Measuring the Success of Transaction Processing Systems ...dagda.shef.ac.uk/dispub/dissertations/2010-11/External/OTayo... · The critical influence that a TPS has on the success

Measuring the Success of Transaction Processing

Systems: An Adaptation of the DeLone and McLean

Information System Success Model

A study submitted in partial fulfilment of the requirements for the degree of

Master of Science in Information Systems

at

THE UNIVERSITY OF SHEFFIELD

by

TAYO, Oluwasikemi

September 2011

ABSTRACT

Background. Recent research indicates the increasing role of Transaction

Processing Systems as key enablers of vast range of commercial activities and the

critical role they play in the economy. This evolution demands the success of these

systems and the need for Transaction Processing System providers to know the

critical factors that can affect the success of the systems they provide.

Aims. This study aims to test a research model adapted from the DeLone and

McLean Information System Success Model to measure the success of a selected

Transaction Processing System; then compare the results of this test with the

results of DeLone and McLean’s findings.

Methods. DeLone and McLean’s (2003) model and hypotheses were adapted and

extended to the context of this research. The questionnaire was constructed using

validated questions and or instruments to measure the various elements in the

model. Users of the case studied Transaction Processing System were selected to

complete the questionnaire. The questionnaire was piloted with five users and then

distributed online via email. A total of 133 complete responses were received and

analysed.

Results. The results from collected responses show a good fit between the data

collected and the research model with the exception of service quality. All of the

research hypotheses were supported except one (service quality had an inverse

association with user satisfaction). The overall results agreed with DeLone and

McLean’s (2003) findings.

Conclusions. It can be concluded that the system quality and information quality

elements have strong impact on the usefulness and users’ satisfaction elements of

the model while the system importance element had a weaker impact on usefulness

and users’ satisfaction.

ACKNOWLEDGMENTS

I would like to express my heartfelt gratitude to my parents, siblings and their

families for their support during this period.

I would like to express my thanks to my supervisor Prof Val Gillet for her guidance

and advice.

Finally, I would like to express my thanks to all the friends that stood by me during

the dissertation period.

Table of Contents

CHAPTER ONE - INTRODUCTION 1

1.1 RESEARCH BACKGROUND 1

1.2 RESEARCH TOPIC 2

1.3 RESEARCH OBJECTIVES 2

1.4 CASE STUDY COMPANY: INTERSWITCH LIMITED 3

1.5 RESEARCH STRATEGY 3

1.6 DISSERTATION OUTLINE 3

CHAPTER TWO - LITERATURE REVIEW 4

2.1 INTRODUCTION 4

2.2 WHAT IS AN INFORMATION SYSTEM? 4

2.3 TRANSACTION PROCESSING SYSTEMS 6

2.3.1 WHAT IS A TRANSACTION? WHAT IS ACID? 7

2.3.2 TYPES OF TRANSACTION PROCESSING SYSTEMS 8

2.4 MEASURING THE SUCCESS OF INFORMATION SYSTEMS 8

2.4.1 INDIVIDUAL DIFFERENCES AND IS SUCCESS 9

2.4.2 USER INVOLVEMENT AND IS SUCCESS 11

2.4.3 IS SUCCESS: THE QUEST FOR THE DEPENDENT VARIABLE 14

2.4.3.1 System Quality 16

2.4.3.2 Information Quality 16

2.4.3.3 System Use 17

2.4.3.4 User Satisfaction 17

2.4.3.5 Individual Impact 17

2.4.3.6 Organizational Impact 18

2.4.3.7 D&M IS Success Model 18

2.4.4 D&M IS SUCCESS MODEL: A TEN-YEAR UPDATE 20

CHAPTER THREE - RESEARCH METHODOLOGY 24

3.1 OVERVIEW 24

3.2 RESEARCH MODEL AND HYPOTHESES 24

3.3 RESEARCH METHODOLOGY 26

3.3.1 SAMPLING 26

3.3.2 DATA COLLECTION 27

3.3.2.1 Reliability 28

3.3.2.2 Validity 29

3.3.2.3 Administration of Questionnaire 29

3.3 ETHICAL ASPECTS 30

3.4 LIMITATIONS OF METHODOLOGY 30

3.4.1 LOW RESPONSE RATE 30

3.4.2 TIME CONSTRAINTS 30

3.4.3 SAMPLE BIAS 30

CHAPTER FOUR - DATA ANALYSIS AND FINDINGS 31

4.1 DEMOGRAPHIC PROFILE 32

4.2 ADAPTED SUCCESS DIMENSIONS 33

4.2.1 SYSTEM QUALITY 33

4.2.2 INFORMATION QUALITY 33

4.2.3 SERVICE QUALITY 34

4.2.4 SYSTEM IMPORTANCE 35

4.2.5 USER SATISFACTION 36

4.2.6 USEFULNESS 37

4.3 DIMENSIONS AND ASSOCIATIONS 37

CHAPTER FIVE - DISCUSSION OF RESULTS 40

5.1 GENDER AND PERIOD OF USE 40

5.2 SYSTEM QUALITY 40

5.3 INFORMATION QUALITY 40

5.4 SERVICE QUALITY 41

5.5 SYSTEM IMPORTANCE 41

5.6 USEFULNESS AS AN IMPACT MEASURE 42

CHAPTER SIX - CONCLUSIONS 43

6.1 CONCLUSION 43

6.2 RESEARCH LIMITATIONS 44

6.3 RECOMMENDATIONS 44

6.4 FUTURE WORK 45

BIBLIOGRAPHY 46

APPENDIX I – SURVEY QUESTIONNAIRE 51

APPENDIX II – FREQUENCY TABLES 56

APPENDIX III –ANOVA TEST TABLES, CORRELATION MATRIX 59

List of Tables and Figures

Figure 1 - Impact of Individual Differences upon MIS Success (Zmud, 1979) ______________________10

Figure 2 - A Descriptive Model of User Involvement (Ives and Olson, 1984) ______________________13

Figure 3 - D&M IS Success Model (DeLone and McLean, 1992) _________________________________19

Figure 4 - Causal Path Implied by D&M's Model ______________________________________________20

Figure 5 - Updated D&M IS Success Model (DeLone and McLean, 2003) _________________________22

Figure 6 - Adapted IS Success Model to Research Study ________________________________________25

Table 1 - Questionnaire Scheme ____________________________________________________________28

Figure 7 - Period of Use____________________________________________________________________32

Figure 8 - Perceived System Quality _________________________________________________________33

Figure 9 - Perceived Information Quality _____________________________________________________34

Figure 10 - Perceived Service Quality ________________________________________________________35

Figure 11 - Perceived System Importance ____________________________________________________36

Figure 12 - User Satisfaction _______________________________________________________________36

Figure 13 - Perceived Usefulness____________________________________________________________37

Appendix II, Table 1 – Gender ______________________________________________________________56

Appendix II, Table 2 - Period of Use _________________________________________________________56

Appendix II, Table 3 - System Quality________________________________________________________56

Appendix II, Table 4 - Information Quality ___________________________________________________57

Appendix II, Table 5 - Usefulness ___________________________________________________________57

Appendix II, Table 6 - User Satisfaction ______________________________________________________57

Appendix II Table 7 - System Importance ____________________________________________________58

Appendix III, Table 1 - System Quality, Usefulness and User Satisfaction _________________________59

Appendix III, Table 2 - Information Quality, Usefulness and User Satisfaction ____________________59

Appendix III, Table 3 - System Importance, Usefulness and User Satisfaction _____________________59

Appendix III, Table 4 – Usefulness and User Satisfaction _______________________________________60

Appendix III, Table 5 - Service Quality and User Satisfaction ____________________________________60

Appendix III, Table 6 - Correlation Matrix of Success Dimensions _______________________________60

Chapter One: Introduction

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CHAPTER ONE

INTRODUCTION

1.1 Research Background

Transaction processing in an organization could be a visible or an invisible back-end

business computing function. However it is a key enabler of a vast range of

commercial activities from travel reservations and electronic banking, to financial

transactions and e-commerce making it a critical component of the world’s economic

market (IBM, 2005). According to an International Technology Report, a Transaction

Processing System (TPS), a type of Information System (IS) is able to handle a very

large number of transactions, averaging 25,000 per second (International

Technology Report, 2001 cited in IBM, 2005:2). For example in the financial sector,

TPSs allow customers to carry out financial requests remotely thus eliminating the

need for their physical presence at the financial institutions. This remote feature in

turn allows institutions to process multiple requests at a time which could lead to

improved customer satisfaction.

The critical influence that a TPS has on the success of industries in addition to the

explosion of e-business and web-based commerce, has placed a greater demand

on the success of Transaction Processing Systems. The need to identify the critical

success factors of TPS became the focus for this research. In 1992, William H.

DeLone and Ephraim R. McLean introduced their first IS Success model, to

understand and measure the success of an IS. This model was based on a

taxonomy that included all the various distinct measures that had been used in past

IS literatures to evaluate IS success. In their model, they tried to describe the

relationships between the proposed IS success dimensions.

Since entire industries, such as finance and travel, literally owe their current place in

the global economy to TPSs (IBM, 2005), TPS is a real-world type of IS to apply

DeLone and McLean‘s model of IS Success to. Also, because the system is not

always exclusively used by internal users of an organization providing or employing

the TPS, it is can be considered customer facing. This feature in addition to other

system characteristics can be easily interpreted to match each dimension of the

DeLone and McLean’s model.


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1.2 Research Topic

This primary aim of this research project is to adapt the DeLone and McLean’s

Information System Success Model in the measure of a TPS, a type of Information

Systems. The research topic is formulated as:

“Measuring the Success of Transaction Processing Systems: An Adaptation of the

DeLone and McLean Information System Success Model”

The reasons for selecting the above topic stemmed from the need to identify and

understand the success factors of TPS and the inability to find previous literatures at

that time which had applied the model to TPS. Working as a 2nd and 3rd level support

engineer for three years had exposed the researcher to the need for TPS providers

to know these factors that can make or break a TPS.

The identification of these factors is a fulfilling area of research as the research

results could serve as a useful guide to TPS providers in identifying the critical

factors that can impact the success of their systems.

1.3 Research Objectives

To achieve the aim of the research, the following objectives were identified:

1) Research existing literature relating to Information System success.

2) Identify a suitable TPS provider as a case study and select the most

appropriate TPS in the company to evaluate.

3) Identify the success dimensions that will be used in this evaluation and

select appropriate variables that will be used to measure each dimension.

4) Develop a survey from the results of the third objective to be completed by

the users of the selected system.

5) Analyse the responses from the users to determine the success dimensions

applicable to the TPS.

6) Validate the hypotheses developed from the DeLone-McLean IS Success

Model.

7) Advice the case study company on results of the research and

recommendations and contribute to the TPSs research field.


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1.4 Case Study Company: Interswitch Limited

InterSwitch Limited, a Nigerian company is a payment and transaction processing

company that provides transaction processing and payment infrastructure to

government, banks and corporate organizations. With its switching infrastructure,

InterSwitch has been able to connect all 24 banks in Nigeria to its network thus

providing online, real-time transaction switching that enables individuals and

businesses access to their funds across different payment channels like the ATMs

(Automated Teller Machines), the POSs (Point of Sales), Mobile Phones, Web and

Bank Branches. The company leverages on this existing infrastructure to develop

TPSs for revenue collection, bill payments, mobile recharge, funds transfer etc.

1.5 Research Strategy

This research project started with the study of existing literatures on IS success to

gain a better understanding of the concept from which the research model was

adapted and its hypotheses formulated. This also aided the selection of the TPS

used to test the research model and the development of a survey questionnaire

completed by the users of the selected TPS. The responses from the survey were

quantitatively analyzed to present the findings that supported or refuted the research

hypotheses.

1.6 Dissertation Outline

This dissertation consists of six chapters. Chapter One presented an overview of the

research project including its aims and objectives. Chapter Two defined key

concepts and presented a survey of relevant literature to IS success. Chapter Three

presented the research methodology selected for the study—measures and

operationalization—as well as the methods for data collection and analysis. Chapter

Four described the results of the analysis. Chapter Five presented discussions on

the results of the analysis and chapter six presented the conclusions,

recommendations and future work.

Chapter Two: Literature Review

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CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

This chapter describes the key concepts relevant to this research. It started with the

definition and categories of IS. Next Transaction Processing Systems were

described and their characteristics highlighted. The last part focused on the review

of previous literatures relevant to this research.

2.2 What is an Information System?

Avison and Fitzgerald defined an Information System (IS) of an organization as a

system that gathers, stores, processes and delivers information relevant to that

organization or the society in an accessible and useful format (Avison and

Fitzgerald, 1995). They emphasized that an Information System is not concerned

with the technology aspect of it but also with the people and organisation aspects.

Here, people are those concerned with the information produced by the system and

organisational aspects are business procedures or processes peculiar to that

organization. For example, a customer management IS used in bank should cater

for customer relationship managers, account officers etc. while taking in

considerations the bank policies about how a customer’s account is managed.

With this revised view of an IS, a better definition would be as a work system whose

internal functions process information such as the capture, transmission, storage,

retrieval, manipulation and display of information (Alter, 1999). A work system to an

organization is a system that is operated by humans and/or machines to perform a

business process using information technology and other resources (such as the

environment, customers or competitions) to deliver a products and/or services to the

organization’s internal or external customers (Alter, 1999). With this definition, an IS

can be viewed as a social system as it describes a human activity system and its

interactions with the environment.

Since an IS can either exist to produce information to its users, provide support to

other systems or automate a work process, it can take many different forms unique

to the organization implementing the system or for the purpose. While the premise

for categorization was not explored in this research, five major categories of


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Information Systems have been commonly identified across literatures: Transaction

Processing Systems (TPS), Management Information Systems (MIS), Decision

Support Systems (DSS), Expert Information Systems (EIS) and Office Automation

Systems (OAS) (Laudon and Laudon, 2006; Alter, 1999; Avison and Fitzgerald,

1995; McLeod, 1990).

TPS – is the most basic type of IS performing daily routine transactions required

in a business demonstrating why its functions and features are usually employed

at the operational level of organizations. At this level, tasks and operations are

predefined and highly structured usually requiring a met set of conditions for a

successful processing (Laudon and Laudon, 2006). For example, a purchase

transaction will require the availability of the product in the desired quantity and

the confirmation of funds (customers’) before it can be successfully processed.

MIS – this type of IS employed at the management level of an organization

provides past, present and future information on the organization’s operations

and the environment it operates in. This system could be used to support an

organizational unit and information captured, processed and stored by TPSs are

usually the input to MIS and the output information can be in a written or oral

form (McLeod, 1990).

DSS – this system also employed at the management level of organizations aids

managers or decision makers in making situation specific and prompt decisions.

This system uses whole or part information about the organization and its

environment to produce information to aid the decision maker (Avison and

Fitzgerald, 1995).

EIS – this system is a subset of Artificial Intelligence Systems and attempts to

simulate the role of the human expert, advising its user on how to solve a

problem (McLeod, 1990). EIS is used to analyse complex problems with a high

degree of uncertainty and problem solutions or guidance are derived from the

reasoning ability of the system using its knowledge base on that particular

domain. This process of using the EIS for guidance or solutions is called a

consultation (McLeod, 1990).

OAS – this represents various applications often found in offices like word

processing, electronic mail, voice mail, facsimile transmission (fax). The aim of

these applications is to improve the productivity of their users (McLeod, 1990).

As a problem solving tool, OASs enables mangers to better communicate with

each other while solving a problem and in turn improving the quality and speed

of their decisions (McLeod, 1990).


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However, for the purpose of this research, Transaction Processing Systems are the

most relevant and will thus be the focus. The next section describes these systems

in more detail.

2.3 Transaction Processing Systems

As defined in the previous section, a TPS is a computerized system that performs

daily routine transactions required to carry out a business function. Transaction

processing is the transformation of symbols (numbers and letters) for the purpose of

increasing their usefulness (McLeod, 1990). This processing involves all the tasks or

actions required to complete a transaction request: capturing, transmitting, storing,

retrieving, manipulating and displaying information. There are characteristics of a

TPS that distinguishes it from other types of Information Systems (Best Price

Computers, 2010; McLeod, 1990).

Rapid response – TPSs are designed to process transactions very fast to ensure

the availability of information as at the time required. Now more than ever, with

technology advancement, the rapid processing of transactions is critical to the

success of a business enterprise as businesses cannot afford to have customers

waiting for a system response beyond the acceptable time frame which is

usually in seconds (Best Price Computers, 2010).

Reliability – an organization usually does not have a choice of not processing

transactions as it is a required activity and as such rely heavily on their TPS; a

breakdown could disrupt operations or can even stop the business (McLeod,

1990). This makes well-designed backup and recovery procedures essential as

an effective TPS must have a low failure rate.

Standardization – Transactions must be processed in the same way each time

to maximise efficiency. TPS interfaces are designed to acquire identical data

and process transactions in the same way regardless of the user, the customer

or the time for day. A flexible TPS would probably create too many opportunities

for non-standard operations (Best Price Computers, 2010). For example, a bank

needs to consistently accept account numbers (online transfer scenario) in a

certain format to avoid future complications with perhaps special characters.

Controlled Access – an organization’s transaction records can be such a

powerful business tool capable of supporting major organization decisions. In

light of this, TPS access must be restricted to only authorized users and have


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the ability to reconstruct past actions and transaction sequence if necessary, this

chronology of actions is referred to as an audit trail (McLeod, 1990).

2.3.1 What is a Transaction? What is ACID?

In the context of TPSs, a transaction is a sequence of actions that is treated as a

unit to fulfilling a system request. This request can either cause a change to the

state of the system (information capture) or not (information retrieval) (TechTarget,

1997). A debit and corresponding credit of funds will be treated as a unit to fulfilling

a payment request. The payment request cannot occur unless all the actions

defined for it had been carried out. A transaction will only be considered one if it has

these properties at every instance (Connolly and Begg, 2010; Date, 2004).

Atomicity: A transaction must be atomic i.e. cannot be split. All defined actions for

that transaction must all be completed or none at all, partial completions are not

permitted. In the event of a failure of any action, effects of all actions that make up

the transaction should be undone, and the system reverted back to its previous

state.

Consistency: A transaction must ensure the transformation of the system from one

consistent state to another consistent state. In other words, a transaction must

preserve all the invariant properties associated with the state. For example, a

transaction that is intended to transfer money from one account to another must

ensure that the correct accounts are debited and credited accordingly. This requires

that the transaction be a correct program.

Isolation: Each transaction should appear to execute independently of other

transactions that may be executing concurrently in the same environment. Any

updates made by a transaction T1 cannot be seen by a transaction T2 unless T1

was done with its process and “saved” these changes.

Durability: Once the above changes to the system had been saved, the updated

state must always be persistent even if the system fails for any reason. This means

that a backup mechanism must be in place to achieve this.

These properties, called as ACID properties, guarantee that a transaction is never

incomplete, the data is never inconsistent, concurrent transactions are independent,

and the effects of a transaction are persistent. To be considered a transaction

processing system the computer must pass the ACID test.


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2.3.2 Types of Transaction Processing Systems

From a review of TPS literatures, four functional categories were identified:

marketing, manufacturing, financial and human resource all of which can have sub

functions (Laudon and Laudon, 2006; Avison and Fitzgerald, 1995). Marketing TPSs

are usually employed in an organization’s customer service, sales management,

pricing, company promotion or dealership functions. Examples of these are sales

order systems and customer feedback systems. Manufacturing TPSs are best

employed in the scheduling, shipping and receiving or purchasing functions of a

manufacturing company. Examples are machine control systems, quality control

systems and purchase order systems. An organization could employ Finance TPSs

like funds management systems and payroll systems for their billing, general ledger

or cost accounting functions. Finally, human resource TPSs can be employed in the

personnel records, employee benefits, compensations and training functions of an

organization. Examples are employee skills inventory systems and employee

records systems.

As described in the previous sections, Transaction Processing Systems are often

central to the operations of an organization that a TPS failure even for a short period

of time could have damaging consequences on the organization and even on

external business units dependent on it. As a result, an organization needs to

constantly monitor the operations of their TPSs but most importantly, TPS providers

need to ensure a high success rate in the Transaction Processing Systems they

develop.

2.4 Measuring the Success of Information Systems

Reviewed literatures were selected based on the observation of information systems

and management information systems or their corresponding abbreviations in the

title, abstract or table of contents. Owing to their relevance to this research project

and the frequency of citation by other studies, three literatures were selected: Zmud

(1979), Ives and Olson (1984) and DeLone and McLean (1992) to be described in

the following sections. Zmud’s study explores how the differences in the information

processing and decision behaviours of IS users can impact its success; Ives and

Olson’s study examined how the levels of user involvement at different stages of an

IS development affect the success of that Information System; and DeLone and

McLean’s study proposed a categorisation of IS success dimensions based on a

synthesis of existing IS success research. These literatures are described in a


9

chronological order because the conclusions of a previous study served as a

premise or part of the premise for the succeeding study.

2.4.1 Individual Differences and IS Success

Though many factors were believed to have had an impact on the success of MIS

experienced by an organization, the influence of individual differences on the

design, implementation and use of MIS produced the largest amount of

interdisciplinary studies whose findings were synthesised in Zmud’s paper to create

a model illustrating the impact of individual differences on MIS success (Zmud,

1979). According to Zmud, Individual differences are the variants in the information

processing and decision behaviours of users of the same system and in studying

their impact on the success of MIS, he identified three classes that individual

differences can fall into: cognitive style, personality and demographic or situational

variables. The cognitive style variables are indicative of the perceptual and thinking

behaviour of the individual characterized or dependent on the task and situational

elements the individual functions in; personality variables indicate cognitive and

sentimental structures such as dogmatism, extroversion/introversion maintained by

an individual, which facilitates their adjustments to people, events and situations

around them; demographic or situational variables indicate a broader spectrum of

personal characteristics like sex, age, professional orientation, experience and so

forth which can vary according to context for example general intellectual abilities or

knowledge of a specific content.

Figure 1 below shows the model illustrating the influences of individual differences

on MIS success. Cognition, a learning process involving mental activities

undertaken by an individual in an attempt to reconcile differences between

perceptions of a concept with what is actually happening in the real world and is

affected by the classes of individual differences. This effect on the cognitive

behaviour in the context of MIS success refers to the amplification or dampening

limitations in human cognition. This limitation in turn relates to the design of the MIS

by suggesting or imposing design alternatives directed towards motivating the MIS

usage and ultimately to the success and failure of the MIS. The individual

differences also affect the attitude of the potential MIS users and the tendency for

these users to involve themselves before or after the MIS development and whose

involvement or lack of can affect the success or failure of the MIS.


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Figure 1 - Impact of Individual Differences upon MIS Success (Zmud, 1979)

The cognitive behaviours of individuals are largely based on task and situational

elements as well as individual differences and the aim of this part of the research

was to identify the critical individual differences that can be used to design a MIS for

the identified profiles (Zmud, 1979). In other to achieve this aim, the author

contrasted these behaviours under each class of individual differences. Under the

cognitive style class, he contrasted complex versus simple individuals and identified

complex users as those who prefer more aggregated information over raw data and

who use more rules when integrating information. However, when making decisions,

complex individuals were able to generate more decision alternatives resulting in

greater flexibility of the system but which cost them more decision time. Under the

personality class, the individuals with greater search activities were found to

possess an internal locus of control and had a high propensity for taking risks. The

demographic class however showed individuals with higher general intelligence

select information more effectively and process it faster while individuals with

greater verbal abilities have enhanced short term memories compared to those with

lesser verbal abilities (Zmud, 1979).

The identified cognitive behaviours along with their limitations can be used in the

designs of MIS that will meet the information requirements of its users. The effects

of these behaviours on the characteristics of the MIS design were explored under

three broad categories: information needs, the decision aids and delivery system

components (Zmud, 1979). Under the information needs aspect of his research,

Zmud learnt that for a MIS that had selected and filtered appropriate information, its

users were found to be satisfied because their information needs were met. Also,

users of a MIS whose information presentation format can be altered were happier

Indiv idual

Dif f erences

Cognitiv e

Behav iour

MIS Design Characteristics

MIS

Success

A Priori

Inv olv ement

Attitude of

MIS User

Posterior

Inv olv ement


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than those whose were not. Under the decision aids, he discovered that while

quantitative models resulted in improved decision performance, it did not boost

users’ confidence because of increased decision time. However, improvements to

the system formats led to increased usage. Finally, under the delivery system, he

found out that easy to use and becoming user interfaces; quality user training;

pleasant rapport with the MIS provider or department could be positively associated

with users’ satisfaction with the MIS (Zmud, 1979).

The effects of individual differences on users’ attitudes were such that extroverted

individuals adopted a more positive attitude towards the MIS while males, older and

lesser education exhibited less positive attitudes and that users with better self-

image or more knowledge in the area were more likely to be involved in the MIS

design which will in turn positively impact the user’s satisfaction with the final MIS

(Zmud, 1979). In relation to individual differences and MIS success, the author

found that individuals with higher task knowledge and of professional status tend to

use the MIS more than those with more education and with longer tenure in the

organization and as a result are less satisfied with it. While the author agrees that

while there is a significant impact of individual differences on MIS success, he also

concludes that there is still remains unknown individual differences in relation to

contextual factors and he proposes areas of research that still need to be explored

like the relationship between user attitudes and MIS design characteristics; the need

to test these concepts in the field so as to validate the results.

2.4.2 User Involvement and IS Success

While Zmud focused on the effect of individual differences on MIS success, Ives and

Olson in their 1984 paper focused on determining when and how much of user

involvement in the development of a computer based information system is

adequate. They defined user involvement as “a participation in the system

development process by representatives of the target user group” Ives and Olson

(1984:587) and identified two theories of Organizational Behaviour relevant to their

research: participative decision making and planned organizational change. In

participative decision making, an increase in the job related inputs of subordinates to

management decisions caused a corresponding increase in job satisfaction and

improved productivity. In the case of computer based information system,

participative decision making occurs when users and system designers work

together with a view to increasing the acceptance of the system. Based on findings

from several studies carried out by the authors, user involvement was predicted to


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improve system quality by: capturing a more accurate and complete interpretation of

the user information requirements; providing more comprehensive particulars about

the organization that is to use the IS; Improving user understanding of the system

and in turn avoid the development of an unusable system. Another possible benefit

of user involvement from the findings was an increase in user acceptance of the

system through: the development of system capabilities from realistic expectations;

creation of an interactive session for bargaining and conflict resolutions regarding

design issues; decreasing users’ resistance to change by increasing their ownership

of the system through their commitments (Ives and Olson, 1984). Planned

organization change on the other hand theorizes that the acceptance and use of the

IS depends on the quality of the implementation process and while user involvement

was considered significant to inducing a positive attitude in the users of the system

to facilitate organizational change, it was not sufficient as research in this area

assumes that change is either a joint effort or negotiation between the manager and

the change agent (Ives and Olson, 1984).

Mapping these researches together, the authors presented a conceptual framework

with a view to improving future research on the User Involvement–IS Success

relationship and increasing understanding in this research area. In their model,

represented in the figure below, the appropriateness of user involvement was

determined by two classes of conditional variables: involvement roles and

development conditions. The involvement role determines who the participants are

to be in the development of the IS. In their paper, three types of users were

identifies: primary users who uses the output from the system; secondary users who

generates system inputs and top level management. The development conditions

represent the characteristics of the development process. Here, they identified two

classes of development conditions that can affect user involvement: the type of

system being developed and the stage in the development process. According to

Ives and Olson, the levels of user involvement vary between types of systems.

There are systems where user involvement is critical (e.g. Decision Support

Systems) and systems where it is inappropriate (e.g. highly technical systems

whose product is unimportant to the user). User involvement will be considered

critical at the requirement gathering and definition stages but less so at the

installation stage (Ives and Olson, 1984).


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Figure 2 - A Descriptive Model of User Involvement (Ives and Olson, 1984)

The different facets of user involvement identified were type and degree. The type of

participation may vary from indirect to direct in forms of consultative, representative

and consensus. The consultative involvement is the traditional user involvement

when the users of the system present their needs and requirements for the system

but the system provider makes the design decisions. Representative involvement

occurs when all levels and functions of the affected user group are present at the

design stage and consensus involvement occurs when all users are consulted

throughout the design period (Ives and Olson, 1984). The degree of user

involvement describes the level of influence the user has over the final product

ranging from no involvement (users do not participate), to symbolic involvement

(user’s input is received but disregarded), to involvement by advice (user’s input is

solicited through interviews/questionnaires), to involvement by weak control (users

sign off responsibilities at each stage), to involvement by doing (user is part of the

design team) and finally to involvement by strong control (users may personally pay

for systems additions). The outcomes of user involvement were represented as

system quality and system acceptance facilitated by two factors: cognitive and

motivational factors. Cognitive factors, facilitating the outcome of user involvement

on system quality include improved system understanding, assessment of system

needs and evaluation of system features while motivational factors, which facilitates

the outcome of user involvement on system acceptance includes improved system

acceptance, increased user perceived ownership, decreased resistance to change

and increasing user commitment to the new system (Ives and Olson, 1984).

Inv olv ement

Roles

User

Characteristics

Organizational

Climate

Dev elopment

Conditions

User

Inv olv ement

Sy stem

Quality

Cognitiv e

Factors

Motiv ational

Factors

Sy stem

Acceptance


14

From their research critique, the authors identified three broad categories of

problems with the research: much of the research studies theorized the effect of

user involvement on system quality or acceptance and there was not a common and

shared view of user involvement and MIS success; in their review methodologies,

they reported that most of the studies were survey data collected after the

development of the system and those systems were either single systems with their

lack of external validity from different methodologies or multiple systems and their

inadequacy in controlling sample selections; the existence of weak measures of

user involvement and system success resulting from lack of a conceptual foundation

to guide the measurement developments and the absence of a measurement

validation tool. In the conclusion on their paper, Ives and Olson reported that the

benefits of user involvement on system success had not been strongly

demonstrated and the consistent lack of rigor in the research then seriously limits

the understanding of the role of user involvement in IS development (Ives and

Olson, 1984).

2.4.3 IS Success: The Quest for the Dependent Variable

In 1992, DeLone and McLean proposed a taxonomy and interactive model known as

the DeLone and McLean IS Success Model as a framework for theorizing and

operationalizing the success of Information Systems. The primary purpose of the

model was to synthesize existing research (including the previously described two)

on Information System success into a more comprehensive multidimensional

framework that can be used to evaluate the success of Information Systems

(DeLone and McLean, 1992).

DeLone and McLean based their taxonomy on the Information theory of Mason

(1978) whose grew out of Shannon and Weaver’s communications research (1949),

and the empirical research studies on Management Information System (MIS) over

a period of seven years from 1981 - 1987. In Shannon and Weaver’s communication

theory, one definition of communication relevant to this research is as a process by

which one system A (say a customer’s credit/debit system in a bank) affects another

system B (customer’s credit check system). The affected system B is the receiver

and the producing system A performs the functions of observing, recording,

collecting, storing, analysing, processing and transmitting. The focus of their

communication theory was in the successful transfer of information and they

identified three different levels of problems that can affect or complicate this

transmission (Shannon and Weaver, 1949).


15

1. Level A or Technical Problem which is concerned with how accurate and

efficient the symbols of communication are.

2. Level B or Semantic Problem which is concerned with the precision of the

communication symbols. How successful were the symbols in conveying

their intended meaning or purpose?

3. Level C or Effectiveness Problem which is concerned with the effect of the

received meaning on the receiver.

Mason then presented a conceptual framework for measuring the output of an

Information System drawing on this communication theory which resulted in the

development of four approaches to IS output measurement (Mason, 1978):

1. Technical Level Output which translates to the Level A of the communication

theory and measures the number of signs and symbols in terms of bits,

characters, physical words, lines, page or block that were accurately

transmitted between a producing system and a receiver.

2. Semantic Level Output which translates to the Level B of the communication

theory and measures the amount of units of meaning in terms of logical

words, sentence expression, texts, and document flowing from the producing

system to the receiver.

3. Functional Level Output which measures the successful completion of an

information conversion process of symbols into meaningful output.

4. Influence Level Output which translates to the Level C of the communication

theory and can be further divided into eight smaller approaches that

measures the output at the influence level:

Receive which measures how the receiver accepts the message

being sent.

Accept which measures the amount of items read and perceived

relevant, useful or acceptable by the receiver.

Retain measures the extent to which the receiver stores and retains

the read data.

Integrate measures the ability of the receiver to compare and

contrast the received data with other data.

Evaluate measures the extent to which the integrated data evokes a

change in the receiver’s attitude or worldview.

Apply measures the extent to which integrated and evaluated data

may be applied in decision making or problem solving processes.


16

Change in Behaviour measures the changes in the receiver’s

behaviour after application.

Change in Systems Behaviour measures changes in the total

system.

In adapting the Shannon and Weaver’s communication theory, Mason used their

three levels of communication problems as specific areas of analysis in measuring

the output of an IS. The fourth level was introduced to analyse information from the

point of the processes which transform or produce this information. The resulting

framework consequently focuses on information as an output rather than the whole

process of transmitting information and sets the foundation for the multidimensional

model of IS success that is now known as the DeLone and McLean Model of IS

Success (D&M IS Success Model). In their model, DeLone and McLean proposed

six dimensions of IS success: “systems quality” which measures the technical output

or success; “information quality” which measures the semantic output of success;

“system use”, “user satisfaction”, “individual impacts” and “organizational impacts”

all of which measures the influence output of success (DeLone and McLean, 1992).

2.4.3.1 System Quality

This success dimension is concerned with the information processing system itself

like how well the various components of the system work together. These

components could be the hardware, software or network components (DeLone and

McLean, 1992). According to the authors, a number of empirical studies had been

carried out to identify suitable and feasible variables that can be used to measure

system quality in an IS. The flexibility of the system, convenience of access,

response time and integration of systems (Bailey and Pearson, 1983), reliability,

response time, ease of use, ease of learning and perceived usefulness of IS were

some of the commonly employed variables in these studies. In a customer

management system, system quality will generally be concerned with characteristics

of the system like reliability of hardware, ease of use, response time to button clicks

for example and so forth. The table below shows a complete list of twelve studies

that identified distinct variables to measure system quality.

2.4.3.2 Information Quality

Measuring the information quality of an IS refers to the quality of the output

produced by the system which is primarily in form of on-screen reports (DeLone and

McLean, 1992). Nine studies were studied and some of the identified variables of

information quality were its accuracy, precision, timeliness, reliability, completeness,


17

relevance and the format it was presented in. According to the authors, these

identified variables are understandably from the perspective of the user of the

information and thus fairly subjective in character. A stock broker using a stock

broking system is likely to access the information quality of the system based on its

accuracy and timeliness while the user of a marketing system might consider the

information quality based on relevance and reliability.

2.4.3.3 System Use

This success dimension was one of the most frequently reported measures of IS

success and it describes the way the system user consumes or uses the output

produced by the Information System (DeLone and McLean, 1992). This use was

measured as actual use rather than reported use but the authors however pointed to

the fact that actual or perceived system use as an IS success measure only makes

sense for voluntary or discretionary users as opposed to mandatory users. From

their findings, motivation to use, frequency of use and time spent per session were

some variables that had been used to measure this dimension.

2.4.3.4 User Satisfaction

This measures the user’s response to their use of an IS output, and is probably the

most widely used single measure of IS success because it is hard to refute the

success of a system when its users have expressed their likeness for it (DeLone

and McLean, 1992). Another reason for its popularity is the availability of a reliable

tool by Bailey and Pearson which measures satisfaction. Researchers like (Ein-Dor

and Segev, 1978) and (Hamilton and Chervany, 1981) have found user satisfaction

to be an appropriate success measure when a specific IS was involved. However, in

a situation where an IS use is mandatory, this success measure becomes more

useful and the previously discussed measure, system use, becomes less useful

(DeLone and McLean, 1992). The table below presents a summary of the empirical

studies that have been carried out to measure user satisfaction using a single

variable or multiple variables

2.4.3.5 Individual Impact

Individual impact is perhaps the most difficult measure to define without ambiguity

(DeLone and McLean, 1992). The authors describes impact as closely related to

performance so an improved performance is considered a positive impact but on the

other hand, impacts could also mean that the IS has improved the user’s

understanding on a particular concept, improved their productivity, cause a change

in their user activity or change their perception on the usefulness or importance of


18

the system. From these possible descriptions, impact thus becomes a general term

that can be used to measure the extent to which the IS has caused a behavioural

change (capabilities and effectiveness) in its key users.

2.4.3.6 Organizational Impact

While academic researchers tend to avoid performance measures such as

organizational impacts due to the difficulty in isolating the changes influenced from

an IS use from other factors that could influence the organization, this measure is of

considerable importance to IS providers (DeLone and McLean, 1992).

Organizational Impact is a measure of the extent to which the IS has caused a

positive change in organizational results and capabilities. This could also be a way

for an organization to justify or count the value of investments that had been made

through the provision of the Information System.

The authors while conceding to the fact that much work is still required in accessing

the business value of information systems, they were been able to present through

twenty empirical researches variables such as cost reductions (e.g. operational

costs); reduced staff costs (e.g. reduced number of staff/hours required for the

process); overall productivity improvement (e.g. key-users are able to achieve more

within certain hours/days compared to previous times); improved outcomes or

outputs (e.g. better quality of produced information); improved business processes;

better positioning for e-Government/Business (e.g. enhancing government to

businesses relationships) which have been used to measure organizational impacts

of an IS.

2.4.3.7 D&M IS Success Model

One of the primary purposes of DeLone and McLean’s 1992 paper was an attempt

to reduce the countless success variables which had been carefully examined in

existing literatures to a more manageable categorization (DeLone and McLean,

1992). While the authors discovered that no single variable is intrinsically better than

the other and that the selection of a success variable was usually a function of the

research objective, the organizational context, the size and the environment of the

organization being studied etc., they identified that many of these variables fell into

one of the six dimensions shown in the figure below.


19

Figure 3 - D&M IS Success Model (DeLone and McLean, 1992)

These six dimensions were proposed to be interrelated rather than independent

based on process and causal considerations; the process model deliberates that a

newly created IS with defined features will exhibit various degrees of system and

information quality which will be experienced by the users of the system creating a

satisfaction or dissatisfaction with the system or the information it produces. This

use can then influence the behaviour of the user in their work and a collection of this

individual influences will in turn impact the organization they belong to; the causal

model considers the relation between random success dimensions to determine if a

causal relationship exists among them. For example, if an increase or decrease in a

dimension causes a corresponding increase or decrease in another dimension

(DeLone and McLean, 1992). The figure below presents the causal path drawn from

the model. However, Seddon and Kiew (1994) argued that the system use

dimension should be removed with the claim that use is behaviour, appropriate in

the process model but not in the causal model. They argued that non-use does not

necessarily mean that a system is not useful, but could mean that its potential user

had more pressing things to do, in other words, use precedes impacts but do not

cause them (Seddon & Kiew, 1994). In their study, they suggested that use should

be replaced with usefulness in contexts where usage of a system is mandatory.

They also proposed the inclusion of a new dimension, system importance, to help

explain the variations in users’ perceptions of the usefulness and use dimensions

(Seddon& Kiew, 1994).


20

Figure 4 - Causal Path Implied by D&M's Model

DeLone and McLean disagreed with this removal in their 2003 update to the model.

According to them, system usage is an appropriate success dimension in most

cases but the problem had been an overly simplistic definition of a complex

dimension. The conclusions of their 1992 paper was the need to further develop and

validate the model empirically before it can be considered the basis for measuring IS

success which provided a motivation for this research project.

2.4.4 D&M IS Success Model: A Ten-Year Update

From the 285 number of citations as at 2002, the adoption of the D&M IS Success

model showed a realisation of one of the purposes of their original paper; for

researchers to validate and further develop the original model (DeLone and McLean,

2003). The emergence of end user computer has created a dual role for IS

providers: as an information provider (providing the IS) and as a service provider

(provision of support to end users) (Pitt et al., 1995). Based on the changes in the

role and management of IS and on research contributions to their original paper,

DeLone and McLean updated their success model in 2003, ten years after their

original paper was published. The changes to their model were the addition of

service quality as a success dimension and the grouping of individual and

organizational impacts into net benefits.

According to Pitt et al., there is a danger of IS success mismeasure if the service

component is not included in any IS assessment instrument (Pitt et al., 1995). To

avoid this, DeLone and McLean added the service quality dimension to their model.

Identifying the variables to measure this dimension had been under continuous

development and validation and no reliable instrument have been identified although

SERQUAL, a 22-item service quality measurement instrument used in the marketing

sector had been applied and tested in the IS context using five dimensions of


21

service quality: tangibles, reliability, responsiveness, assurance and empathy (Pitt et

al., 1995).

Tangibles describes the physical facilities of the IS provider such as their up-

to-date software, building and the appearance of the personnel.

Reliability describes the ability of the provider to render the promised service

dependably and accurately.

Responsiveness describes the promptness and willingness of the provider to

help customers.

Assurance describes the confidence and trust the customer has regarding

the ability and expertise of the provider on the service problem.

Empathy describes the ability of the provider to reflect the customer’s best

interests in their interactions with them.

SERVQUAL measures the service quality for each dimension by finding the

difference score between the service that customers expect and the performance

they perceive to have received. Van Dyke et al. has challenged the SERVQUAL

instrument indicating it suffered from both empirical (e.g. reduced reliability, poor

convergent validity and poor predictive validity) and conceptual (difficulty in

operationalizing perceived service quality as a gap score, the ambiguity of the

expectations constructs and the unsuitability of using a single measure of service

quality across different industries) difficulties (1997). While the authors agree that

the SERVQUAL instrument needs to be further developed, they believed that

service quality should be added to system quality and information quality as another

IS success dimension (DeLone and McLean, 2003).

According to DeLone and McLean, the impacts of an IS have evolved over the years

intimating suggestions from researchers a need to create more types of impact such

as consumer impacts, societal impacts. After considering these suggestions, the IS

success model was further refined by grouping of all impacts into a category called -

net benefits. Net benefits raised three issues that they advised researchers to take

into account when in use: “what qualifies as a benefit”? “for whom”? and “at what

level of analysis”? According to the authors, the original term “impacts” could have

been interpreted positively or negatively, possibly creating confusion as to whether

the impact results were good or bad. In light of this, the term “net benefits” is a more

accurate descriptor as it recognizes that no outcome is wholly positive. Who

ultimately appreciates these benefits? Is it the organization that sponsored the

system? Is it the users of the system? These are decisions to be made by


22

researchers based on their research context including whose perspective the

benefits are measured from. Will it be from the perspective of an individual, the

organization they belong to or the society the organization exists in? The rationale

behind this decision was to maintain a parsimonious IS success model and provide

the choice of where impacts should be measured depending on the system being

evaluated and for what purpose. The figure below presents the updated model.

Figure 5 - Updated D&M IS Success Model (DeLone and McLean, 2003)

With this update, the quality of an IS can be measured from three dimensions:

system, information and service in addition to the other dimensions. A final

conclusion was also presented regarding the multidimensional nature of system use.

They suggested that intention to use (an attitude) might be a sensible alternative to

use (a behaviour) depending on the context, and reasoned this suggestion might

resolve some of the previously discussed concerns Seddon and Kiew (1994) raised

on the process vs. causal nature of system use. The D&M model as a process

model, use, they reported must precede user satisfaction; as a causal model, use

will lead to greater satisfaction and user satisfaction will in turn increase intention to

use and ultimately use. From these use and user satisfaction, certain net benefits

(positive or negative) are inherent which will influence subsequent use and user

satisfaction validating the feedback loops in the model. The updated model’s arrows

demonstrates the proposed process associations between the success dimensions,


23

however, the authors recommend future researchers who uses this model to

hypothesize its causal associations according to the context of their studies.

Chapter Three: Research Methodology

24

CHAPTER THREE

RESEARCH METHODOLOGY

3.1 Overview

The purpose of this research project was to adapt the D&M IS Success Model to the

measurement of TPS. The chapter starts with a description of the hypotheses

implied from the updated DeLone and McLean’s model followed by a justification of

the selected research methodology and questionnaire design used. The final

sections present an overview of how data collection was carried out and the

limitations to the chosen methods.

3.2 Research Model and Hypotheses

The literature review from the previous chapter highlighted D&M proposed

dimensions to the success of an IS. It also presented some of the validated

variables that had been used to measure each of these dimensions and the implied

causal associations between them. Six success dimensions were chosen to

measure the success of the selected TPS from the case study. Four were from the

updated D&M model: system quality, information quality, service quality and overall

user satisfaction. The last two dimensions: usefulness and system importance were

adopted from Seddon and Kiew’s partial test of the D&M IS Success Model (Seddon

and Kiew, 1994). In their paper, they held that when the usage of a system is

mandatory, the number of hours spent on the system does not wholly convey the

system’s usefulness and so success. Usefulness, they argued was a more

meaningful success dimension. This was adapted to the research model as the case

studied TPS is a mandatory system. This usefulness dimension was also used to

measure the impact of the TPS from the users’ perspectives. Seddon and Kiew

added system importance to the D&M model to explain the variations between

usefulness and user satisfaction. According to them, it seemed likely that the user’s

opinions about the relevance of the system to their job functions (the importance of

their tasks) will influence their opinions on the value of the system and ultimately the

success of the system (Seddon and Kiew, 1994). They were right, the conclusions

on their tests showed that while task importance was independent of the system, it

had a major influences on the perceived usefulness of the system. In the

conclusions of their paper, they cautioned that in adapting the usefulness


25

dimension, system importance should be measured (Seddon and Kiew, 1994). The

final adapted success model is presented below.

Figure 6 - Adapted IS Success Model to Research Study

In the above model, the model elements in the dotted rectangle are the

interdependent variables in the model. From this model, the research hypotheses

were formulated and are presented below.

H1: Perceived System Quality predicts User Satisfaction

H2: Perceived Information Quality predicts User Satisfaction

H3: Perceived Service Quality predicts User Satisfaction

H4: Perceived System Quality predicts Usefulness

H5: Perceived Information Quality predicts Usefulness

H6: Perceived System Importance predicts Usefulness

H7: Perceived System Importance predicts User Satisfaction

H8: Perceived Usefulness predicts User Satisfaction


26

3.3 Research Methodology

A quantitative approach to research is inherently deductive. A deductive approach is

a test of theories, going from more general to the more specific (a top-down

approach) (Trochim, 2006). DeLone and McLean updated model gave rise to eight

hypotheses which were to be confirmed, rejected or modified through findings from

data collected and analysed. As a result, the deductive approach was used in this

research. The main point of a quantitative research method is that measurement is

valid, reliable and can be generalized with its clear anticipation of cause and effect

(Cassell and Symon, 2004). With its particularistic and deductive nature, the

quantitative method depends on the formulation of a research hypothesis and an

empirical confirmation using a specific data set (Frankfort-Nachmias and Nachmias,

1992). In a quantitative method of research, the scientific hypothesis is of no value;

this means that the researcher’s personal opinions, subjective preferences and

prejudices are not applicable. The quantitative approach was useful as it helped

prevent prejudice in the collection and presentation of research data. The

quantitative data collection methods (controlled observations, mass surveys, and

laboratory experiments) are useful and reliable especially when a study needs to

measure the cause and effect relationships evident between pre-selected and

discrete variables (Creswell, 2003). Using the survey collection method in this

research helped specify clearly and precisely variables, both dependent and

independent, that were needed in the study, analyse their findings independent of

subjectivity of judgement and deliver a more objective investigation, discussion, and

conclusion.

3.3.1 Sampling

To validate the D&M IS success model on TPS, the Figure 5 D&M IS model was

adapted to a specific TPS, provided by Interswitch Limited, a Nigerian company.

This TPS is a web-based system that enables corporates and government

organizations receive payments made by their distributors, agents and customers

into their accounts from all of the banks in Nigeria. This system was selected after

an informal discussion with the head of the development team on the need for a

case study system. In the end, the TPS was selected because there had been some

difficulties with its implementation, and a wide range of satisfaction scores was

likely. In addition, the system is used by bank staff, corporate staff and Interswitch

staff (a total of 5372 at the time of study) all of which had been trained using the

same set of training materials provided by Interswitch. There were therefore a

smaller number of extraneous factors that could cause variance in the usefulness


27

and satisfaction scores. The case study approach was an opportunity to study a

specific case of “real” users’ perceptions of a “real” system in “real” time. Even

though the study focused only on one system in one organization, the research

setting could be expected to increase the internal validity of the study and to some

extent its external validity as well. To attain a 95% confidence level that the sample

results are inferable to the sampling frame, 357 users were selected to participate in

the survey (a recommendation from the online survey tool). However, knowing the

response rate may be less than 50%, over sampling was estimated to compensate

for this lack of coverage. For this reason, the sample was adjusted to 380 users.

3.3.2 Data Collection

The survey questionnaire was constructed from the six dimensions in the adapted IS

success model. Validated variables to measure each dimension were found in

relevant literatures whose reliability of questions and scales had been tested. Each

of these dimensions was represented per section in the questionnaire including a

demographic section that was added to give a general overview to the study

sample. Each section presented a number of questions and/or statements which

represent the dimension’s validated variables and negative sentences that were

used to measure dimensions will be reworded prior to data analysis, this is to have

the same positive rating for the whole dimension. The table below shows a summary

of the dimensions and variables used.


28

Success Dimension Dimension Variables Question Format

Demographics

Gender Multiple choice **only one choice should be selected Period of use

System Quality Reliability Likert scale rating from 1-7 Response time

Ease of use

Ease of learning

Information Quality Accuracy Likert scale rating from 1-7 Precision

Timeliness

Reliability

Completeness

Conciseness

Format

Relevance Service Quality Reliability Star rating from 1-9

Responsiveness

Rapport

Tangibles User Satisfaction Adequacy Likert scale rating from

1-7 System Efficiency

System Effectiveness

Satisfaction Perceived Usefulness Productivity Likert scale rating from

1-7 Performance

User Effectiveness

User Efficiency

System Usefulness

System Importance Importance Star rating from 1-7

Relevance

Key/Fundamental/Central

Essential

Needed

Table 1 - Questionnaire Scheme

3.3.2.1 Reliability

To ensure the reliability of the survey questionnaire, the survey questions were

adapted from validated instruments and scales: the ten questions on System Quality

were adapted from Doll and Torkzadeh’s (1988) two questions on Ease of Use, four

questions from Davis’s (1989) Perceived Ease of Use and four scales adopted from

Bailey and Pearson (1983): response/turnaround time, understanding of systems,

accuracy and reliability; the nine questions on Information Quality were all adapted

from Doll and Torkzadeh’s (1988) End-User Satisfaction instrument; the four

questions on User Satisfaction were adapted from Seddon and Kiew (1994); the six

questions on Perceived Usefulness were adapted from Davis’s (1989) Perceived


29

Usefulness; the five questions on System Importance were adapted from Seddon

and Kiew (1994); and the fifteen questions on Service Quality grouped under

responsiveness, reliability, tangibles and rapport categories were all adapted from

Kettinger and Lee’s (2005) Zones of Tolerance SERVQUAL (ZOT SERVQUAL)

instrument. Their instrument was developed to address the concerns with the

original SERVQUAL instrument discussed in chapter two, ZOT SERVQUAL

approached the measurement of service quality as a range of levels rather than the

single expectation point the original SERVQUAL instrument measured. Using ZOT,

the IS provider’s service quality was measured on three levels of service: desired

service (the level of service desired), adequate service (the minimum level of service

the customers were willing to accept) and received service (the level of service

received from their most recent interaction with the provider) (Kettinger and Lee,

2005). From these levels of service, the IS provider can recognize the service areas

where they were not meeting the minimum levels of service and can in turn, focus

on getting the levels up.

3.3.2.2 Validity

The validity of the constructed questionnaire was tested from the content

perspective. To test the content validity of the questionnaire, five respondents were

asked to complete the survey. This was done to obtain feedback on the ease of

navigation, comprehension of questions and time spent completing the

questionnaire. Their answers were not part of the actual study process and were

only used for testing purposes. The respondents were asked to provide suggestions

or any corrections to further improve and validate the survey instrument. With these

feedbacks, the survey questionnaire was revised changing ambiguous questions

into clearer ones to ensure comprehension.

3.3.2.3 Administration of Questionnaire

The survey questionnaire was web-based because it was the most cost effective

way to administer the questionnaire. In addition to this, the survey tool provided

greater flexibility to displaying the questions with radio buttons, star rankings,

instruction screens and likert matrices; it also provided immediate data validation

and was used to enforce rules such as required questions, or a minimum number of

answers per row or column, which cannot be done in email or with paper. Anonymity

was another reason for this choice as the respondents’ replies were of their own free

will with no influences as might have been in the case of a face to face interview.

The link to the survey questionnaire was sent via an email to a total of 380 system

users whose email addresses were provided by the company.


30

3.3 Ethical Aspects

The involvement of human participants or human related data is was required for

this research project. This was through an informal interview and survey

questionnaires. Therefore, descriptions of the study and a guarantee that all

responses will remain confidential and anonymous were presented at the beginning

of the survey with a clause that respondents’ consent will be implied upon

completion of the survey. The researcher confirms to have read and abided by the

University’s Ethics Policy for Research Involving Human Participants, Data and

Tissue.

3.4 Limitations of Methodology

3.4.1 Low Response Rate

The number of respondents to the online survey questionnaire was not large enough

as statistical tests require a larger sample size to ensure a representative

distribution of the population and to be considered representative of groups of

people to whom results will be generalized or transferred.

3.4.2 Time Constraints

The time available for the research was insufficient to collect as much data as would

have been adequate for an exhaustive statistical analysis.

3.4.3 Sample Bias

Respondents may avoid using extreme response categories (central tendency bias);

agree with statements as presented (acquiescence response bias); or try to portray

themselves or their group in a more favourable light (social desirability bias).

Chapter Four: Data Analysis and Findings

31

CHAPTER FOUR

DATA ANALYSIS AND FINDINGS

This chapter describes the analysis on the data collected from the survey

questionnaire and its results will be used to determine if the hypotheses implied from

the research model hold true or not. It is made of 3 main sections that described the

data collected on each success dimension, and presented the results of the

analyses. Section 4.1 presented the demographic results (gender and period of

use). Section 4.2 presented the results on the success dimensions (system quality,

information quality, service quality, system importance, user satisfaction and

usefulness). The last section, discusses the results of the analysis carried out to test

the hypotheses. SPSS software version 18, provided by the university, was used to

process the survey results. The sample size for this research was 380 registered

TPS users (bank, corporate and Interswitch users). A total of 133 completed

responses were obtained, 179 partially completed and no responses from the rest.

The 133 valid feedbacks received presented a response rate of 35%. Preece et al.

(2002) stated that “forty percent return is generally acceptable for many surveys but

much lower rates are common.” Therefore, the response rate was considered to be

fairly successful.

Different levels of analysis were carried out on the collected data and techniques

selected based on the type of data (ordinal, interval). With the research aims and

objectives in mind, a descriptive analysis, describing the distribution and range of

responses to each success dimension was performed. These dimensions were

independently analysed by univariate analysis using pie charts and bar charts. The

collected data were “summed” where appropriate to enable meaningful statistical

comparisons of subgroups. After this, bivariate analysis was carried out in order to

explore the relationships between two dimensions and the significant level of those

relationships, using Spearman’s rank correlation coefficient, which is the most

suitable statistical technique for exploring the relationship between two likert scales

responses (e.g. system quality and user satisfaction) (MASH, 2009; Jamieson;

2004). In addition, the ANOVA test was used to determine any significant variances

in responses.


32

4.1 Demographic Profile

Gender

The composition of responses from the aspect of gender revealed that 41 out of 133

responses, almost 31% of the received responses were female users while the

remaining 91 were male users (approximately 69%). Appendix II, Table 1 shows the

frequency distribution of the respondents’ gender.

Period of Use

For the analysis of this demographic variable, original range of values (in months

and years) were collapsed and converted into the final three ranges (in years)

shown in the figure below. The initial categorization was done to make the

completion of the survey questionnaire easier for the respondents. Findings showed

that most of the users (80 out of 133) had been using the TPS for over two years,

(28 out of 133) had been using the system between 1 to 2 years and (25 out of 133)

users had been using the system for less than a year. This indicated that most of the

respondents for this survey questionnaire have had substantial experience using the

system which is helpful to the validity of this research. For frequencies, refer to

Appendix II, Table 2.

Figure 7 - Period of Use

< 1 year 19%

1-2 years 21% > 2 years

60%

Period of Use


33

4.2 Adapted Success Dimensions

4.2.1 System Quality

The system quality dimension was measured using a matrix of likert scales. Each of

its ten questions required an individual response on the scale and all responses

were grouped together to report the findings on this dimension. The data from this

scale was treated as ordinal because while the responses categories had a ranked

order, the intervals between these values cannot be presumed equal (Jamieson,

2004). For example, is the same difference between “strongly disagree” and

“disagree” the same as “agree” and “strongly agree”? Majority of the users’, almost

60%, agreed that the TPS was easy to use, easy to understand, reliable and

responds to their system requests on time, as illustrated in Figure 8 below while the

rest of the users, a combination of those who either slightly agreed, were neutral or

disagreed with the system quality of the TPS. See Appendix II, Table 3 for the

frequencies.

Figure 8 - Perceived System Quality

4.2.2 Information Quality

The responses obtained from each of the nine questions on information quality were

grouped and treated as ordinal for the same reasons as the system quality

dimension. Findings revealed that majority of the users (over 70%) agreed positively

to the accuracy, timeliness and completeness of the information produced by the

TPS, presented in a usable format and relevant to their information needs. A smaller

2.8

8.2 5.8

7.7

15.9

40.5

19.1

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

StronglyDisagree

Disagree SlightlyDisagree

Neutral SlightlyAgree

Agree StronglyAgree

Pe

rce

nta

ge

System Quality

%


34

fraction (17%) was less agreeable and less than 9% of the users disagreed (Figure

9 and Appendix II, Table 4).

Figure 9 - Perceived Information Quality

4.2.3 Service Quality

This success dimension was measured under four categories: reliability,

responsiveness, rapport, tangibles and by a range of levels. These range of levels

ranked (here, rank meant to rate and not order) between 1 and 9 measured the level

of service the user considered adequate, desired and perceived from the TPS

support team. The responses from the questions under each category were

collapsed to present the average rank per level per category (See Figure 10 below).

The reliability category, measuring the ability of the provider to render the promised

service dependably and accurately recorded an average of 6.05, 6.85 and 6.43 for

the adequate, desired and perceived service quality. This shows that while the TPS

provider is yet to meet the desired level of reliability, it had exceeded the minimum.

The responsiveness category measured the promptness and willingness of the

support team to assist users presenting an average rank of 6.59, 7.38 and 6.79 to

the adequate, desired and perceived levels respectively. While the support team did

meet the minimum level of responsiveness, they did not the desired level. The

rapport category is concerned with the empathy and assurance the TPS user

considered adequate, desired and perceived, an average ranking of 6.54, 7.25 and

6.69 were assigned to these respectively. The last category, tangibles, concerned

with the physical support infrastructure of the TPS provider and the availability of

0.5 0.5 2.4

5.3

17.5

39.2

34.7

0

5

10

15

20

25

30

35

40

45

Never AlmostNever

SlightlyNever

Neutral SlightlyAlways

AlmostAlways

Always

Pe

rce

nta

ge

Information Quality

%


35

support materials gained average rankings of 6.40, 7.20 and 6.73 to the adequate,

desired and perceived levels of tangibles respectively.

Figure 10 - Perceived Service Quality

4.2.4 System Importance

The importance of the system, added to the research model to explain possible

variance in user’s perception of the TPS's usefulness and their satisfaction with it

was measured with five elements ranked (to rate, not order) between 1 and 7. The

elements: importance, relevance, fundamentality, essentiality and need were used

to obtain the users’ views of the TPS. The highest average rank, 6.04 indicate that

the users view the TPS as needed in their job functions. The following average

rankings in descending order, 5.97, 5.93 and 5.77 shows the relevance, importance

and essentiality of the TPS to the users’ job. The least average rank, 5.56 shows

fewer users considered the TPS central to their job. See Appendix II, Table 7 for the

frequency table.

6.05 6.59 6.54 6.40

6.85 7.38 7.25 7.20

6.43 6.79 6.69 6.73

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

Reliability Responsiveness Rapport Tangibles

Ave

rage

Ra

nk

Service Quality

Adequate Service Desired Service Perceived Service


36

Figure 11 - Perceived System Importance

4.2.5 User Satisfaction

Figure 12 below shows a very high level of user satisfaction with the TPS. Over

three quarters of the users were either satisfied or very satisfied with the TPS. A

lesser fraction of users (16%) were slightly satisfied and the rest of the users (8%)

were either neutral or dissatisfied with the system. See Appendix II, Table 6 for the

frequency table.

Figure 12 - User Satisfaction

5.93 5.97

5.56

5.77

6.04

5.27

5.37

5.47

5.57

5.67

5.77

5.87

5.97

6.07

Ave

rage

Ran

k

System Impotance

Average Rank

0.2 1.1 2.6 3.9

16.2

53.9

22.0

0.0

10.0

20.0

30.0

40.0

50.0

StronglyNot

Not SlightlyNot

Neutral Slightly Is Is Strongly Is

Pe

rce

nta

ge

User Satisfaction

%


37

4.2.6 Usefulness

As with the user satisfaction dimension, a large number of users (67%) believed the

TPS to have improved their productivity, performance and effectiveness at their jobs

(See Figure 13 below). The rest of the users (33%) were either less inclined, neutral

or disagreed to the usefulness of the TPS

Figure 13 - Perceived Usefulness

4.3 Dimensions and Associations

System Quality, Usefulness and User Satisfaction

The correlation coefficient of 0.556 (greater than p = 0.05) between system quality

and usefulness could be interpreted to represent a moderate positive linear

association between the two dimensions such that an increase in system quality will

increase the perceived usefulness of the system. An ANOVA was carried out to

identify the possible variances in the responses on the dimensions. From the test

results (Appendix III, Table 1, 6), the level of significance observed between the two

dimensions was statistically significant with a value less than 0.01. This means that

probability of the association being due to chance is less than 1%. With user

satisfaction, a slightly increased positive association was recorded with a correlation

coefficient of 0.587 with an increase in system quality causing a corresponding

0.1 2.0 2.5

9.0

19.5

44.6

22.2

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

StronglyDisagree

Disagree SlightlyDisagree

Neutral SlightlyAgree

Agree StronglyAgree

Pe

rce

nta

ge

Usefulness

%


38

increase in user satisfaction. The results of the ANOVA test for this association was

also statistically significant with the probability of chance less than 1%.

Information Quality, Usefulness and User Satisfaction

The association between information quality and usefulness was stronger with a

coefficient of 0.615 confirming that an increase in the information quality of the TPS

will result in a corresponding increase in the perceived usefulness of the system. A

significance of 0.000 further established the occurrence of this association was not

by chance. A higher association was present between information quality and user

satisfaction with a coefficient of 0.770 with a statistical significance of 0.000 showing

that there were no variances in the responses obtained on the dimensions (See

Appendix III, Table 2, 6).

System Importance, Usefulness and User Satisfaction

The association between the importance of the TPS and perceived usefulness

though present, was weak with a coefficient of 0.484 (< 0.50). This could mean that

an increase in the importance of the system might produce a corresponding

increase if the perceived usefulness of the system. This weakness was further

highlighted with the results of the ANOVA test between the dimensions (See

Appendix III, Table 3, 6) which showed a high probability (> 0.01) of chance from the

variances in the responses obtained on these dimensions. With user satisfaction,

the association got weaker. Negligible association could be seen between system

importance and user satisfaction with a coefficient of 0.338 (< 0.50) showing an

increase in the importance of the system with almost no effect on user satisfaction.

This almost lack of association was emphasized with the variances recorded in the

data collected on these dimensions.

Usefulness and User Satisfaction

Perceived usefulness showed ability to cause an effect on user satisfaction with a

coefficient of 0.673 indicative of a strong association. So an increase in the

perceived usefulness of a system is able to cause a corresponding increase in the

user’s satisfaction with the system. The ANOVA test showed statistical significance

in the association between the two dimensions with a probability of chance less than

0.01 (See Appendix III, 4, 6).


39

Service Quality and User Satisfaction

The correlation indicated a negative association between service quality and user

satisfaction with a coefficient of -0.037 indicative if an inverse association. This

means that an increase in service quality will cause a decrease in the level of user

satisfaction and vice versa. However, the results from the ANOVA test shows the

association as statistically significant with a probability to change less than 1% (See

Appendix III, Table 5, 6)

Chapter Five: Discussion of Results

40

CHAPTER FIVE

DISCUSSION OF RESULTS

This chapter discusses the analysis results from the previous chapter and accepts

or rejects the hypotheses formulated from the research model. Each success

dimension in the survey will be discussed separately with the exception of user

satisfaction which will be discussed within each dimension. Any association between

these dimensions will be subsequently clarified.

5.1 Gender and Period of Use

The addition of the demographic profile on respondents was not to identify possible

associations between gender, period of use and any of the other six success

dimensions; rather it was for data presentation. However, noteworthy was the large

fraction of experience users (using the TPS over 2 years) that completed the survey

questionnaire. This, the researcher expected increased the internal validity of the

causal associations identified in the research study.

5.2 System Quality

The results of the data analysis carried out on the system quality highlighted a

positive association with the usefulness and user satisfaction dimensions. This

supports the hypotheses on the positive effect of system quality on the usefulness of

a TPS and its users’ satisfaction. DeLone and McLean (2003) and Seddon and Kiew

(1994) also established the predictive characteristic of the system quality dimension

of an IS over its users’ satisfaction and perceived usefulness respectively. It can

therefore be inferred that the users of the case studied TPS perceived that the more

reliable, easy to use and prompt responding the system was, the more useful it was

to them invariably, increasing their satisfaction with it.

5.3 Information Quality

Information quality was established to have a high positive effect on the perceived

usefulness of the TPS to its users and their satisfaction with it. Seddon and Kiew

(1994) were able to show these strengths of association in their research; as did

DeLone and McLean (2003). Not surprising though, considering the rising the

dependency on these types of systems. A TPS that can be guaranteed to always


41

produce the right information, in the right format at the required time will most likely

have users who will come to depend on it for their related business functions,

increasing its usefulness to them and ultimately their satisfaction with it.

5.4 Service Quality

The results from data analysis on service quality however, did not support the

implied hypothesis which stated that was an increase in users’ satisfaction is

predicted when there is an increase in the level of quality service rendered by the

TPS provider. Rather, findings presented an inverse association which was not

supported by any of the researched literature. However the researcher suggests two

likely reasons for this:

1. Fewer empirical studies have been done on the updated D&M model which

was adapted in this research. Therefore, there are no exhaustive tests

showing the possible associations of service quality as an element of the IS

success model.

2. Due to the number of experienced users that responded to the survey

questionnaire, the service quality dimension might not have had an influence

of their perceptions on the TPS success especially if they do not require

frequent or extensive support. This likelihood could be further supported with

the ratings on the levels of service. As it was, the average ratings leaned

more towards the positive scale.

5.5 System Importance

The results on the system importance dimension showed a weak support of its

hypothesis. This implied that a positive increase in the importance of a system from

the perspective of the user might not guarantee an increase in the system’s

perceived usefulness and or satisfaction. However, Seddon and Kiew (1994) were

able to substantially support this increase in their research though they warned that

a change in the perceived importance of a system is what will result in

corresponding positive associations. Suffice to say there were minimal changes to

the users’ perceptions of the TPS's importance to cause strong effects on perceived

usefulness and satisfaction. The distribution on the period of use could be a reason

for the minimal change to perceptions as majority of the users probably have

already established their perceptions on the importance of the system prior to taking

the survey.


42

5.6 Usefulness as an Impact Measure

The questions presented under the usefulness dimension were also used to

measure the impact of the TPS on the users. This was not to establish a causal

association between independent variables and individual impacts but to measure

how much the use of the TPS has caused a change in the behaviour of its users.

Appendix II, Table 5 shows a high degree of impact on users.

Chapter Six: Conclusions

43

CHAPTER SIX

CONCLUSIONS

This chapter presents the conclusions from the research, summarising the research

findings. The limitations of the research were discussed, recommendations for

Transaction Processing System providers were presented and future work revealed.

6.1 Conclusion

The increasing role of Transaction Processing Systems as key enablers of vast

range of commercial activities and as a critical component to the world’s economy

demands that extra efforts be made to ensure the success of these systems. While

a lot of studies have focused on measuring the success of Information Systems as a

whole or as types of Information System, very little attention has been paid to

Transaction Processing System and its success measures.

In light of this, the research’s aim was to measure the success of Transaction

Processing Systems adapting the D&M IS Success Model. To do this, a model

adapted from the updated D&M model to the context of the research, was

developed to measure the success of a particular TPS. From this model, the

research hypotheses were formulated to be supported or refuted by the findings of

the research. The study focused on exploring the degrees of influences each

independent elements of the model (system quality, information quality, service

quality and system importance) had on the one or more dependent elements

(usefulness and users’ satisfaction). An online survey was conducted, featuring

various types and numbers of questions grouped appropriately to measure each

element in the research model. A total of 133 completed survey responses were

received and a significant number of all the respondents revealed positive effects of

independent elements on one or two of the dependent elements.

The research findings revealed that the system quality of a TPS influences users’

perception of its usefulness and a TPS that its users perceive useful will increase

their satisfaction with it. Information quality also showed a strong positive influence

on perceived usefulness and user’s satisfaction. A system that is reputed to produce

precise reports will be regarded as more useful and thus more satisfying. The

findings also revealed that the weak influence system importance had on usefulness

and user satisfaction so that is the importance of a system increases, it does not


44

have to influence its usefulness or users’ satisfaction. However, the findings did not

support a positive influence of service quality on user satisfaction; rather it revealed

an inverse influence on which suggestions were presented as to its cause. The

impact of system use on users of the TPS was measured and was found to be high.

Users attested that using the system increased their productivity, their performance

and their efficiency. It is safe to infer that if the users experienced these changes in

behaviour from use of the system; this will ultimately lead to a cumulative benefit to

the organization they are part of. Finally, the findings of this study support DeLone

and McLean (2003) and Seddon and Kiew (1994) results on similar studies with the

exception of the service quality dimension which could be subject to a plausible

unknown factor.

6.2 Research Limitations

Sample Size

Strength in numbers characterizes the many advantages of quantitative research

and while the number of respondents for this research was not overly small, it was

not enough. With more data, extensive statistically tests could have been done.

Time

The major constraint of this research project was time as the researcher believes

the three month duration of this research project was insufficient. It took a while for

to get a final approval from the case study company which encroached into data

collection time affecting the size of collected data.

6.3 Recommendations

The research findings provide TPS providers with an understanding on the aspects

of a TPS that are critical to its success; in particular, the awareness that different

factors, even non-system factors (like system importance) can affect users’

satisfaction which is usually one of the goals of producing the systems. As service

providers, the zones of tolerance could help them develop an efficient service

improvement plan if required such that more focus could be placed on areas where

the adequate levels of service have not been met.


45

6.4 Future Work

This is not the end of this research; more work is still required before the research

model can be a valid TPS success model. First, the inverse association between

service quality and user satisfaction still needs to be explored to find the causal

associations between them. Second, the weakness of the system importance

dimension needs to be investigated and measured in another context to conclude on

its validity. Lastly, a large sample size that can give a true representation of the

population should be used.

Word Count: 14,500

46

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51

APPENDIX I – SURVEY QUESTIONNAIRE

52

53

54

55

56

APPENDIX II – FREQUENCY TABLES

Gender

Frequency Percent Valid Percent

Cumulative

Percent

Valid Female 41 30.8 30.8 30.8

Male 92 69.2 69.2 100.0

Total 133 100.0 100.0

Appendix II, Table 1 – Gender

PeriodOfUse


Cumulative

Percent

Valid 7-12 Months 10 7.5 7.5 7.5

0-6 Months 15 11.3 11.3 18.8

1-2 Years 28 21.1 21.1 39.8

Over 2 years 80 60.2 60.2 100.0

Total 133 100.0 100.0

Appendix II, Table 2 - Period of Use

SystemQuality


Cumulative

Percent

Valid Disagree 1 .8 .8 .8

Strongly Disagree 1 .8 .8 1.5

Slightly Agree 3 2.3 2.3 3.8

Slightly Disagree 3 2.3 2.3 6.0

Neutral 6 4.5 4.5 10.5

Strongly Agree 51 38.3 38.3 48.9

Agree 68 51.1 51.1 100.0

Total 133 100.0 100.0

Appendix II, Table 3 - System Quality

57

InformationQuality


Cumulative

Percent

Valid Never 1 .8 .8 .8

Slightly Never 2 1.5 1.5 2.3

Neutral 7 5.3 5.3 7.5

Slightly Always 22 16.5 16.5 24.1

Always 46 34.6 34.6 58.6

Almost Always 55 41.4 41.4 100.0

Total 133 100.0 100.0

Appendix II, Table 4 - Information Quality

Usefulness


Cumulative

Percent

Valid Disagree 3 2.3 2.3 2.3

Slightly Disagree 4 3.0 3.0 5.3

Neutral 13 9.8 9.8 15.0

Strongly Agree 28 21.1 21.1 36.1

Slightly Agree 29 21.8 21.8 57.9

Agree 56 42.1 42.1 100.0

Total 133 100.0 100.0

Appendix II, Table 5 - Usefulness

UserSatisfaction


Cumulative

Percent

Valid Dissatisfied 2 1.5 1.5 1.5

Slightly Dissatisfied 4 3.0 3.0 4.5

Neutral 5 3.8 3.8 8.3

Slightly Satisfied 23 17.3 17.3 25.6

Very Satisfied 28 21.1 21.1 46.6

Satisfied 71 53.4 53.4 100.0

Total 133 100.0 100.0

Appendix II, Table 6 - User Satisfaction

58

SystemImportance


Cumulative

Percent

Valid 1.00 3 2.3 2.3 2.3

2.00 3 2.3 2.3 4.5

3.00 6 4.5 4.5 9.0

4.00 15 11.3 11.3 20.3

5.00 26 19.5 19.5 39.8

6.00 38 28.6 28.6 68.4

7.00 42 31.6 31.6 100.0

Total 133 100.0 100.0

Appendix II Table 7 - System Importance

59

APPENDIX III –ANOVA TEST TABLES, CORRELATION

MATRIX

ANOVA

Sum of Squares df Mean Square F Sig.

Usefulness Between Groups 2843.926 35 81.255 4.602 .000

Within Groups 1712.601 97 17.656

Total 4556.526 132

UserSatisfaction Between Groups 1077.080 35 30.774 6.038 .000

Within Groups 494.364 97 5.097

Total 1571.444 132

Appendix III, Table 1 - System Quality, Usefulness and User Satisfaction

ANOVA



Within Groups 2338.499 107 21.855

Total 4556.526 132


Within Groups 417.197 107 3.899

Total 1571.444 132

Appendix III, Table 2 - Information Quality, Usefulness and User Satisfaction

ANOVA



Within Groups 3264.358 110 29.676

Total 4556.526 132


Within Groups 1125.373 110 10.231

Total 1571.444 132

Appendix III, Table 3 - System Importance, Usefulness and User Satisfaction

60

ANOVA

UserSatisfaction


Between Groups 1096.231 22 49.829 11.534 .000

Within Groups 475.213 110 4.320

Total 1571.444 132

Appendix III, Table 4 – Usefulness and User Satisfaction

ANOVA

UserSatisfaction


Between Groups 1077.080 35 30.774 6.038 .000

Within Groups 494.364 97 5.097

Total 1571.444 132

Appendix III, Table 5 - Service Quality and User Satisfaction

Appendix III, Table 6 - Correlation Matrix of Success Dimensions (Next Page)

61

Correlations

SystemQuality

InformationQual

ity ServiceQuality

SystemImportan

ce Usefulness

UserSatisfactio

n

Spearman's rho SystemQuality Correlation Coefficient 1.000 .617** -.130 .301

** .556

** .587

**

Sig. (2-tailed) . .000 .136 .000 .000 .000

N 133 133 133 133 133 133

InformationQuality Correlation Coefficient .617** 1.000 -.095 .349

** .615

** .770

**

Sig. (2-tailed) .000 . .277 .000 .000 .000

N 133 133 133 133 133 133

ServiceQuality Correlation Coefficient -.130 -.095 1.000 -.049 -.037 -.049

Sig. (2-tailed) .136 .277 . .577 .675 .574

N 133 133 133 133 133 133

SystemImportance Correlation Coefficient .301** .349

** -.049 1.000 .484

** .338

**

Sig. (2-tailed) .000 .000 .577 . .000 .000

N 133 133 133 133 133 133

Usefulness Correlation Coefficient .556** .615

** -.037 .484

** 1.000 .673

**

Sig. (2-tailed) .000 .000 .675 .000 . .000

N 133 133 133 133 133 133

UserSatisfaction Correlation Coefficient .587** .770

** -.049 .338

** .673

** 1.000

Sig. (2-tailed) .000 .000 .574 .000 .000 .

N 133 133 133 133 133 133

**. Correlation is significant at the 0.01 level (2-tailed).

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