i

MINI DISSERTATION

Assessment of Big Data Analytics Readiness in

South African Governmental Parastatals

by

Mokgadi Motau 212494879

Tel: 0722049646 E-mail: mokgadimotau@gmail.com

Submitted in partial fulfillment of the requirement of the degree Masters: Business Information System

at the

Tshwane University of Technology

Supervisor: Dr. BM Kalema

May 2016

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DEDICATION

This dissertation is dedicated to Dr. Billy Kalema who guided me throughout this

study, to my Husband Philip who always believed in me, and to the almighty God

who gave me strength continuously and to everyone who contributed towards the

success of this study. And also dedicated this dissertation to my late father Philemon

Motau and my late brother Bobo Motau.

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ACKNOWLEDGMENT

Firstly, I wish to thank the almighty God for making it all possible (Jeremiah 29:11).

My sincere gratitude and many thanks to my special husband Philip Lemekwane for

his patience, sleepless nights and unwavering support; Special thanks to Dr. Kalema

and my friends; Mathilda Mphofela, Mpho Makhumisane and Mpho Motase.

In the special category that really demands mention in no specific order is Mr.

Arends Phaiphai, Mr. and Mrs. Masebe, my lovely Family, Pastor Steve and Promise

Mamabolo, Smakie Lemekwane, Fannie Motau, My mother Esther Motau, Salome

Lemekwane, Phagamang teachers (Mrs Rapholo, Mr Kgomo, Mr Leshabela, Mr

Serongwa, Mrs Mphasha ,Ms Kgoale, Mrs Maseko, Mr Mpsa), Dr MEC and MM

Moleki, Shingai Shiri.

Last but not least, I would like to extend my appreciation to Tshwane University of

Technology (TUT) for allowing me to further my studies. Special thanks to everyone

who contributed to this study.

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DECLARATION

I hereby declare that this dissertation is my own work and that it has not previously

been submitted for assessment to any other University or for another qualification.

Signed: ………………………. Date: ………………… Mokgadi Salminah Lemekwane Student Signed: ………………………. Date: ………………… Dr BM Kalema Supervisor

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ABSTRACT

The amount of data in our personal and professional life will continue to grow as we

interact more online. The analysis of that data can help organizations get actionable

insights to improve business. Government organizations recognize the opportunities

offered by Big Data technologies, but are postponing Big Data implementation

decisions as they are unsure if they are actually ready for its implementation.

Organizations struggle with the assessment of their readiness for Big Data use

implementation. By identifying factors from literature and combining them with

factors affecting technology readiness in general to assess Big Data Analytics

readiness within South African parastatals, a Big Data Readiness framework is

established, which helps South African parastatals sector organizations with

determining their Big Data readiness.

Although management of most organizations recognize the growing importance of

Big Data, there is still a struggle with its proper application. An important thing is for

management to understand the strategic importance of Big Data. However they

admit their knowledge of its applications is limited. IT management across all

functions believe that Big Data applications are too large to be left jus IT specialists.

The study collected data using close-ended questionnaires that were developed

based on the technology-organization-environment (TOE) framework. The South

African Revenue Services (SARS) head office was used for data collection.

Collected data was analyzed quantitatively by using the Statistical Package for

Social Scientists (SPSS).There will be no need for ethics since the data collected is

not sensitive. Empirical findings shows that technology infrastructure, security,

reliability, finances, competitors, customers and vendors are vital constituents in

assessing Big Data readiness. More so, results from this study will be used by other

researchers to further research in this direction and in other various environments.

By so doing, this study will be making a significant contribution to information

systems body of knowledge.

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Table of Contents

DEDICATION ........................................................................................................................... I

ACKNOWLEDGMENT .......................................................................................................... II

DECLARATION ..................................................................................................................... III

ABSTRACT ............................................................................................................................ IV

LIST OF FIGURES.............................................................................................................. VII

LIST OF TABLES ............................................................................................................... VIII

LIST OF ACRONYMS .......................................................................................................... IX

GLOSSARY............................................................................................................................. X

CHAPTER 1: INTRODUCTION ............................................................................................ 1

1.1. Introduction and background of the study ............................................................ 1

1.2. Problem Statement ..................................................................................................... 4

1.3 Research goal and objectives .................................................................................... 5

1.4 Research questions ..................................................................................................... 5

1.5 Justification of the study .............................................................................................. 6

1.6 Research Contribution ................................................................................................. 7

1.6.1 Theoretical contribution ........................................................................................ 7

1.6.2 Practical contribution ............................................................................................ 7

1.7 Research Structure ...................................................................................................... 7

CHAPTER 2: LITERATURE REVIEW ................................................................................. 9

2.1. The Big Data analytics concept ................................................................................. 9

2.1.1 Characteristics of Big Data ................................................................................ 11

2.1.2 Factors influencing Big Data readiness assessment ..................................... 11

2.2 Technology readiness assessment ......................................................................... 14

2.2.1 Related work for technology readiness assessment ................................. 15

2.3 Theories of Big Data and Technology Readiness ............................................. 17

2.3.1. Technology readiness Index ............................................................................ 18

2.3.2 Technology Acceptance Model Framework .................................................... 19

2.3.3 Change Management theories .......................................................................... 20

2.3.4 Technology Organization Environment Framework ...................................... 21

2.4 Big Data Theoretical framework ........................................................................... 22

2.5 Big data readiness ...................................................................................................... 24

2.5.1 Big Data readiness assessment ................................................................... 24

2.6 Related works.............................................................................................................. 25

2.9 Summary ...................................................................................................................... 27

CHAPTER 3: RESEARCH METHODOLOGY .................................................................. 28

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3.3 Research paradigm .................................................................................................... 30

3.4 Research strategy ...................................................................................................... 31

3.5 Sampling of Participants ............................................................................................ 31

3.6 Data collection methods ............................................................................................ 32

3.6.1 Close-ended questionnaires .......................................................................... 32

3.6.3 Research techniques ...................................................................................... 33

3.7 Piloting the Study .................................................................................................... 33

3.8 Main Survey ............................................................................................................. 33

3.8.1 Unit of analysis .................................................................................................... 34

3.9 Data analysis ............................................................................................................... 34

3.10 Reliability and validity of constructs ....................................................................... 35

3.10.1 Reliability ....................................................................................................... 35

3.11 Summary .............................................................................................................. 36

CHAPTER 4: RESULTS AND DATA ANALYSIS ............................................................ 37

4.1. Frequency and demographics of participants ....................................................... 37

4.2 Descriptive statistics of constructs ........................................................................... 42

4.3. Correlations of variables ........................................................................................... 44

4.4. Chi-square (χ2) analysis and reporting .................................................................. 46

4.5. Summary ..................................................................................................................... 48

CHAPTER 5: DISCUSSION, INTERPRETATION, CONCLUSION AND RECOMMENDATION ........................................................................................................... 49

5.1 Overview of the research ...................................................................................... 49

5.2 Discussions and implication of results .................................................................... 50

5.2.1 Discussion and Implications in Relation to the Hypotheses ......................... 52

5.3 Discussions in relation to the research questions ................................................. 55

5.3.1. Research question one ..................................................................................... 55

5.3.2. Research question two ...................................................................................... 55

5.3.3 Research question three .................................................................................... 56

5.4 Research Contributions ............................................................................................. 56

5.4.1 Contribution to Practice and Management ...................................................... 56

5.4.2 Theoretical and Methodological Contributions ............................................... 56

5.5 Limitations of the Study ............................................................................................. 57

5.6 Recommendations ..................................................................................................... 57

5.7 Conclusion ................................................................................................................... 57

REFERENCES ...................................................................................................................... 59

APPENDIX A: QUESTIONNAIRES.................................................................................... 64

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LIST OF FIGURES

Figure1.1: Research Disposition ………………………………………………………...8

Figure 2.1: The 3 v’s of Big Data ............................................................................. 11

Figure 2.2: The Technology Readiness Index ………………………………………….19

Figure 2.3: The Technology Acceptance Model ...................................................... 20

Figure 2.4 The Technology organizational and environmental .................................. 2

Figure 2.5: Big data readiness Framework ............................................................. 25

Figure 3.1: Research process ……………………………………………………………28

Figure 4.1: Education Levels ................................................................................... 39

Figure 4.2 Age Group ............................................................................................... 40

Figure 4.3 Overall working experience ..................................................................... 40

Figure 4.4 Big data awareness ................................................................................. 41

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LIST OF TABLES

Table 3.1: Quantitative and Qualitative Approach…………………………………..30 Table 3.2: Respondent rate …………………………………………………………...34 Table 3.3: Questionnaires reliability statistics ……………………………………….35 Table 3.4: Constructs reliability statistics …………………………………………….37 Table 4.1: Frequencies and descriptive statistics of participants…………..………39 Table 4.3: Descriptive statistics of constructs ………………………………………..43 Table 4.4: Correlation of constructs ………………….………………………………..45 Table 4.5: Chi-square case processing summary ……………………………………46 Table 4.6: Chi-square significance ……………………………………………...….......47 Table 5.1: Testing the suggested hypotheses.…………..…………………………….50

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LIST OF ACRONYMS

ERP Enterprise resource planning

OLTP Online transaction processing

IT Information Technology

SARS South African Revenue services

TAM-CT Technology Acceptance Model for

Collaboration Technology

SPSS Statistical package for social scientists

TEO Technology organization Environment

TAM Technology Acceptance Model

TRA Theory of Reasoned Action

RDBMS Relational database management system BI Business intelligence BDA Big Data Analytics

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GLOSSARY

Construct Refers to a proposed attribute of a person that often cannot be

measured directly, but can be assessed using a number of

indicators or manifest variables

Correlation

Refers to a single number that describes the degree of

relationship between two variables

Demographics

Refers to particular properties of a population on such as gender,

age, position, level of education, and experience among others.

Hypothesis

Refers to a proposition, a fixed forth as a description for the

occurrence of specified group of phenomena. It describes in

concrete terms what one expects will happen in the study

Univariate

analysis

Refers to the investigation carried out with the description of a

single variable in terms of the applicable unit of analysis

Bivariate

analysis

Refers to the investigation of two variables for the purpose of

determining the empirical relationship between them

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CHAPTER 1: INTRODUCTION

The main focus of this chapter is to give an introduction and the background of the

study and the areas that have been investigated. The chapter introduces the concept

of Big Data, Big Data Analytics and describes how its readiness may be assessed.

Furthermore, the study described the research problem from which the research

questions are derived. The justification of the study explains the significance of

assessing Big Data readiness. Lastly the chapter discusses the expected

contribution of the research along with the structure that will be followed for the rest

of the dissertation

1.1. Introduction and background of the study

The ever increasing changes in the business environment are making many

organizations to strive for competitiveness so as to keep abreast with the global

standards. Organizations are increasing their operations by leveraging information

technology (IT) to expand their network by opening up branches both locally and

internationally. These global trends have seen many large organizations across

industries joining the data economy. By so doing, many of these organizations strive

to find how best they can improve on their traditional data analytics in order to

remain competitive in the business arena. This implies that these organizations need

to improvise means that will lead to change of skills, leadership, organizational

structures, technologies and architectures. This in turn will help organizations to

cope with technological innovations and to have a wide global market share.

According to Ferguson (2012), for many years organizations have been building data

warehouses to create insights for decision makers to act on improving business

performance and to study their business activities. He noted that these traditional

analytical systems capture, clean, transform and integrate data from several

operational systems before loading it into a data warehouse. However, he argues

that even though these traditional environments continue to advance, many new

more complex types of data have now emerged that businesses ought to use in

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order to analyse and build on what they already know. In addition, Wielki (2013)

observed that new data is being created and generated at high speed.

The desire for differentiation, the power of the connected consumer, the pressure for

digital influence and the ubiquitous use of technology are among the factors that are

propelling organizations to open up branches and extend their networks (Brown et

al., 2011). As a result, private enterprises, public sectors and government

departments and parastatals seek greater insight into the data they collect, manage

and store. Hence, organizations need to use numerous analytical techniques to

study both structured and unstructured forms of data that could assist in process

discovery, productivity and policy making. Michael and Miller (2013) observed that

organizations are currently employing the best technologies and data architectures

together with the powerful analysis and reporting tools. They however noted that, the

dramatic growth of data within organizations continues to overpower these traditional

analytic tools. This implies that software vendors need to offer new data analytical

solutions that will handle the massive volumes of data which is also known as Big

Data.

The Big Data concept may be looked at as the formation of datasets that

continuously expand so much that it becomes challenging to manage using existing

database management concepts and tools (Singh & Singh, 2012). Researchers

Kaisler et al. (2013) describe the concept of Big Data as the amount of data just

beyond technology’s ability to process, manage and store efficiently. Notwithstanding

the data volumes, Big Data can help an organization to gain insights and make

better decisions (Goss & Veeramuthu, 2013).

Among all the definitions offered for Big Data, this study opted to use the definition

based on that of researchers Singh and Singh (2012) that looks at Big Data as the

datasets which continue to grow so much that it becomes difficult to manage using

existing database management concepts and tools. From this perspective, Big Data

refers to that data that is too big, too fast, or too hard for existing tools to process.

Here, “too big” means that organizations must increasingly deal with petabyte-scale

collections of data that come from click streams, transaction histories, sensors, and

elsewhere. “Too fast” means that not only is data big, but it must be processed

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quickly. “Too hard” is all-encompassing for data that an existing processing tool is

unable to manage and so it needs some improved analysis (Madden, 2012).

Although Big Data can produce exceptionally useful information, it also presents new

challenges with respect to storage, cost and security and how long it must be

sustained (Michael & Miller, 2013). Big Data also supplies more raw materials for

statistical mischiefs and influenced fact finding excursions (Lohr, 2012). When an

organization can leverage all the data available rather than just a subset of its data

then it has a great advantage over the competitors (Singh & Singh, 2012).

Organizations ought to start thinking deeply about whether they are prepared to

exploit Big Data’s potential benefits and also to manage the threats it can pose.

Once it is acknowledged that data is to be treated as a corporate resource, serving

several purposes, management of which must satisfy the three criteria that is too big,

too fast and too hard the organizational implications may be startling. When these

implications are worked out to the practical level and management accept the need

to manage information throughout the organization, major changes may result in

terms of the structure, staff and systems; but before this can happen, techniques to

administer and account for information must be developed.

Most organizations anticipate their data to grow over time as the organizations

increase their businesses, their services and business partners and clients as well as

their projects and facilities and employees (Kaisler et al., 2013). Few organizations

adequately consider data increase, which occurs when the data records grow in

productivity, and this may evolve to additional information as new techniques,

processes and information demands evolve. Due to the large diversity of sources

from which data is collected and integrated, it is impossible to manually specify data

quality rules. Hence, Big Data comes with a major promise, having more data allows

the data to speak for itself instead of depending on unconfirmed assumptions and

weak correlations (Saha & Srivastava, 2014).

In many government parastatals, privacy is more attached to the sensitivity of data

they are working with. Such data may include but not limited to; clients list,

prospective projects, financial data and all other valuable data that may or may not

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be disclosed. The amount of data that is being stored is not always one hundred

percent valuable (Tole, 2013). Therefore, organizations should ensure their storage

systems have enough flexibility to scale up to meet Big Data requirements without

procuring capacity they don’t need. Big Data itself cannot produce business results;

what’s needed is a clear vision of how the business can gain results using Big Data.

Also with the right technology, trained professionals are required to leverage

resources and deliver results for Big Data projects. Hence, organizations need to

assess their readiness for this new technological development.

SARS embarked on a modernization programme in 2007. The modernisation

programme was aimed at automating the high volumes of manual processing in

order to free up human capacity that will instead focus on providing improved

services to taxpayers. The modernization programme has made significant

improvements specifically related to personal income tax, the eFiling web system

and the introduction of electronic channels supporting Customs and Excise

transactions.As a results they are overwhelmed with huge volume of data and the

data need to be analyzed in real time.

This study therefore sought to assess Big Data analytics readiness within

government parastatals. The study was informed by data collected from the South

African Revenue Services (SARS) and analyzed quantitatively to determine the

factors relevant for Big Data readiness.

1.2. Problem Statement

The lack of capacity and capability to handle Big Data within organisations has led to

inability to leverage IT and failure to make informed decisions. Customers are

interacting with organizations in an increasing number of ways and channels

globally. As a result, the data growth rate within organizations is overwhelmingly high

(Kaisler et al., 2013). However, many organizations cannot support the required level

of engagement of growing data to fulfill the increasing customers’ demands. More

so, organizations are also experiencing the inability to cater for this growth of data.

This rapid expansion of data is increasingly exceeding the organizations ability to

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design appropriate systems that could handle Big Data effectively and analyze it to

extract relevant meaning for decision making (Michael & Miller, 2013).

It is therefore not clear whether organizations have the capability to leverage data

integration and therefore interact with their increasing stakeholders through multiple

channels in real time situations. . In order to assess Big Data Analytics readiness

within South African parastatals, the factors were identified from literature on

Technology readiness in general.

1.3 Research goal and objectives

The goal of this study was to assess Big Data readiness for South African

government parastatals

The specific objectives were:

1. To determine technological, environmental and organizational readiness

related to Big Data processes and tools

2. To determine the benefit of Big Data analytics within an organization

3. To determine the level of influence of the identified factors to Big Data

Analytics readiness

1.4 Research questions

The main research question that was focused on this study aimed at assessing the

Big Data Analytics readiness within governmental parastatals. The following

secondary questions were explored:

1. What are the organizational, environmental and technological readiness

factors related to Big Data processes and tools?

2. What are the benefit of Big Data analytics within an organization

3. What are the level of influence of the identified factors to Big Data Analytics

readiness

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1.5 Justification of the study

Most organizations face challenges when assessing Big Data needs and realising

the benefits thereof. Goss and Veeramuthu (2013) argue that Big Data analytics has

the capability to reveal insights on previously hidden data that were too costly to

process. They further argue that with Big Data technologies, organisations are able

to process every required item in a reasonable time (Madden, 2012). Organizations

need a new approach to harness unstructured and multi-structured information and

to combine it with ever-growing volumes of big transaction data from data

warehouses, enterprise resource planning ERP applications, and online transaction

processing (OLTP) systems.

Organizations are at the point now where it is difficult to manage the huge amount of

data because the volume is increasing speedily in comparison to the computing

resources (Katal et al., 2013). This is particularly the case for security where

sensitive data needs to be protected. The challenge is major, in terms of

management of the data they collect. However, despite this challenge, Big Data has

arrived as another trend of computing (Ferguson, 2012). Emerging academic

research advises that companies that use data and business analytics to guide

decision making are more productive and hence experience higher returns on equity

than companies that don’t (Brown et al., 2011).

Big Data offers a chance to create unprecedented business advantage and better

service delivery. It also requires new infrastructure and a new way of thinking about

the way business and IT industry work. The concept of Big Data is going to change

the way things are done today (Singh & Singh, 2012). There is no doubt about the

fact that the South African governmental parastatals are already experiencing the

Big Data in their everyday operations, so it is very important for them to be ready for

the era of Big Data.

Before an organization can perform the analysis needed to support informed

decision-making, it needs to know what data resources are available. Discovery

includes not only inventorying data assets but also preparing and organizing these

assets (Miller & Mork, 2013). Davenport and Dyché (2013) argue that in order to

properly use data to positively impact business strategy, organizations need to align

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the data to the problem that they aim to solve. Hence, many organizations have

different problems, but most will have Big Data needs. Big Data Analytics can reveal

insights previously hidden by data that were too costly to process, such as sensor

logs. They should be able to process every required item in a reasonable time (Goss

& Veeramuthu, 2013).

1.6 Research Contribution

The contribution this study makes is twofold namely;

1.6.1 Theoretical contribution

So far the literature available on Big Data readiness is mostly in whitepapers and

company websites. Very little literature has been academically recorded on the

assessment of Big Data Analytics readiness within organizations. This study tested

Technology-Organization-Environment (hereafter TOE) to assess Big Data Analytics

readiness within a South African parastatal. Hence, this study will be contributing

significantly to the literature of the information systems body of knowledge.

1.6.2 Practical contribution

The process of decision making fails many organizations mainly due to lack of

guidelines. The identified factors by this study will assist management in organization

to address issues related not only to Big Data but other technologies’ readiness. This

will be a significant contribution to practice and management as the empirical

evidence of this study will be used to guide decision making.

1.7 Research Structure

The main focus of chapter one of this dissertation discusses the introduction of the

study, the research problem as well as the questions that determine the direction of

the whole research. The chapter also demonstrates how the research questions

drawn from the research problem are to be answered by carefully following the

research objectives.

Big Data concept, Big Data readiness assessment as well as the Technology

readiness as a whole are discussed in Chapter two. Based on the factors identified

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from the literature review, a theoretical framework which underpinned the study is

presented. This framework is crucial for data collection as it clearly indicates what

type of data is to be collected and from whom.

The main focus of chapter three is to presents the methodology of the study. In this

chapter, the steps and procedures of how data necessary for research is to be

gathered is clearly shown, Also it gives the methodological approach to the research

as well as the research paradigm.

Chapter four of the study presents the results obtained from the analysis of the

collected data. This done by generating the descriptive analysis, correlation, and

regression analysis together with the Chi-Square test analysis for testing the

hypotheses developed.

The main focus of chapter five is discussion of the results. In this chapter, the

meaning and implication of the results in relation to the set hypotheses are

discussed. This chapter also makes a narrative comparison of what has been

obtained from this research in relation to the literature and actual practice. The

findings set grounds for the research’s recommendations and direction for future

studies.

Figure1.1: Research Disposition

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CHAPTER 2: LITERATURE REVIEW

The main focus of this chapter is to review the literature and related work on models

relevant to the current study. It also presents a critical synthesis of empirical

literature according to factors influencing Big Data Analytics. In particular this chapter

provides an e-readiness definition in the context of previous research and scholarly

material, based on that it defines Big Data readiness and also gives a theoretical

framework used to assess Big Data readiness. Finally, the hypothesis developments

are drawn from the research model are hypothesized and the summary of the

chapter is given.

2.1. The Big Data analytics concept

Big Data is a concept often used when an organization’s existing traditional relational

database and file systems processing capacities are exceeded in high transactional

volumes, velocity responsiveness, and the quantity and or variety of data. The data

are too big, move too fast, or don’t fit the structures of RDBMS architectures. Scaling

also becomes a problem. To gain value from these data, organizations must choose

alternative ways to process the data sets (Goss & Veeramuthu, 2013).

Organizations could improve their decision-making thereby realize their objectives if

they use information embedded in Big Data sets at their disposal intelligently. As

reported by Manyika et al. (2014), organizations may lose competitiveness if they fail

to systematically analyze the available information needed for decision making. Big

Data can improve decision making and increase organizational efficiency and

effectiveness, but only if organizations employ a variety of analytical tools and

methods to make sense of the data (Joseph & Johnson, 2013).

The Big Data era has of late descended on many communities, from governments,

commerce and to health organizations. With an overwhelming amount of web-based,

mobile, and sensor generated data arriving at terabyte and exabyte scales new

science, discovery and insights can be obtained from the highly detailed,

contextualized, and rich contents of relevance to any business or organization (Chen

et al., 2012).

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The use of Big Data technologies should be done by architectural groups and

analytics groups with an operations research title, that exist within IT organizations.

In most cases these central service organizations are aligned to Big Data initiatives

with analytically-oriented functions or business units marketing. Furthermore, the

organizations that have close relationships between the business groups addressing

Big Data and the IT organizations supporting them, had initiatives that seemed most

effective and likely possible to succeed (Davenport & Dyché, 2013)

It is important to note that Big Data is not a single technology, but a combination of

new and old technologies that support companies gain insight while effectively

handling data load and storage problems. Big Data analytics require the ability to

collect, manage, and analyze potentially massive volumes of different data, at the

right speed, within the right time frame, while providing the right-time analysis and

activity to the end consumer (Halper & Krishnan, 2013)

Additionally, Big Data includes structured databases and unstructured data from

various internal and external sources such as streaming data, social media,

geospatial data, and so on. To successfully leverage these various kinds of data

structures, organizations require infrastructure, data analytics, organizational

structure and governance processes to make Big Data analytics operational and

actionable.

According to Halper and Krishnan (2013) many organizations begin to utilize Big

Data solutions because they have a specific business problem to solve that requires

Big Data. For example, an Internet company might monetize Big Data as part of its

business model. This is often a new class of company that is very fast and

aggressive, where data is driving business. Others have started to realize that data

is a corporate asset they can use to become more competitive often because of a

business imperative such as losing market share.

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2.1.1 Characteristics of Big Data

There are four characteristics that define big data:

Volume - The amount data collect managed and stored.

Velocity - The speed at which is received

Variety – The different type of data from multiple sources.

Value – The insight of which the organization gain

Figure 2.1: The 3 v’s of Big Data (Source: Singh & Singh, 2012)

2.1.2 Factors influencing Big Data readiness assessment

Several researchers have discussed and argued that numerous factors influence Big

Data readiness within an organization. These factors may differ from one

organization to another and they include but not limited to;

a) Management Support

Management support is usually stated as a critical success factor in the field of data

business intelligence, warehousing and Big Data analysis (Chen et al., 2012).

Rajpurohit (2013) also argues that in order to resolve the Big Data problem,

managers need to realize that Big Data ownership can no more be left simply to

statisticians or business intelligence units. Deriving the maximum value from

analytics would need configuring and customizing the analytics implementation to

meet your business goals.

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b) IT infrastructure

As the data grows in the industry, new techniques and approaches need to be

adopted (Bakshi, 2012).Big Data difficulties affect information technology

infrastructures. The capability to collect and process big data necessitates sufficient

transmission and storage capacities, as well as computing power.

c) Security

Big Data forces decision makers to adopt cloud computing but security are on their

minds for every cloud project. Researchers (Chen et al., 2012) state that

organizations of different sizes are facing the daunting task of defending against

cybersecurity threats and protecting their intellectual assets and infrastructure.

Processing and analyzing security-related data, however, is increasingly difficult. The

privacy of data is another big concern, and one that rises in the context of Big Data

(Jagadish et al., 2014).

d) Finances

Most organization prefer to archive a large amount of data that is not even being

used since there is no cost effective way to process it and get value out of it. Big

Data analytics can reveal insights previously hidden by data that were too costly to

process, such as sensor logs (Goss & Veeramuthu, 2013). There is also the issue of

resource constraints in some organization when deciding on appropriate strategies

to tackle Big Data. Resource availability in terms of money and human resources is a

major factor when assessing Big Data readiness in an organization. Businesses

operating in the Big Data space continue to face obstacles in accessing finance and

skilled labour.

e) Competition

As organizations work to extract competitive business values and ultimately revenue

from a growing sea of data, Big Data implementations leverage diverse sets of

distributed semi-unstructured and unstructured data types, which frequently start

with mathematics, statistics and data aggregation efforts (Villars & Olofson, 2011).

Villars & Olofson, (2011) also noted that competitive advantage can be greatly

13

improved by leveraging the right data. When an enterprise can leverage all the

information available with large data rather than just a subset of its data then it has a

powerful advantage over the market competitors. Big Data can help to gain insights

and make better decisions (Singh & Singh, 2012).

f) Customers

The private and public sectors are starting to use Big Data in their everyday

activities. Big retail companies, such as Walmart, Sears and Amazon are using Big

Data to attempt to better understand their customers and their buying choices (Kelly,

2013).

g) Vendor

There are many vendors offering Big Data analytics solutions like IBM, Kognitio,

ParAccel & SAND (Singh & Singh, 2012). A clear vision and proper business case

often should not only include the current business needs regarding BI &BDA but

should also support future business requirements (Ebner et al., 2014).

h) Organization size

Organization that have long handled massive volumes of data are beginning to

enthuse about the ability to handle a new type of data while organizational size is

often positively correlated with the availability of resources (Ebner et al., 2014).

2.1.2 Benefits of Big Data Analytics

There is a plethora of literature on studies that have been conducted relating to Big

Data and its benefits thereof; some of the benefits are listed below :( Kuketz, 2012)

1. Timely insights from the huge amounts of data. This includes those already

stored in company databases, from external third-party sources, the Internet,

remote sensors and social media.

2. Real-time monitoring and forecasting of events that impact business operations

and performance.

3. Ability to acquire, extract, manipulate, analyse, connect and visualize data with

the tools of choice

4. Convergence of the BDI solution for variety with the speed of SAP HANA for

velocity

14

5. The capability to manage huge amounts of data, in or out of the Cloud, with

validation and verification.

6. Identifying important information that can improve decision making

7. Mitigating risk by optimizing the complex decisions of unplanned events more

rapidly

2.2 Technology readiness assessment

Information and communication technology has been implemented worldwide in

many organizations. In particular, organizations are using ICT as a tool to run

businesses, to support work, and to serve customers, which must work within their

strategies and master plans. Furthermore, technology has been presented in the

government sector in the past decades in an effort to achieve better operational

efficiency and effectiveness (Alghamdi et al., 2011).

To this end, readiness assessment tools related to ICT have been developed in

many organizations (Chanyagorn & Kungwannarongkun, 2011). Assessing the

readiness of an organization to embrace new innovations is a vital requirement for

evolving and keeping informed of market demands in today’s volatile environment.

Owing to advances in information and communication technology and its role in

business and industry, e-readiness concept has been developed to rationalize

action, enhance competitiveness, and manage resources efficiently (Mutula & van

Brakel, 2006). E-readiness can be defined as the ability of an organizational unit to

be prepared, willing to adopt, use and benefit from e-innovations such as e-

procurement, e-learning, e-business and e-government (Aboelmaged, 2014).

For most organizations, their readiness in adopting the technologies is expected to

lead the organizations to manage the relationships with their key stakeholders such

as suppliers, customers, employees and investors in a more positive direction

(Janom & Zakaria, 2008). Scientifically measuring technology and system maturity is

a multi-dimensional process that cannot be performed broadly by a one-dimensional

metric. Although the Technology readiness levels metric has been recognized by the

15

government and many industries, it captures only a small part of the information that

stakeholders need to support their decisions (Azizian et al., 2009).

Organizations make considerable investments each year in the development and

implementation of the latest technologies (Alghamdi et al., 2012). However,

readiness is always an issue; it becomes a critical factor to successful

implementation of such technologies. Assessment for readiness is feasibly the best

measure before the inception of technologies (Isa et al., 2013). In this regard, El-

Darwiche et al. (2014) reported that organizations foster new decision-making

culture by exploiting the opportunities presented by Big Data and prepare their own

internal capabilities to handle this new era.

2.2.1 Related work for technology readiness assessment

Technology readiness can be described as the capability of an enterprise or

organizational unit to be ready, keen to embrace, use and benefit from e-innovations

such as e-business, e-government, e-procurement, e-learning e-health, few studies

have examined e-readiness on organization-level.

According to a study by Erdo˘gmus and Esen (2011), personality influences

technology use and therefore organizations should be aware of this relationship

when introducing information systems projects. In other words organizations must

adopt their strategy on how to increase technology acceptance on the basis of user’s

personalities. They also linked technology readiness index (TRI) to technology

acceptance model (TAM) to investigate the effect of technology readiness of Human

resource managers on the acceptance of e-HRM system. Their results show that

personality influences technology use. Organizations should be aware of this

relationship when initiating information systems. However their study focused more

on individual readiness to technology than the organization as a whole.

The study of Janom and Zakaria (2008) stated that theories and frameworks

probably need to be tailored to the type of technology and its adoption context.

Thus, theories that cover different areas of adoption (innovation, management,

16

organization, and environment) provide better descriptive influence than models that

mainly depend on only one of the other opinions. Therefore, a combination of

success factors from the four perspectives of innovation, management, organization

and environment have given organizations the grounds to measure their

preparedness in these to implement B2B e-commerce.

Researchers Alghamdi et al. (2011) proposed e-government framework comprising

of seven dimensions of ICT readiness assessment for government organizations.

These included e-government organizational ICT strategy, user access, e-

government program, ICT architecture, business process and information systems,

ICT infrastructure, and human resource. They recommended quantitative empirical

research to test the framework.

In Borgman et al. (2011) the link between the TOE framework and the decision of

organizations to adopt cloud computing, as well as the moderating effect of IT

governance structures and processes on these relationships was conceptualized.

They developed hypotheses and were able to test these for the main independent

variables, showing that, specifically, the technology and organization context factors

affect the decision whether organizations should adopt cloud computing.

Researchers Olschewski et al. (2013) proposed an extension of the Technology

Acceptance model in order to assess the effect of social influence and technology

readiness on collaboration technology adoption and alternative collaboration

technology use. In their first pretest to measure the technology readiness within an

organization, their study proposed Technology Acceptance Model for Collaboration

Technology (TAM-CT). However the framework is used to evaluate and test

individual-level acceptance of technologies rather than organization.

According to Naseebullah et al. (2013), Technology readiness refers to the current

use and potential adoption of e-procurement by organizations. Naseebullah et al.

(2013) highlighted the technology readiness in terms of IT infrastructure, IT expertise

and IT compatibility. The model is based on intensive literature survey of

organization’s readiness of technology in general. They recommended that the

model can be extended with other organizational contexts.

17

2.3 Theories of Big Data and Technology Readiness

Technology readiness (TR) refers to people's tendency to adopt and use new

technologies (Parasuraman, 2000). In this regard, TR represents the personal

perspective and inclination to use technology products and services in everyday life.

The TR is also related to whether new technologies that can assist people in

completing professional purposes exist in the area of users (Kuo, 2012). The

innovation in technology changed how services are delivered. Technology increases

the productivity and efficiency by allowing consumers to conveniently access

services (Yieh et al., 2012).

The rapid initiation and convergence of ICT have triggered a vast array of cultural,

social and economic changes. Global ICT, which are growing in capacity and

improving in terms of interactive and dynamic operations have become major drivers

of competitiveness in the organizations. This is achieved by taking into consideration

the benefits in the adoption and effective use of technologies (Yunis, 2011). Toufani

and Montazer (2010) also argues that the high level of technology readiness allows

organizations to manage business electronically in order to achieve less turn-around

time, faster delivery of services, enhanced product selection, international

competitiveness, a broader market reach,reduced costs, faster and limitless access

to new customers and suppliers, increased depth of communication, exchange of

information.

Information Technology has long been applied to support the exchange of goods,

services and information among organizations (Naseebullah et al., 2011). To meet

the intensifying market needs, one of the primary duties of technology forecasting

within organization is to; identify relevant technologies, determine technology

readiness, predict future development and to estimate suitability of technology within

the organization (Ardilio et al., 2012). The importance of information and

communications technologies as powerful tools for socio-economic development is

now widely acknowledged not only among large organizations but also among the

small organizations (Mutula & Brakel, 2007).

18

The need to identify and explain the factors that address technology readiness and

its assessment has been the basis for several researches. In this viewpoint two

theories have been at the lead of many studies. These theories are the TRI

(Parasuraman, 2000) and the TAM (Davis et al., 1989). Others that have been used

are Change management and TOE (Tornatzky & Fleischer, 1990).

2.3.1. Technology readiness Index Technology Readiness Index was first issued in 2000 in the Journal of Service

Research and has become an extensively recognized metric for studying the

propensity to adopt and use cutting-edge technology (Parasuraman,

2000).Technology readiness can be viewed as a belief resulting from four personality

dimensions: optimism, innovativeness, discomfort, and insecurity. According to

Parasuraman (2000), these personality dimensions affect people's propensity to

embrace and use new technologies. The four main constructs are briefly described

in the following:

Optimism is described as "a positive view of technology and a belief that it

[technology] offers people flexibility, better control and efficiency in their lives"

(Parasuraman & Colby, 2001). It generally captures positive feelings about

technology.

Innovativeness is described as "a propensity to be a technology pioneer and

leader" (Parasuraman & Colby 2001). This dimension generally measures the

degree to which individuals perceive themselves as being at the forefront of

technology adoption.

Discomfort is described as "an alleged lack of control over technology and a

feeling of being overwhelmed by it" (Parasuraman & Colby 2001). This

dimension generally measures the fear and concerns people experience

when confronted with technology.

Insecurity is described as a "doubt of technology and uncertainty about its

ability to work properly" (Parasuraman & Colby, 2001). This dimension

19

focuses on concerns people may have in face of technology-based

transactions.

Figure 2.2: The Technology Readiness Index (Source: Parasuraman, 2000)

Optimism and innovativeness are the positive drivers of technology readiness index

(TRI); they encourage customers to use technological products/services, and to hold

a positive attitude towards technology. The relative strength of positive drivers in TR

indicates a person’s openness towards technology. On the contrary, discomfort and

insecurity are the negative attitudes, i.e., inhibitors; they make customers reluctant or

have less intention to adopt new technology (Chen & Li, 2010).

2.3.2 Technology Acceptance Model Framework

Technology Acceptance Model is an amendment of the Theory of Reasoned Action

(TRA) in the field of information systems Davis (1986).TAM proposes that perceived

usefulness and perceived ease of use determine an individual's intention to use a

system with the intention to use serving as a mediator of actual system use.

Perceived usefulness is also seen as being directly impacted by perceived ease of

use. TAM have been simplified by removing the attitude construct found in TRA from

the current specification (Venkatesh et al., 2003). TAM defines and predicts an

individual’s acceptance behavior toward a new technology, the model is not suited

for testing of organizational-level acceptance of technologies (Lippert &

Govindarajulu, 2015).

20

TAM has three key variables: Perceived usefulness (PU), perceived ease of use

(PEU) and behavioral intention to use (BIU). Perceived usefulness looks at the

user’s subjective probability that using a specific application system will increase his

or her job performance within an organizational context. Perceived ease of use looks

at the degree to which the user expects the target system to be free of effort. While,

behavioral intention to use shows an individual’s requests and efforts to perform a

behavior. TAM emphasizes that the influence of external variables upon user

behavior is mediated through user beliefs and attitudes (Erdo˘gmus & Esen,

2011).They also noted that TAM is one of the most dominant, robust and

parsimonious model for predicting user acceptance especially in IS context.

However TAM is not suitable for this study as the factors identified on the research

objectives are not covered in the TAM model. TAM addresses for individual

readiness not to firm or organization readiness.

Figure 2.3: The Technology Acceptance Model (Source: Davis et al., 1989)

2.3.3 Change Management theories

Studies using change management theories have shown that the change process

goes through different sets of phases (Pryor et al., 2008). Each set of phases lasts

for a certain amount of time and mistakes that occur at any phase may impact on the

success of the change process. Kotter (1996) suggests eight critical steps to change

management in order to avoid common pitfalls and increase chances of succeeding:

- Establishing a Sense of Urgency

- Forming a Powerful Guiding Coalition

21

- Creating a Vision

- Communicating the Vision

- Empowering Others to Act on the Vision

- Planning for and Creating Short-Term Wins

- Consolidating Improvements and Producing Still More Change

- Institutionalizing New Approaches

According to Shield’s (1999) model, change fails because of insufficient attention to

cultural and human aspects of business. He suggests that there are critical

components that are necessary for leaders to change an organization. He asserts

that a change that occurs in one component must be aligned to the other

components to avoid inefficient work processes. This is supported by Kalema et al.

(2011) who suggests that people must be involved in planning and change

management so as to embrace change and avoid resistance of users. This system

aids in integrating human resources management with business process

innovations.

Since most of the available literature on Big Data is still in Whitepapers, this study

has found no theoretical framework that completely addresses all the aspects of its

readiness. Therefore, this study intends to use Technology Organization

Environment ,henceforth referred to as TOE framework(Tornatzky & Fleischer,

1990), because the factors influencing Big Data readiness identified from literature

are addressed by it. For the purpose of this study we have adopted the TOE

framework as an organization-level theory "that explains how the organization

context influences the adoption and implementation of innovations"

2.3.4 Technology Organization Environment Framework

The TOE framework was developed by Tornatzky and Fleischer (1990) and identifies

three aspects of an enterprise's context that influence the process by which it adopts

and implements a technological innovation, namely; technological context,

organizational context, and environmental context. For the purpose of this study all

three constructs were adopted as appropriate. Technological, organizational and

Environmental readiness played a big role in the literature when assessing the

readiness of an organization as shown in figure 2.4 below.

22

Figure 2.4: The Technology organizational and environmental (Source: Tornatzky & Fleischer, 1990).

2.4 Big Data Theoretical framework

The proposed research model, Figure 2.5, organizes the Big Data readiness

framework determinants into Tornatzky and Fleischer’s (1990) TOE framework to

establish the three principal contexts – technological, organizational, and

environmental.

Technological construct looks at whether the organization’s existing infrastructure

will still be fit with the Big Data and therefore this construct relates to the evaluation

of the existing technology in the organization. Also, this construct looks at the

organization IT infrastrusure, reliability and security of the data they collect.

Organizations construct looks at support in the adoption of new technology from

top management. It’s easier for the management to influence the whole organization.

This construct view the size, structure of the organization.

Environmental construct looks the organization’s relationship with their customers

and services offered to them. External factors includes customers, vendors, and

competitors, economic, political, social and technological.

23

From the TOE frameworks, we formulated our own framework to assess Big Data

readiness as depicted in Figure 2.5

Figure 2.5: Big data readiness Framework

Reliability

ICT Infrastructure

Security

Top Management

Organizational Size

Finances

Competition

Customers

Vendor

Big Data Analytics

Readiness

Organizational

Context

Technological

Context

Environmental

Context

H1

H2

H3

H4

H5

H6

H7

H8

H9

24

2.5 Big data readiness

As the data grows in the industry, new techniques and approaches are adopted

(Bakshi, 2012). Researchers Kaisler et al. (2013) put it that, while the government

seems to assume that Big Data users will be more successful, more productive, and

have differential impacts across many industries, their underlying concern seems to

be a lack of tools and trained personnel to properly work with Big Data. Furthermore,

he asserts that it is important for government agencies to establish a Big Data

strategy.

According to Tekiner and Keane, (2013) it is impossible to manually specify data

quality rules due to the large diversity of sources from which data is collected and

integrated for its sheer volume and changing nature. However, the wide technology

gap between industrial applications and decision makers remains a challenge.

Decision makers need to be prepared and understand the data and technologies

better in order to extract information to aid strategic decision making.

Big Data comes with a major promise of having various information from which

decisions could deduced. This is because; having more data allows the acquisition of

reliable information instead of depending on unconfirmed assumptions and weak

correlations (Saha & Srivastava, 2014). Many organizations that appreciate the

advantages of Big Data and prepare for it enjoy the benefits and provide better

services. More so, such organizations become more competitive than those that fail

to utilize the data they have.

2.5.1 Big Data readiness assessment

Technology has changed significantly in the past years. The trend of digitization of

information traditionally maintained as documents (examples being emails,

contracts, logfiles, policies, claims), finding structure in data traditionally viewed as

digital “blobs” (examples being call logs, digital audio and video, weblogs, social

media generated data), along with the unprecedented rise of social networks has

given rise to new information sources spewing out vast quantities of data

continuously. This is the phenomenon of Big Data (Oracle, 2012). According to

25

Informatics (2012), the first step in a Big Data journey is to understand the maturity

of the organization from an analytics perspective.

According to Villars et al. (2011), culture plays a critical role in many industries when

it comes to the pace of adoption of Big Data practices. He argues that organizations

that have been generating or dealing with large data sources for decades will be

early movers. This group includes many government organizations and mostly the

parastatals organizations.

2.6 Related works

Pearson et al. (2012) put it that while technology steals the spotlight in many

discussions of Big Data, it’s only one part of the equation. He asserts that to operate

and assess readiness of big data solution, it is critical to consider whether you have

the right people and processes in place. Similarly a report by T-System (2013)

mentions that relevant experience, skills, processes, initiatives and projects provides

insight into organizational strengths and identifying areas of improvement when

evaluating Big Data maturity. However, all these studies only illustrate the benefits

and challenges to big data without indicating how best organizations can get ready

for Big Data utilization.

Probst at el. (2013) argues that there will be an increasing demand for skilled labour

and data scientists. They also put it that most organizations do not have the

appropriate infrastructure to capture the amount of data they are generating, let

alone deploy techniques to analyze it. More so, they noted that the demand for

statisticians and data scientists to analyze data in organizations is too high. However

clear model or steps to follow in order to be prepared for big data still needs to be

investigated.

According to Romijn (2014), Big Data is a new concept, and therefore there is no or

few assessment methods for the organizational readiness are available. To this end,

integrating insights from practice with established concepts from literature, a suitable

framework for Big Data readiness assessment was proposed and reported in Romijn

(2014),. The framework contains aspects for organizational alignment, maturity and

26

capabilities in order to determine how best to organizations can prepare for Big Data.

Furthermore, they asserted that by linking the concept of Big Data with current

grounded academic knowledge of technology readiness, organizations will be in

position to handle new technological innovations.

2.7 Theoretical foundations

As reported in Aboelmaged, (2014), the TOE framework has been examined across

different disciplines and contexts to prove its theoretical strength, empirical support

and usefulness in investigating the readiness, adoption and deployment of various

forms of innovations. Although specific factors identified within the three contexts

may vary across different studies, the technology-organization-environment

framework has a strong theoretical basis, stable empirical support , and promise of

applying it to other IS innovation domains.

2.8 Hypotheses development

The study is underpinned by technology-organization-environment (TOE) framework,

for assessing Big Data Analytics readiness within an organization. Based on the

literature reviewed, nine hypotheses and identified factors (firm size, technology

readiness, financial resources, competition intensity, customer, vendor, reliability,

Security and Top management) that may affect Big Data analytics readiness within

an organization were drawn. The suggested hypotheses (H) were drawn from the

research model as follows:

H1: Reliability of information positively affects the Big Data readiness.

H2: Technology infrastructure positively affects Big Data readiness

H3: Security of information and systems positively affects Big Data readiness

H4: Top management support is positively associated with Big Data Readiness

H5: The larger the size of an organization, the greater the potential for Big Data

Readiness

H6: The greater the perceived benefits by the organization, the greater the potential

for Big Data Readiness

H7: Competitive pressure positively affects big data readiness.

27

H8: The organization’s customers’ demands influence Big Data analytics readiness. H9: The greater the trust in the vendor, the more likely the organization will adopt Big

Data analysis

2.9 Summary

This chapter reviewed the related literature on technology and Big Data readiness.

Firstly the concepts of Big Data Analytics and models that are used for their

deployment in organizations are presented. Secondly, literature relating to

technology readiness assessment was reviewed. Based on this literature, the factors

influencing Big Data analytics readiness were identified. Further, a theoretical

framework that could be used for Big Data readiness assessment within organization

based on the factors identified was formulated. In the next chapter we discuss the

research methodology and methods that were used for data collection and analysis.

The methods that were used to analyze the data and to validate the framework are

also discussed.

28

CHAPTER 3: RESEARCH METHODOLOGY

The main goal of this chapter is to situate the study within a particular

methodological tradition, provide a rationale for that approach, describe the research

setting and sample, and describe data collection and analysis methods. The chapter

provides a detailed description of all aspects of the design and procedures of the

study.

3.1 Research process and design

According to De Vaus (2006) research design refers to the overall strategy chosen to

integrate the different components of the study in a coherent and logical way,

thereby ensuring that the research problem is addressed effectively. He asserts that

research design establishes the draft for the collection, measurement and analysis of

data.

This study sought to assess Big Data readiness within South African parastatals. In

the process of conducting this research, both primary and secondary data were

used. Secondary data was obtained by extensive review of the literature in which

several factors already identified by other researchers were established and

categorized.

The information acquired was then used to design the close-ended questionnaire

measuring items. Furthermore the researcher designed the questionnaires based on

the construct of TOE framework using factors that have been identified from the

literature. The questionnaires were then sent to participants.

The questionnaires were coded and transcribed in the statistical package for social

scientists (SPSS) for analysis . Validation of constructs and quantitative analysis

was then carried out. Figure 3.1 depicts the research process followed in this study;

Problem Identification

Questionnaire development

and distribution

Problem definition and

planning

Data Collection,

Analysis and Processing

Reporting and

Conclusion

29

Figure 3.1: Research process

3.2 Research approach

Quantitative approach is characterized by its concern for objective data collection,

development of systematic and standardized procedures, and emphasis on

researcher control. This approach was used as it was considered to be the best

approach in order to effectively address the objectives of the study, and also

because the study needed to focus on a large number of participants in order to

assess the Big Data readiness in an organization.

Quantitative approach produces real and unbiased results as it eliminates biasness

and caters for greater accuracy and objectivity of results. Quantitative study is

constructed in such a way that it enables other researchers to get similar results

when re-performing certain analysis as it uses mathematical and statistical methods

for measuring data. Furthermore, due to the high volume of data collected,

quantitative approach makes it easier to summarize large datasets and make

comparisons across different categories (Galt et al., 2008). The study adopted

quantitative approach.

Adequate literature with respect to the similarities and differences between

quantitative and qualitative approach was conducted (Kelemen & Rumens, 2012;

Galt et al., 2008). Table 3.1 outlines the identified similarities and differences.

30

Table 3.1: Quantitative and Qualitative Approach (Source: http://atlasti.com/quantitative-vs-

qualitative-research/)

Qualitative Quantitative

Is suitable to gaining understanding of

underlying reasons and motivations

To quantify data and generalize results from a

sample to the population of interest

To provide understandings into the setting of

a problem, generating ideas and/or

hypotheses for later

To measure the incidence of various views and

opinions in a chosen sample

To uncover prevalent trends in thought and

opinion

Sometimes followed by qualitative research which

is used to explore some findings further

Usually a small number of non-

representative cases. Respondents selected

to fulfil a given quota

Usually a large number of cases representing the

population of interest. Randomly selected

respondents.

Unstructured or semi-structured techniques

e.g. individual depth interviews or group

discussions.

Structured techniques such as online

questionnaires, on-street or telephone interviews

Non-statistical. Statistical data is usually in the form of

tabulations.

Exploratory and/or investigative in nature.

Findings cannot be used to make

generalizations about the population of

interest.

Findings are conclusive and usually descriptive in

nature.

Validates the accuracy of the findings Quantitative approach tests theories or

explanations

Moreover, the researcher engaged with different divisional managers of IT in order to

detect which divisions are aware of Big Data analytics. The researcher also

approached relevant divisional managers for assistance in distribution of

questionnaires and buy-in.

3.3 Research paradigm

Research paradigm can be classified in three categories: Positivist, Critical and

interpretative (Yin, 2009). The importance of paradigms is that they shape how the

researchers see the world and are reinforced by those around them, the community

of practitioners. Within the research process the beliefs of a researchers are

reflected in the manner the research was designed, how data is both collected and

analyzed and how research results were presented. The positivism paradigm was

opted for because in order to answer research questions 1. What are the

organizational, environmental and technological readiness factors related to Big Data

31

processes and tools? The researcher needed to get objective viewpoint of what the

users’ beliefs are, in order to be able to analyse the why this is so and get a general

view on the subject.

3.4 Research strategy

To achieve more information and complete data, SARS head office was surveyed.

Due to expected big number of people involved, this study employed the survey

strategy. In this case, close-ended questionnaires were used for data collection.

Questionnaires were hand delivered to the division heads with whom the researcher

made arrangement for distribution.

To protect the anonymity of the participants, a sealed box with an opening at the top

was put at the Head of division’s office and participants were requested to slot in the

filled questionnaires inside the box. The strategy was considered to be the best fit for

the researcher as it was easy to use, cost efficient, promotes anonymity, flexible,

saves time, and it was more convenient for the respondents.

3.5 Sampling of Participants The targeted population for this study was comprised of six divisions from the IT

department. Participants were selected based on their experience, role in decision

making and relevancy to the study. The population of this study was the employees

in the IT departments at SARS Head office in Brooklyn Pretoria. The IT department

has over six divisions with the representation of data management, data analytics,

data warehouse development, business intelligence and data storage. These

divisions were selected for data collection since they deal with huge volume of data

on a daily basis.

The advantage of the sampling method used in this study is that it eliminates

biasness in the results. Furthermore, the method is considered to be cost efficient,

saves time and provides accurate results that can be calculated mathematically or

using statistical packages. This approach enabled the researcher to determine her

sampling procedure, sample size and population of interest.

32

The study targeted a group of professionals who are knowledgeable or expected to

be knowledgeable of Big Data, thus the participants involved were thought to be of

relevance to the study. Nine divisions were invited to participate with each division

having IT functions embedded in them. These divisions are Strategy and

Enablement, Human Resources, Procurement, Revenue and Analysis, Finance,

Compliance and Audit, Modernizations and Technology (M&T), Operations, and

Large Business Centre.

3.6 Data collection methods

The data collection method that was used in this study included administering

surveys with closed-ended questions in the form of questionnaire. The questionnaire

was hand delivered to the participants and completed questionnaires collected from

the sealed box with opening on top.

3.6.1 Close-ended questionnaires

To enable the researcher to meet the research objectives the close-ended

questionnaires were used as they were considered to be the best method. The

advantage for using this type of questionnaires is that large amounts of information

can be collected from a large number of people in a short period of time and in a

relatively cost effective way and results of the questionnaires can be quickly and

easily quantified through the use of a software package such as SPSS other than

methods such as observations, interviews and focus groups.

3.6.2 Questionnaire development

The sections of the questionnaire were developed by using the constructs of the

framework shown in figure 2.4. The attributes of these constructs were then used to

formulate the question items of the questionnaire (Appendix A). Furthermore, to

ensure that the questionnaires are developed in line with the objective of the study

each construct was carefully analyzed. The questionnaires was designed based on

a five-point scale where;

1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree and 5 = strongly agree.

33

3.6.3 Research techniques

The researcher hand delivered the questionnaires to the division heads with whom

the arrangements were made for distribution. A sealed box with an opening at the

top was put at the division’s offices and participants were requested to slot in the

filled questionnaires inside the box so as to protect the anonymity of the participants.

The researcher constantly followed-up with relevant participants to encourage

maximum participation. Furthermore, the researcher conducted an informal interview

with relevant divisional managers requesting assistance in distribution of

questionnaires to their respective staff and also to obtain buy-in.

3.7 Piloting the Study

Twenty staff members were used to experiment the questionnaire with the objective

to determine the effectiveness of the questionnaires and also to validate whether the

set measurement items belong to their constructs. Some items on the questionnaire

were amended after the piloting of the study and final questionnaire issued for data

collection. One question was a bit confusing; most did not respond some commented

on it. Later the question was rephrased for better meaning. The question was “Are

you aware vendors as service provider for big data Analytics tools” it was rephrased

to “I’m aware of vendors as service provider for big data Analytics tools.”

3.8 Main Survey

Data for the major survey was collected using close-ended questionnaire. Prior to

the major survey, the researcher conducted informal interviews with divisional

managers. The collected data was important in acquainting the researcher with the

individual perceptions on Big Data readiness. Furthermore, the collected data played

a significant role in the designing of the questionnaire items used in the major

survey.

The questionnaire included thirty six (36) items grouped into three sections; Section

A covered the general information, Section B covered the construct to measure Big

Data readiness and Section C covered the demographics of participants. The

34

participants’ response rate based on the data collected is shown on the table 3.2

below:

Table 3.2: Respondent rate

Office Questionnaires

Sent Questionnaires

Returned Usable

Response rate (%)

Usable rate (%)

South African Revenue Services

(Head Office) 203 164 127 80.79 77.44

One hundred and sixty four (164) questionnaires were received out of two hundred

and three (203) questionnaires sent to participants. From the received

questionnaires one hundred and twenty seven (127) were usable. Thus response

rate of 80.79% and usability rate of 77.44% were achieved. The researcher

considered the response rate and usable rate to be sufficient for conducting the

study.

3.8.1 Unit of analysis

The unit of analysis is the major entity that was being analyzed in a study. Generally,

it encompasses the 'what' or 'who' that is being studied. In social science research,

typical units of analysis include groups, social organizations, social artifacts,

individuals (most common). In this research, the unit of analysis was individuals as

the data was collected from individuals in SARS head office in the form of

questionnaires. The researcher established the factors affecting Big Data Analytics

readiness from different divisions. Divisions were analyzed individually and as a

group. Thus, the unit of analysis was individual and group analysis.

3.9 Data analysis

The data was collected, transcribed and coded in the SPSS v21 package. The data

was coded accordingly to ensure that the original meaning of data is maintained. For

instance, technology readiness was coded as TER, organizational readiness as

OGR, environmental readiness as ENR, Big data readiness as BDR, etc. Both

univariate and bivariate analysis were undertaken and patterns of relationships

between factors were identified including the correlation between constructs during

regression analysis.

35

3.10 Reliability and validity of constructs

The reliability of questionnaires was tested to ensure that the data collected is

reliable and can be used to draw reasonable conclusion.

3.10.1 Reliability

The reliability of the questionnaires was tested using SPSS Cronbach's Alpha (α).

Cronbach (1951) developed Cronbach's Alpha with the purpose of providing a

measure of the internal consistency of a test or scale. According to Tavakol et al.

(2011), Cronbach's Alpha is expressed as a number between 0 and 1. They assert

that there are different reports about the acceptable values of alpha, ranging from

0.70 to 0.95. The low value of alpha could be due to heterogeneous constructs, a

low number of questions or poor interrelatedness between items.

Table 3.3 represents the outcome of the reliability tests of questionnaires. Overall,

the questionnaires used in the current study were considered reliable because the α-

coefficient was 0.881 which is greater than the required minimum value of 0.7.

Table 3.3: Questionnaires reliability statistics

Cronbach's

Alpha

Cronbach's Alpha Based on

Standardized Items

N of Items

.881 .886 36

Furthermore, the reliability and validity per construct was also tested. There were

seven constructs as shown on Table 3.4 and all the constructs were found to be

above the required minimum α coefficient value of 0.7 while the Cronbach's Alpha

based on standardized item for the customer Environmental Readiness was found to

be 0.695.

36

Table 3.5: Constructs reliability statistics

Construct Cronbach's

Alpha

Cronbach's Alpha Based on

Standardized Items

N of Items

Technology Readiness (TER) 0.841 0.846 8

Top Management (TM) 0.896 0.897 4

Organizational Size (OS) 0.708 0.760 3

Financial Resources (FR) 0.770 0.762 3

Competitors (COM) 0.795 0.797 3

Customer (CUS) 0.707 0.695 3

Vendors (VEN) 0.710 0.720 3

3.10.2 Validity

The study checked both content and constructs validity. Content validity ensures the

content cogency. The measuring instrument was designed using items that had

been used and tested by previous researchers to ensure validity of the collected data

but parameterized to fit the context of the study. On the other hand, construct validity

ensures the validity of the constructs and that the questionnaire was developed

based on the constructs of the framework. The constructs were then used as the

categories or sections of the questionnaire while the factors for each construct were

used to formulate the questionnaire items.

3.11 Summary

The chapter outlined the research process and design that was followed in

conducting this study. In particular, the research process was formulated via problem

identification, definition and planning, followed by development and distribution of

questionnaires. After the collection of data then the data was analysed, processed

37

and the findings were reported and concluded. Furthermore the research approach,

paradigm and strategy were discussed. Sampling of participants and various data

collection and analysis methods were discussed. The chapter concludes by testing

the reliability and validity of questionnaires using Cronbach's Alpha.

CHAPTER 4: RESULTS AND DATA ANALYSIS

The main focus of this chapter is to report the analytical and descriptive analysis of

the data gathered. The chapter starts with the analysis and discussion of the

frequency and demographics of participants, followed by the analysis of the

descriptive statistics of constructs. The descriptive results are then used to analyze

the relevance of the constructs to inform Big Data Analytics readiness. The

correlation between constructs was also measured to demonstrate the

interdependencies and the patterns in which the constructs are related. The analysis

and reporting of chi-square and hypotheses testing concludes the chapter.

4.1. Frequency and demographics of participants

The participants in this study were believed to work directly with Big Data within the

organization. The researcher carefully identified the participants with the help from

the divisional heads or management within SARS. The identified participants were

then invited to voluntarily participate by sharing their opinion and experiences. This

was done through the completion of questionnaires.

The information about the demographics of participants and their perception on Big

Data readiness was collected for analysis. The researcher saw it fit to collect

information about the participants’ gender, age group, business unit or division,

highest qualifications, number of years working at the current organization and their

overall working experience. This was done in order to demonstrate the relationship

between age, qualifications and experience and how that influence technology

readiness.

Furthermore, the participants were requested to indicate their awareness of Big Data

so as to ensure relevancy and accuracy of information gathered. The data collected

38

was then coded, transcribed and analyzed using SPSS v 22.0. The frequency and

descriptive statistics of the participants are shown in table 4.1 below.

39

Table 4.1: Frequencies and descriptive statistics of participants

Factor Items Frequency Percent (%)

Cumulative Percent (%)

Gender

Male 83 65.4 65.4

Female 44 34.6 100

Total 127 100

Age Group

21 - 25 Years

16 12.6 12.6

26 - 30 Years

36 28.3 40.9

31 - 35 Years

35 27.6 68.5

36 - 40 Years

20 15.7 84.3

41+ Years 20 15.7 100

Total 127 100

Highest Qualification

National Diploma

31 24.4 24.4

Degree 68 53.5 78

Masters 17 13.4 91.3

Doctorate 1 0.8 92.1

Other 10 7.9 100

Total 127 100

Business Unit

Strategy and Enablement

7 5.5 5.5

Revenue Analysis

12 9.4 15

Finance 5 3.9 18.9

Compliance and Audit

8 6.3 25.2

M&T 72 56.7 81.9

Operations 20 15.7 97.6

Large Business Centre

3 2.4 100

Total 127 100

Organization Duration

0 - 3 Years 48 37.8 37.8

4 - 7 Years 51 40.2 78

8 - 11 Years

22 17.3 95.3

12 - 15 Years

3 2.4 97.6

16+ Years 3 2.4 100

Total 127 100

Overall Working Experience

0 - 3 Years 22 17.3 17.3

4 - 7 Years 36 28.3 45.7

8 - 11 Years

34 26.8 72.4

12 - 15 Years

20 15.7 88.2

16+ Years 15 11.8 100

Total 127 100

40

Factor Items Frequency Percent (%)

Cumulative Percent (%)

Big Data Awareness

Yes 106 83.5 83.5

No 21 16.5 100

Total 127 100

From the table above, 13.4% obtained masters while 53.5% of participants obtained

after degree and 24.4% had a national diploma, while a further 0.8% of participants

obtained the Doctorate. This indicates that a significant number of participants are

well educated enough to answer the questionnaires and are expected to some

degree to have awareness of Big Data. Figure 4.1 shows the graphical

representation of participants with respect to their level of education.

Figure 4.1: Education Levels

Looking at the age group, the majority of participants equaling to 84.3% were forty

years and younger, thus the participants were mostly youth. Figure 4.2 shows the

graphical representation of participants’ age group.

41

Figure 4.2: Age Group

Figure 4.3 shows a graphical representation of the participants’ overall working

experience. 82.7% of participants have over four years working experience.

Figure 4.3: Overall working experience

Figure 4.4 shows the graphical representation of Big data awareness, 83.46% of

participants are aware of Big data while 16.54% of participants are still not aware of

Big data. Looking at the participants’ level of education in figure 4.1, the age group in

figure 4.2 and their overall working experience in figure 4.3 it is worrying to discover

that 16.54% of participants are still not aware of Big data. Thus, it is a bit difficult for

organizations to be ready for Big Data while they lack awareness.

42

Figure 4.4: Big data awareness

Further investigations and discussions will be conducted in the next chapter in order

to determine factors needed for the assessment of Big Data readiness.

4.2 Descriptive statistics of constructs

The descriptive analysis of the constructs was performed as demonstrated in Table

4.3. The minimum, maximum, mean, standard deviation and skewness of the

constructs was determined. The questionnaire used for the constructs (see

Appendix B) was based on the 5 level Likert scale where 1 and 5 represented

strongly agree and disagree, 3 represented neutral whereas 2 and 4 where

respective intermediate values.

Results from the descriptive statistics shows that technology readiness (TER) had

the highest mean of 4.63 and the highest standard deviation of 1.141. The mean

range from 3.30 to 4.63 which implies that the majority of respondents agree (4) with

what has been said, whereas high standard deviation shows that the data is spread

out over a large range of values.

Organisational readiness top management (TM) has the highest mean of 3.78 and

standard deviation of 1.053. Organisational readiness organizational size (OS) has

the highest standard deviation of 1.017 and the mean ranging from 4.08 to 4.59, thus

the mean implies that most participants agree with what has been said.

43

Environmental Readiness Vendor (VEN), Environmental Readiness Customer

(CUS), Environmental Readiness Competitor (COM) and Technology Readiness

(TER) have the highest standard deviation of 1.233, 1.248, 1.116 and 1.141

respectively which implies that there is high variation thus the data is spread out over

a large range of values.

Table 4.3: Descriptive statistics of constructs

N Minimum Maxim

um

Mean Std. Deviation Skewness

Statistic Statistic Statist

ic

Statistic Statistic Statistic Std. Error

Technology Readiness1 126 1 5 3.30 1.119 -.099 .216

Technology Readiness2 126 3 5 4.48 .701 -.976 .216

Technology Readiness3 126 1 5 3.65 .958 -.301 .216

Technology Readiness4 126 1 5 4.07 .878 -.861 .216

Technology Readiness5 126 1 5 3.81 1.002 -.287 .216

Technology Readiness6 126 1 5 3.73 1.141 -.534 .216

Technology Readiness7 126 1 5 4.63 .690 -2.326 .216

Technology Readiness8 126 1 5 4.49 .746 -1.561 .216

Top Management1 126 1 5 3.82 .958 -.234 .216

Top Management2 126 1 5 3.78 .937 -.073 .216

Top Management3 126 1 5 3.42 1.053 -.288 .216

Top Management4 126 1 5 3.62 .987 -.490 .216

Organisation Size1 126 1 5 4.08 1.017 -1.136 .216

Organisation Size2 126 1 5 4.59 .623 -2.051 .216

Organisation Size3 126 3 5 4.51 .642 -.952 .216

Financial Resource1 126 2 5 4.45 .733 -1.193 .216

Financial Resource2 126 1 5 3.46 .952 -.082 .216

Financial Resource3 126 1 5 3.56 .926 -.227 .216

Competitors1 126 1 5 2.78 1.072 -.137 .216

Competitors2 126 1 5 2.83 1.056 .009 .216

Competitors3 126 1 5 3.78 1.116 -.390 .216

Customer1 126 1 5 2.89 1.195 .075 .216

Customer2 126 1 5 2.87 1.248 .260 .216

Customer3 126 1 5 3.89 1.097 -.774 .216

Vendor1 126 1 5 3.45 1.009 -.010 .216

Vendor2 126 1 5 3.68 1.063 -.226 .216

Vendor3 126 1 5 3.17 1.233 -.209 .216

Big Data Readiness1 126 1 5 4.07 .973 -.780 .216

44

N Minimum Maxim

um

Mean Std. Deviation Skewness

Statistic Statistic Statist

ic

Statistic Statistic Statistic Std. Error

Big Data Readiness2 126 1 5 3.21 .941 -.016 .216

Valid N (listwise) 126

4.3. Correlations of variables

Correlation coefficient refers to a number between -1.00 and +1.00 inclusive which

indicates statistical relationships between two or more random variables or observed

data values. The direction of the relation is indicated by the sign (*). When the

variable value is positive, it indicates that there is a direct relationship between both

variables, that is, both variables increase at the same time. The negative value

indicates that the relation is inversed, in other words, one variable decreases while

the other increases. When there is no correlation between the variables, the

correlation coefficient is 0.

45

Table 4.4: Correlation of constructs

TER TM OS FR COM CUS EN BDR

TER Pearson Correlation 1

Sig. (2-tailed)

N 126

TM Pearson Correlation .648** 1

Sig. (2-tailed) .000

N 126 126

OS Pearson Correlation .466** .211

* 1

Sig. (2-tailed) .000 .018

N 126 126 126

FR Pearson Correlation .620** .674

** .524

** 1

Sig. (2-tailed) .000 .000 .000

N 126 126 126 126

COM Pearson Correlation .308** .265

** .213

* .300

** 1

Sig. (2-tailed) .000 .003 .017 .001

46

Management (TM) is significant to technology readiness (TER) with Pearson

correlation of 0.648 at 0.01 significance level. Organisational readiness with respect

to organisational size (OS) is significant to technology readiness at 0.01 level, while

it is significant to TM at 0.05 level. Organisational readiness in terms of financial

resources (FR) is significant to TER, TM and OS at 0.01 level.

Environmental readiness in terms of competitors (COM) is significant to TER, TM

and FR at 0.01 level while it is significant to OS at 0.05 level. Environmental

readiness in terms of customers (CUS) is significant to TER, TM, OS, FR and COM

at 0.01 level. Environmental readiness in terms of vendor (VEN) is significant to

TER, TM, FR, COM and CUS at 0.01 level but insignificant to OS. Big data

readiness is significant to TER, TM, OS, FR, COM, CUS and VEN at 0.01 level.

4.4. Chi-square (χ2) analysis and reporting

Chi-square is a statistical test that tests for the existence of a relationship between

two variables. This test can be used with nominal, ordinal, or scale variables, so it is

a very versatile test, but it is sensitive to sample sizes too. It is vital to have at least a

few cases in each of the values of both of the variables involved in this test or the

results will be skewed.

The Chi-square processing summary of the constructs is given in table 4.5.

According to Janes (2001) Chi-square refers to a multipurpose statistical test used to

observe the significance of relationships between two or more nominal-level

variables. He asserts that the Chi-square is best used for the data with categories

N 126 126 126 126 126

CUS Pearson Correlation .447** .410

** .232

** .425

** .655

** 1

Sig. (2-tailed) .000 .000 .009 .000 .000

N 126 126 126 126 126 126

VEN Pearson Correlation .396** .331

** .160 .309

** .338

** .424

** 1

Sig. (2-tailed) .000 .000 .074 .000 .000 .000

N 126 126 126 126 126 126 126

BDR Pearson Correlation .409** .466

** .350

** .359

** .415

** .481

** .358

** 1

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000

N 126 126 126 126 126 126 126 126

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

47

without any inherent sense of order in the order, thus it can be useful with a lot of

data generated from questionnaires and surveys. The relationships that will be

tested between the variables are shown on the table below;

Table 4.5: Chi-square case processing summary

Cases

Valid Missing Total

N Percent N Percent N Percent

TER * BDR 126 99.2% 1 0.8% 127 100.0%

TM * BDR 126 99.2% 1 0.8% 127 100.0%

OS * BDR 126 99.2% 1 0.8% 127 100.0%

FR * BDR 126 99.2% 1 0.8% 127 100.0%

COM * BDR 126 99.2% 1 0.8% 127 100.0%

CUS * BDR 126 99.2% 1 0.8% 127 100.0%

VEN * BDR 126 99.2% 1 0.8% 127 100.0%

The significance value of the relationship between constructs is shown in table 4.6.

The table outlines chi-square statistics value, the degree of freedom (df) and the

significance value (P-Value). In this study we use the standard 5 percent (i.e. 0.05)

cut-off for determining statistically significant difference.

According to researchers (Laszlo et al., 2013) the chi-square (X2) is calculated as

follows;

X2 = ∑ (O - E)2

E

O = Observed frequency

E = Expected frequency

The degree of freedom (df) is calculated by multiplying the number of rows minus

one and the number of columns minus one as shown in the formula below;

df = (no. of rows - 1) x (no. of columns - 1).

SPSS was used for the calculation in this study.

Table 4.6: Chi-square significance

48

Chi-square Statistics

degree of freedom(df)

Significance Value(P-Value)

Supported

TER * BDR 318.610 a 133 0.000 Yes

TM * BDR 236.546 a 98 0.000 Yes

OS * BDR 120.028a 49 0.000 Yes

FR * BDR 218.138a 70 0.000 Yes

COM * BDR 191.083a 84 0.000 Yes

CUS * BDR 207.488a 84 0.000 Yes

VEN * BDR 108.039a 70 0.002 Yes

4.5. Summary

This chapter covered the analysis of data. Generally, the chapter covered the

analysis of the frequency and demographics of participants as well as descriptive

statistics of constructs. The graphical representation of demographics was used to

analyze the level of education, as well as Big Data awareness of participants. From

the analysis, a large number of participants were aware of Big Data. Furthermore,

correlation of variables to indicate the degree of relationships between variables was

done followed by Chi-square analysis and reporting.

49

CHAPTER 5: DISCUSSION, INTERPRETATION, CONCLUSION AND RECOMMENDATION

The main focus of this chapter is to discuss the results and give the interpretation

and implications of the findings. It further evaluates the study in relation to its

significance, contribution and achievement of the set questions. The goal of this

study was to assess Big Data Analytics readiness in a South African parastatals.

Hence, each research question is discussed and how it was addressed to achieve

the research goal. This section further stipulates whether and how the objectives of

the study were achieved. This is followed by an evaluation of the study and the

discussion of the contributions this research has made to the information systems

body of knowledge. Lastly, the chapter concludes by discussing limitations of the

study and possible direction for future research.

5.1 Overview of the research

This section outlines the whole study by giving a brief summary of what is presented

in each chapter. The chapters are summarized in their sequential order as follows.

The first chapter presented the concept of Big Data, and the benefits of Big Data

Analytics tools. The chapter highlighted the problem the study was set to address

and the research questions that needed to be answered in order to address the

identified problem. The major objective of this study was to assess Big Data

readiness in South African parastatals. Finally, in the chapter a brief discussion of

the contribution the study makes to the information systems body of knowledge that

are further discussed in this chapter.

The second chapter presented Big Data Analytics concepts and theories that are

used by previous researchers to assess technology readiness. Based on this

literature, the factors influencing Big Data analytics readiness were identified. A

theoretical framework that could be used for Big Data readiness assessment within

organizations was based on the factors identified.

50

The third chapter discussed a particular methodological approach, provided a

rationale for that approach, described the research setting and sample, and

described data collection and analysis methods. The chapter provided a detailed

description of all aspects of the design and procedures of the study. The validity and

reliability of the measuring instrument, construct and collected data were discussed.

Lastly, the chapter also gave a step by step ethical procedures that were put into

consideration during the collection, analysis and reporting of the findings.

The forth chapter presented the analysis and discussions of the frequency and

demographics of participants, followed by the descriptive statistics of constructs.

Additionally, correlation of variables was discussed. The results that were obtained

from chi-square analysis were presented from which results of the suggested

hypotheses were derived.

The current chapter consists of three sections; the overview of the whole study, the

interpretation of findings and the evaluation of the whole research process. In

particular, the first section gives an overview of the whole study detailing how and

what was covered in each chapter. The next section interprets the findings of the

study in relation to the research questions. The findings of the study including the

results of the tested hypotheses are interpreted in relation to Assessment of Big Data

Analytics readiness. Lastly the chapter evaluates the study in relation to its

significance, contribution and achievement of set goals. Limitations and discussions

are also discussed.

5.2 Discussions and implication of results

The goal of this study was to assess Big Data analytics readiness for South African

government parastatals

The specific objectives were:

1. Determine technological, environmental and organizational readiness related

to Big Data processes and tools

2. Determine the benefit of Big Data analytics within an organization

51

3. Determine the level of influence of the identified factors to Big Data Analytics

readiness

The technological, organizational and environmental factors were identified from the

literature. The technological factors that were established related to reliability of the

networks, security of data collected and IT infrastructure. Thus, the organizational

factors included support from management and executives, shortage of skills, the

size of an organization in terms of the data they collect, financial support. The

environmental factors related to all of those internal and external issues apart from

technological and organizational that could influence Big Data Analytics readiness.

Among others benefit of Big Data analysis was also identified as the main factor in

the assessment of technology readiness. Related literature was reviewed to find the

factors that influence Big Data Analytics readiness. The Chi-Square (X2) test results

are presented on the Table 5.1 below:

Table 5.1: Testing the suggested Hypotheses

Hypothesis

Comment

H1: Reliability of information positively affects the Big

Data readiness.

Accepted

H2: Technology infrastructure positively affects Big

Data readiness

Accepted

H3: Security of information and systems positively

affects Big Data readiness

Accepted

H4: Top management support is positively associated

with Big Data Readiness

Accepted

H5: The larger the size of the firm, the greater the

potential for Big Data Readiness

Accepted

H6: The greater the perceived benefits by the

organization, the greater the potential for Big Data

Readiness

Accepted

H7: Competitive pressure positively affects Big Data

Accepted

52

readiness.

H8: The organization’s customers’ demands influence Big Data analytics readiness.

Accepted

H9: The greater the trust in the vendor, the more likely

organization will adopt Big Data analysis tools the

vendor is offering.

Accepted

5.2.1 Discussion and Implications in Relation to the Hypotheses

As illustrated in table 5.1 eight relationships were hypothesized, four of these

hypothesized relationships are discussed in the following subsections.

H1: Reliability of information positively affects the Big Data readiness. This

hypothesis was accepted

The result of this hypothesis suggested that, organizations should ensure their

storage systems have enough flexibility to scale up to meet Big Data requirements

without procuring capacity they don’t need ( Tole ,2013).

H2: Technology infrastructure positively affects Big Data readiness. This

hypothesis was accepted

The result of this hypothesis is in agreement with those reported in Naseebullah

et al., (2013) and Singh and Singh (2012) who concluded that new infrastructure is

needed when preparing for Big Data analytics in order to successfully leverage these

various kinds of data structures, organizations require infrastructure, data, analytics,

organizational structure and governance processes to make Big Data analytics

operational and actionable.

H3: Security of information and systems positively affects Big Data readiness. This

hypothesis was accepted

The result of this hypothesis suggested that, technology related issues of data

security are crucial for the assessment of Big Data readiness within an organization.

These findings corroborate those reported in Chen et al., (2012). Chen at al., (2012)

53

particularly averred that organizations of different sizes are facing the daunting task

of defending against cybersecurity threats and protecting their intellectual assets and

infrastructure. Processing and analyzing security-related data, however, is

increasingly difficult because in many government parastatals, privacy is more

attached to the sensitivity of data they are working with. Such data may include but

not limited to; clients list, prospective projects, financial data and all other valuable

data that may or may not be disclosed.

H4: Top management support is positively associated with Big Data Readiness. This

hypothesis was accepted

The result of this hypothesis implies that management support ought to be

considered as a critical success factor in the field of data warehousing, business

intelligence and Big Data analysis (Chen et al. 2012).This is also in agreement with

Janom and Zakaria (2008) who predicted management as one of the success factors

for the preparedness of B2B e-commerce.

Additionally, Rajpurohit (2013) also argues that in order to resolve the Big Data

problem, managers need to understand that Big Data ownership can no more be left

simply to statisticians or business intelligence units. Deriving the maximum value

from analytics would need configuring and customizing your analytics

implementation to meet your business goals.

H5: The larger the size of the firm, the greater the potential for Big Data Readiness.

This hypothesis was accepted

The result of this hypothesis suggested that, It is also suggested that the bigger the

organization the more likely that they will adopt new technology. According to Villars

et al. (2011), organizations that have been generating or dealing with large data

sources for decades will be early movers. This group includes many government

organizations and mostly the parastatals organizations. Organization size are

important organizational factor for technology adoption (Tornatzky and Fleischer,

1990).

54

H6: The greater the perceived benefits by the organization, the greater the potential

for Big Data Readiness. This hypothesis was accepted

Toufani and Montazer (2010) also argues that the high level of technology readiness

allows organizations to manage business electronically in order to achieve less turn-

around time, faster delivery of services, enhanced product selection, international

competitiveness, a broader market reach, costs, faster and limitless access to new

customers and suppliers, increased depth of communication, exchange of

information

The results of this hypothesis also imply that, resource availability in terms of money

and human resources contributes towards Big Data readiness. Goss & Veeramuthu

(2013) noted that businesses operating in the Big Data space continue to endeavour

in accessing finance and skilled labour.

H7: Competitive pressure positively affects big data readiness. This hypothesis

was accepted.

According to Villars and Olofson (2011) competitive advantage can be greatly

improved by leveraging the right data. Further, Manyika et al. (2014) noted that

organizations may lose competitiveness if they fail to systematically analyze the

available information needed for decision making. Big Data can improve decision

making and increase organizational efficiency and effectiveness, but only if

organizations employ a variety of analytical tools and methods to make sense of the

data (Joseph & Johnson, 2013). All these conclusions seem to corroborate results of

the study reported in this dissertation.

H8: The organization’s customers’ demands influence Big Data analytics readiness.This hypothesis was accepted

The private and public sectors are starting to use Big Data in their everyday

activities. Consumers are increasingly interacting with companies through various

means that include but not limited to social media, mobile, e-commerce sites, stores,

55

and more that dramatically increase the complexity and variety of data types

received and need to be analyzed within an organization. Hence, companies from

retail to corporate need to use Big Data to better understand their customers’

characteristics and choices

5.3 Discussions in relation to the research questions

This section discusses and presents how the study’s primary questions were answered.

1. What are the organizational, environmental and technological readiness

factors related to Big Data processes and tools?

2. What are the cost benefit analyses of Big Data?

3. How could the identified factors be used in assessment of Big Data Analytics

readiness?

5.3.1. Research question one

What are the organizational, environmental and technological readiness factors

related to Big Data processes and tools?

The technological, environmental and organizational factors that influence Big

Data readiness were tested. All factors identified were found significant for the

assessment of Big Data readiness. These factors are as illustrated in Table 5.1

5.3.2. Research question two

What are the benefits of Big Data analysis?

The literature suggests that the ability to find, acquire, extract, manipulate,

analyze, connect and visualize data with the tools of choice (SAP HANA, SAP

Sybase®, SAP Intelligence Analysis for Public Sector application by Palantir,

Kapow®, Hadoop) will motivate the organization ‘s readiness for Big Data

analysis. (Kuketz, 2012). Expected benefits positively influence the

organization‘s Big Data readiness.

56

5.3.3 Research question three

The results obtained from the Chi-Square analysis supported the goal of the

research in terms of assessing Big Data Analytics Readiness by indicating the

relationship between the constructs. Also they indicated how each of these

constructs influences the readiness of Big Data in an organization.

5.4 Research Contributions

The contribution of this research to the information systems body of knowledge and

management from the results and findings of the study, is as discussed below.

5.4.1 Contribution to Practice and Management

Big Data Analytics is a new concept. Therefore, most of its literature is still in the

websites and white papers. This study has reviewed the literature and identified

factors that could influence organizations to make sound technological decisions

before adopting Big Data Analytics innovations. This implies that this study makes a

significant contribution to practice and management of the assessment of Big Data

Analytics readiness. Furthermore, results from this study could be applied not only to

assessment of Big Data Analytics but generally to any new technology in an

organization.

5.4.2 Theoretical and Methodological Contributions

This study’s contribution to the information systems body of knowledge is twofold;

theoretically, this study contributes to the little literature on Big Data as few academic

literature acknowledged in this study has been published. Hence, future researchers

will extend to the identified factors to Big Data readiness studies and add to the ones

identified.

The study also makes a major contribution to practice and management in the

assessment of Big Data analytics readiness. Likewise, results from this study could

be applied to investigate other Technology readiness within an organization and not

only to Big Data Analytics.

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5.5 Limitations of the Study

The study mostly depended on factors that influence technological implementation in

general and later applied them to Big Data because of the lack of academically

related literature relating to Big Data. Hence, the results of this study may lack

generalization to all Big Data Readiness projects.

This study also focused on one organization and therefore might be non-

representative. A survey of other South African parastatals could yield more detailed

findings pertaining Big Data Readiness. Hence, generalization of the results should be

interpreted with caution.

Also; this study used cross-sectional survey as data was collected at a specific time.

Adoption of technology and the change in the business environment may change

over time due to the exponential developments in technological innovations and the

results of this study may be limited in forecasting the future trends of Big Data

Readiness. Eventually, some of the factors established in this study may not hold

due to advancement in technology.

5.6 Recommendations

More qualitative studies, both regarding the factors influencing the decision itself as

well as the decision process over time, may also help to interpret survey results and

offer more detailed explanations for these results. Furthermore, using a longitudinal

study in future research would provide more comparative insights into Big Data

readiness at different time periods.

5.7 Conclusion

Big Data has the potential of generating new opportunities for organizations to serve

customers and markets while also creating and extracting value in new ways.

Traditional technologies struggle to accommodate the velocity, variety and volume of

Big Data. The main aim of the study was to assess Big Data Readiness in South

African parastatals. From the literature reviewed, organizational, technological and

58

environmental factors were identified which influence Big Data Analytics readiness in

an organization.

This research was underpinned by TOE framework and the decision of organizations

to assess its Big Data analytics readiness. The collected data was analyzed with the

use of Statistical Package for Social Scientist (SPSS). From the results of the study,

technological, environmental and organizational factors were all found to be highly

significant as related to Big Data readiness assessment. Organizational readiness is

a critical scenario as it governs optimal implementation of technology. Optimal

implementation of a technological innovation is paramount for its success. Failure to

assess organizational readiness to implement and use technology could be

considered catastrophic. This study sought to investigate public sector readiness for

Big Data analytics. Many public sector enterprises handle big volumes of data on a

daily basis since they are directly involved with those activities intended to the serve

the public. Hence, being ready for Big Data analytics is essential for generating new

opportunities for public sectors to serve citizens and markets while creating and

extracting value in the voluminous collected data.

Traditional technologies struggle to accommodate the velocity, variety and volume of

Big Data that is currently generated in many organizations. Leveraging the findings

of this study, public sector enterprises will be in position to know what to do, when to

do it and how when they are faced with situations of Big Data analytics. This study

contributes to scanty literature of Big Data analytics and its findings will be used by

Management to make informed decisions regarding Big Data analytics.

59

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Appendix A: Questionnaires

PROJECT TITLE: Assessment of Big Data Analytics Readiness in South African Governmental

Primary investigator: Ms. M.S Motau, M-Tech (BIS) - Candidate

Study leader: Dr. B. Kalema, D.Tech, Department of Informatics, Tshwane University of Technology, Pretoria

Dear Potential research participant,

You are invited to complete a survey questionnaire that forms part of my formal MTech-studies.

The ever increasing changes in the business environment are making many organizations to strive for

competitiveness so as to keep abreast with the global standards. Organizations have been building

data warehouses to study business activity and create insights for decision makers to act on

improving business performance for many years. The traditional analytical systems capture, clean,

transform and integrate data from multiple operational systems before loading it into a data

warehouse. However, even though these traditional environments continue to evolve, many new more

complex types of data have now emerged that businesses want to analyse to enrich what they

already know, and data growth rate within organisations is overwhelmingly high. More so,

organizations are also experiencing the inability to cater for this growth of data. This rapid expansion

of data is increasingly exceeding the organizations ability to design appropriate systems that could

handle Big Data effectively and analyze it to extract relevant meaning for decision making. This study

is therefore set to identify factors that will assess the Big Data Analytics readiness within South

African parastatals.

You are therefore invited to participate in this research leading to assessment of Big Data Analytics

with South African parastatals. Kindly note that, participation in this study is voluntary and in case you

feel like not continuing to participate, simply discard the questionnaire. You don’t even have to provide

the reason/s for your decision. Your withdrawal will in no way influence your continued relationship

with the research team. All responses will be completely anonymous and therefore your name will

NOT appear anywhere on the survey that is why it is not requested. All information obtained from the

questionnaire is strictly confidential.

FACULTY OF INFORMATION AND COMMUNICATION TECHNOLOGY

DEPARTMENT OF INFORMATICS

65

Completing and returning the questionnaire constitutes your consent to participate and you are kindly

appreciated. Answering this questionnaire will only take 10-15 minutes of your time. Your corporation

is highly appreciated and will contribute to the success of this study.

Completion of the questionnaire involves no foreseeable emotional discomfort or inconvenience to

you or your family. The results of the questionnaire will have no direct personal benefit to you, but you

will make a contribution towards a better understanding the factors affecting the Big Data readiness

within the South African parastatals. Kindly note that, you are not waiving any legal claims, rights or

remedies because of your participation in this research study.

Your identity will not be revealed while the study is being conducted or when the study is reported in

scientific journals and/or research reports. All the hard copies of the questionnaires that have been

completed will be stored in a secure place for three years, after which they will be destroyed. Any

information that is obtained in connection with this study and that can be identified with you will

remain confidential and will be disclosed only with your permission or as required by law. The

information received during the project will only be used for research purposes and not be released

for any employment-related performance evaluation, promotion and/or disciplinary purposes.

It is a policy at TUT that a research questionnaire complies with the integrity standards. The

questionnaire is first validated and approved by the research and ethics committee before it is

distributed to the respondents and these standards are strictly followed.

The primary researcher, Ms MS Motau, can be contacted during office hours at her cellular phone at

072 204 9646. The study leader, Dr. B.M. Kalema, can be contacted during office hours at Tel (012)

382-9624. Should you have any questions regarding the ethical aspects of the study, you can contact

the chairperson of the Faculty Committee of Research Ethics of the Faculty of ICT, Tshwane

University of Technology, Dr A.B. Pretorius, during office hours at Tel 012 382 9965/(013) 653-

3136/65 and E-mail pretoriusab1@tut.ac.za. Alternatively, you can report any serious unethical

behaviour at the University’s Toll Free Hotline 0800 21 23 41.

Your Corporation is highly appreciated and will contribute to the success of this study.

THANK YOU.

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PLEASE MAKE A CROSS IN THE BOX CORRESPONDING TO AN ANSWER THAT YOU FILL SUITABLE FOR THE QUESTION

In this study the term Big Data refers to the datasets which continues to grow so

much that it becomes difficult to manage using existing database management

concepts and tools.

SECTION A: GENERAL INFORMATION

1. In which business unit are you working

Strategy and Enablement

Human Resources

Procurement

Revenue Analysis

Finance

Compliance and Audit

M&T

Operations

Large Business Centre

2. For how long have you been working with this organization?

0 -3years

4- 7 years

8 -11years

12-15 years

16+ years

3. What is your overall working experience?

0 -3years

4- 7 years

8 -11years

12-15 years

16+ years

4. Have you ever heard of Big Data?

Yes No

67

SECTION B: CONSTRUCTS TO MEASURE BIG DATA READINESS

By using the rating scale from 1-5 where; 1 = strongly disagree, 2 =Disagree, 3 = Neutral, 4= Agree, 5 = strongly agree. Indicate your level of agreement or disagreement on the following statements

.5.TECHNOLOGICAL

READINESS (this construct refers to

how ready you and your organization

are technically ready for Big Data)

Questionnaire Item

1

2

3

4

5

5.1 I have enough technology expertise to

deal with Big Data analytics

5.2 Our organization have disaster recovery

plan in place

5.3 Employees in our department are willing

to learn and acquire knowledge of Big

Data Analysis tools

5.4 Our department has a clear plan for new

IT projects and changes

5.6 Our department put in place steering

committees for new IT projects

5.7 Management clearly explains and

communicate the need for IT changes

5.8 Reliability and availability of data is

important to our organization

5.9 Our organization controls access to

confidential data by various system

functions a channels.

6.ORGANIZATIONAL

READINESS(this construct refers to

how ready your organization are for Big

Data)

Questionnaire Item

1

2

3

4

5

6.1

a) Top Management

6.1.1 Our executives have sufficient

knowledge about Big Data.

6.1.2 Our executive supports the Big Data

initiatives.

6.1.3 We receive support from senior

management in terms of money,

68

training Big data analytic.

6.1.4 Our organizational strategic plan

includes the IT investment for Big Data.

6.2 b) Organization size

6.2.1 We have a proper IT department to

support Big Data initiatives.

6.2.2 The size of the organization is big.

6.2.3 Our organizations have enough network

users in the IT department.

6.3 c) Financial resource

6.3.1 Our organization has a good IT

infrastructure.

6.3.2 Our organization allocates enough

budget to Big Data and Analytics

initiatives.

6.3.3 Our organization provides enough

resources like time, support and human

resource for Big Data initiatives.

7.ENVIROMENTAL

READINESS(this construct refers to

how the external factors affect

organization’s readiness for Big Data)

Questionnaire Item

1

2

3

4

5

7.1 a) Competitors

7.1.1 Our organization is under pressure to

adopt Big Data due to competitors.

7.1.2 We closely follow our competitors’ Big

Data initiatives to determine our

strength and weakness.

7.1.3 Adoption of Big Data will give the

organization a competitive advantage.

7.2 b) Customer

7.2.1 Customers are pressurizing the

organization to implement Big Data.

7.2.2 Customers are demanding the use of

Big Data Analytics tools to increase

system performance.

69

7.2.3 Our customers are ready to access our

services from multiple channels such as

cell phone and internet.

7.3 c) Vendor

7.3.1 Our vendors are selling Big Data

Analytics innovations to our

organization

7.3.2 Our organizations are loyal to specific

vendors for IT infrastructure

7.3.3 I’m aware of vendors such as service

provider for big data Analytics tools

8.BIG DATA READINESS

Questionnaire Item

1

2

3

4

5

8.1 a) Big Data

8.1.1 Given the necessary resources,

opportunities and knowledge to use Big

Data, our organization will be ready adopt it.

8.1.2 Our organization has begun to focus on big

data opportunities, but are not yet planning

SECTION C: BIOGRAPHICAL DATA 9. What is your age group?

16-20 years

21-25 years

26 – 30 years

31-35 years

36-40 years

41+ years

10. What is your gender?

Male Female

11. What is your highest qualification?

National Diploma

BTech/Degree

Postgraduate Masters

DTech

Other (Please specify )