an organization (Obrien, 1983). These
systems can either be manual systems or computerized systems. Computerized
information systems consist of hardware, software, telecommunication and other
information technologies that transform data into a variety of information resource.
Information systems are classified in respect to their functions. According to James A.
Obrien; there are two main classifications these are operational information systems and
Management information systems. Under Management information systems (those that
support managerial decision making), are information reporting system, Decision support
systems and Executive information systems knowledge management systems, and
database management systems. While under operational information systems (those that
support business operation), are Transaction processing system, Process control systems,
and Office automation systems.
DSS stand out from the other types of IS because of their ability to support unstructured
decision and that‟s why they are preferred compared to the rest in loan allocation process.
There are five classifications of DSS; Communication-driven DSS are used to help users
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to collaborate; Data-driven DSS are used to query a database or data warehouse to seek
specific answers for specific purposes. It is deployed via a main frame system,
client/server link, or via the web; Document-driven DSS are used to search web pages
and find documents on a specific set of keywords or search terms; Knowledge-driven
DSS or „knowledgebase‟ are essentially used to provide management advice or to choose
products/services; Model-driven DSS are complex systems that help analyze decisions
or choose between different options. These are used by managers and staff members of a
business, or people who interact with the organization, for a number of purposes
depending on how the model is set up - scheduling, decision analyses etc.
2.5 Conceptual framework
The conceptual framework below depicts the IS value at the loan allocation (dependent
variable) and is largely affected by successful implementation of DSS.
The Challenges that affect successful implementation of DSS (independent variable)
include; Lack of management support, Lack of user involvement, User resistance,
Improper change management approach, Lack of Skills and technological know how to
implement it, Communication barrier between end user and developer, Security
problems.
Affect
Challenges in DSS implementation
Lack of management support,
Lack of user involvement,
User resistance,
Improper change management approach,
Lack of Skills and technological know how to
implement it,
Communication barrier
Security problems.
DSS in loan allocation
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CHAPTER THREE: RESEARCH METHODOLOGY
3.1 Research Design
This study is a survey of the challenges facing commercial banks in their DSS
implementation. This research basically generated quantitative data due to the nature of
the study being challenges facing commercial banks in their DSS implementation. This is
a descriptive topic which will require the respondents to report the way things are.
Descriptive research portrays an accurate profile of persons, events or situation Robson
2002(as cited by Thornhill 2007). This research portrays the situation at the loans
allocation DSS implementation hence this was the most appropriate research design. It is
popular with business studies because they allow the collection of large amounts of data
from a sizeable population in an economical way. Considering the time constraint and the
vast size of the population this is the most appropriate strategy.
3.2 Population
The study targets all the 44 commercial bank in Kenya most of which are based in
Nairobi. ICT managers of the 44 banks were the main respondents to the questionnaire.
The survey answered the questions who, what, where, how etc Thornhill (2007). The
main reason of carrying out the census survey is because of the size of the population in
question as well as the fact that there are no random errors or systematic errors caused by
sampling in addition; it often results in enough respondents to have a high degree of
statistical confidence in the survey results
3.4 Data Collection
This study relied on primary data. The instrument of collecting this data was a self
administered questionnaire that was be delivered to the respondents and collected at a
later date once it is filled. This afforded the respondents the time to answer the questions
at their own pace as well as give them time to think through the questions and recollect
events in case the process in question was completed awhile ago. The questionnaire had
an assortment of questions both closed and open ended. The closed positioned the
17
respondent to the actual topic while the open ended ones gave them the flexibility they
needed to highlight the issues asked.
3.5 Data Analysis
First the data was checked for accuracy and completeness. Then the data was coded and
entered into Statistical Package for the Social Sciences which helped in performing
analytical induction on the data collected by performing descriptive statistics mostly
using the mean and the standard deviation to show the level of consensus in the industry
on parameters that were used to test the benefits and challenges of DSS implementation,
and to show the level of dispersion on a particular parameter.
18
CHAPTER FOUR: DATA ANALYSIS AND PRESENTATION
4.1 Introduction
This chapter involves analysis, discussion and presentation of the research findings. The
questionnaire used in the study was divided into three sections. The aim of the study was
to identify challenges that may hamper the successful implementation of Decision
Support Systems in loan Allocation by Commercial banks in Kenya as well as examine
the benefits of resultant successful implementation. Section one gives general
information about the organization responding to the questionnaire, section two addresses
the benefits of DSS in loans allocation and section three addresses the challenges
involved in Decision Support Systems implementation.
Out of the 44 commercial banks that the research targeted, 28 responded to the
questionnaire making the gaining a response of 63.6% with 28 out of the total 44
commercial banks.
4.2 General Company information
Data in this part of the questionnaire were analyzed using frequency distributions and
percentages to determine where most banks had a strong desire to have a DSS installed
with installations being as old as 1995 and the most recent being in 2010. Out of the 28
respondents, 12 were part of the team that implemented the system constituting 42.9% of
the total response.16 of the respondents constituting 57.1% of the total respondents were
not part of the teams but responded from either company documented sources.
Out of all the questions that were asked in this section the one that the research was very
keen on was the reason for DSS implementation. This is because it was giving an insight
or justification for the need of a DSS implementation in loans allocation. The data is
analyzed is provided in the table 1 below.
19
Table 1: what necessitated the implementation?
Frequency Percent Valid Percent Cumulative Percent
Valid None 3 10.7 10.7 10.7
Increased volumes and desire for accuracy 5 17.9 17.9 28.6
Need for a comprehensive loan allocation module
2 7.1 7.1 35.7
Need to provide better customer service 6 21.4 21.4 57.1
Need to make loan allocation more efficient 12 42.9 42.9 100.0
Total 28 100.0 100.0
From table 1, 10.7 % of the banks did not say what necessitated their installation
while 17.9% were pushed by increased volumes and desire for accuracy.7.1% of the
banks needed a comprehensive loan allocation module while 21.4% needed software
that would help them provide better services to their customers. The highest
percentage 42.9% which constituted 12 banks wanted to make their loans allocation
process more efficient than the way they were doing it before the implementation.
The above deductions show that on implementation of the system, close to half of the
organizations were in dire need for the system to improve their efficiency or were
unsatisfied about the system that they were using. Similarly need to provide better
services to customers was rated very highly and this shows that banks wanted to
provide services that would give them a competitive edge against it competitors if
they improved their customer satisfaction.
4.3 Benefits of DSS
A 4 point likert scale was used as the basis of measurement, where 4 represented big
impact, 3 significant impact, 2 small impact and 1 represented no impact. The various
responses were averaged to arrive at mean score. A standard deviation was computed
to indicate how responses varied from one respondent to the other. A standard
deviation of less than one indicate consensus among the respondents and a standard
deviation of greater than one indicate considerable dispersion in responses obtained.
20
Table 2: Benefits of DSS implementation in loans allocation
N Minimum Maximum Mean Std. Deviation
Central access 28 2 4 2.86 .756
Central distribution of information 28 1 4 2.82 .819
Creates a competitive advantage over competition
28 1 4 2.79 .917
Easy backup 28 2 4 3.11 .832
Easy customer trait identification 28 2 4 2.96 .793
Easy record-keeping 28 2 4 3.11 .832
Easy tax preparation 28 2 4 2.96 .744
Encourages Decentralization 28 2 4 3.11 .786
Encourages exploration and discovery on the part of the decision maker
28 2 4 3.04 .838
Expedites problem solving 28 3 3 3.00 .000
Facilitates interpersonal communication 28 2 4 3.25 .844
Facilitates planning 28 2 4 3.21 .686
Generates new evidence in support of a decision 28 2 4 2.89 .685
Helps automate the managerial processes. 28 2 4 3.14 .756
Improves personal efficiency 28 2 4 3.36 .731
Increases organizational control 28 2 4 3.14 .756
Minimizes information overload 28 2 4 3.25 .752
Promotes learning or training 28 2 4 3.14 .848
Reveals new approaches to thinking about the problem space
28 2 4 3.00 .816
Easy Collection of data 28 0 4 1.18 1.744
Easy Creation of report from data collected 28 0 4 1.18 1.744
Valid N (listwise) 28
21
The respondent unanimously agreed that DSS Expedites problem solving in that the
standard deviation of this parameter was 0.00 and it had a mean of 3.00 meaning the
impact of having a decision support system was significant to the organization. Similarly
improves personal efficiency registered the highest mean with 3.36 meaning DSS
implementation have significant impact in improving personal efficiency. The standard
deviation on the same was 0.731 meaning most respondents were in agreement.
Facilitates interpersonal Communication and Minimizes information overload had a
mean of 3.25 giving the indication that it had significant impact in most organization with
the former having a standard deviation of 0.844 and the latter 0.752. Both standard
deviations are less than 1 implying that there was close consensus among respondent on
these parameters. Facilitates planning had a mean of 3.21 meaning the use of DSS in
planning is significant in an organization. Furthermore, it had a standard deviation of
0.686 implying that most of the respondents had a close agreement on this parameter
Easy backup, Easy record-keeping and Encourages Decentralization had a mean of 3.11
implying that most organizations were in agreement that DSS encourages
decentralization, ease record keeping and backup. This is further proven by the margin
difference in their standard deviations of 0.832, 0.832 and 0.786. This indicates that these
organizations were in close consensus of the same.
Some of the parameters that scored lowly included; Generates new evidence in support of
a decision, Easy tax preparation, Easy customer trait identification Creates a competitive
advantage over competition, Central access, Central distribution of information which
had the following means 2.89, 2.96, 2.96, 2.79, 2.82, 2.86 respectively. This means imply
that the responding organizations felt that DSS were strong on delivering on other
parameters but had a small impact on the above listed ones. These deduction is supported
by the standard deviation of the same parameters which scored high less than 1 i.e 0.685,
.744, .793, .917, .819, .756 denoting the high level of agreement that DSS
implementation will have a small impact in respect to these parameters.
22
Lastly some parameters namely; Easy Collection of data, Easy Creation of report from
data collected registered the least mean of 1.18 hence falling in the category no impact.
Their standard deviation is 1.744 hence implying there were very few respondent in who
thought that these parameters had an impact in their organization hence very few rated it
strongly or none at all. From this we deduce that DSS would be of better benefit to the
organization in expediting problem solving but of least impact in collection of data.
4.4 Challenges of DSS implementation
In analyzing challenges facing DSS implementation in loans allocation, A 3 point
likert scale was used as the basis of measurement, where 3 represented Strong
Challenge, 2 Manageable Challenge and 1 represented No Challenge at all . The
various responses were averaged which resulted in a mean score. A standard
deviation was computed to indicate how responses varied from one respondent to the
other. A standard deviation of less than one indicate consensus among the
respondents and a standard deviation of greater than one indicate considerable
dispersion in responses obtained.
Table 3: Challenges of DSS implementation
N Minimum Maximum Mean Std. Deviation
Availability of technical support 28 2 3 2.25 .441
Comfort Zones 28 2 3 2.50 .509
Communication barrier between end user and developer
28 2 3 2.50 .509
Compliance to standards 28 2 3 2.25 .441
Cost implications 28 1 2 1.75 .441
Data migration 28 1 3 2.00 .720
Degree of diffusion of technologies 28 1 2 1.50 .509
External relationships(electronic data interchange)
28 1 3 2.00 .720
Failure to match systems needs to system requirements
28 1 3 2.00 .720
Government regulation 28 1 3 2.25
.844
23
Improper change management approach 28 1 3 2.25 .844
Interaction of technology and the organization 28 2 3 2.25 .441
Lack of attention from senior executives 28 2 3 2.50 .509
Lack of education and training. 28 1 3 2.00 .720
Lack of management support 28 2 3 2.50 .509
Lack of proper planning 28 2 3 2.75 .441
Lack of proper technology adaptation 28 2 3 2.25 .441
Lack of resources allocation from senior management
28 1 3 2.00 .720
Lack of Skills and technological know how to implement it
28 2 3 2.25 .441
Lack of team work and cooperation. 28 2 3 2.25 .441
Lack of understanding of the potential benefits 28 2 3 2.75 .441
Lack of user involvement 28 1 2 1.50 .509
Organization size 28 1 3 1.75 .844
Organizational culture 28 2 3 2.75 .441
Organizational dependence to the technology
28 2 3 2.25 .441
Organizational politic 28 1 3 2.00 .720
Overall competition in the industry 28 2 3 2.25 .441
Poor attitude towards quality improvement. 28 2 3 2.25 .441
Productivity paradox 28 2 3 2.25 .441
Quick technology advancements 28 1 3 2.25 .844
Sacred Cow Systems 28 1 3 2.00 1.018
Security problems 28 2 2 2.00 .000
Turnover among Implementer and Users 28 2 3 2.25 .441
User expectation 28 2 2 2.00 .000
User resistance 28 1 2 1.75 .441
Complex user needs 28 0 3 .75 1.323
Valid N (listwise) 28
24
From the data above the following deductions can be made. User expectation, Security
problems were unanimously considered manageable challenges by all Banks. This is so
because the parameter gained a mean of two hence putting it under manageable challenge
category. The interesting thing here is the level of consensus. All banks agree that user
expectation and security issues are manageable through the 0.00 standard deviation
meaning there was a 100% agreement on those parameters being manageable threats.
No challenge was rated as a strong challenge to the banking industry. This is so because
no challenge had a mean of three. This doesn‟t mean that no banks faced any strong
challenges but cumulatively they could not cite one challenge that posed as a strong
challenge to the industry as a whole. But a few challenges came close this include;
organizational culture, Lack of understanding of the potential benefits, Lack of proper
planning which all had a mean of 2.75 this means that at least a good number of the
respondents faced this as a strong challenge that‟s why the mean was almost 3.0. But also
there was a 6 to 4 consensus on these parameters as depicted by the standard deviation of
0.441.these indicated for every 10 banks 6faced the above challenges strongly while 4
didn‟t.
With the legalization of credit information sharing, external relationships (electronic data
interchange), was strong manageable challenge with a mean of 2.0 and a standard
deviation of 0.720. This means 7 out of every 10 respondents felt that this was a
manageable challenge while 3 didn‟t. But that wasn‟t the only parameter that the banks
felt so. Others include; Lack of resources allocation from senior management,
Organizational politic, Lack of education and training, Failure to match systems needs to
system requirements which all had a mean of 2.0 and a standard deviation of 0.720.
There was a near unanimity in the following parameters being manageable challenges.
These are Government regulation, improper change management approach and Quick
technology advancements which had a standard deviation of 0.844 meaning for every 10
banks 8 agreed that the above were manageable while 2 didn‟t. This means government
25
regulations in relation to IS we‟re not oppressive to banks and that technological
advancement wasn‟t rendering their IT infrastructure obsolete more often. It also means
that most banks exercise proper change management approaches in relation to
information systems.
The following parameters were rated as not a challenge to the banks with Degree of
diffusion of technologies and Lack of user involvement having a mean of 1.50. This
means technological diffusion among banks is very high hence they did not consider it a
challenge. This also applies to user involvement. This shows that system implementation
is all inclusive in the banking industry hence wasn‟t a challenge to most banks. Also in
this category are two other parameters which scored slightly higher in mean these are
Cost implications, Organization size and User resistance
26
CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS
5.1 Introduction
This chapter summarizes the findings gathered from the analysis of the data collected as
well as the conclusions drawn from the analysis. The summary has been done in line with
the objectives of the study and the conclusion drawn from the study. Recommendations
are made in light of the findings of the study and areas that more focus is needed for
successful implementation. And like any study, there were limitations to the study which
have been outlined to guide future researchers who wish to carry out research in a similar
area.
The aim of the study was to establish the challenges facing commercial banks in Kenya
in their implementation of decision support systems as well as the benefits that result in a
successful implementation of the same.
5.2 Conclusion
The overall findings of this study indicate that there are significant benefits realized by
companies that have implemented a decision support system in loan allocation in that
issues like expedited problem solving which was among the main locus of the study
didn‟t disappoint the researcher to find out that all commercial banks felt that a decision
support system in loans allocation would expedite problem solving in their operation.
Other factors that were a point of locus to the research include facilitating interpersonal
communication as well as improving interpersonal efficiency also scored very highly an
indication that the said system actually delivers to these parameters.
The challenges the study sort to identify were also confirmed but none was put beyond
the capability of the locally available knowledge base of technically trained personnel.
Implementation issues like user resistant as well as lack of their participation was also
cited as manageable hence showing that there is high confidence in the level of user
awareness and training.
27
On the infrastructure, there was a good indication that the country‟s information
infrastructure as well as those of responding organizations have improved greatly for
these organizations them not to cite it as a challenge that isn‟t easily manageable.
Lastly most organization cited issues of having a sacred cow system as a thing of the past
by scoring over 1 in standard deviation. This shows that a lot of consultation with the
concerned stakeholders goes on before a system is agreed upon for implementation in a
good number of organizations.
5.3 Recommendations
Based on the study, implementation of a decision support system in loans allocation
seems to be the most prudent thing for banks to do in order to provide better services to
their customer hence have an edge in the very competitive industry. In light of the current
developments in the industry where banks are now allowed to share credit information, a
decision support system will be a better system to handle this kind of pressure while
delivering on the intended function of using the information acquired from the shared
credit information analyzing it in line with an application for a loan facility and still
advice on the best decision that a credit officer can make in a short period compared to
having to correspond with other banks using other means which might take a lot of time.
As a means of precaution, there were a few challenges that scored very highly meaning
that it would be important for any bank intending to implement a Decision support
system to take a closer look at them before in the system implementation. Organizational
culture, Lack of understanding of the potential benefits, Lack of proper planning gave an
indication that most organizations had very rigid organizational cultures that conflict with
a system hence posing as a challenge. Planning was also cited as a strong point to put
more emphasis on while implementing a system. Outlining the potential benefits a
system will give yield to goes a long way in marshaling support for the system hence
giving it a high degree of success.
28
5.4 Limitations of the study
The targeted response of the study was 44 commercial banks but only managed to get 28
to respond. This is as a result of some commercial banks citing issues of confidentiality
and sensitive nature of the information as a reason not to respond to the questionnaire.
Also some of the banks cited being too busy as a reason not to respond which also
contributed to the 37.4% none response. Bureaucratic system in other banks made it
impossible to get data from them due to the time available and that it would take for them
to respond to the questionnaire.
5.5 Suggestions for further study
The research identified that some companies implemented their systems over 15 years
ago. In this time many changes have occurred in the information systems field hence a
research can be done to investigate how this old systems are coping the new technologies
of the 21st
century
29
References
Alter, S.L. (1980), Decision Support Systems: Current Practices and Continuing Challenges,
Addison-Wesley, Reading, MA.
Bel G. Raggad Effects of information structure and problem solving on decision-support
system choice
Bion B. Howard, (1953), introduction to business finance p,g 352
Christian W. Dawson, (2005), Projects in computing and information systems
Daniel J Power (2002 ) Decision support systems: concept and resources for managers
Daniel Power (2007), DSSResources.COM
Daniel Power (2009), decision support basics
Daniel Power(2005) DSSResources.COM
Daniel Power(2009) DSSResources.COM
Efraim Turban, Jay E. Aronson, Ting Peng Liang, Ramesh Sharda.(2007),Decision support
Systems and Business Intelligence.
Elias M. Awad(1997), Systems Analysis and Design.
Fred young Philips(1992), thinkwork: Learning, and managing in a computer interactive
society
Gary B.Shelly, Thomas J. Cashman, Harry J. Rosenblatt, systems analysis and design, 2008
pg.483-484
Hossein Bidgoli(1998), intelligent management support systems
Jack R Kapoor,Les R. Dlabay and Robert J. Hughess, Personal finance 1994 pp125
James A. Obrien (1983), Management Information Systems
James A. Obrien (1999), Management Information Systems pg.104
James A. Obrien (1999), Management Information Systems pg.61
John l. Bennett(1983) Building Decision Support Systems
Lloyd B. Thomas( 2006), Money, Banking and Financial Market,
30
Mark S. Silver, M. Lynne Markus, Cynthia Mathis Beath (1995) The Information
Technology Interaction Model: A Foundation for the MBA Core Course, MIS Quarterly,
Vol. 19, No. 3, Special Issue on IS Curricula and Pedagogy (Sep., 1995)
Mark Saunders, Philip Lewis, Adrian Thornhill 2007,Research Methods for Business
Students
Michael E. Porter(1998), competitive advantage: creating and sustaining superior
performance: with a new introduction.
Palitha R.Kuruppuarachchi, Purnendu Manadal and Rose Smith IT Project Implementation
Strategies for effective change: critical review, 2002.
Sean B. Eom Decision support systems research: current state and trends
Steve Clarke(2001), Information Systems Strategic Management: an integrated approach
Terry lucey 2005, Management information Systems pg .285
Ting-Peng Liang and Shin-Yuan Hung, Information Technology & People,Vol. 10 No. 4,
1997, pp. 303-315. © MCB University Press, 0959-3845
Vidyaranya B. Gargeya and Cydnee Brady, Success and failure factors of adopting SAP in
ERP system implementation.
31
APPENDIX I
QUESIONNAIRE
NB This questionnaire consists of three sections section A is to be filled by an IT expert,
Section B by a credit/loans manager/representative and the last section C can be filled by
either of the two respondents. Please fill all sections appropriately.
Name of institution ………………………………………..Title............................…….....
Years worked in that institution…………………………………………………………….
Section A: Implementation of DSS
Do you have a loans allocation decision support system?
Yes [ ] No [ ]
If yes, what is it
called………………………………………………………………………………………
………………………………………………………………………………………………
Is it separate from the main banking system?
Yes [ ] No [ ]
When was it installed?
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
What system were you using prior to the new one?
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
Were you part of the team that carried out the implementation of the new system?
32
Yes [ ] No [ ]
How well is the system performing compared to the old system?
Excellent [ ] Fair [ ] No change [ ] Worse
[ ]
Comment……………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
What necessitated the implementation of the DSS?
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
……………………………………………………………………………………………..
Section B: Benefits of DSS implementation in Loan Allocation
To what extent has your bank realized the following benefits in loan allocation through
the use of DSS? Please rank them by ticking appropriately using the guideline provided
below
1: No impact 2: Small impact 3: Significant impact 4: Big impact
No Benefit 1 2 3 4
1 Improves personal efficiency
2 Expedites problem solving
3 Facilitates interpersonal communication
33
4 Promotes learning or training
5 Increases organizational control
6 Generates new evidence in support of a decision
7 Creates a competitive advantage over competition
8 Encourages exploration and discovery on the part of the
decision maker
9 Reveals new approaches to thinking about the problem space
10 Helps automate the managerial processes.
11 central access
12 easy backup
13 central distribution of information
14 easy record-keeping
15 easy tax preparation
17 Facilitates planning
18 Minimizes information overload
19 Encourages Decentralization
20 easy customer trait identification
Has your organization experienced other benefits from the above listed?
Yes [ ] No [ ]
If yes please list them below
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
34
………………………………………………………………………………………………
………………………………………………………………………………………………
Section C: Challenge of DSS Implementation
The following are the commonly anticipated DSS implementation challenges. Please rank
them by ticking appropriately using the guideline provided below if it was a challenge to
your organization.
3: Strong challenge 2: Manageable Challenge 1: No Challenge at all
NO CHALLENGE 1 2 3
1 Lack of management support
2 Lack of attention from senior executives
3 Lack of resources allocation from senior management
4 Lack of user involvement
5 Lack of education and training.
6 User resistance
7 Lack of team work and cooperation.
8 Improper change management approach
9 Lack of Skills and technological know how to implement it
10 Communication barrier between end user and developer
11 Security problems
12 Productivity paradox
13 Quick technology advancements
14 Comfort Zones
15 Failure to match systems needs to system requirements
16 Lack of proper technology adaptation
17 Sacred Cow Systems
18 Data migration
35
19 Interaction of technology and the organization
20 User expectation
21 Organization size
22 Turnover among Implementer and Users
23 Organizational culture
24 External relationships(electronic data interchange)
25 Cost implications
26 Lack of proper planning
27 Lack of understanding of the potential benefits
28 Poor attitude towards quality improvement.
29 Degree of diffusion of technologies
30 Overall competition in the industry
31 Compliance to standards
32 Organizational dependence to the technology
33 Availability of technical support
34 Organizational politic
35 Government regulation
Has your organization experienced other challenges from the above listed?
Yes [ ] No [ ]
If yes please list them below
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
36
………………………………………………………………………………………………
………………………………………………………………………………………………
Were there any business processes in your department that were affected by the DSS
implementation?
Yes [ ] No [ ]
If yes, can you mention a few?
………………………………………………………………………………………………
………………………………………..……………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………..……………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………..……………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………………………………………………………………
………………………………………..……………………………………………………
37
APPENDIX II
List of Commercial Banks in Kenya
1. Bank of Africa Kenya Ltd.
2. Bank of Baroda (K) Ltd.
3. Bank of India
4. Barclays Bank of Kenya Ltd.
5. CFC Stanbic Bank Ltd.
6. Charterhouse Bank Ltd
7. Chase Bank (K) Ltd.
8. Citibank N.A Kenya
9. Commercial Bank of Africa Ltd.
10. Consolidated Bank of Kenya Ltd.
11. Co-operative Bank of Kenya Ltd.
12. Credit Bank Ltd.
13. Development Bank of Kenya Ltd.
14. Diamond Trust Bank (K) Ltd.
15. Dubai Bank Kenya Ltd.
16. Ecobank Kenya Ltd
17. Equatorial Commercial Bank Ltd.
18. Equity Bank Ltd.
19. Family Bank Ltd
20. Fidelity Commercial Bank Ltd
21. Fina Bank Ltd
22. First community Bank Limited
23. Giro Commercial Bank Ltd.
24. Guardian Bank Ltd
25. Gulf African Bank Limited
26. Habib Bank A.G Zurich
27. Habib Bank Ltd.
28. I & M Bank Ltd
38
29. Imperial Bank Ltd
30. Jamii Bora Bank Ltd.
31. Kenya Commercial Bank Ltd
32. K-Rep Bank Ltd
33. Middle East Bank (K) Ltd
34. Middle East Bank (K) Ltd
35. National Bank of Kenya Ltd
36. NIC Bank Ltd
37. Oriental Commercial Bank Ltd
38. Paramount Universal Bank Ltd
39. Prime Bank Ltd
40. Standard Chartered Bank (K) Ltd
41. Trans-National Bank Ltd
42. UBA Kenya Bank Ltd.
43. Victoria Commercial Bank Ltd