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CRITICAL SUCCESS FACTORS FOR ACCOUNTING INFORMATION SYSTEMS

DATA QUALITY

BY

UKAH CHIBUEZE KALU PG/MBA/10/54752

DEPARTMENT OF ACCOUNTANCY FACULTY OF BUSINESS ADMINISTRATION

UNIVERSITY OF NIGERIA, ENUGU CAMPUS.

OCTOBER 2011.

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TITLE PAGE

CRITICAL SUCCESS FACTORS FOR ACCOUNTING INFORMATION SYSTEMS DATA QUALITY

BY

UKAH CHIBUEZE KALU PG/MBA/10/54752

BEING A PROJECT REPORT SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER OF BUSINESS ADMINISTRATION (MBA) DEGREE IN

ACCOUNTANCY

DEPARTMENT OF ACCOUNTANCY FACULTY OF BUSINESS ADMINISTRATION

UNIVERSITY OF NIGERIA, ENUGU CAMPUS,

SUPERVISOR: DR. R.O. UGWOKE

OCTOBER 2011.

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CERTIFICATION

I UKAH, CHIBUEZE KALU, a postgraduate student in the Department of

Accountancy with registration number PG/MBA/10/54752 has satisfactorily

completed the requirements for course and research work for the award of

Master of Business Administration (MBA) degree in Accountancy.

To the best of my knowledge, the work embodied in this project is original

and has not been submitted in part or in full for any other Diploma or degree

of this or any other university.

………………………. ………………… Ukah, Chibueze Kalu Date PG/MBA/10/54752. Candidate.

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APPROVED This project work was carried out by Ukah, Chibueze Kalu, a postgraduate

student with registration number PG/MBA/10/54752 in the department of

Accountancy and have been approved and accepted by the Department of

Accountancy, Faculty of Business Administration, University of Nigeria,

Enugu Campus, Enugu.

……………………. ………………… Dr. R.O. Ugwoke Date Supervisor ............................... ………………….. Dr. R.O. Ugwoke Date Head of Department

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DEDICATION

This project is entirely dedicated to God Almighty for his infinite mercies,

guidance and protection and for making my dream come true. Also to my

friends, and sponsors, CSP Kalu Ukah whose financial support remains the

“back-bone” of my academic pursuits.

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ACKNOWLEDGMENTS I would like to acknowledge the assistance of many people who provided

help, support, and encouragement, enabling me to complete my M.B.A

project. In particular, I would like to acknowledge the contribution of my

principle supervisor and Accountancy Head of Department, Dr. R.O.

Ugwoke who guided and encouraged me from the beginning and throughout

my whole M.B.A candidature.

Equally, I will not forget to acknowledge the support extended to me by all

the lecturers of the Faculty of Business Administration, especially

Department of Accountancy. My appreciation also goes to the management

of First Bank Plc, Zenith Bank Plc, Nigeria Breweries Plc and Nigeria

Bottling Company Plc all in Enugu State for their co-operation in making

available information necessary to complete this work. All my friends and

colleagues are equally appreciated for their spiritual, moral and financial

support and particularly in the family of Okechukwu Nonso Beloved that

provided invaluable assistance and support. Special thanks to Agu O. Agu,

who helped me at many critical stages of my research and to Chikwendu

Henry A., Sam-Igbeta Felisha and Postgraduate Students Fellowship

University of Nigeria Enugu Campus, whose friendships helped me greatly

on completion of this project work.

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To my family members, I wish to express my gratitude and love to my

parents for their unreserved love, support and encouragement. The courage

and determination they taught me have made my life so wonderful.

Finally, my profound gratitude goes to Almighty God who is the Alpha and

Omega, Giver of Wisdom and Knowledge, for His provision, guidance and

protection throughout my Masters programme at University of Nigeria.

UKAH, CHIBUEZE KALU PG/MBA/10/54752

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TABLE OF CONTENTS

Title Page … … … … … … … … … i

Certification … … … … … … … … … ii

Approval Page … … … … … … … … iii

Dedication … … … … … … … … … iv

Acknowledgments … … … … … … … … v

Table of Contents … … … … … … … … vii

List of Figures … … … … … … … … xi

List of Tables … … … … … … … … vii

Abstract … … … … … … … … … x

CHAPTER ONE: INTRODUCTION

1.1 Background of the Study … … … … … … 1

1.2 Statement of the Problems … … … … … 3

1.3 Objectives of the Study … … … … … … 5

1.4 Research Questions … … … … … … 5

1.5 Research Hypothesis … … … … … … 6

1.6 Significance of the Study … … … … … 7

1.7 Scope of the Study … … … … … … 8

1.8 Limitations of the Study … … … …. … … 9

1.9 Operational Definition of Key Terms … … … … 10

1.10 Profile of the Organization used for the Study … … 11

1.10.1 First Bank Plc … … … … … … … 11

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1.10.2 Zenith Bank Plc … … … … … … … 12

1.10.3 Nigeria Breweries Plc … … … … … … 13

1.10.4 Nigeria Bottling Company Plc … … … … … 13

References … … … … … … … … 15

CHAPTER TWO: REVIEW OF RELATED LITERATURE

2.1 Introduction … … … … … … … 16

2.2 Data Quality … … … … … … … 16

2.3 Accounting Information System … … … … 19

2.4 Data Quality of Accounting Information System … … 19

2.4.1 Data Quality in AIS for this research … … … … 22

2.5 Quality Management … … … … … 24

2.5.1 Quality management: Just-in-time … … … … 27

2.5.2 Product Perspective on Data Quality Management … … 29

2.5.3 Product Quality and Service Quality … … … … 30

2.5.4 Data Quality in Database System … … … … 36

2.6 Critical Success Factors for Quality Management and TQM… 32

2.7 Total Data Quality Management … … … … 35

2.7.1 Data Quality in e-Business … … … … … 36

2.7.2 Framework for Data Quality Research … … … 36

2.7.3 Important Steps in Ensuring Data Quality … … … 40

2.8 Data Quality in Accounting Information System … … 43

2.8.1 The Model for factors Influencing Data Quality in AIS … 43

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2.8.2 Possible factors that Impact on Data Quality in AIS… … 46

2.8.3 Critical factors for Ensuring Data Quality in AIS … … 46

2.9 Stakeholder Groups for Data Quality in AIS … … … 47

References … … … … … … … … 53

CHAPTER THREE: RESEARCH METHODOLOGY

3.1 Introduction … … … … … … … 53

3.2 Research Design … … … … … … … 53

3.3 Sources of Data … … … … … … … 54

3.4 Area of Study … … … … … … … 55

3.5 Instruments for Data Collection … … … … 55

3.6 Population of the Study … … … … … … 56

3.7 Sampling Procedure … … … … … … 56

3.8 Sample Size Determination … … … … … 57

3.9 Validity of the Research Instrument … … … … 59

3.10 Reliability of the Research Instrument … … … … 59

3.11 Questionnaire Design and Administration … … … 61

3.12 Data Treatment Technique(s) … … … … … 61

References … … … … … … … … 64

CHAPTER FOUR: PRESENTATION AND ANALYSIS OF DATA

4.1 Introduction … … … … … … … 65

4.2 Analysis of Questionnaire … … … … … 66

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4.3 Test of Hypothesis … … … … … … 89

4.4 Discussion of Results … … … … … … 105 CHAPTER FIVE: SUMMARY OF MAJOR FINDINGS,

CONCLUSION AND RECOMMENDATIONS

5.1 Introduction … … … … … … … 108

5.2 Summary of Major Findings … … … … … 108

5.3 Conclusion … … … … … … … … 109

5.4 Recommendations … … … … … … … 110

5.5 Contributions to Knowledge … … … … … 111

5.6 Suggested Area for Future Research … … … … 113

Bibliography … … … … … … … 115

Appendix I … … … … … … … … 119

Appendix II … … … … … … … … 126

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LIST OF FIGURES

2.1 Preliminary Theoretical Framework of this Research … … 23

2.2 The Model for Factors Influencing Data Quality in AIS … 45

2.3 The framework for Understanding Relationships Between

Stakeholders groups and Data Quality in AIS … … 50

5.1 Information Quantity Management in AIS

Adoption Framework … … … … … … 112

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LIST OF TABLES

2.1 Data Quality Dimensions … … … … … … 18

2.2 Deming’s 14 Principles of Quality Management … … 25

2.3 Product vs. Information Manufacturing … … … 29

2.4 A Framework for Data Quality research … … … 37

2.5 Critical Success Factors in Data Quality … … … 41

3.1 Staff Strength of the Four Selected Organizations … … 56

3.2 Pearson Product Moment Correlation Coefficient … … 60

4.0 Questionnaire Distribution … … … … … 65

4.1 Sex Distribution of Respondents … … … … 65

4.2 Marital Status of Respondents … … … … … 65

4.3 Educational Qualification of Respondents … … … 67

4.4 Years of Experience/ Length of Service … … … 67

4.5 Age of Respondents … … … … … … 68

4.6 Industry of the Organization … … … … … 68

4.7 Main role Relative to Accounting Information System … 69

4.8 Accounting Information System in Organization … … 70

4.9 Quality Data from Organization AIS … … … … 70

4.10 Top management commitment to Data Quality … … 71

4.11 Understanding of the System and Data Quality … … 72

4.12 Personnel Competency … … … … … … 73

4.13 Physical Environment … … … … … … 74

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4.14 Teamwork (Communication) … … … … … 75

4.15 Data Quality policies and Standards … … … … 76

4.16 Establish DQ manager position to manage overall DQ … 76

4.17 Clear DQ vision for entire Organization … … … 77

4.18 Data Quality Controls … … …. … … … 78

4.19 Middle Management Commitment to Data Quality … … 79

4.20 Continuous Improvement … … … … … 80

4.21 Data Supplier Quality Management ... … … … 81

4.22 Risk Management … … … … … … … 81

4.23 Management of Changes … … … … … … 82

4.24 Effective Employee relations … … … … … 83

4.25 User Focus … … … … … … … … 84

4.26 Input Controls … … … … … … … 85

4.27 Organizational Culture of Focusing on Data Quality … 86

4.28 Audit and Reviews … … … … … … 87

4.29 Evaluate Cost/ Benefit Tradeoffs … … … … 88

4.30 Education and Training … … … … … … 89

4.31 Condensed Outcome of the five Questions Administered for

Testing Hypothesis One … … … … … … 90

4.32 Descriptive Statistics … … … … … … 91

4.33 Chi-Square Test … … … … … … … 92

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4.34 Condensed Outcome of the Four Questions Administered for

Testing Hypothesis Two … … … … … … 94

4.35 Paired Sample Statistics … … … … … … 95


Testing Hypothesis Three … … .. … … 98



Testing Hypothesis Four … … … … … … 100



Testing Hypothesis Five … … … … … … 103


4.42 Chi-Square Test … … … …… … … … 105

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ABSTRACT

Today’s organizations are operating and competing in an information age. Quality information is critical to organizations’ success in today’s highly competitive environment. Accounting information systems (AIS) as a discipline within information systems require high quality data. However, empirical evidence suggests that data quality is problematic in AIS. Therefore, knowledge of critical factors that are important in ensuring data quality in accounting information systems is desirable. A literature review evaluates previous research work in quality management, data quality, and accounting information systems. It was found that there was a gap in the literature about critical success factors for data quality in accounting information systems. Based on this gap in the literature and the findings of the exploratory stage of the research, a preliminary research model for factors influence data quality in AIS was developed. A framework for understanding relationships between stakeholder groups and data quality in accounting information systems was also developed. The major stakeholders are information producers, information custodians, information managers, information users, and internal auditors. A model for critical success factors for AIS DQ was proposed and examined in four Nigerian organizations were carried out, where two of them were banking organizations and the other two are manufacturing organizations. Each case was examined as a whole to obtain an understanding of the opinions and perspectives of the respondents from each individual organization as to what are considered to be the important factors in the case Furthermore, the variations between five different stakeholder groups were also examined. The results of the four main case studies suggested 26 factors that may have impact on data quality in AIS. The major findings from the survey are: 1. respondents rated the importance of the factors and the actual performance of those factors consistently higher. 2. Four factors were found to be significantly different between different stakeholder groups: user focus, measurement and reporting, data supplier quality management and audit and reviews. 3. The top three critical factors for ensuring data quality in AIS were: top management commitment, education and training, and the nature of the accounting information systems. The key contribution of this project is the theoretical framework developed from the analysis of the findings of this research, which is the first such framework built upon empirical study that explored factors influencing data quality in AIS and their interrelationships with stakeholder groups and data quality outcomes. That is, it is now clear which factors impact on data quality in AIS, and which of those factors are critical success factors for ensuring high quality information outcomes. In addition, the performance level of factors was also incorporated into the research framework. Since the actual performance of factors has not been highlighted in other studies, this research adds new theoretical insights to the extant literature. In turn, this research confirms some of the factors mentioned in the literature and adds a few new factors. Moreover, stakeholder groups of data quality in AIS are important considerations and need more attention. The research framework of this research shows the relationship between stakeholder groups, important factors and data quality outcomes by highlighting stakeholder groups’ influence on identifying the important factors, as well as the evaluation of the importance and performance of the factors.

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CHAPTER ONE

INTRODUCTION

1.1 BACKGROUND OF THE STUDY

Quality information is one of the competitive advantages for an organization. In an

accounting information system, the quality of the information provided is

imperative to the success of the systems. Accounting Information System (AIS) as

one of the most critical systems in the organization has also changed its way of

capturing, processing, storing and distributing information. Information has

become a key resource of most organizations, economies, and societies. Indeed, an

organization’s basis for competition has changed from tangible products to

intangible information. More and more organizations believe that quality

information is critical to their success (Wang, R.Y 2006).

However, not many of them have turned this belief into effective action. Poor

quality information can have significant social and business impacts (Strong, Lee

and Wang, 1997). There is strong evidence that data quality problems are

becoming increasingly prevalent in practice (Redman, T.C 1998). Most

organizations have experienced the adverse effects of decisions based on

information of inferior quality (Huang, Lee and Wang, 1999). It is likely that some

data stakeholders are not satisfied with the quality of the information delivered in

their organizations. In brief, information quality issues have become important for

organizations that want to perform well, obtain competitive advantage, or even

just survive in the 21st century.

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In particular, Accounting Information Systems (AIS) maintain and produce the

data used by organizations to plan, evaluate, and diagnose the dynamics of

operations and financial circumstances (Anthony, Reese and Herrenstein, 2005).

Providing and assuring quality data is an objective of accounting. With the advent

of AIS, the traditional focus on the input and recording of data needs to be offset

with recognition that the systems themselves may affect the quality of data

(Fedorowicz and Lee, 1998). Indeed, empirical evidence suggests that data quality

is problematic in AIS (Johnson, Leith, and Neter, 1981). AIS data quality is

concerned with detecting the presence or absence of target error classes in

accounts (Kaplan, Krishnan, Padman and Peters, 1998).

Thus, knowledge of the critical factors that influence data quality in AIS will assist

organizations to improve their accounting information systems’ data quality.

While many AIS studies have looked at internal control and audit, Data Quality

(DQ) studies have focused on the measurement of DQ outcomes. It appears that

there have been very few attempts to identify the Critical Success Factors (CSFs)

for data quality in AIS. Thus, there is a need for research to identify the critical

success factors that affect organizations’ AIS DQ.

Information technology has changed the way in which traditional accounting

systems work. There is more and more electronically captured information that

needs to be processed, stored, and distributed through IT-based accounting

systems. Advanced IT has dramatically increased the ability and capability of

processing accounting information. At the same time, however, it has also

introduced some issues that traditional accounting systems have not experienced.

One critical issue is the data quality in AIS. IT advantages can sometimes create

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problems rather than benefiting an organization, if data quality issues have not

been properly addressed. Information overload is a good example. Do we really

need the quantity of information generated by the systems to make the right

decision? Another example is e-commerce. Should the quality of data captured

online always be trusted?

Data quality has become crucial for the success of AIS in today’s IT age. The need

arises for quality management of data, as data processing has shifted from the role

of operations support to a major operation in itself (Romney, M. and Steinbart, P.

J., 2009). Therefore, knowledge of those factors impact on data quality in

accounting information systems is desirable, because those factors can increase the

operating efficiency of AIS and contribute to the effectiveness of management

decision making.

1.2 STATEMENT OF THE PROBLEM

The proliferation of computerized database with relative increase in errors of such

stored data base in organizations which depend on them to support business

process and decision making has been questioned by many analysts.

The number of errors in stored data and the organizational impact of these errors is

likely to increase (Klein 1998).

Also, inaccurate and incomplete data may adversely affect the competitive success

of an organization (Redman 1992). Indeed, poor quality information can have

significant social and business impacts. For example, NBC News reported that

“dead people still eat!” Because of outdated information in US government

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databases, food stamps continued to be sent to recipients long after they died.

Fraud from food stamps costs US taxpayers billions of dollars.

Equally, losses in millions incurred by business organizations who were caught

unawares by dramatic changes in interest rates is of great concern to both owners

and management.

In particular, there are consequences of poor data quality in AIS. For example,

errors in an inventory database may cause managers to make decisions that

generate overstock or under-stock conditions (Bowen 1993). One minor data entry

error, such as the unit of product/service price, could go through an organization’s

AIS without appropriate data quality checks, and cause losses to an organization

and / or harm its reputation.

More so, most of the information system research into data quality focuses on the

theoretical modeling of controls and measurement while few studies have

attempted to understand what causes the difference in AIS data quality outcomes,

and what should be done to ensure high quality accounting information.

Most organizations have experienced the adverse effects of decision based on

information of inferior quality. However, not many of them have turned this belief

into effective action. Poor quality information can have significant social and

business impacts.

Therefore, there is lack of knowledge of the CSF for data quality in AIS that can

assist organizations to ensure and improve accounting information quality.

These has necessitated the conduct of this research.

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1.3 OBJECTIVES OF THE STUDY

The main objective of this study is to examine the critical success factors for

accounting information systems data quality. The subsidiary objectives include the

following:

(1) To determine the factors that affects the variation of data quality in

accounting information systems.

(2) To ascertain the variations with regard to the perceptions of importance of

those factors that affect data quality in accounting information systems.

(3) To examine the stakeholder perceptions on importance of critical factors

for accounting information systems.

(4) To investigate the factors that are critical success factors to ensure a high

quality of data in accounting information systems

(5) To examine the organizations perspective in the importance and

performance of critical success factors for accounting information system

data quality.

1.4 RESEARCH QUESTIONS

In order to explore the research problem, the focus of this project is on five

research questions which reflect on the objectives of the study are fielded.

(1) What factors affect the variation of data quality in accounting information

systems?

(2) Are there any variations with regard to the perceptions of importance of

those factors that affect data quality in accounting information systems?

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(3) What are the perceptions of stakeholder groups in importance of critical

factors for accounting information systems?

(4) Which of these factors are critical success factors to ensure a high quality of

data in accounting information systems data quality?

(5) What are organizations perspective in the importance and performance of

critical success factors for accounting information system data quality?

1.5 RESEARCH HYPOTHESES

In analyzing the critical success factors for accounting information systems’ data

quality, some tentative statements were formed to help answer the research

questions hence the following hypotheses that have to be tested were put forward

for this study.

Hypothesis One

Ho: There are no significant factors that affect the variation of data quality in

accounting information system.

H1: There are significant factors that affect the variation of data quality in


Hypothesis Two

H0: There are no significant differences between the perceptions of importance

of critical factors for accounting information systems’ data quality, and

actual performance of those factors.

H1: There are significant differences between the perceptions of importance of

critical factors for accounting information systems’ data quality, and actual

performance of those factors.

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Hypothesis Three

H0: There are no significant differences between different stakeholder groups in

their perceptions of importance of critical factors for accounting

information systems’ data quality.

H1: There are a significant difference between different stakeholder groups in



Hypothesis Four

H0: There are no significant critical success factors to ensure a high quality of

data in accounting information systems.

H1: There are significant critical success factors to ensure a high quality of data

in accounting information systems.

Hypothesis Five

H0: Different organizations have the same perspective in the importance and

performance of critical success factors for accounting information systems

data quality.

H1: Different organizations have different perspective in the importance and


data quality.

1.6 SIGNIFICANCE OF THE STUDY

Identifying the critical success factors for AIS could enhance the ability of AIS’s

to gather data, process information and prepare reports. Outcomes of this research

will contribute to the body of knowledge both in AIS and data quality field, and it

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may benefit other research into these areas. For example, it can help arouse the

awareness of data quality issues in AIS field, and to make it possible to establish

the linkage of the identified CSFs with the existing data quality dimensions for

outcomes assessment.

Thus, understanding how these factors affect organizations’ AIS performance may

be useful to practitioners. Focusing on those factors that are more critical than

others will lead to efficiency and effectiveness AIS’s procedures. In brief, the

results from this research are likely to help the academic community for future

researchers, organizations’ top management, accountants, and IT managers obtain

better understanding of AIS DQ issues.

1.7 SCOPE OF THE STUDY

This study is limited to four Nigerian companies selected for the case study in this

research. Two of them were chosen from banking industry, and two from

manufacturing industry. As there is no one set of criteria to distinguish banking

industry and manufacturing industry for the purpose of the case study analysis of

this research, employee number was use to define the size of the organizations.

Although criteria defining organizations as bank, manufacturing vary, in this

research organizations with more than 1000 employees were categorized as

manufacturing companies while those organizations with fewer than 1000

employees were categorized as banking industries. In order to respect the privacy

of the participating organizations and individual interviewees they were not

identified by their real names or actual position titles.

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1.8 LIMITATIONS OF THE STUDY

As part of the research experience by researchers all over the globe; certain

limitations hindered the effective and smooth collection of data for the work.

These in specific terms include: inadequate working fund, lack of time and

difficulties (minimal) in obtaining needed data relevant to the subject matter of

critical success factors for accounting information systems data quality.

Financial Constraint: The finance needed to carry out this work is too much and

cannot be afforded by the student. This to an extent hampered the success of this

work.

Time Constraint: Time was really a big constraint in carrying out this research

study. The researcher had to combine the collection of materials for the study with

other academic activities. The study was not easy to carryout due to distant part of

the organizations and the huge financial burden involved.

Non-Challant Attitude of Respondents: Another limitation in the course of

carrying this study was the non-chalet attitude of the respondents in supplying the

necessary information. This was probably due to their ignorance of the main

purpose of the study. Also many refused to grant interviews or answer question

bordering on the activities of the organizations.

Scope of the Research: The study was constrained to Nigerian organizations;

therefore, the conclusions drawn from this study may have a potential problem on

generalization.

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1.9 OPERATIONAL DEFINITIONS OF KEY TERMS

This section develops the definition of core terms for this research because precise

definitions of core terms are the foundation of any research project.

Accounting Information System: Accounting information system (AIS) is a

system of records, usually computer based, which combines accounting principles

and concepts with the benefits of an information system and which is used to

analyze and record business transactions for the purpose to prepare financial

statements and provide accounting data to the organizations studied.

Critical Success Factor: Critical success factor (CSF) is the term for an element

that is necessary for an organization or project to achieve its mission.

Data Quality: Data Quality (DQ) is the state of completeness, validity,

consistency, timeliness and accuracy that makes data appropriate for a specific

use.

Data Suppliers: Data suppliers are those who provide raw, un-organized data to

the accounting systems which include both internal and external such as, other

departments within the organization (internal), and trading partners (external).

Information Users: Information users are the users of the accounting information

which include both internal and external users. Such as: top management and

general users within the organization (internal), banks and government (external).

Middle Management: is responsible for implementing the strategic decisions of

top management. Middle managers make tactical/short-range decisions.

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Non-management Employees: who include production, clerical, and staff

personnel.

Stakeholder: Stakeholder is a person, group, organization, or system who affects

or can be affected by the organization's actions

Small to Medium Organizations: Small to medium organizations (SMEs) are

companies whose headcount or turnover falls below certain limits.

Top Management: Executive or senior management includes the highest

management positions in an organization.

1.10 PROFILE OF THE ORGANIZATION USED FOR THE STUDY

1.10.1 First Bank Plc

First Bank Plc is one of the oldest financial institutions in Nigeria and was the first

bank to be established in West Africa. The bank was incorporated as a limited

liability company in March 1894 and was listed on The Nigerian Stock Exchange

in March 1971. Following the Central Bank of Nigeria’s (“CBN”) induced

industry-wide consolidation in 2005, the bank acquired its merchant banking

subsidiary, FBN (Merchant Bankers) Limited and MBC International Bank Plc.

The bank offers a wide array of financial services to a diverse customer base

through its local and offshore offices, including 465 branch offices country wide

and 532 ATM’s. In addition to growing organically through new products and

branch development, other viable domestic acquisitions are being explored as the

Bank marked its 110 years of existence during which it pioneered the art and

science of modern banking in the country.

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First Bank of Nigeria maintains a subsidiary in the United Kingdom, FBN Bank

(UK), which has a branch in Paris. The bank also has representative offices in

South Africa and China. In October 2011, the bank acquired Banque International

de Credit (BIC), a leading bank in the Democratic Republic of Congo (DRC).

(http://www.firstbanknigeria.com/Portals/2/pdf/Rating_rep/FirstBank%20-

%20GCRFinal%20%20rpt%2008.pdf)

1.10.2 Zenith Bank Plc

Zenith Bank was established in May 1990. It became a public limited company in

July 2004, and had an initial public offering on the Nigerian Stock Exchange

(NSE) on October 21 of that year. Also in 2004, credit rating agency Fitch Ratings

identified its credit as AA- on their long-term scale.

Zenith Bank Plc is a Nigeria-based commercial bank engaged in the provision of

universal banking services to corporate, commercial and individual customers.

The Bank provides services as savings and current accounts, treasury and

financing services, investment banking, mortgage loans, trade financing, fund

management and investment banking, import and export finance, and cash and

liquidity management services to the wholesale and retail market, among others.

In addition, various types of credit and debit cards, Internet and telephone

banking, as well as money transfer services. The Company operates a number of

subsidiaries include, among others, Zenith Realtors Ltd, Zenith Registrars, Zenith

General Insurance, Zenith Pension Custodian, Zenith Securities, Zenith Life

Assurance, Zenith Capital, Zenith Medicare and Zenith Trustees Limited.

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1.10.3 Nigeria Breweries Plc

Nigerian Breweries Plc (NB), incorporated in 1946, is the pioneer and largest

brewing company in Nigeria with current annual production capacity estimated at

10 mn hectolitres. The company is engaged in brewing, marketing and selling of

alcoholic and non-alcoholic products such as lagers, stouts, non-alcohol malt

drinks and soft drinks. Nigerian Breweries Plc (NB) is a subsidiary of Dutch

brewer, Heineken N.V. and distributes its products across Nigeria. NB offers beer

under the Star and Gulder brands, lager under the Heineken brand, malt drinks

under the Maltina and Amstel Malta brands, premium stout under the Legend

brand, and sparkling soft drinks under the Fayrouz brand. The company operates

five breweries in Lagos, Aba, Kaduna, Ibadan and Ama regions in Nigeria, as well

as a malting plant in Aba region providing a geographical spread across the

country, albeit bias for cities in the southern part of Nigeria. It classifies its sales

regions into six units namely, Lagos, Central, East, West, North, and South. The

company is headquartered in Lagos, Nigeria. Listed on the Nigerian Stock

Exchange in 1990, NB is one of the most capitalized and actively traded

companies outside the banking and insurance sectors.

(http://proshareng.com/news/download.php?item=Nigerian%20Breweries%20Plc

%)

1.10.4 Nigeria Bottling Company Plc

The Nigerian Bottling Company Plc (NBC) was incorporated in November 1951,

as a subsidiary of the A.G. Leventis Group with the franchise to bottle and sell

Coca-Cola products in Nigeria. From a humble beginning as a family business, the

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company has grown to become a predominant bottler of non-alcoholic beverages

in Nigeria, responsible for the manufacture and sale of over 33 different Coca-

Cola brands. Other popular brands of beverage produced by the company are Eva

Water, Five Alive fruit juice and the newly introduced Burn energy drink.

Production began in 1953 at a bottling facility in Ebute-Metta, Lagos. The

company presently has 13 bottling facilities and over 80 distribution warehouses

located across the country. Since production started, NBC Plc has remained the

largest bottler of non-alcoholic beverages in the country in terms of sales volume,

with about 1.8 billion bottles sold per year, making it the second largest market in

Africa. Today, the company is part of the Coca-Cola Hellenic Bottling company

(CCHBC), one of Coca-Cola Company’s largest anchor bottlers worldwide.

CCHBC operates in 28 countries, serving 540 million consumers and selling over

1.3 billion unit cases of beverage annually.

31

REFERENCES

Anthony, R. S. et al, (2005) Accounting Text and Cases, Irwin.

Bowen, P. (1993), “Managing Data Quality Accounting Information Systems”: A Stochastic Clearing System Approach, Unpublished Ph.D Dissertation, University of Tennessee.

Fedorowicz, J. and Lee, Y. W. (1998), “Accounting Information Quality: Reconciling Hierarchical and Dimensional Contexts”, in Proceedings of 1998 Association of Information Systems (AIS) Conference.

Huang, H.-T. et al, (1999), Quality Information and Knowledge, Prentice Hall,

New Jersey.

Johnson, J. R., et al, (1981), 'Characteristics of Errors in Accounts Receivable and Inventory Audits', The Accounting Review, vol. 56, no. 2, pp. 270-293.

Kaplan, D., and Krishnan, R (1998), “Assessing Data Quality in Accounting Information Systems”, Communications of the ACM, vol. 41, no. 2,

pp. 72-78.

Klein, B. D. (1998), “Data Quality in the Practice of Consumer Product Management: Evidence from the Field”, Data Quality, Vol. 4, No. 1.

Redman, T. C. (1992), Data Quality: Management and Technology, Bantam Books, New York.

Redman, T. C. (1998), 'The Impact of Poor Data Quality on the Typical Enterprise', Communications of the ACM, Vol. 41, No. 2.

Romney, M. B. and Steinbart, P. J. (2009), 'Accounting Information Systems,' (Pearson Prentice Hall). Strong, D. M., (1997), 'Data Quality in Context', Communications of the ACM,

Vol. 40, No. 5, pp. 103-110. Wang, R. Y. and Strong, D. M.( 2006), 'Beyond Accuracy: What Data Quality

Means to Data Consumers', Journal of Management Information Systems, vol. 12, no. 4, pp. 5-34.

http/en/Wikipedia.org/wiki/data quality

http://www.firstbanknigeria.com/Portals/2/pdf (First Bank Plc) 03/11/2011.

www.umsl.edu/.-joshi/msis 480/chapt 15.htm (Accounting Information Systems) 04/09/2011.

32

CHAPTER TWO

REVIEW OF RELATED LITERATURE

2.1 INTRODUCTION

The aim of this chapter is to review the literature concerning data quality,

accounting information systems, and quality management that are relevant to the

research problem. The background theories / parent disciplines used to develop the

theoretical framework are discussed first broadly and then in a more focused way

on the research problem.

2.2 DATA QUALITY

The core term of data quality needs to be clarified from information. Information

and data are often different, for example, data is a collection of symbols which

signify real world system states and are brought together because they are

considered relevant to some purposeful activity. Information is an objective

commodity carried by symbols and relates to who produced it, why and how it

was produced and its relationship to the real world state it signifies (Shanks and

Darke 1999). Although data and information are different concepts, data quality is

often treated as the same as information quality in some literature and real-world

practice. Therefore, in this research, data quality and information quality are

synonymous.

The general definition of data quality is ‘data that is fit for use by data consumers’

(Huang et al, 1999). Many data quality dimensions have been identified.

Commonly identified data quality dimensions are:

33

Accuracy, which occurs when the recorded value is in conformity with the

actual value;

Timeliness, which occurs when the recorded value is not out of date;

Completeness, which occurs when all values for a certain variable are

recorded, and

Consistency, which occurs when the representation of the data values, is the

same in all cases.

Four other data quality dimensions have been identified (Wang and Strong 1996)

that are also widely accepted:

Intrinsic dimensions define the quality of data in its own right;

Contextual dimensions define data quality within the context of the task at

hand;

Accessibility dimensions emphasize the role of information systems in

providing data, and;

Representational dimensions define data quality in terms of the

presentation and delivery of data.

A set of comprehensive essential dimensions of data quality for delivering high

quality data has been determined as follows:

34

Table 2.1 Data Quality Dimensions (Source: Wang and Strong 1996) Dimension Definitions

Accessibility The extent to which data is available, or easily and quickly retrievable.

Appropriate Amount of Data

The extent to which the volume of data is appropriate for the task at Hand.

Believability The extent to which data is regarded as true and credible.

Completeness The extent to which data is not missing and is of sufficient breadth and depth for the task at hand.

Concise Representation The extent to which data is compactly represented.

Consistent Representation

The extent to which data is presented in the same format.

Ease of Manipulation The extent to which data is easy to manipulate and apply to different tasks.

Free-of-Error The extent to which data is correct and reliable.

Interpretability The extent to which data is correct and reliable

Objectivity The extent to which data is unbiased, unprejudiced, and impartial.

Relevancy The extent to which data is applicable and helpful for the task at hand.

Reputation The extent to which data is highly regarded in terms of its source or content.

Security The extent to which access to data is restricted appropriately to maintain its security.

Timeliness The extent to which the data is sufficiently up-to-date for the task at hand.

Understandability The extent to which data is easily comprehended.

Value-Added The extent to which data is beneficial and provides advantages from its use.

35

2.3 ACCOUNTING INFORMATION SYSTEM DEFINED (AIS)

DEFINED

In order to understand data quality issues in AIS in particular, it is important that

the term AIS is clearly defined. There are various definitions of AIS. AIS is seen

as a subsystem of a management information systems, and its major function is to

process financial transaction, as well as non-financial transactions that directly

affect the processing of financial transactions (Siegel and Shim; Hall 1998). An

AIS comprises four major sub-systems that are relevant to this research:

The transaction processing system, which supports daily business

operations with numerous documents and messages for users throughout

the organization;

The general ledger/financial reporting system, which produces the

traditional financial statements, such as income statements, balance sheets,

statements of cash flows, tax returns, and other reports required by law;

The fixed asset system, which processes transactions pertaining to the

acquisition, maintenance, and disposal of fixed assets, and

The management reporting system, which provides internal management

with special purpose financial reports and information needed for decision

making, such as budgets, variance reports, and responsibility reports. (Hall

1998).

2.4 DATA QUALITY OF ACCOUNTING INFORMATION SYSTEM (AIS)

In accounting and auditing, where internal control systems require maximum

reliability with minimum cost, the key data quality dimension used is accuracy –

defined in terms of the frequency, size, and distribution of errors in data (Wang,

36

Storey and Firth 1995). In assessing the value of accounting information,

researchers have also identified relevance and timeliness as desirable attributes.

The emphasis of accounting information systems literature on data quality is on

internal control systems and audits. Accounting professionals have been concerned

with data quality measurement for some time (Wang et al. 1995).The global

business environment is changing and creating new strategic management

challenges, as well as accounting information management challenges. The United

States Government Accounting Office (GAO) defines information management

as: Strategic information is one critical, integrated part of any general management

framework. Similar to the way modern organizations have gradually become

dependent on information technologies, it has become an indispensable lens

through which to view most vital general management decisions. Strategic

information management typically involves defining a mission based on customer

segments and needs, establishing core processes that accomplish the mission;

understanding the key decisions that guide mission delivery processes; supporting

those decisions with the right information available to the right people at the right

time; and using technology to collect, process, and disseminate information in

ways that improve the delivery of products, goods, and services to customers (The

United States Government Accounting office).

A very early attempt at AIS data quality measurement was a statistical approach to

measure errors in outputs of internal control systems. Another mathematical model

of the accounting internal control system and measures of reliability and cost was

developed by Cushin. Later researchers moved on to address data quality as it

37

relates to audit populations (Johnson, Leitch and Neter 1981; Groomer and Murthy

1989). Others presented models of the internal control process that responded to

guidelines and regulations calling for auditors to evaluate management’s effort to

assure that accounting data was correct (Hamlen 1980; Stratton1981; Fields, Sami

and Sumners 1986). Some AIS research extended the models developed in the

accounting literature, and analyzed cost/quality control trade-offs for information

systems, and furthermore extended to spreadsheet models (Ballou and Pazer 1985,

Ballou, Belardo and Klein 1987). A model of internal control from a survey of

audit data was developed by Nichols (1987). Some researchers have presented a

review of information systems research as it applied to accounting and auditing.

Others have developed a methodology which provides management with a

quantitative measure for determining the quality of data in information systems

(Paradice and Fuerst 1991). Researchers have also used a decision support systems

approach-combining human judgment and model-based procedures. This allows

auditor-determined variability in establishing quality thresholds to assessing data

quality in AIS (Kaplan et al 1998).

The management accountants within AIS were viewed as involving the design and

operation of financial advisory and information systems in organizational settings.

Three factors influence quality of management accounting:

Compliance, which focuses on the design and operation of systems

concerned with technical compliance with external regulations and

reporting requirement.

38

Control, which is the systems to support resource management and control

including standard costing and variance analysis, flexible budgeting,

responsibility accounting and accounting performance measures; and

Competitive support, which is the provision of financial services to the

management team in order to enhance the firm’s competitiveness. The

accounting function is seen as one of producing financial services, which

add value, and the management team is seen as a consumer of those

services.

2.4.1 What is Data Quality in AIS for this Research?

The dimensions that have been identified by Ballou et al (1993) will be adopted in

this research because they cover the most important dimensions that have been

addressed in the AIS literature and have been reasonably widely accepted in the

data quality field. Therefore, quality data in AIS in this research means accurate,

timely, complete, and consistent data.

39

Figure 2.1: Preliminary Theoretical Framework of this Research

Legend: Rectangles: main components of the framework

Oval: accounting information systems

Arrows: relationships between components and systems

Source: Developed for this Research.

CRITICAL FACTORS FOR DQ IN AIS

AIS characteristics

DQ characteristics

Stakeholders’ related factors

Organizational factors

External factors

Dimensions of DQ Performance

(Outcome Measurement)

Accuracy

Timeliness

Completeness

Consistency

Stakeholder Groups

Information producers

Information custodians

Information consumers

Data/database managers

Data quality (DQ) in accounting information systems (AIS)

40

There are some similarities between quality data manufacturing and quality

product manufacturing. For instance, both quality data and quality product need to

conform to specification, lower defect rates and improved customer satisfaction

(Wang, Kon and Madnick, 1993). Therefore, quality management concepts in

general and CSFs developed for quality management could aid the development of

the theoretical framework of this research. The discussion in this section is about

quality management that is the first parent discipline, while data quality

management in particular as the second parent discipline will be covered in the

next section.

2.5 QUALITY MANAGEMENT

Quality management in general has been a major concern of businesses and

research for many years, and is managed by using quality measurements,

reliability engineering, and statistical quality control (Crosby, 1979; Garvin,

1988). Many attempts have been made to define quality. One of the fundamental

definitions for quality is ‘fitness for use’ that includes quality of design, quality of

conformance, abilities, and field service (Juran, 1979). Some focus on the cultural

and behavioral aspects of quality, such as, Crosby (Crosby, 1979) identified major

steps to achieve quality improvement, which consist of management commitment,

quality measurement, cost of quality evaluation, quality awareness, and

commitment to the ‘zero defects’ performance standard. Deming states that ‘A

product or a service possesses quality if it helps somebody and enjoys a good and

sustainable market’ (Deming, 1993). His philosophy focuses on bringing about

improvements in product and service quality by reducing uncertainty and

variability in the design and manufacturing process (Evans and Lindsay, 1996).

41

These quality management experts identify sets of key variables that are critical to

achieve high quality outcomes. For example, Deming has summarized his

philosophy in ‘a system of profound knowledge (SPK)’, which consists of:

appreciation of a system, some knowledge of the theory of variation, theory of

knowledge, and psychology. He identifies 14 principles of quality management,

each of which can be derived from one or more of his SPK parts. According to

Deming, all those points cannot be implemented selectively; they are an all-or-

nothing commitment. Table 2.2 lists those 14 principles.

Table 2.2 Deming’s 14 principles of quality management (Deming, 1982)

Point 1 Create a vision and demonstrate commitment.

Point 2 Learn the new philosophy

Point 3 Understand inspection

Point 4 Stop making decisions purely on the basis of cost

Point 5 Improve constantly and forever

Point 6 Institute training

Point 7 Institute leadership

Point 8 Drive out fear

Point 9 Optimize the efforts of teams

Point 10 Eliminate exhortations

Point 11 Eliminate numerical quotas and management by objective

Point 12 Remove barriers to pride in workmanship

Point 13 Encourage education and self-improvement

Point 14 Take action

42

In comparing quality philosophies (i.e. Deming, Juran, and Crosby), it is clear that

quality is viewed as crucial for organizations to obtain competitive advantages,

and it requires a total commitment from everyone in the organization.

The philosophies of Deming, Juran, and Crosby provide fundamental principles on

which quality management, and total quality is based. However, those principles

are only proposed by the experts without rigorous supporting evidence. Therefore,

sometimes, they might not be able to provide sufficient specificity for real-world

organizations’ initiation of quality improvements and quality performance

evaluation (Motwani, 2001). There are also practical frameworks of quality

awards, such as the Malcolm Baldrige National Quality Award, the Deming Prize

and the European Quality Award. Together with experts’ philosophies they

comprise the principal theories, concepts and frameworks that direct real-world

quality management practice.

Research in quality management has evolved from the analysis of specific success

cases to scientific theory building. For example, in the early seventies, a scientific

theory building model was formulated by Wallace (1971), which involves

observation, empirical generalization, turning empirical generalizations into

theories, hypothesis generation and testing and logical deduction. More recent

quality management empirical studies focus on hypothesis generation and testing

(Flynn, Schoeder and Sakakibara, 1994; Black and Porter, 1996; Ahire, Golhar

and Waller, 1996) and logical deduction (Anderson et al., 1995; Rungtusanatham

et al., 1998). The next section details the major studies into the development of

constructs of critical success factors for quality management.

43

2.5.1 Quality Management: Just-In-Time

In comparison to TQM, in the field of Just-in-Time (JIT), a survey was carried out

(Zhu and Meredith 1995) of published articles on JIT inventory control strategies

to study the critical factors affecting JIT applications. They provided a list of JIT

implementation elements. Among those elements those that were related to quality

where: quality circle, cross-training, JIT education, relationship with suppliers,

communication, JIT team, quality certificate of suppliers, top management

commitment and co-worker relations.

Procedures / Processes Improvement and Training

In order to stay competitive in their respective industries, many organizations are

pursuing quality improvement operational strategies. However, they often only

focus on improving products and services for their customers, not the

improvement of the procedures for the production and distribution of their

products and services

(McCahon, Ryes and Ward, 1996). The International Quality Study showed that

approximately 80 per cent of Nigerian businesses did not focus on process

improvement compared to 50 per cent of Japanese firms (1991). The study further

defined process improvement as the practice of continuously reviewing, analyzing,

incorporating changing consumer expectations and refining the process so that

products and services continuously improve. In addition, the study suggested that

organizations should invest more in process improvement, and therefore, needed

to realign employee training to meet this need.

44

Training is critical for organizations’ quality improvement efforts to achieve their

goals. The challenge for organizations that are already aware of quality

improvement lies in their unfamiliarity with the amount of training and education

required to support the implementation of effective quality improvement strategies

(Johnson, 1993). Because lack of appropriate training has led to negative

outcomes or not being able to achieve the proposed objectives, some organizations

have failed their quality initiatives (Revelle, 1993). Therefore, proper investment

in the workforce – education and training - is crucial in ensuring the success of the

implementation of quality strategies (Aguayo, 1990). However, many quality

initiatives have failed, in spite of the large amount of resources spent on training

(Chang, 1993), because many obstacles impeded the effectiveness of training:

improper needs assessment, unskilled trainers and poor training techniques.

Organizations have often rushed into training programs without thoughtful needs

assessments (Johnson, 1993). Overly ambitious quality directors sometimes

implemented unnecessary training programs that were exercises in information

overload, dooming them to failure (Chang, 1993).

Therefore, in order to ensure positive training results, organizations need to

complete necessary phases for training: first, needs assessment; second,

development; and third evaluation. There has been research into the evaluation of

the effectiveness of different training techniques in meeting different training

objectives. Although results from these studies were slightly different, they all

highlight the important role that training performs in the quality management

process.

45

2.5.2 A Product Perspective on Data Quality Management

There exist some similarities between quality issues in product manufacturing and

information manufacturing. Information manufacturing can be viewed as a system

that produces information products from the raw data, similar to product

manufacturing, which produces physical products from raw materials, as shown in

Table 2.5 (Wang, 1998).

Table 2.3 Products vs. Information Manufacturing (Wang, 1998)

Product Manufacturing Information Manufacturing

Input Raw materials Raw data

Process Assembly line Information system

Output Physical product Information products

To treat information as a product is done because the information output from an

information manufacturing system has value that can be transferred to the

information consumer. Therefore, like a physical product, an information product

has quality dimensions, and the information quality can be viewed as fitness for

use by the information consumer. Clearly, there are also some differences between

product manufacturing and information manufacturing. For instance, the raw

materials used in information manufacturing are data, which can be consumed by

more than one consumer without depletion, not like raw materials in product

manufacturing that can only be used for single physical products (Wang, 1998).

46

In the semantic data modeling area, research suggests capturing more meaning

about application data. Semantics in data models have various dimensions and

categories, such as the quality and context of data that have significant

implications for users in the business community (Madnick, 1992). Data of poor

quality and mismatched context may lead to erroneous decisions. Therefore,

capturing data quality and context semantics at an early stage of database design is

a critical issue for both database researchers and practitioners (Tu and Wang,

1993).

Several research efforts have addressed the issue of explicitly representing quality

information, such as attribute-based research to facilitate cell-level tagging of data

to enable consumers to retrieve data that conforms to their quality requirements

(Wang and Madnick, 1990; Wang, Kon and Madnick, 1993a; Wang, Reddy and

Kon, 1993).

2.5.3 Product Quality and Service Quality

Information should be treated as both a product and a service. The literature draws

distinctions between product quality and service quality of information (Zeithaml,

Berry and Parasuraman, 1990). Product quality includes product features that

involve the tangible measures of information quality, such as accuracy,

completeness, and freedom from errors. Service quality includes dimensions

related to the service delivery process, and intangible measures such as ease of

manipulation, security, and added value of the information to consumers (Kahn,

Strong and Wang, 2002).

47

2.5.4 Data Quality in Database Systems

In a conventional database management system (DBMS), the quality of data has

been treated implicitly through functions such as recovery, concurrency, integrity,

and security control. However, from the data consumer’s perspective, those

functions are not sufficient to ensure the quality of data in the database (Wang,

Kon and Madnick, 1993b). For example, although there are some essential built-in

functions for ensuring data quality in a database like integrity constraints and

validity checks, they are often not sufficient to win consumers’ confidence on data

(Maxwell, 1989). In fact, data is used by a range of different organizational

functions with different perceptions of what constitutes quality data, and therefore

it is difficult to meet all data consumers’ quality requirements. Thus, data quality

needs to be calibrated in a manner that enables consumers to use their own

yardsticks to measure the quality (Wang, Reddy and Gupta, 1993).

In database design, although the primary focus is not on data quality itself, there

are many tools that have been developed for the purpose of data quality

management. For example, it is recommended to build integrity constraints and

use normalization theory to prevent data incompleteness and inconsistencies, as

well as through transaction management to prevent data corruption. However,

those tools are only related to system design and control. Although they can help

for making sure of the quality of data in the system, by themselves they are not

sufficient to solve the issue of imperfect data in the real world.

Data quality is affected by other factors rather than only by the system, such as

whether it reflects real world conditions, and can be easily used and understood by

the data user. If the data is not interpretable and accessible by the user, even

48

accurate data is of little value. Therefore, a methodology for designing and

representing corporate data models is needed. The use of scenarios, subject areas

and design rationale was found to be effective in enhancing understanding of

corporate data models (Shanks and Darke, 1999).

2.6 CRITICAL SUCCESS FACTORS FOR QUALITY MANAGEMENT

AND TQM.

Research into quality management and TQM has identified many critical success

factors that affect an organization’s position. Many of those studies were based on

the principles of quality experts including Deming, Juran, Feigenbaum

(Feigenbaum, 1991), and Crosby, as well as the practical frameworks like the

Baldrige Award criteria. In addition, the Ernst and Young and American Quality

Foundation (1992) study reviewed the quality management practices in the USA

and the other major economies of the world. The study found that not all methods

are equally beneficial to all organizations. Instead, it found that the level of quality

performance should be used to define practices.

In order to evaluate quality management implementation status, valid instruments

for measurement are important. A questionnaire to measure management policies

related to total quality management has been developed by Saraph, Benson, and

Schroeder (1989). Their research identifies eight critical factors of quality

management in business enterprises, which include:

• Role of divisional top management and quality policy;

• Role of the quality management;

• Training;

• Product/ service design;

49

• Supplier quality management;

• Process management /operating;

• Quality data and reporting and

• Employee relations.

CSFs for quality management identified by Saraph et al (1989) have been tested

by other researchers, and the results have confirmed the reliability and construct

validity of the instrument (Badri, Davis and Davis1995). Those eight factors have

been widely accepted in the TQM field. Other researchers of TQM study critical

success factors have used different methodologies (Porter and Parker 1993;

Tamimi and Gershon 1995; Ahire, Golhar and Waller 1996). Different sets of

factors have been found by other researchers (Black and Porter 1996; Ramirez and

Loney 1993). Furthermore, there is also research concerning the critical success

factors for implementation of TQM in small and medium enterprises (Yusof and

Aspinwall 1999).

For example, there is a 10-dimensional 32-item instrument of critical factors of

quality management developed by Black and Porter (1996) based on the Malcolm

Baldrige National Quality Award (MBNQA) of the USA. The 10 dimensions

included:

• People and customer management;

• Supplier partnerships;

• Communication of improvement information;

• Customer satisfaction orientation;

• External interface management;

• Strategic quality management;

50

• Teamwork structures for improvement;

• Operational quality planning;

• Quality improvement measurement systems; and

• Corporate quality culture.

This instrument was further tested by a recent cross-sectional study of quality–

oriented companies in Hong Kong (Lai, Weerakoon and Cheng, 2002). The study

surveyed companies of four types of industries: manufacturing, service,

construction and public utility. The results showed that although the companies in

all the industry types view quality management as an integrated approach, giving

generally equal importance to all aspects of quality management implementation,

those companies in the public utility and service industries appear to have a higher

level of quality management. Furthermore, among the quality management

implementation factors, the companies in service and construction industries were

perceived to be relatively weak in the factors of teamwork structures for

improvement, and those in the manufacturing industry put less effort into the

communication of improvement information.

A recent study by Motwani (2001) provided a comparative analysis of the

empirical studies of critical factors of TQM (Table 2.3), which found that

validated scales for integrated TQM developed by the empirical studies

complement one another. Furthermore, those empirical studies had higher validity

and were more comprehensive than the non-empirical TQM studies; and also

‘incorporated most of the TQM implementation constructs’ as proposed by quality

management experts (Motwani, 2001). By grouping the similar constructs from

51

those studies, seven factors could be identified, which contributed to an integrated

TQM:

• Top management commitments;

• Quality measurement and benchmarking;

• Process management;

• Employee training and empowerment;

• Suppler quality management; and

• Customer involvement and satisfaction. (Motwani, 2001)

2.7 TOTAL DATA QUALITY MANAGEMENT (TDQM)

To achieve a state of high data quality, an organization needs to implement Total

Data Quality Management (TDQM). Different industries with different goals and

environments can develop more specific and customized programs for data quality

management to suit their own needs. However, some researchers argue that

regardless of differences organizations must follow certain steps in order to enable

the successful implementation of a viable TDQM:

1) Clearly define what the organization means by quality in general and data

quality in particular;

2) Develop a set of measures for the important dimensions of data quality for

the organization that can be linked to the organization’s general goals and

objectives. (Avison, D. and Myers, M. D. 2002)

52

2.7.1 Data Quality in e-Business

Data quality in the context of eBusiness has some different features from the

issues in the traditional environment, because of the increasing utilization of

Internet and online transactions in the eBusiness environment. The e-Business

organization has more interactions with the environment, which adds complexities

to data quality. Therefore, it is imperative for e-Business organizations to establish

data quality strategies and implementation methodologies that suit their eBusiness

transformation approaches (Segev and Wang, 2001). While basic principles of

traditional information systems methodologies still apply, the scope and context

have changed significantly in the eBusiness environment.

2.7.2 A Framework for Data Quality Research

To aid the understanding of data quality in theory and practice, a framework for

data quality analysis was developed by Wang, Storey and Firth (1995),.This

framework comprises seven elements: management responsibilities, operation and

assurance costs, research and development, production, distribution, personnel

management, and legal function (see Table 2.6). The framework was further

employed to analyze articles relevant to data quality research in the same study. It

covered articles from a wide range of different disciplines and across the period

from 1970 up to 1994, which provided a comprehensive review of studies in data

quality and related areas.

53

Table 2.4: A Framework for Data Quality Research (Wang, Storey and Firth,

1995)

Element Description

Management

responsibilities

Development of a corporate data quality policy;

Establishment of a data quality system

Operation and

Assurance costs

Operating costs include prevention, appraisal, and failure costs;

Assurance costs relate to the demonstration and proof of

quality as required by customers and management

Research and

development

Definition of the dimensions of data quality and measurement

of their values;

Analysis and design of the quality aspects of data products;

Design of data manufacturing systems that incorporate data

quality aspects

Production Quality requirements in the procurement of raw data

components and assemblies needed for the production of data

products;

Quality verification of raw data, work-in-progress and final

data products;

Identification of non-conforming data items

Distribution Storage, identification, packaging, installation, delivery, and

after-sales servicing of data products;

Quality documentation and records for data products.

Personnel

management

Employee awareness of issues related to data quality;

Motivation of employees for produce high quality data

products;

Measurement of employee’s data quality achievement

Legal function Data product safety and liability

54

Due to the significance of Wang et al’s (1995) study, this section discusses some

relevant components of the framework of the study. The citations of this section

are mainly from Wang et al’s (1995) article. The first important component that

needs to be addressed is management responsibilities. The importance of top

management’s commitment and involvement has been recognized by many quality

management studies as described in Section 2.3 of this chapter when discussing

the parent discipline one of this research. Similarly, the importance of top

management commitment has also been addressed by data quality studies.

However, despite the increasing awareness of the need for corporate policy for

data quality management, there is a lack of research into what constitutes the

success of data quality policies and systems. In particular, in order to convince top

management of the importance of data quality to the survival of the organization

in the dynamic global environment, research that assists management in

identifying data quality factors that affect a company’s position is needed (Wang,

Storey and Firth, 1995). The cost of data quality effort is another important area in

data quality research.

There are two different types of costs for a data quality system. They are operating

costs (prevention, appraisal and failure), and assurance costs (Wang, Storey and

Firth, 1995). In particular, information systems research has looked into

cost/quality tradeoffs of internal control for ensuring the quality output from an

information system that covers processing activities, corrective procedures, and

penalties for failing to detect errors. Furthermore, researchers have also found that

there is a need to obtain better estimates of the penalty costs of poor data quality.

However, it is very hard to quantify the cost of data errors, though it is very costly.

55

One of the critical aspects for data quality research is to identify the appropriate

dimensions and measurement methods for the quality of information. Wang et al’s

(1995) study indicates that researchers in data quality, information systems

success and user satisfaction, and accounting and auditing areas have attempted to

identify data quality dimensions. In the previous discussion, some commonly

accepted definitions of data quality have been included. Dimensions identified by

other studies are discussed in this section.

In the information systems field, information quality has been assessed from the

users’ viewpoint, such as Orlikowski, W. J. and Baroudi, J. J. 1991), which

identifies usability, reliability, independence as major information quality

dimensions. Studies in evaluating the quality and value of information systems

have identified information quality attributes like accuracy, timeliness, precision,

reliability, relevancy, completeness. In addition, research into user satisfaction and

user involvement has also identified similar attributes. Furthermore, researchers

have also looked into the measurement of data quality attributes. For example, the

reliability attribute is divided and measured by internal reliability, relative

reliability that related to fulfilling the information user’s requirements, and

absolute reliability as to how well the data represents reality. Other relevant

aspects have also been addressed, such as, the measurement of usefulness of

information, the effectiveness of the system, the quality of information systems

and the evaluation of the structure of executive information systems.

One important contribution of Wang et al’s framework is to include personnel

management as part of the data quality management, which is the area that has

been overlooked by data quality research. Not too many studies have looked into

56

human related factors that impact on data quality. Among those few attempts into

personnel issues, a framework for understanding data production that incorporates

the person environment fit and the effect of employees’ ability and motivation has

been developed (Nicolaou, A. I. 2000). This study discovered that when data

production is separated from data use, such as one worker creates data and another

uses it, data quality problems are more likely to occur. Another study, showed how

a company’s employees identified the importance of data quality improvement and

aroused top management’s awareness, which then led to further action in dealing

with the issues. The attention to data quality issues by the company’s management

and employees helped the improvement of data quality in the company’s large

MIS database.

2.7.3 Important Steps in ensuring Data Quality

In the data quality field, not much research has been conducted directly into the

investigation of critical success factors for ensuring high quality data. Only a few

researchers have attempted to identify critical success factors of data quality, for

example Table 2.7 shows the seven factors suggested by English (1999).

57

Table 2.5 Critical Success Factors in Data Quality (Source: English 1999)

Understand fully what information quality improvement is and why you are

doing it.

Implement information quality improvement effectively.

Implementing information quality improvement on the right problem.

Training and communication.

Incentives for information quality.

Management commitment to information quality improvement as a

management tool.

Managing change.

Although other researchers in DQ area have not proposed critical success factors,

they have suggested some important areas and steps that may be taken to ensure

DQ. For example, four steps for the initiation and implementation of successful

systems’ data quality were recommended by Bovee, M. (2004):

(1) Establish a data quality position;

(2) Formulate a data quality policy;

(3) Determine objectives and

(4) Obtain management and employee commitment.

Furthermore, six important points in managing data quality were proposed by

Segev (1996):

(1) Establish organizational awareness of the importance of data quality, and

parties responsible for it;

(2) Define what organizations mean by data quality;

(3) Establish an information flow and processes map;

58

(4) Identify data quality problems and their location on that map;

(5) Identify technologies and practices that can be used to solve such DQ

problems.

(6) Evaluate the cost/benefit tradeoffs associated with improving the quality of

particular data or processes.

In order to maintain reasonable level of data quality, organizations need to treat

information as a product, not as a by-product, and they should follow four

principles:

(1) Understand consumers’ information needs;

(2) Manage information as the product of a well-defined production process;

(3) Manage information as a product with a life cycle. (Information product life

cycle was defined as the stages through which information passes from

introduction to obsolescence. The life cycle can be divided into four stages:

introduction (creation), growth, maturity, and decline.)

(4) Appoint an information product manager (IPM) to manage the information

processed and the resulting product (Choe, J.-M. 2004).

In addition, most researchers also recommend organizations establish information

quality programs. According to Huang et al (1999), in order to do this,

organizations should:

Articulate an information quality vision in business terms:

Set standards;

Information quality vision must be clearly identified with top-level

management;

59

Chief information officer must make it clear to the entire organization

that information quality has become a top priority;

Establish central responsibility for information quality within through

the organization;

Educate information product suppliers, manufacturers and consumers;

Educate key people in the organization who will take charge of

continuous improvements of information quality;

Teach new information quality skills; and

Institutionalize continuous information quality improvement

2.8 DATA QUALITY IN ACCOUNTING INFORMATION SYSTEMS

In order to ensure data quality in AIS, it is important to understand the underlying

factors that influence the AIS’s data quality. Knowledge of the critical factors that

constitute an AIS having high data quality is desirable but is still unclear at this

time. This section first proposes the possible factors that might impact on data

quality in accounting information systems from a summary of the thoroughly

review of the relevant literature. It then describes two pilot case studies conducted

to examine the proposed possible factors, followed by an analysis of the case

studies.

2.8.1 The Model for Factors Influencing Data Quality in Accounting

Information Systems

A model for critical success factors of accounting information systems' data

quality was developed based upon the AIS, DQ, quality management literature and

the pilot case studies. Several categories of factors were identified that according

60

to the theoretical and empirical literature have the potential to influence data

quality in AIS. These categories were: AIS characteristics, DQ characteristics,

stakeholders’ related factors, organizational factors, and external factors.

The first level is the external environment that consists of external factors, the

second level is the organizational environment that consists of organizational

factors, and the third level is the accounting information systems, which has AIS

characteristics and DQ characteristics. Stakeholders of AIS could come from

within the AIS, outside the AIS but within the organization, and outside the

organization. For example, AIS could have both internal and external information

suppliers and customers. Within each of those identified categories, a list of

factors was grouped. Factors were identified by the comprehensive literature

review and the empirical pilot case studies. The relationship between factors and

categories is shown in Figure 2.4, and forms the model for factors influencing data

quality in accounting information systems.

Although there is only one factor, nature of the AIS, under the category of AIS

characteristics, this factor has many attributes, such as the number of the systems

/packages, the number of staff, what kind of the system it is, the age and maturity

of the system, and the organizational structure of the system. There are seven

factors listed under the category of DQ characteristics, those factors are all related

directly to the data quality itself. They are: appropriate DQ policies and standard

and its implementation, DQ approaches (control and improvement), Role of DQ,

Internal control, Input control, Understanding of the systems and DQ, and

Continuous improvement of DQ. As previously mentioned the stakeholders of AIS

61

could come from both inside and outside the AIS and the organization. Human

related factors have always been the focus within social science and IT research.

The category of stakeholders’ related factors in this research deals with the

human/people related factors’ influence on DQ in AIS. They include, top

management’s commitment to DQ, role of DQ manager/manager group, customer

focus, employee/personnel relations, information supplier quality management,

and audits and reviews. In the organizational level, there are seven factors,

training, organizational structure, organizational culture, performance evaluation

and rewards, management of change, evaluation of cost/benefit tradeoffs, and

teamwork (communication).

External factors have been identified as factors outside the organization from the

external environment, and the organization has little or no control over them.

Figure 2.2: The model for factors influencing data quality in accounting

information Systems

External factors

Nature of the AIS

AIS characteristics

Stakeholders’ related factors

Top management’s commitment to DQ Role of DQ manager /manager group; Customer focus; Employee/personnel relations Information supplier quality management; Audit & reviews

DQ characteristics

Training; Organizational structure & culture Performance evaluation & rewards Manage change; Evaluate cost/benefit tradeoffs Teamwork

Accounting Information Systems

Relevant DQ policies & standards & its implementation

DQ approaches (control & improvement); Role of DQ;

Organizational factors

62

2.8.2 Possible factors that Impact on Data Quality in Accounting

Information Systems

Although the critical success factors for high data quality in AIS have not been

addressed, there have been many studies of critical success factors in quality

management such as Total Quality Management (TQM) and Just-In-Time (JIT)

(Davis and Davis 1995; Yusof and Aspinwall 1999). Some of the data quality

literature has addressed the critical points and steps for DQ (Segev 2001; Huang et

al 1999; English 2009). Table 2.8 indicates the related research efforts and reflects

whether these research efforts addressed certain issues or elements of critical

factors of quality or data quality management.

2.8.3 Critical Factors for Ensuring Data Quality in AIS

From comparing the ranking of the most critical factors and the ranking based on

the mean importance rating, it is clear that there were some similarities, most

critical factors were also ranked in the mean importance rating, and therefore, they

can be identified as critical success factors for data quality in AIS. The first three

are:

1) Top Management Commitment, which means top management recognize

the importance of data quality in AIS and support data quality activities;

2) Nature of Accounting Information Systems, which means to have

suitable and adequate systems / packages; and;

3) Input Controls, which means get the information right in its initial phase,

that is, input, so as to prevent input errors (“Garbage-In-Garbage-Out”). In

addition, three other factors were deemed to be critical success factors for

63

data quality in AIS, although they were not considered to be as crucial as

the first three.

They are:

4) Personnel Competency, which means the employment of well-trained,

experienced and qualified individual personnel at all levels, from top and

middle management to employees. In other words, there should be highly

skilled and knowledgeable people in both the technical and the business

areas;

5) Teamwork (Communication), which means working as a team and having

sufficient communication between different departments and within

departments, and between different professionals, such as accounting and

IT; and

6) Middle Management Commitment to Data Quality, which means the

acceptance of responsibility for data quality performance by middle

managers, and having effective procedures at middle management level.

2.9 STAKEHOLDER GROUPS FOR DQ IN AIS

In order to understand the stakeholders groups’ impact on accounting information

quality, it is essential to identify their relationships with accounting information

systems. The framework for understanding stakeholders in accounting information

systems proposed in this chapter combines the stakeholder concepts from data

quality, data warehouse, accounting information systems and quality management

areas.

64

In data quality and data warehouse fields, there are four stakeholder groups that

have been identified who are responsible for creating, maintaining, using, and

managing data. They are data producers, data custodians, data consumers, and data

managers (Strong et al, 1997; Wang, 1998; Sharks and Darke 1998). In the

accounting information systems area, auditors were recognized as fulfilling the

role of monitoring how the accounting information systems work and the quality

of the information which has been generated by the systems. Internal auditors

especially perform the internal policing and quality adviser role within the

organization.

Data quality research focuses on processing. Accounting management research

focuses on results checking and monitoring. In the quality management area the

source where raw data comes from is also addressed. In the quality management

literature, suppliers’ quality management has been highlighted as the important

aspect of the total quality management. In accounting information systems, data

suppliers also play a role in data quality management. Therefore, they are also

included in the framework.

Thus, in summary and combination of the above mentioned areas, for the purpose

of this research, the stakeholders in accounting information systems have been

identified as follows:

Information producers: create or collect information for the AIS;

Information custodians: design, develop and operate the AIS;

Information users: use the accounting information in their works;

65

Information managers: are responsible for managing the information

quality in the AIS;

Internal auditors: monitor the AIS and its data quality, check internal

controls in the AIS; and

Data suppliers: provide the unorganized raw data to the AIS

The framework components and their interrelationships are shown in Figure 2.5.

In accounting information systems, different stakeholders have different functional

roles in relation to the quality of the information. The framework relates all

stakeholders to accounting information systems on three different levels. The

lower level has only one stakeholder group - the data suppliers who provide

unorganized raw data to the AIS. It represents the input stage, which is getting raw

data into the AIS. In the middle level, there are four stakeholder groups, namely,

information producers, information custodians, information managers, and internal

auditors, who are responsible for creating and collecting the information,

designing, developing and operating the AIS, managing information, and

monitoring AIS and information respectively. This important level contains the

processing, storing, maintaining, and monitoring stages. The final and highest

level distributes the organized, useful information to the information users, and it

is the output stage.

66

Figure 2.3: The framework for understanding relationships between

stakeholder groups and data quality in accounting information

systems

Data suppliers

Raw data

Information Producers E.g. Accountants

Information Custodians E.g. IT managers

Information Managers

Internal Auditors

Accounting Information Systems (AIS)

Useful information

Information users E.g. top management & general users

67

REFERENCES

Avison, D. and Myers, M. D. (2002), “Qualitative Research in Information Systems, A Reader,” SAGE Publications. Black, S. A. and Porter, L. J. 1996, 'Identification of the Critical Factors of TQM',

Decision Sciences, vol. 27, pp. 1-21. Bovee, M. (2004), 'Empirical Validation of the Structure of an Information Quality Model,' International Conference on Information Quality. Choe, J.-M. (2004), "The Relationships among Management Accounting Information, Organizational Learning and Production Performance," Journal of Strategic Information Systems 13, 61-85. English, L. P. 2009, Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits, John Wiley and Sons. Evans, J. R. and Lindsay, W. M. 1996, The Management and Control of Quality,

3rd edition, West Publishing Company, St. Paul, MN. Hall, J. A. 1998, Accounting information Systems, 2nd edition, South-Western

College Publishing. Huang, H.-T., Lee, Y. W. and Wang, R. Y. 1999, Quality Information and

Knowledge, Prentice Hall, New Jersey. Kahn, B. K., Strong, D. M. and Wang, R. Y. 2002, 'Information Quality

Benchmarks: Product and Service Performance', Communications of the ACM, Vol. 45, No. 4.

Kovac, R., Lee, Y. W. and Pipino, L. L. 1997, 'Total Data Quality Management:

The Case of IRI', in Proceeding of the 1997 Conference on Information Quality, pp. 63- 79

Lai, K.-H., Weerakoon, T. S. and Cheng, T. C. E. 2002, 'The State of Quality

Management Implementation: A Cross-sectional Study of Quality-oriented Companies in Hong Kong', Total Quality Management, Vol. 13, No. 1, pp. 29-38.

68

Management Accounting and Integrated Information Systems: A Literature Review, International Journal of Accounting Information Systems 8, 40-68. Motwani, J. 2001, 'Measuring Critical Factors of TQM', Measuring Business

Excellence, Vol. 5, No. 2, pp. 27-30.

Nicolaou, A. I. (2000). "A Contingency Model of Perceived Effectiveness in Accounting Information Systems Organizational Coordination and Control Effects," International Journal of Accounting Information Systems 1, 91-105. Orlikowski, W. J. and Baroudi, J. J. (1991), "Studying Information Technology in Organizations: Research Approaches and Assumptions," Information Systems Research 2, 1-28. Segev, A. and Wang, R. 2001, 'Data Quality Challenges in Enabling eBusiness Transformation', in Proceedings of the Sixth International Conference on Information Quality, pp. 77-82. Shanks, G. and Darke, P. 1998, 'Understanding Data Quality in Data Warehousing: A Semiotic Approach', in Proceeding of the 1998 Conference on Information Quality, Boston, Massachusetts. Strong, D. M., Lee, Y. W. and Wang, R. Y. 1997, 'Data Quality in Context',

Communications of the ACM, Vol. 40, No. 5, pp. 103-110. Wang, R. Y., Storey, V. C. and Firth, C. P. 1995, 'A Framework for Analysis of Data Quality Research', IEEE Transactions on Knowledge and Data Engineering, Vol. 7, No. 4, pp. 623-639. Wang, R. Y. and Strong, D. M. 2006, 'Beyond Accuracy: What Data Quality

Means to Data Consumers', Journal of Management Information Systems, Vol. 12, No. 4, pp. 5-34.

Yusof, S. M. and Aspinwall, E. 1999, 'Critical Success Factors for Total Quality

Management Implementation in Small and Medium Enterprises', Total Quality Management, p. 803.

69

CHAPTER THREE

RESEARCH METHODOLOGY

3.1 INTRODUCTION

This chapter describes the steps, tool and techniques used in carrying out this

study deriving from the theoretical empirical literature of previous chapters. This

chapter, first of all builds a framework which will permit the analysis of the

linkages between the variables. Therefore the chapter further throws light on the

research design, source of data, method of data collection and population of the

study. Other issues that it will examined are sample size determination, sampling

techniques, validity and reliability of the instrument, statistical tools for data

analysis.

3.2 RESEARCH DESIGN

In the view of Onwumere (2009:111), research design is a kind of blue print that

guides the researcher in his or her investigation and analysis. For the purpose of

this study, descriptive survey design was used for the study. The method is

considered adequate and most appropriate because it helped the researcher to

describe, examine, record, analyze and interpret the variables that exit in the study.

It is also worthwhile in view of the relatively large population from which the

information was collected. More so it is very economical for independent

researchers. Furthermore, the researcher also find it necessary to employ oral

interview because of its factual implication on the study. Some historical

documents were also reviewed for a more in depth and comprehensive coverage.

70

3.3 SOURCE OF DATA:

The data used for this research work were obtained specifically from two sources

namely: Primary and Secondary sources of data.

3.3.1 Primary Sources

Primary data: The primary data include the data collected from questionnaire

administered by the researcher, the personal interview, observation. The primary

data also include information collected from direct survey, which involves direct

contact with the respondents. In this study, the research used questionnaire as the

main tool for the primary data collection. The researcher distributed the questioner

to potential respondents who filled and returned them.

3.3.2 Secondary Sources

The secondary sources involve the use of existing but related but related literature,

which was produced by earlier researchers for the purpose of contributing their

quota to the problem under study. Specifically the data collection sources to be

used for extracting secondary data for this study include relevant documents, such

as position descriptions, policy manuals, organizational structure charts and

training documents; as well as some published information about organizations,

such as financial statements and annual reports, journals, magazines, text-books

and internet.

71

3.4 AREA OF STUDY

This study covers banks and manufacturing companies in Enugu, Enugu state.

These are First Bank Plc, Zenith bank Plc, Nigeria Bottling Company and Nigeria

Breweries Plc. For the purpose of this work, two banks and two manufacturing

companies were selected.

3.5 INSTRUMENTS FOR DATA COLLECTED

The instruments that were used for data collection include: structured

questionnaire, observation and interview.

1. Questionnaire: Structured questionnaire was administered to respondents

and the contents of the questionnaire were clear enough to elicit accurate

responses from the selected organizations as attached in Appendix II. The

instrument was structure in liker scale form, it contained twenty-nine

questions and three hundred-seventeen copies were administered to the

respondents. The scale grading follows:

Not important (NI) / Disagree (D) ------------- 1

Little importance (LI) / Strongly Disagree (SD) ------------- 2

Average importance (AI) / Undecided (U) ------------- 3

Very important (VI) / Agree (A) ------------- 4

Extremely (E) / Strongly Agree (SA) ------------ 5

2. Interview Schedule: Some respondents were interviewed personally using

interview schedule to get more details about the study especially those not

covered by the questionnaire. Semi-structured interviews were conducted

with key stakeholders of AIS. In data quality studies, four types of

stakeholders have been identified; they are data producers, data custodians,

72

data consumers, and data managers. These interview questions given in

Appendix I of this study were fewer stakeholders of AIS in manufacturing

industry than in banking industry.

3.6 POPULATION OF THE STUDY

The population for the study comprised of the number of top and middle

management stakeholder group of AIS staff from four vibrant companies in Enugu

state of which two of them are banks and the other two are manufacturing

companies.

Table 3.1 Staff Strength of the Four Selected Organizations

ORGANIZATIONS NAMES OF COMPANY NUMBER OF

AIS STAFF

Banks

A First bank Plc 415

B Zenith bank Plc 401

Manufacturing

C Nigeria bottling company 350

D Nigeria breweries Plc 364

TOTAL 1530

Source: Fieldwork 2011

3.7 SAMPLING PROCEDURE

The sample method to be adopted in this study is the stratified but systematic

random sampling method. Using this method, the researcher had applied an

appropriate scientific sample size determination method to determine the sample

size from the population. According to Onwumere (2009) this method involves

73

division of the population into classes or groups with each group or stratum having

some definite (similar) characteristics or features.

3.8 SAMPLE SIZE DETERMINATION

The formula adopted in determining the sample size for this study is that pro-

pounded by Taro- Yamane (1964). The mathematical formula is given as:

n = ___N__ 1+Ne2

Where n = Sample size desired

N = Population size

e = Maximum acceptable margin of error (0.05).

1 = Theoretical constant

In applying this formula in determining the sample size for this study, we

substitute as follows:

n = N 1+Ne2 n = 1530 1+1530(0.05) 2

n = 1530 1+1530(0.0025) n = 1530 1+3.825 n = 1530 4.825 n = 317.09

n = 317

74

By percentage representation, we have:

(1) First Bank Plc Enugu

415 x 100 = 27% 1530

(2) Zenith Bank Plc Enugu 401 x 100 = 26% 1530 (3) Nigeria Bottling Company Enugu

350 x 100 = 23% 1530

(4) Nigeria Breweries Plc Enugu

364 x 100 = 24% 1530

Determination of specific number of respondents for each company:

(1) First Bank Plc Enugu

27 x 317 = 86 100

(2) Zenith Bank Plc Enugu

26 x 317 = 82 100 (3) Nigeria Bottling Company Enugu

23 x 317 = 73 100 (4) Nigeria Breweries Plc

24 x 317 = 76 100

75

3.9 VALIDITY OF THE RESEARCH INSTRUMENT

Onwumere (2005:66), defines validity as “the extent to which a measuring

instrument on application performs the function for which it was designed,”

To ascertain the validity of the instrument, content validity was adopted, in which

the researcher subjected the instrument to face to face validity by giving it to two

executives of the Institute of Chartered Accountant of Nigeria ICAN), Enugu

chapter, who examined the items and made sure they were in line with the

objectives of the study. The structure and language of the questionnaire was

modified in the light of the corrections. The instrument was structured in such a

way as to minimize the effect of errors like inconsistency and ambiguity. The

extent to which differences in sore represent true differences in the attributes being

measured. Whether they reflect the influence of other factors and to what degree

they do so.

3.10 RELIABILITY OF THE RESEARCH INSTRUMENT

Aham (2000:87) defines reliability as “the ability of a particular measuring

instrument to yield similar result when applied to the same situation at different

times.”

The reliability of the instrument was determined by a reliability test through the

use of pilot study. The pre-test was done using twenty copies of questionnaire

administered to the respondents of the selected firm. All questionnaire distributed

were completed and returned using the Pearson product moment correlation

coefficient of reliability was found to be high, r = 0.95 showing that there is

consistency in the items of the survey.

76

Table 3.2 Pearson Product Moment Correlation Coefficient showing the

reliability of instrument

Organization First

Average

Response (X)

Second

Average

Response (Y)

XY X2 Y2

First Bank Plc 4 5 20 16 25

Zenith Bank Plc 5 4 20 25 16

Nig. Bottling Company 3 4 12 9 16

Nig. Breweries Plc 3 4 12 9 16

TOTAL 12 13 52 50 57

Source: Research’s Field Survey 2011

Formula _______n∑xy – (∑x) (∑y)___________ [(n∑ x2 – (x)2] x [n∑y2 – (y)2] 22 (52) – (12) (13)_________ 22(50) – (12) 2 x (22) (57) – (13) 2

1,144 – 156____________ (1,100 – 144) (1,254 – 169) 988_____ (956)(1,085) 988_____ 1,037,260 r = 0.95

77

3.11 QUETIONNAIRE DESIGN AND ADMINSTRATION

One type of questionnaire was designed and distributed for the study. The basic

questions that were asked in the questionnaire bothered on the main issues raised

in the study objectives as derived from problem statement and reflected on the

research hypotheses. The type of questionnaire issued in this study is the

structured questionnaire which is tailored and designed to guide the respondents

on the direction of the objective.

Questionnaire was designed and served on the respondents for a period of two

weeks an ample time of weeks after which they were collected back.

3.12 DATA TREATMENT TECHNIQUE(S)

For data presentation and analysis, both descriptive and inferential statistical tools

were employed. The descriptive statistical tools employed under this study

include: Tables, Pearson product Moment Correlation, Chi-square and T-test.

3.12.1 The Pearson Product Moment Correlation

For the purpose of determining whether there is significant relationship between

accounting information system and data quality, the Pearson product moment

correlation coefficient was used. It provides an index of the strength magnitude

and degree of the critical success factor in AIS DQ. The Pearson product moment

correlation coefficient is therefore, suitable for the purposes of the present study

attempted to describe the relationship between working environment and AIS DQ.

Formula:

r = _________n∑xy – (∑x)(∑y)__ [(n∑ x2 – (x)2] x √[n∑y2 – (y)2]

78

3.12.2 Chi-Square (X2) Test

The data that will be collected will be analyzed using tables and percentage and

hypotheses will be tested using chi-square (x2).

x2 = (Fo - Fe)2 Fe Where; x2 = Value of the chi-square

C = Summation

Fo = Observed frequency

Fe = Expected frequency

The calculated chi-square (x2) will be compared with tabulated chi-square using

the normal level significance of 5% which leaves 95% confidence interval. Level

of freedom is (R - 1).

(C - 1) = Number of column in the contingency table, the contingency show the

observed and expected frequencies.

The difference between the calculated values of the chi-square will form the based

for accepting or rejecting the null hypothesis. It was used for testing hypothesis 1

and 5.

3.12.3 Z-Test

Formula:

Z = X -µ nS

__

79

Where:

X = Population mean

µ = Sample mean

S = Standard deviation

n = Sample size

This tool was used to test hypotheses 3 and 4.

3.12.4 Pair of Sample T-Test

The paired sample t-test compares the means of two variables. It is used to test the

null hypothesis that the average of the difference between a series of paired

observations is zero.

Formula:

Pair of sample T-Test = X n Where: X = Sample mean of the data

n = Sample size

= Population standard deviation

This tool was used to test hypotheses 2.

__

__

80

REFERENCES

Asika, N. (2001), Research Methodology in Behavioral Sciences, Lagos,

Longman. Eboh, E. C. (1998), Social and Economic Research. Principles and Methods,

Lagos. Academic Publications and Development Limited. Fruends, J. E. and Williams F. J. (1979), Modern Business Statistics London;

Pitman Publishing Limited. Fubara, B. A. and Mguni B. S. (2005), Research Methods in Management, Port-

Harcourt: Pearl Publishers. Ikeagwu, E. K. (1998), Ground Work of Research Methods and Procedures,

Enugu: Institute for Development Studies. Nzelibe C. G. and IIogu, G. C. (1996), Fundamentals of Research Methods,

Enugu: Optimal International Limited. Onwumere, J. U. J. (2009), Business and Economic. Research Methods, Enugu:

Vougasen Limited. Onwura, E. A. (1998) Introduction to Academic Research Methods, Enugu:

Gostak Printing and Publishing Co. Limited.

81

CHAPTER FOUR

PRESENTATION AND ANALYSIS OF DATA

4.1 INTRODUCTION

The focus of this chapter is on the presentation and analysis of data generated

through a set of interviews and questionnaire administered to the respondents.

Tables, simple percentage and other common statistical tools such as Pearson

correlation coefficient test, Chi-square and Pair sample t-test were used in

presenting and analyzing the data generated. The descriptive statistic of simple

percentage was used to analyze the variables measured in a nominal and an ordinal

scale to show the types of relationship or associations that existed between these

variables. Furthermore brief explanatory discussions were attached to tables for

clarity purpose.

It must be stressed here however that the data presented and subsequently

analyzed and discussed are only those which bear direct relevance to the problem

and objectives of the study and which apparently are relevant to the hypothesis

formulated in this study.

4.1 QUESTIONNAIRE DISTRIBUTION

S/NO Names of

Companies

Number

Distributed

Number

Returned

Number Not

Returned

First Bank Plc 86 81 5

Zenith Bank Plc 82 79 3

Nig. Bottling Company 73 69 4

Nig. Breweries Plc 76 73 3

Total 317 302 15

Source: Researcher’s Field Survey 2011

82

In the table above, it indicates that out of 317 copies of questionnaire distributed,

302 were returned, while 15 copies were not returned.

4.2 ANALYSIS OF QUESTIONNAIRE

This section analyzes the questions asked in the questionnaire.

BIOGRAPHICAL DATA

Table 4.1: Sex Distribution of Respondents

Option First Bank Zenith Bank NBC Plc NB Plc Total

Freq % Freq % Freq % Freq % Freq %

Male 31 10 24 8 25 8 27 9 107 35

Female 50 17 55 18 44 15 46 15 195 65

Total 81 27 79 26 69 23 73 24 302 100


The sex distribution of respondents as presented in table 4.1 shows that from 302

respondents, 107 representing 35% are males while 195 representing 65% are

females.

Table 4.2: Marital Status of Respondents



Single 41 14 39 13 20 7 19 6 119 40

Married 35 12 33 10 39 13 41 14 148 49

Widow 4 1 5 2 7 2 9 3 25 8

Divorced 1 - 2 1 3 1 4 1 10 3

Total 81 27 79 26 69 23 73 24 302 100


83

From table 4.2 above it was observed that out of 302 respondents, 119(40%)

respondents are single, majority of the respondents 148(49%) are married, 25(8%)

are respondents widow while 10(3%) respondents are divorced.

Table 4.3: Education Qualification of Respondents


Freq % Freq % Freq % Rep % Freq %

OND/NCE 15 5 18 6 21 7 23 7 77 25

B.Sc/HND 36 12 39 13 38 13 40 13 153 51

ICAN/MBA 25 8 20 7 9 3 8 3 62 21

PHD 5 2 2 1 1 - 2 1 10 3

Total 81 27 79 26 69 23 73 24 302 100


The data on education qualification of the respondents as shown in table 4.3 above

reveals that respondents with OND/NCE is 77(25%), 153(51%) respondents have

B.Sc/HND as qualification, 62(21%) respondents have ICAN/MBA while 10(3%)

respondents said their academic qualification was PHD.

Table 4.4: Years of Experience/ Length of Service of Respondents



2 years/less 10 4 13 4 7 2 9 3 39 13

3-10 years 25 8 27 9 31 10 31 10 114 38

11-20 years 34 11 35 12 28 9 30 10 127 42

21 or more 12 4 4 1 3 1 3 1 22 7

Total 81 27 79 26 69 23 73 24 302 100

Source: Researcher’s Field Survey 2011.

84

Table 4.4 above shows that out of 302 respondents, 39(13%) have 2 years or less

experience on AIS DQ, 114(38%) have 3-10 years experience on AIS DQ, biggest

group 127(42%) have 11-20 years experience, further 22(7%) respondents have

21 or more years of experience on AIS DQ.

Table 4.5: Respondents Age

Option First Bank Zenith Bank NBC Plc NB Plc Total Freq % Freq % Freq % Freq % Freq %

21-30 12 4 11 4 13 4 14 5 50 17

31-40 30 10 34 11 25 8 26 9 115 38

41-50 25 8 27 9 30 10 31 10 113 37

51 above 14 5 7 2 1 1 2 1 24 8

Total 81 27 79 26 69 23 73 24 302 100


The table 4.5 above show that 50 (17%) of the respondents are within the age

bracket of 21 – 30 years old, 115 (38%) are between 31 – 40 years old, 113 (37%)

are 41 – 50 years old while 24 (8%) 51 years and above.

Question 6: What industry does your organization belong?

Table 4.6: Industry of the organization.

Option First Bank Zenith Bank NBC Plc NB Plc Total Freq % Freq % Freq % Freq % Freq %

Services 14 5 15 5 8 3 9 3 46 15

Finance 59 19 51 17 10 3 11 4 131 43

Education 8 3 13 4 6 2 5 2 32 11

Manufacturing - - - - 45 15 48 16 93 31

Total 81 27 79 26 69 23 73 24 302 100


85

The table 4.6 shows out of 302 respondents, 46 (15%) offer services, 131 (43%)

are finance organizations, 32 (11%) are into education while 93 (31%) represent

manufacturing organizations.

Question 7: What is your main role in relative to Accounting Information

Systems (AIS)?

Table 4.7: Main Role Relative to Accounting Information Systems (AIS)



Create data 8 3 9 3 7 2 8 3 32 11

Collect data 10 3 11 4 12 4 14 4 47 15

Design data 5 2 6 2 3 1 4 1 18 6

Operate data 18 6 12 4 15 5 17 6 62 21

Use AIS 21 7 19 6 17 6 20 6 77 25

Audit data 9 3 10 3 7 2 5 2 31 10

Manage data 10 3 12 4 8 3 5 2 35 12

TOTAL 81 27 79 26 69 23 73 24 302 100


The table 4.7 above indicates that out of 302 respondents, 32 (11%) create data for

the AIS, 47(15%) collect data for the AIS, 18 (6%) design the AIS, 62(21%)

operate the AIS. While 77 (25%) use AIS, 31(10%) audit in AIS and 35(12%)

manage data quality in AIS.

Question 8: Which of the following categories best describe the Accounting

Information Systems (AIS) in your organization?

86

Table 4.8: Accounting Information Systems (AIS) in your organization.



Developed 15 5 18 6 11 4 10 3 54 18

Commercial 50 16 45 15 30 10 33 11 158 52

Customized 16 5 16 5 28 9 30 10 90 30

TOTAL 81 27 79 26 69 23 73 24 302 100


The table 4.8 above reveals that out of 302 respondents, 54 (18%) are developed in

house, 158 (52%) are commercial software package, 90 (30%) are customized

package.

Question 9: Do you receive quality data from your AIS?

Table 4.9: Quality Data from Organizational AIS.



Very High 33 12 32 11 22 7 31 10 118 39

High 28 9 31 10 18 6 21 7 98 32

Neutral 10 3 8 2 15 5 13 4 46 15

Low 6 2 5 2 9 3 5 2 25 8

Very Low 4 1 3 1 5 2 3 1 15 5

Total 81 27 79 26 69 23 73 24 302 100


87

Table 4.9 above indicates that out of 302 respondents, 118 (39%) indicates very

high quality data in AIS, 98 (32%) indicates high quality data in AIS, 46 (15%)

are neutral with the statement, 25 (8%) indicates low data quality and 15 (5%)

indicates very low quality data.

Ho: There are no significant factors that affect the variation of data quality

in accounting information system.

Question 10: Do you believe that top management recognize the importance of

DQ in AIS and support DQ activities?

Table 4.10: Top Management Commitment to Data Quality (DQ)

Options First Bank Zenith Bank NBC Plc NB Plc Total


Strongly Agree 70 23 63 21 53 18 61 20 247 82

Agree 5 2 10 3 9 3 6 2 30 10

Undecided 3 1 3 1 4 1 3 1 13 4

Strongly Disagree 2 1 1 - 1 - - - 4 1

Disagree 1 - 2 1 2 1 3 1 8 3

Total 81 27 79 26 69 23 73 24 302 100


The table 4.10 above reveals that out of 302 respondents, 247(82%) respondents

strongly agree that top management commitment to data quality is a success factor

for AIS DQ, 30 (10%) respondents agree top management commitment to data

quality as a critical success factor in AIS DQ. 13 (4%) respondents were recorded

88

for undecided, 4 (1%) respondents indicates strongly disagree and 8 (3%)

respondents disagree to the statement.

Question 11: Do you agree that understanding how the systems work and the

importance of DQ by everyone that is involved in AIS is a success factor in AIS

DQ?

Table 4.11: Understanding of the Systems and DQ



Strongly Agree 71 24 64 21 55 18 64 21 254 84

Agree 5 1 11 4 9 4 6 2 31 10

Undecided 3 1 3 1 4 1 3 1 13 4


Disagree - - - - - - - - - -

Total 81 27 79 26 69 23 73 24 302 100


From table 4.11 it indicate that out of 302 respondents, 254 (84%) respondents

representing 31 (10%) strongly agree that understanding how the systems work,

and the importance of DQ by everyone that is involved in AIS is a success critical

factor in AIS DQ. 13 (4%) respondents were indifferent to the statement, 4 (1%)

respondents disagreed while and none disagreed was recorded.

Question 12: Do you believe that employing well-trained, experienced and

qualified individual personnel at all levels, from top, middle management to

employees is a critical success factor in AIS DQ?

89

Table 4.12: Personnel Competency



Strongly Agree 71 24 64 21 55 18 64 21 254 84

Agree 5 1 11 4 9 4 6 2 31 10

Undecided 3 1 3 1 4 1 3 1 13 4


Disagree - - - - - - - - - -

Total 81 27 79 26 69 23 73 24 302 100


The table 4.12 show that out of 302 respondents, 254 (84%) respondents strongly

agree that employing well-trained, experienced and qualified individual personnel

at all levels, from top, middle management to employees is a success factor in AIS

DQ, 31 (10%) respondents were recorded for agree, 13 (4%) respondents were

indifferent, 4 (1%) respondents strongly disagree and none was recorded for

disagree.

Question 13: Do you agree that pleasant physical working environment, such as a

modern environment with air-conditioning, and adequate office space among AIS

staff is a success factor in AIS DQ?

90

Table 4.13: Physical Environment



Strongly Agree 65 21 51 17 48 16 60 20 224 74

Agree 10 3 15 5 12 4 4 1 41 14

Undecided 3 2 8 2 6 2 7 2 24 8

Strongly Disagree 3 1 5 2 3 1 2 1 13 4

Disagree - - - - - - - - - -

Total 81 27 79 26 69 23 73 24 302 100


From table 4.13 it indicates that out of 302 respondents, 224 (74%) respondents

strongly agree that pleasant physical working environment, such as a modern

environment with air conditioning, and adequate office space from top, middle

management to employees is a success factor in AIS DQ representing, 41 (14%)

respondents agree to the statement, 24 (8%) respondents were indifferent, 13 (4%)

respondents strongly disagree and none was recorded for disagree.

Question 14: Do you agree that working as a team and having sufficient

communication among AIS staff is a critical success factor in AIS DQ?

91

Table 4.14: Teamwork (Communication):



Strongly Agree 75 25 65 22 61 20 70 23 271 90

Agree 5 2 12 4 7 2 3 1 27 9

Undecided 1 - 2 1 1 - - - 4 1

Strongly Disagree - - - - - - - - - -

Disagree - - - - - - - - - -

Total 81 27 79 26 69 23 73 24 302 100


The table 4.14 indicated that out of 302 respondents, 271 (90%) respondents

strongly agree that working as a team and have sufficient communication from

top, middle management to employees is a success factor in AIS DQ, 27 (9%)

respondents was recorded for agree,4(1%) respondents were indifferent, none was

recorded for strongly disagree and disagree.

H0: There are no significant difference between the perceptions of importance

of critical factors for accounting information systems’ data quality and

actual performance of those factors.

Question 15: Do you agree that appropriate (simple, relevant and consistent) DQ

policies and standards is a critical success factor for AIS DQ?

92

Table 4.15: DQ Policies and Standards

Options First Bank Zenith Bank NBC Plc NB Plc Total Freq % Freq % Freq % Freq % Freq %

Strongly Agree 33 11 39 13 23 7 25 8 120 40

Agree 27 9 34 11 17 6 20 7 98 33

Undecided 14 5 - - 20 7 21 7 55 18


Disagree 2 1 2 1 5 2 5 1 14 5

Total 81 27 79 26 69 23 73 24 302 100



agree to appropriate DQ policies and standards for AIS DQ, 98 (33%) agree with

the statement, 55 (18%) are indifferent about the statement. 15 (5%) was recorded

on strongly disagree and 14 (5%) disagree to the statement.

Question 16: Do you believe that setting up a skilled person or a group of people

as DQ manager/s to manage information flow: from input to process, and to output

is a critical success factor for AIS DQ?

Table 4.16: Establish DQ manager position to manage overall DQ:


Strongly Agree 35 12 40 13 38 13 37 12 150 50

Agree 30 10 36 12 27 9 30 10 123 41

Undecided 15 5 - - - - - - 15 5

Strongly Disagree 1 - 2 1 3 1 4 1 10 3

Disagree - - 1 - 1 - 2 1 4 1

Total 81 27 79 26 69 23 73 24 302 100


93

The table 4.16 reveals that out of 302 respondents, 150 (50%) respondents

strongly agree to establishing DQ manager position to manage overall DQ for

AIS, 123 (41%) agree with the statement, 15 (5%) are indifferent about the

statement, 10 (3%) were recorded on strongly disagree and 4 (1%) respondents

disagree to the statement.

Question 17: Do you believe that allocating sufficient funds, technical tools,

expertise, skilled personnel will ensure AIS DQ?

Table 4.17: Clear DQ vision for entire organization



Strongly Agree 30 10 31 10 26 9 30 10 117 39

Agree 26 9 29 9 23 8 25 8 103 34

Undecided 6 2 3 1 7 2 5 2 21 7


Disagree 10 3 7 3 5 2 4 1 26 9

Total 81 27 79 26 69 23 73 24 302 100


Question 17 was set to validate or disprove the assertion that clear DQ vision for

the entire organization pave way to success for AIS DQ. Out of 302 respondents,

117 (39%) respondents strongly agree that allocating sufficient funds, technical

tools, expertise, skilled personnel will ensure AIS DQ, 103 (34%) agree with the

statement and 21 (7%) were recorded on undecided, 35 (11%) were strongly

disagree and 26 (9%) disagree to the statement.

94

Question 18: Do you agree that having appropriate DQ controls, approaches, and

adequate processes for DQ improvement activities create success factor in AIS

DQ?

Table 4.18: DQ Controls



Strongly Agree 40 13 38 12 30 10 34 11 142 47

Agree 16 5 19 6 20 7 19 6 72 24

Undecided 10 3 12 4 11 4 13 4 46 15


Disagree 7 3 1 - - - 2 1 10 4

Total 81 27 79 26 69 23 73 24 302 100


The table 4.18 indicates that out of 302 respondents, 142 (47%) respondents

strongly agree to having appropriate DQ controls, approaches, and adequate

processes for DQ improvement activities for AIS DQ, 72 (24%) agree with the

statement, 46 (15%) respondent are indifferent about the statement, 30 (10%)

respondents strongly disagree and 10 (4%) respondents disagree to the statement.

H0: There is no significant difference between different stakeholder groups

in their perceptions of importance of critical factors for accounting


Question 19: Do you agree that acceptance of responsibility for DQ performance

by middle managers is a critical success factor in AIS DQ?

95

Table 4.19: Middle Management Commitment to Data Quality



Strongly Agree 41 14 37 12 32 10 41 14 151 50

Agree 30 10 31 11 25 9 22 7 108 36

Undecided 7 2 5 2 4 1 3 1 19 6


Disagree 1 - 2 1 3 1 3 1 9 3

Total 81 27 79 26 69 23 73 24 302 100


Question 4.19 was administered to find out if middle management commitment to

data brings positive success factor for AIS DQ. Out of 302 respondents, 151

(50%) respondents strongly agree that acceptance of responsibility for DQ

performance by middle managers will create effective procedures at middle

management, 108 (36%) agree with the statement, while 19 (6%) was recorded for

undecided, 15 (5%) strongly disagree and 9 (3%) disagree to the statement.

Question 20: Do you believe that continuous and consistent improvement of

system and human DQ controls is a critical success factor in AIS DQ?

96

Table 4.20: Continuous Improvement



Strongly Agree 30 11 39 13 35 12 37 12 141 47

Agree 28 9 35 11 28 9 28 9 119 39

Undecided 19 6 - - - - - - 19 6


Disagree 1 - 2 1 3 1 3 1 9 3

Total 81 27 79 26 69 23 73 24 302 100


Question 20 was administered to find out if continuous and consistent

improvement of system and human DQ controls is a success factor. Out of 302

respondents, 141 (47%) respondents strongly agree that continuous and consistent

improvement of system and human DQ controls is a success factor, 119 (39%)

agree with the statement, 19 (6%) are indifferent about the statement, 14 (5%)

respondents strongly disagree and 9 (3%) respondents disagreed to the statement.

Question 21: Do you believe that having effective DQ management relationships

with raw data suppliers brings success factor in AIS DQ?

97

Table 4.21: Data supplier Quality Management


Strongly Agree 30 10 29 10 30 10 31 10 120 40

Agree 26 9 26 9 28 9 25 8 105 35

Undecided 15 5 14 5 8 3 11 3 48 16


Disagree 4 1 5 2 1 - 2 1 12 4

Total 81 27 79 26 69 23 73 24 302 100


From table 4.21 above, it indicates that out of 302 respondents, 120 (40%)

respondents strongly agree that having effective DQ management relationships

with raw data suppliers is a critical success factor, 105 (35%) agree with the

statement, 48 (16%) are indifferent about the statement, 17 (7%) respondents

strongly disagree and 12 (4%) disagreed respondents were recorded.

Question 22: Do you believe that identifying key risk areas and key indicators of

DQ and monitor these factors brings success in AIS DQ?

Table 4.22: Risk Management


Strongly Agree 39 13 35 12 34 11 38 12 146 48

Agree 21 7 23 7 23 7 25 9 92 30

Undecided 10 3 12 4 5 2 4 1 31 10


Disagree 2 1 4 1 5 2 4 1 15 5

Total 81 27 79 26 69 23 73 24 302 100


98


agree that identifying key risk areas and key indicators of DQ and monitor these

factors is a success factor, 92 (30%) agree with the statement, 31 (10%) were

indifferent about the statement, 18 (6%) strongly disagree and 15 (5%)

respondents disagreed to the statement.

H0: There are no significant critical success factors to ensure a high quail of


Question 23: Do you agree that organization’s abilities and skills to manage

internal and external changes pave way to critical success factor in AIS DQ?

Table 4.23: Management of changes



Strongly Agree 33 11 39 13 26 9 26 9 124 41

Agree 32 11 37 12 24 8 24 8 117 38

Undecided 12 4 - - 16 5 19 6 47 16

Strongly Disagree 3 1 2 1 - - 3 1 8 3

Disagree 1 - 1 - 3 1 1 - 6 2

Total 81 27 79 26 69 23 73 24 302 100


The table 4.23 reveals that out of 302 respondents, 124(41%) respondents strongly

agree that organization’s abilities and skills to manage internal and external

changes, 117 (38%) agree with the statement, 47 (16%) are indifferent about the

statement, 8 (3%) respondents strongly disagree that organization’s abilities and

99

skills to manage internal and external changes and 6 (2%) respondents disagreed

to the statement.

Question 24: Do you believe that high employee self-satisfaction, job security,

and career development motivates staff to success factors in AIS DQ?

Table 4.24: Effective Employee Relations



Strongly Agree 19 6 18 6 39 13 33 11 109 36

Agree 21 7 22 7 25 8 23 8 91 30

Undecided 20 7 21 7 - - - - 41 14


Disagree 2 1 - - 5 2 10 3 17 6

Total 81 27 79 26 69 23 73 24 302 100


The question 24 was administered to find out if high employee self-satisfaction,

job security, and career development is a success factor. Out of 302 respondents,

109 (36%) respondents strongly agree that high employee self-satisfaction, job

security, and career development is a success factor, 91 (30%) agree with the

statement, 41 (14%) are indifferent about the statement, 44(15%) strongly disagree

that high employee self-satisfaction, job security, and career development is a

success factor, 17 (6%) respondents disagree to the statement.

100

Question 25: Do you agree that focus on information users' needs and their

quality requirements enable active participation from users to ensure and improve

AIS DQ?

Table 4.25: User Focus



Strongly Agree 33 11 39 13 23 7 25 8 120 40

Agree 25 8 29 10 17 6 19 6 90 30

Undecided 14 5 - - 20 7 21 7 55 18


Disagree 3 1 4 1 3 1 2 1 12 4

Total 81 27 79 26 69 23 73 24 302 100



agree that focus on information users' needs and their quality requirements enable

active participation from users to ensure and improve DQ, 90 (30%) agree with the

statement, 55 (18%) are indifferent about the statement, 25 (8%) respondents were

strongly disagree and 12 (4%) respondents disagree to the statement.

Question 26: Do you believe that getting the information right in its initial phase,

i.e. input, so as to prevent input errors (Garbage-In-Garbage-Out) is a success

factor in AIS DQ?

101

Table 4.26: Input Controls



Strongly Agree 39 13 37 12 29 10 30 10 135 45

Agree 19 6 15 5 25 8 26 9 85 28

Undecided 11 4 13 4 10 3 17 6 51 17

Strongly Disagree 7 2 9 3 5 2 - - 21 7

Disagree 5 2 5 2 - - - - 10 3

Total 81 27 79 26 69 23 73 24 302 100


Question 26 was set to find if getting the information right in its initial phase, i.e.

input, so as to prevent input errors (Garbage-In-Garbage-Out) is a success factor.

Out of 302 respondents, 135 (45%) respondents strongly agree that if getting the

information right in its initial phase, i.e. input, so as to prevent input errors

(Garbage-In-Garbage-Out) is a success factor AIS DQ, 85 (28%) agree with the

statement, 51 (17%) were indifferent about the statement, 21 (7%) respondents

strongly disagree to the statement and 10 (3%) respondents disagree.

H0: There are no significant difference between different organizations with

different perspective in the importance and performance of critical

success factors for accounting information systems data quality.

Question 27: Do you believe that promoting the DQ culture within the

organization will enable high quality data in AIS?

102

Table 4.27: Organizational Culture of focusing on DQ



Strongly Agree 53 17 49 16 41 14 41 14 184 61

Agree 20 7 15 5 13 4 15 5 63 21

Undecided 4 1 7 2 6 2 8 3 25 8


Disagree 2 1 4 1 5 2 4 1 15 5

Total 81 27 79 26 69 23 73 24 302 100


The essence of the question was to validate or disprove on assertion that

organizational culture of focusing on DQ is critical success factor in AIS DQ. Out

of 302 respondent under study, 184 (61%) respondents strongly agree that

promoting the DQ culture within the organization will enable high quality data in

AIS, 63 (21%) agree with the statement, 25 (8%) were indifferent about the

statement, 15 (5%) respondents strongly disagree and disagree to the statement

respectively.

Question 28: Do you agree that independent internal and external audit on the

systems and the DQ will ensure that appropriate controls are in place AIS DQ?

103

Table 4.28: Audit and Reviews



Strongly Agree 33 11 39 13 26 9 26 9 124 41

Agree 32 11 37 12 24 8 24 8 117 38

Undecided 12 4 - - 16 5 19 6 47 16


Disagree 1 - 1 - 3 1 1 - 6 2

Total 81 27 79 26 69 23 73 24 302 100


Question 28 indicates that out of 302 respondents, 124 (41%) respondents strongly

agree that independent internal and external audit on the systems and the DQ will

ensure that appropriate controls are in place in AIS DQ, 117 (38%) agree with the

statement, 47 (16%) were indifferent about the statement, 8 (3%) respondents

were recorded for strongly disagree and 6 (2%) respondents were recorded for

disagree.

Question 29: Do you believe that having a systematic cost /benefit analysis of DQ

controls and activities will maximize benefits at minimum cost in AIS DQ?

104

Table 4.29: Evaluate Cost/Benefit Tradeoffs



Strongly Agree 31 10 24 8 25 8 27 9 107 35

Agree 28 9 22 7 22 7 24 8 96 31

Undecided 12 4 16 5 13 4 15 4 56 19


Disagree 3 1 6 2 3 1 2 1 14 5

Total 81 27 79 26 69 23 73 24 302 100


The table 4.29 above shows that out of 302 respondents, 107 (35%) respondents

strongly agree that having systematic cost /benefit analysis of DQ controls and

activities will maximize benefits at minimum cost in AIS DQ, 96 (31%) agree

with the statement, 56 (19%) respondents were indifferent about the statement, 29

(10%) respondents strongly disagree and 14 (5%) respondents disagreed to the

statement.

Question 30: Do you agree that providing effective and adequate training for staff

will enable them to understand and efficiently use AIS in order to obtain quality

information?

105

Table 4.30: Education and Training



Strongly Agree 40 13 38 13 35 12 37 12 150 50

Agree 28 10 31 10 25 8 27 9 111 37

Undecided 8 3 4 1 5 2 4 1 21 7


Disagree 2 - 2 1 1 - 2 1 7 2

Total 81 27 79 26 69 23 73 24 302 100


The table 4.30 above indicate that out of 302 respondents, 150 (50%) respondents

strongly agree that education and training enable staffs to understand and

efficiently use AIS in order to obtain quality information, 111 (37%) agree with

the assertion. While 21 (7%) was recorded for undecided, 13 (4%) indicates

strongly disagree and 7 (2%) disagree to the statement.

4.4 TEST OF HYPOTHESES

To test the hypotheses listed in chapter one the work adopted the following

statistical tools: Pearson’s correlation coefficient test, Chi-square, Pair sample t-

test, aided by computer Microsoft Statistical Package for Social Science (SPSS)

for analysis to test the five major hypothesis as earlier stated in chapter one.

Pearson’s correlation coefficient was used in testing hypothesis one and chi-square

was used in testing hypotheses two and three while Pair sample t-test was used in

testing hypothesis four and five and below are the analyses and the testing of the

hypotheses formulated to answer the research questions asked to guide the study.

106

Research Question 1:

What factors affect the variation of data quality in accounting information

systems?

Hypothesis One

Ho: There are no significant factors that affect the variation of data quality in


H1: There are significant factors that affect the variation of data quality in


Questions (10 – 14) were designed and administer to validate or disprove the

above hypothesis. Reactions by respondents were analyzed as follows.

Having exhaustively analyzed the question administered for testing hypothesis

one, a compressed result of the five individually analyzed questions were

presented below in accordance with the respondents from the selected

organizations in respective manufacturing and banking industries.

Table 4.31: Condensed Outcome of the five question administered for testing

hypothesis one.

Option Organizations Total Freq

Total % FBN ZBP NBC NBP

Strongly Agree 352 307 272 319 1250 82.78

Agree 30 59 46 25 160 10.60

Undecided 13 19 19 16 67 4.44

Strongly Disagree 9 8 6 2 25 1.65

Disagree 1 2 2 3 8 0.53

Total 405 395 345 365 1510 100.00


107

The Test:

Our goal is to analyze significant factors that affect the variation of data quality in

accounting information system data quality among selected manufacturing and

banking industries. Based on the condensed outcome of the five questions

administered for testing hypothesis one and aggregate response from the four

selected organizations. Chi-square test for statistic was employed using the

relevant area of the computer special package for social science (SPSS) as related

to research question one and hypothesis one respectively the result below

emerged.

Table 4.32: Descriptive Statistics – Organizations Cross tabulation.

Organizations Total FBP ZBP NBC NBP

There are

significant

factors that

affect the

variation of

data quality

in accounting

information

system.

Strongly agree Count

Expected Count

44

36.2

48

35.4

11

23.0

17

25.4

120

120.0

Agree Count

Expected Count

27

29.5

34

28.9

29

18.8

8

20.8

98

98.0

Undecided Count

Expected Count

17

16.6

4

16.2

10

10.6

24

11.7

55

55.0

Strongly disagree Count

Expected Count

0

4.5

0

4.4

3

2.9

12

3.2

15

15.0

Disagree Count

Expected Count

3

4.2

3

4.1

5

2.7

3

3.0

14

14.0

Total Count

Expected Count

91

91.0

89

89.0

58

58.0

64

64.0

302

302.0

108

Table 4.32 above demonstrates the observed and expected frequencies of

responses of strongly agreed to disagreed. By careful observation of the

description statistics, respondents had an opposing view to the statement that there

are significant factors that affect variation of data quality in accounting

information system.

Table 4.33: Chi-Square Tests

Value df Asymp. Sig

(2-sided)

Pearson Chi-Square 88.166a 12 .000

Likelihood Ratio 90.388 12 .000

Linear-by-linear Association 29.819 12 .000

No of Valid Cases 302

a. 8 cells (40.0%) have expected count less than 5. The minimum expected

count is 2.69

Table 4.33 above shows the chi-square test computed from the frequency

distribution and significant values. The chi-square computed value X²c = 88.166 is

greater than chi-square table X²t = 21.03 with 12 degree of freedom at 0.05

significant level.

Decision Rule:

Hence, since the calculated chi-square X²c value of 88.166 which is greater than

the critical chi-square table of 21.03, it indicates that the null hypothesis should be

rejected. Therefore, factors affect the variation of data quality in accounting

information system among organizations in Enugu state.

109


Are there any variations with regard to the perceptions of importance of those

factors that affect data quality in accounting information systems?

Hypothesis Two

H0: There is no significant difference between the perceptions of importance of



H1: There is a significant difference between the perceptions of importance of



Questions (15 – 18) were considered relevant for the testing the validity of the

above hypothesis. The analysis of the various responses were compressed result of

four individually analyzed questions presented below in accordance with the

category of respondents and organizations respectively.

110

Table 4.34: Condensed outcome of the four questions administered for

testing hypothesis two.

Option Organizations Total

Freq

Total

% FBN ZBP NBC NBP

Strongly Agree 138 148 117 126 529 43.79

Agree 99 118 87 94 398 32.95

Undecided 45 15 38 39 137 11.34


Disagree 19 11 11 13 54 4.47

Total 324 316 276 292 1208 100.00


The Test:

Our aim is to find out whether or not significant difference between the

perceptions of importance of critical factors for accounting information systems’

data quality, and actual performance of those factors. Based on the condensed

outcome of the four questions administered for testing hypothesis two and

aggregate response from the four selected organizations in Enugu state. Pair of

sample t-test statistics were employed using the relevant area of the computer

special package for social science (SPSS) as related to research question two and

hypothesis two respectively the result below emerged.

111

Table 4.35: Paired Samples T-Test Statistics

Mean N Std.

Deviation

Std. Error

Mean

Pair

1

Accounting information

system data quality and

Actual performance

factors

2.1589

1.6854

302

302

1.14464

.88004

.06587

.05064

Paired Samples Corrections

N Correlation Sig

Pair 1 Accounting information system

data quality and actual

performance factors

302 .878 .00

112

Paired Samples Correlations

Table 1 above displays the paired sample statistics of the number of respondents

(=302) mean (m=2.1589) standard deviation (Std. deviation = 1.14464) and

standard error of mean (Std. Error m = .06587) for accounting information system

data quality while for actual performance number of respondents (N = 302) mean

(m =.47351) standard deviation (Std. deviation = .88004) and standard error of

mean (Std. error m = .05064). By careful observation of mean score and standard

deviation scores of both variables compared; there is some differences in term of

the mean scores and variability of data points.

Table 2 is the output of paired sample T-test. The result reveals that the paired

differences of the independent variable of accounting information system data

quality and actual performance had mean score of .47351, Std deviation of .56265

and standard error of mean .3238; paired sample T-test statistics computed value

of (tc = 14.625) is greater than critical value (tt = 1.960) with 302 degree of

freedom (df) at 0.5 level of significance for 2 – tailed test (tc = 14.625 P<0.5).

Thus there are significant difference between the perceptions of importance of

Paired Difference

T

Df

Sig.

(2-tailed)

Mean

Std.

Deviation

Std. Error

Mean

95% confidence

interval of the

Difference

Lower Upper

Pair

1

AIS DQ -

actual

performanc

e factors

.47351 .56265 .03238 .40980 .53722 14.625 301 .000

113




What are the perceptions of stakeholder groups in importance of critical factors for

accounting information systems?

Hypothesis Three

H0: There is no significant difference between different stakeholder groups in



H1: There is a significant difference between different stakeholder groups in



Having exhaustively analyzed the questions administered for testing hypothesis

three, a compressed result of the four individually analyzed questions (19-22) were

presented below.

114

Table 4.36: Condensed outcome of the four questions administered for testing

hypothesis three.


Freq

Total

% FBN ZBP NBC NBP

Strongly Agree 140 140 131 147 558 46.19

Agree 105 115 104 100 424 35.10

Undecided 51 31 17 18 117 9.68


Disagree 8 13 12 12 45 3.73

Total 324 316 276 292 1208 100.00


The Test:

Our target is to identify whether or not there is a significant difference between

different stakeholder groups in their perceptions of importance of critical factors

for accounting information systems’ data quality. Based on the condensed

outcome of the four questions administered for testing hypothesis three and

aggregate response from the four selected organizations in Enugu state. Z-test

statistics were employed using the relevant area of the computer special package

for social science (SPSS) as related to research question two and hypothesis two

respectively the result below emerged.

115

Table 4.37: Descriptive Statistics

N

Mean Standard

Deviation

Minimum Maximum

There are a significant difference

between different stakeholder

groups in their perceptions of

importance of critical factors for

accounting information systems’

data quality

302 2.0232 1.09490 1.00 5.00

One-Sample Kolmogorov-Smirnov Test

N

Normal Parameters a, b Mean

Std. Deviation

Most Extreme Absolute

Differences Positive

Negative

Kolmogorov-Smirnov Z

Asymp. Sig. (2-tailed)

302

2.0232

1.09490

.230

.230

-.175

4.002

.000

a. Test distribution is normal

b. Calculated from data

From the table 4.37, the computed 2-value of 4.002 against 1.96 and a significance

of 0.0000, the null hypothesis should be rejected and alternate accepted. Thus,

there is significant difference between stakeholder groups in their perceptions of

importance of critical success factors for accounting information data quality.

116


Which of these factors are critical success factors to ensure a high quality of data

in accounting information systems?

Hypothesis four

H0: There are no significant critical success factors to ensure a high quality of


H1: There are significant critical success factors to ensure a high quality of data

in accounting information systems.


four, a compressed result of the four individually analyzed questions (23-26) were

presented below.


testing hypothesis four.


Freq

Total

% FBN ZBP NBC NBP

Strongly Agree 124 133 117 114 488 40.40

Agree 97 103 91 92 383 31.71

Undecided 57 34 46 57 194 16.06


Disagree 11 10 11 13 45 3.72

Total 324 316 276 292 1208 100.00


117

The Test:

Our goal is to ascertain whether or not there is a significant critical success factors

to ensure a high quality of data in accounting information systems. Based on the

condensed outcome of the four questions administered for testing hypothesis four

and aggregate response from the four selected organizations in Enugu state. Z-test


for social science (SPSS) as related to research question four and hypothesis four



N Mean Standard Deviation

Minimum Maximum

There are significant critical

success factors to ensure a high

quality of data in accounting

information systems.

302 2.2417 1.24606 1.00 5.00

One-sample Kolmogorov-Smirnov Test

N

Normal Parameters a, b Mean

Std. Deviation

Most Extreme Absolute

Differences Positive

Negative

Kolmogorov-Smirnov Z

Asymp. Sig. (2-tailed)

302

2.2417

1.24606

.239

.239

-.159

4.156

.000

118

a. Test distribution is Normal.

b. Calculated from data.

From the table 4.39, the computed Z-value of 4.156 against 1.96 and a

significance of 0.000, the null hypothesis should be rejected and alternate

accepted. Thus, there are significant critical success factors to ensure a high

quality of data in accounting information systems among organizations in Enugu

state.


What are organizations perspective in the importance and performance of critical

success factors for accounting information system data quality?

Hypothesis Five

H0: Different organizations have the same perspective in the importance and


data quality.

H1: Difference between have different perspectives in the importance and


data quality.


four, a compressed result of the four individually analyzed questions (27-30) were

presented below.

119


testing hypothesis five.


Freq

Total

% FBN ZBP NBC NBP

Strongly Agree 157 150 127 131 565 46.77

Agree 108 105 84 90 387 32.04

Undecided 36 27 40 46 149 12.33


Disagree 8 13 12 9 42 3.48

Total 324 316 276 292 1208 100.00


The Test:

Our goal is to determine whether or not different organizations have different

perspectives in the importance and performance of critical success factors for

accounting information systems data quality. Based on the condensed outcome of

the four questions administered for testing hypothesis five and aggregate response

from the four selected organizations in Enugu state. Chi-square and T-test


for social science (SPSS) as related to research question five and hypothesis five


Organizations

Total

120


Table 4.41; above demonstrate the observed and expected frequencies of

responses of strongly agreed to disagreed. By careful observation of the

description statistics, respondents had an opposing view to the statement that

different organizations have the different perspective in the importance and

performance of critical success factors for accounting information system.

Table 4.41: Chi-square Test

FBP ZBP NBC NBP

Different

organizations have

different perspective

in the importance and

performance of critical

success factors for

accounting

information system.

Strongly agree Count

Expected Count

25

38.3

11

17.7

46

26.7

27

26.3

109

109.0

Agree Count

Expected Count

44

31.9

17

14.8

12

22.3

18

22.0

91

91.0

Undecided Count

Expected Count

18

13.7

4

6.3

4

9.6

13

9.4

39

39.0

Strongly disagree Count

Expected Count

17

16.1

13

7.5

9

11.3

7

11.1

46

46.0

Disagree Count

Expected Count

2

6.0

4

2.8

3

4.2

8

4.1

17

17.0

Total Count

Expected Count

106

106.0

49

49.0

74

74.0

73

73.0

302

302.0

121

Value Df Asymp. Sig

(2-sided)

Pearson Chi-Square 51.587a 12 .000

Likelihood Ratio 51.025 12 .000

Linear-by-linear Association 1.140 1 .286

No of Valid Cases 302

a. 3 cells (15.0%) have expected count less than 5. The minimum expected

count is 2.76.

Table 4 above shows the chi-square test computed from the frequency distribution

and significant values. The chi-square computed value X²c = 51.587 is greater

than chi-square table X²t = 21.03 with degree of freedom of .000

4.4 DISCUSSION OF RESULT

The study was evaluated through use of questionnaire with questions tailored

towards determining the critical success factors for accounting information

systems’ data quality. All the five hypotheses were subject to statistical test and

these tools were employed: Pearson’s correlation coefficient, Friedman Chi-square

and T-test. Computer aided Microsoft special package for social science (SPSS)

were used to aid analysis to ensure accuracy and eliminate mistake arising from

manual computation.

Hypothesis one was tested with Chi-square to determine the strength magnitude

and direction of the factors that affect the variation of data quality in accounting

information system. To further validate the result of the result, additional test

statistics of t-test was conducted to test the significance of the correlation

122

coefficient (r). With a computed t-value of against the critical t-value of the null

hypothesis was rejected giving to the conclusion that there are significant factors

that affect the variation of data quality in accounting information system among

the selected manufacturing and banking industries.

Based on other scholars view, accounting information system is perceived as an

accurate, timely, complete, and consistent accounting data; AIS is an information

system that is designed to make the accomplishment of accounting function

possible. Many AIS staffs are of the opinion that downsizing, right sizing and

reengineering give AIS staffs is an opportunity to dispose of those workers who

are a liability to the organization.

Hypothesis two was tested using the pair of sample T-test test to examine the

stakeholder perceptions on importance of critical factors for accounting

information systems among selected organizations. With a computed pair of

sample T-test, the null hypothesis was rejected resulting in the conclusion that the

variations with regard to the perceptions of importance of those factors that affect

data quality in accounting information systems.

Again hypothesis three was tested using T-test to examine the stakeholder

perceptions on importance of critical factors for accounting information systems

among manufacturing and banking industries in Enugu state. It was discovered

that AIS staff members are more committed to their organization, based on the

computed T-test of 4.002 against T-critical 1.96 and a significance of 0.000 and

the null hypothesis should be rejected and alternate accepted. Thus, which showed

that there are significant difference between different stakeholder groups in their

123

perceptions of importance of critical factors for accounting information systems’

data quality.

Furthermore, hypothesis four sought to be test using T-value to evaluate the

success factors which are critical to ensure a high quality of data in accounting

information systems data quality in manufacturing and banking industries. With a

computed T-value of 4.156 against Z-critical 1.96 and significance of 0.000. The

null hypothesis was rejected resulting in the conclusion that there are significant

critical success factors to ensure a high quality of data in accounting information

systems.

Lastly hypothesis five sought to ascertain the significant difference between

different organizations with different perspectives in the importance of critical

success factors for accounting information systems data quality among selected

manufacturing and banking industries. The hypothesis was tested with Chi-square

to examine the extent of significance between the variables.

With a computed chi-square of 51.587 against the tabulated chi-square value of

21.03, there was a very strong significance between different organizations with

different perspectives in the importance of critical success factors for accounting

information systems’ data quality. Therefore the null should be rejected and the

alternate hypothesis should be accepted. Thus, there are significant difference

between different organizations with different perspectives in the importance of

critical success factors for accounting information systems data quality among

selected manufacturing and banking industries in Enugu state.

CHAPTER FIVE

124

SUMMARY OF MAJOR FINDINGS, CONCLUSIONS AND

RECOMMENDATIONS

5.1 INTRODUCTION

This chapter provides an overview of salient findings emanating from the research.

The results are aligned with the various objectives and hypotheses set out in

chapter one of the project research. Conclusions were drawn and necessary

recommendations were made from the research findings. Contribution and

suggestions for further research were also made.

5.2 SUMMARY OF MAJOR FINDINGS

The result based on the descriptive statistics reveals the following:

1. The result revealed that there are significant factors that affect the variation

of data quality in accounting information system. (88.166 > 21.03)

2. It was found from the study that there are significant difference between the

perceptions of importance of critical factors for accounting information

systems’ data quality and actual performance of those factors among the

selected manufacturing and banking industries. (14.625 > 1.960)

3. It was observed from the study that there are a significant difference

between different stakeholder groups in their perceptions of importance of

critical factors for accounting information systems’ data quality. (4.002

>1.96)

4. It was discovered from the study that there are significant critical success

factors to ensure a high quality of data in accounting information systems.

(4.156 > 1.96).

125

5. It was further observed that different organizations have different

perspectives in the importance and performance of critical success factors

for accounting information systems data quality. (51.587 >21.03).

5.3 CONCLUSION

On the basis of research findings the following conclusions were made:

The study concluded that AIS staff of organizations in Enugu state cherish top

management commitment, nature of accounting information systems, input

controls, personnel competency, teamwork and middle management commitment

to data quality as critical success factors in accounting information system data

quality.

This study also indicate that the surveyed manufacturing and banking

organizations were aware of the importance and the performance of the critical

success factors that impact on data quality of accounting information systems.

Comparing to their consideration of the importance of the factors, actual

performance of these factors is not up to the satisfactory level.

In brief, this research has provided an understanding of the importance of critical

success factors for data quality in accounting information systems. That is, data

quality management is crucial for the successful implementation of accounting

information systems. The critical factors identified by the study can serve

practitioners in accounting and IT fields as well as management as a useful guide

to data quality management activities, and improvement efforts.

126

High-level data quality management practice is one of the keys to success for

many organizations. Specification of the critical success factors of DQ

management in AIS can permit managers to obtain a better understanding of

accounting information system data quality management practices. If

organizations focus on those critical success factors, they may be able to evaluate

the perception of data quality management in their organizations’ AIS, and ensure

the quality of the accounting information. In addition, they will be able to identify

those areas of AIS data quality management where improvements should be made,

and improve overall data quality in the future.

Furthermore, the findings of the study would help organizations to focus on

important factors to obtain better benefit from less effort. Top management, IT and

accounting professionals should be able to gain the better understanding of

accounting information systems data quality.

5.4 RECOMMENDATIONS

Based on the findings the following recommendations were made:

1. Authorities need to develop strategies to recruit AIS staffs who have great

commitment and passion for AIS DQ. Part of interview could be subjecting

AIS staffs to practical work to ascertain if highly skilled and their passion

for the AIS DQ.

2. The management authorities should demonstrate their preparedness to show

concern in attending to AIS staffs related problems and support data quality

activities. This will give them confidence to remain and be committed.

127

3. Accountancy professional bodies should set strong and nation minded team

of supervisors who will not be settled at the management table to visit the

organizations and monitor the activities of AIS staff from time to time.

4. Stakeholders of the systems and data quality controls need to work together

as a team and having sufficient communication between different

departments and within departments and between different professionals to

ensure the data quality in AIS.

5. Management should upgrade the salary scale of AIS staffs so that the

profession will be attractive for quality and competent personnel with

working environment more friendly and conducive for maximum

productivity.

6. Government should enact laws to ensure that there are standards for

accounting information data quality which must be met for effectiveness

and efficiency among organizations.

7. In order to have a successful accounting information systems

implementation, organizations should pay attentions to both systems and

organizational factors.

5.5 CONTRIBUTION TO KNOWLEDGE

The following contributions to knowledge were made:

The result of this research has provided basis for better understanding of the

critical success factors for accounting information data quality but other variables

have been mentioned which contribute to the achievement of this work, but

without mentioning passion this work will have a gap. Management should adopt

128

attempts to identify the critical success factors that organizations should focus on,

to ensure IQ during the systems adoption process.

Framework for Information Quality Management in Accounting Information

Systems Adoption

Fig 5.1: IQ Management in AIS Adoption Framework (Source:

Researcher)

Information is the key resource of today’s organizations, and therefore, quality

information is critical to organizations success. Accounting information systems

(AIS) in particular, requires high quality information. This research provides

empirical evidence that adopting AIS information from internal or external

organization requires recognition that information quality affects decision making,

suggesting that IQ dimensions should be considered in developing AIS in order to

improve the effectiveness of AIS systems. This study investigates information

quality dimensions in accounting information systems adoption. The framework

measured IQ dimensions from sixteen dimensions including relevance, reliability,

Internal Organization

AIS Adoption

AIS Adoption Processes

IQ Management

IQ Dimensions

Information Quality (IQ)

IQ management in AIS adoption

AIS Adoption Performance

External Organization

AIS System Selection

AIS System Implementation

AIS System Use

129

comparability, understandability, availability, effectiveness, efficiency,

confidentiality, accessibility, integrity, compliance, accuracy, objectivity, security,

completeness, and timeliness for adopting AIS. The overall results indicate that IQ

dimensions have a positive relationship with AIS adoption processes. The

evidence in this study suggests that an IQ criterion promotes AIS adoption process

performance. Furthermore, IQ dimensions play a vital role in the process of AIS

adoption. This evidence suggests that organizations should obtain knowledge of

appropriate information quality dimensions for accounting information systems

adoption to improve work performance as well as help organizations to make

profits.

5.6 SUGGESTED AREAS FOR FUTURE RESEARCH

Firstly, replication of this study in other non-profit organizations including both

non-governmental organizations and government organizations like parastatals

may give interesting insights into national practice. It can not only advance the

literature but also provide a useful benchmark for real-national practice, as

organizations nowadays need to become more competitive in order to survive in a

more open international trading environment.

Secondly, research into building the relationship between the performance of

critical factors and business output is needed. This research focused on identifying

the critical factors. Further research that links the performance of those factors to

business output can provide a wider picture of the issues and aid towards building

an understanding of cause-effect relationships between different variables in data

quality management area. The measurement of the business output could be the

130

overall level of quality achieved as well as financial data such as return on

investment.

Thirdly, a longitudinal experiment may be useful to further test the theory built in

this study. This would be to investigate whether continuous improvement effort on

data quality management can lead to better business performance. If there are any

variations on performance, an attempt could be made to develop the measurement

of how much those variations are caused by improvement activities.

Future studies can do cross-country surveys to address more issues in this field. In

addition, it will be better to expand the sample with more respondents from

different industries to enhance the empirical study. Also, future studies can focus

on connecting other fields to critical success factors, such as organization’s size

and respondent’s job level.

131

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APPENDIX I

Interview Protocol

Critical Success Factors for Data Quality in

Accounting Information Systems

Business Name:

Interviewee’s Name:

Business Profile:

Location:

Date:

Start time of the Interview:

Finish time of the Interview:

Section 1: General Information

Please tell me about yourself.

1. Your Background:

1) Education, and working experience

2) Your experience with accounting information systems

2. Your organization.

1) Major industry:

- Manufacturing

- Servicing

- Financial

- Government

- Other

136

2) Your department

- Finance, Accounting

- Information systems /IT

- Senior Executive

- Other.

3) Your main role relative to accounting information. Do you

primarily:

- Collect accounting information

- Manage those who collect accounting information

- Use accounting information in tasks

- Manage those who use accounting information in tasks

- Work as an information systems professionals

- Manage information systems professionals

4) Annual revenue naira

- Over N100 million

- Under N 100 million, but over N 10 million

- Under N 10 million

5) Company total assets

- Over N 100 million

- Under N 100 million, but over N 10 million

- Under N 10 million

137

Section 2: Accounting Information Systems (AIS)

Please tell me something about your organization’s accounting information

systems (AIS)?

Do you think those category of AIS will influence DQ?

1. How large is the AIS? (Number of different systems /packages; Number of

staff)

2. What kind of systems are you using for AIS? SAP? Please name.

3. How old is the AIS? (The age, maturity of the system)

4. What is the organizational structure of the AIS and how you fit in the

structure?

Section 3: Data Quality (DQ) in Accounting Information Systems

For each of the following question, does it help to ensure and improve DQ in AIS?

1. Is data quality issue a top priority in your AIS?

2. Do you have DQ polices?

3. What kind of data quality polices or standards do you have or adopt?

4. Do you think they are appropriate

5. Do you think standard and polices are important to ensure DQ?

6. What sort of trainings do you have in data quality?

Initial training to new employees

Regular training

7. What data quality controls and improvement approaches do you have in

your AIS?

8. What internal controls do you have in your AIS?

138

9. What input controls do you have in your AIS?

10 What is the role of audit and review in relation to AIS?

Internal

External

11. What sort of data quality performance evaluation and rewards do you have

in your AIS?

12. What employee/ personnel relations do you have in data quality area?

Section 4: Factors

What factors do you think may influence the data quality in accounting

information system?

1. Does your organization allocate enough funds, technical tools, experts,

skilled personnel available for ensuring data quality in AIS?

2. Do you have data quality manager or similar roles to ensure the quality of

the (accounting) information?

If yes, how can he/she help to improve DQ?

If no, do you think it will help to have one;

or do you think it is not necessary or impossible?

3. How the top management’s role in relation to data quality issues in AIS

will impact on DQ?

4. Who are the information suppliers of your AIS? Will information supplier

quality management influence the DQ in AIS?

5. Who are the customers of your AIS? Will the different requirements from

different customers influence the DQ in AIS?

139

6. How does your organization manage change? (Technology, regulation,

economy, marking changes)

Do you think skills to manage change can help to improve DQ?

7. Are there any external factors that you think may influence DQ in AIS?

8. Does your organization evaluate cost / benefit tradeoffs of DQ in AIS? Are

there any incentives for DQ?

If yes, does it help to improve DQ?

Section 5: Critical Success Factors

We have defined some factors that might impact on data quality of accounting

information systems. Which of these factors do you think are critical success

factors? Would you be able to give a mark for each of these factors on a ten -

point scale, 10 as very critical, 1 as not important at all?

1. Top management’s commitment to DQ.

2. Appropriate (simple, relevant and consistent) data quality policies and

standards and its implementation.

3. Role of data quality

4. Role of data quality manager.

5. Training.

6. Organizational: Structure

: Culture.

7. Nature of the AIS.

8. Data quality (control and improvement activities) approaches and processes

9. Customer focus –user involvement.

140

10. Employee/personnel relations (employee’s responsibility to DQ).

11. External factors.

12. Information supplier quality management.

13. Performance evaluation and rewards (responsibility for DQ).

14. Manage change

15. Evaluate cost/benefit tradeoffs

16. Audit (internal and external) and reviews

17. Internal controls (systems, process), such as: access control and security

and segregation of duties

18. Input control.

19. Understanding of the systems and DQ (importance, improvement)

20. Teamwork (between different departments and within departments)

(communication)

21. Continuous improvement

Do you think these factors are appropriate? Why, why not?

Which of these factors do you think are critical success factors?

Are there other factors that you think may be important but were not included in

this list?

Conclusion:

Is there anything I have not asked that you feel is important when discussing

critical success factors of data quality in accounting information systems?

Is there anyone else that you would recommend talking to in relation to AIS DQ?

With hindsight what would you have done differently?

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Would you like some of the feedback from this research regarding to your

organization’s DQ issues or the findings of the research?

If you would like, we will supply a copy of what we believe you told us, and how

we have interpreted what you said, so that you can correct the impressions that we

have taken from your responses. We will also provide you with factors suggested

by other respondents, you could then comment on the responses of others and

accept or reject factors.

Thank you very much for your precious time and your valuable help!

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APPENDIX 11 Survey Questionnaires

Department of Accountancy, Faculty of Business Administration, School of Postgraduate Studies, University of Nigeria, Enugu Campus. August 8, 2011.

………………

……………....

………………

Dear Sir/Madam,

RESEARCH QUESTIONNAIRE ON “CRITICAL SUCCESS FACTORS

FOR ACCOUNTING INFORMATION SYSTEMS DATA QUALITY

I am a postgraduate student in the above named department and institution, presently carrying out a research work on the critical success factors for accounting information systems data quality.

This questionnaire is design in the completion of above named project research in partial fulfillment of the requirement for the award of a master degree of business administration in Accountancy. I assure you that the information given will be treated with utmost confidentiality and will be used strictly for academic purpose.

Therefore I solicit your patient and brevity in answering these questions and will be grateful if you could assist me in this direction. I hereby attach the questionnaire for your completion.

Thanks for your anticipated co-operation.

Yours faithfully,

……….………………. Ukah, Chibueze Kalu.

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SECTION A: PERSONAL DATA

Name of your company___________________________________

Please read carefully and tick () in the appropriate box.

1. Sex:

(a) Male [ ] (b) Female [ ]

2. Marital Status:

(a) Single [ ] (b) Married [ ] (c) Windowed [ ] (d) Divorced [ ]

3. Education:

(a) OND/NCE [ ] (b) HND/B.Sc [ ] (c) ICAN/MBA [ ] (d) PHD [ ]

4. Years of experience:

(a) 2 years [ ] (b) 3-10 years [ ] (c) 11-20 years [ ]

21 years and above [ ]

5. Age:

(a) Between 20 - 30 [ ] (b) Between 31 - 40 [ ] (c) Between 41-50 [ ]

(d) Between 51- 60 [ ]

6. What industry does your organization belong? (a) Manufacturing [ ]

(b) Services [ ] (c) Finance and insurance [ ] (d) Education [ ]

(e) Manufacturing [ ]

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There are some DEFINITIONS that you might need while answering the

questionnaire

Data Quality (DQ): quality data in Accounting Information Systems (AIS) in

this research means accurate, timely, complete, and consistent data.

Information Users: the users of the accounting information include both internal

and external users. Such as: top management and general users within the

organization (internal), banks and government (external)

Data Suppliers: are those who provide raw, un-organized data to the accounting

systems, include both internal and external. Such as, other departments within the

organization (internal), and trading partners (external)

Top management: executive or senior management includes the highest

management positions in an organization.

Middle management: is responsible for implementing the strategic decisions of

top management. Middle managers make tactical/short-range decisions.

Non-management employees: who include production, clerical, and staff

personnel.

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Thank you for participating in this research, please answer the following

PRELIMINARY QUESTIONS first

7) Please indicate your MAIN ROLE relative to Accounting Information

Systems (AIS); do you PRIMARILY: (Please tick one box only)

[ ] 1 Create or collect data for the AIS

[ ] 2 Manage those who create or collect data for the AIS

[ ] 3 Design, develop and operate the AIS

[ ] 4 Manage those who design, develop and operate the AIS

[ ] 5 Use accounting information in tasks

[ ] 6 Audit or review data in AIS

[ ] 7 Manage data and / or data quality in AIS

8) Which of the following categories best describe the Accounting

Information Systems (AIS) in your organization? (Please tick one box

only)

[ ] 1 Developed in-house

[ ] 2 Commercial software package

[ ] 3 Customized package

9) Do you receive quality data from your AIS?

How would you rate the overall data quality in AIS in your organization?

[ ] 1 Very Low [ ] 2 Low [ ] 3 Neutral [ ] 4 High [ ] 5 Very High

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Section B: CRITICAL SUCCESS FACTORS FOR ACCOUNTING

INFORMATION SYSTEMS

DATA QUALITY

NOTE: Please rate the importance of each factor in ensuring Data Quality (DQ)

in Accounting Information Systems (AIS) from your perceptions, opinions and

rate the actual performance (achievement) on each of those factors by your

organization. The Option to select are in the following scale. Please indicate your

view by circling the number which most closely matches your opinion in the table

below.

1. - Not important (NI)

2. - Little importance (LI)

3. - Average importance (AI)

4. - Very important (VI)

5. - Extremely (E)

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QUESTIONNAIRE

NI LI AI VI E

10. Top management recognize the importance of DQ

in AIS and support DQ activities

11. Understand how the systems work, and the

importance of DQ by everyone that is involved in

AIS.

12. Employ well-trained, experienced and qualified

individual personnel at all levels, from top, middle

management to employees

13. Pleasant physical working environment, such as a

modern environment with air conditioning, and

adequate office space.

14. Working as a team and have sufficient

communication.

15. Establishment of appropriate specific DQ goals

and implementation /enforcement of policies and

standards.

16. Set up a skilled person or a group of people as DQ

manager/s to manage information flow: from input

to process, and to output.

17 Allocate sufficient funds, technical tools,

expertise, skilled personnel to ensure DQ.

18. Have appropriate DQ controls, approaches, and

adequate processes for DQ improvement

activities.

19. Acceptance of responsibility for DQ performance

by middle managers.

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20. Continuous and consistent improvement of system

and human DQ controls.

21. Have effective DQ management relationships with

raw data suppliers.

22. Identify key risk areas and key indicators of DQ

and monitor these factors.

23. Organization’s abilities and skills to manage

internal and external changes.

24. High employee self satisfaction, job security, and

career development.

25. Focus on information users' needs and their

quality requirements. Enable active participation

from users to ensure and improve DQ.

26. Get the information right in its initial phase, i.e.

input, so as to prevent input errors (Garbage-In-

Garbage Out).

27. Promote the DQ culture within the organization

that there must be high quality data in AIS.

28. Independent internal and external audit on the

systems and the DQ to ensure that appropriate

controls are in place.

29. Have systematic cost/benefit analysis of DQ

controls and activities in order to maximize

benefits at minimum cost.

30. Providing effective and adequate training for staff

to be able to understand and efficiently use AIS in

order to obtain quality information.

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