I Need help with chapter 3 in the dissertation paper for quantitative methodology.
I need at least 15-18 pages for this chapter 3 with references. 
I have already worked on chapter 1 and 2, do align with this content and work on chapter 3.
DO ENSURE TO READ THE ATTACHED PAPER before starting to work on chapter 3 which will make the existing paper and the new content in chapter 3 sink well. 
I am attaching sample chapter 3 content in a ppt file – pls refer this as mandatory
1
An Empirical Analysis of Organizational Factors Hindering Blockchain Adoption among Startups and Small Enterprises.
Draft version – Chapter 1, 2 and 3.
Abstract
Blockchain technology is a new distributed ledger technology that is gaining popularity as an upgrading technology that records the source class and incorporates data-related processes to ensure the defined data is retained securely as a digital asset. Blockchain technology holds promising benefits by enabling organizations to operate in a secured pattern while allowing digital information to record and distribute. Its capacity to enhance security makes transactions more reliable with transparency, and this database can be shared across the network of computer systems. Blockchain can also facilitate the expansion of SMEs internationally as it creates trust between the parties involved. It provides SMEs with access to financial services such as loans often not offered by traditional banking systems.
Further, it reduces transaction costs and enhances operational efficiency within the business. Blockchain focuses on having a clear decentralized structure for end-to-end encryption for its network connecting users, making it more reliable and greater scalability. Looking at the latest evolution, we can ensure that the central authority of Blockchain can secure digital transactions in a quality environment. Also, Blockchain improves business efficiency as it reduces the number of activities and lead times among orders and ensures that customers can track down their orders on time.
However, despite these benefits, not many SMEs have embraced the technology due to underlying barriers. For SMEs to take full advantage of the opportunities presented by the technology, it is essential to understand the obstacles that limit blockchain adoption among SMEs. Therefore, the primary purpose of this research is to investigate the factors that restrict blockchain adoption among SMEs. The study is performed using the Resource Dependency Theory and the Diffusion theory.
Table of Contents
Chapter 1. Introduction 5
Research Background 6
Blockchain Technology 6
The Integration of New Technologies 8
Benefits of Blockchain Technology for SMEs 9
Problem Statement 10
Aim of the Study 11
Significance of the Research 12
Hypothesis 13
Research Questions 13
Limitations for the adoption process 14
Chapter 2. Literature Review 15
Introduction 15
Research Scope 16
Literature Search Strategy 16
Phase 1: Selecting Reputable Databases. 17
Phase 2: Literature Search 17
Theoretical Approach 18
Diffusion of innovation theory (dit). 18
The Resource Dependence Theory (RDT). 23
Blockchain Technology 24
Assumptions about the usage. 27
Current Technology trends. 27
Literature Review: Theory in Action 29
The Diffusion of Innovation Theory (DOI). 29
Resource Dependency Theory (RDT). 31
Blockchain Adoption 32
Characteristics of Blockchain 32
Impact Analysis 34
Reduced Operational Costs. 35
Enhancing Brand Image. 36
Contributes to organizational culture. 36
Enhances data security and privacy. 36
Access to financial services. 37
Internationalization. 38
Increased network size. 39
Improved security in data transfer. 39
Improved efficiency in the supply chain. 40
Factors involved in the Decision-making Process 41
Cost of implementing and maintenance. 42
Organizational culture. 43
Management Support 43
The Readiness of an Organization. 44
The size of the organization. 45
Blockchain Adoption among SMEs 46
Characteristics of SMEs. 47
Blockchain in the context of SMEs. 48
The limitations of blockchain technology. 49
Analytical and systematic approach 50
Summary 50
Chapter 1. Introduction
Blockchain presents significant opportunities for organizations. It is considered a disruptive technology that will change how most organizations conduct their operations as it bears huge innovative potential. Innovation is one of the sources of competitive advantage and economic growth, especially in today’s ever-changing economic condition (Holotiuk & Moormann, 2019). Disruptive innovations are the ones that lead to the development of new markets or value networks. They completely transform the current market or replace existing products and services. Therefore, for organizations to remain competitive and increase their market share, it is essential to consider the adoption of Blockchain technology.
However, several challenges limit the implementation of blockchain technology. Choi et al. (2020) also noted that the adoption of Blockchain among organizations has been very slow due to factors that limit the firm’s decisions. Additionally, the epidemic theory recognizes that the process of introducing new technology into an industry can be lengthy and time-consuming. While some organizations prefer to be at the forefront of adopting new technology, others prefer playing it safe. Moreover, other organizations could be considering their decision to adopt the technology based on their available resources, or in other cases, the proposed benefits are not as convincing (Choi et al., 2020).
Blockchain is a highly complex procedural activity requiring a high degree of data management and IT skills. This makes it a challenge for small businesses with financial resource constraints and limited access to other resources. Blockchain has become one area that such organizations can only implement in partial intervals despite the objectives of such business establishments expanding sales and diversifying. Strategies to ensure that organizations adapt to business technology changes such as Blockchain are crucial to both the business and the stakeholders.
Managers are seeking to position their companies by concentrating on technological megatrends and emerging growth sectors. PwC and Gartner collaborated to create a list of significant technological megatrends for 2017. The top five combined areas include 1.) Analytics including machine learning and artificial intelligence, 2.) Cloud computing, 3.) Internet of Things and connected systems such as drones, 4.) Virtual and augmented reality, and 5.) Blockchain including distributed ledgers and value exchange transactions. This study focuses primarily on the megatrend of Blockchain and related distributed ledger capabilities. As blockchain technology can dramatically change numerous sectors, interest in this field increases (Woodside, Augustine Jr & Giberson, 2019). While considerable interest is, most blockchain implementations are still in the alpha or beta stages due to substantial technological difficulties.
Research Background 
Blockchain Technology 
Blockchain was first publicized to trade bitcoin, but its significant vital functions, such as smart contracts and ledgers, make it unique. Blockchain is a decentralized digital ledger that allows for recording all peer-to-peer transactions without a centralized authority. Blockchain technology facilitates collective bookkeeping. This enables users to agree on the validation of transactions through a mathematical hash function (Holotiuk & Moormann, 2019). The transaction information is registered in blocks that are reviewed and verified by the network. The network provides distributed ledger of verified transactions. 
The blockchain idea is comparable to the Internet, including a diverse set of underlying technology and uses. Continuing in this vein, some experts predict Blockchain will have a similar impact on business as the Internet did. Blockchain technology can replace central banking systems and other use cases like business process optimization, trading, health information sharing, automobile ownership, and voting. Blockchain technology enables cryptocurrency.
Data is stored in blocks and forms into a chain. When a new record of information is added to the chain, we can no longer change the chain block. This makes it more reliable to use the system in that once the information is stored, it cannot be altered or edited by other parties in access to the report. Organizations suffer data loss or loss of evidence to investigations due to the suspected parties’ edited data to eliminate factors that would incriminate the parties. It becomes easier for organizations to make more reliable decisions towards operations and improve performance due to reliance on a more reliable data source.
With the advancement in technology in recent decades, with most organizations shifting their data from the physical storage units such as backup drives and other sources, there has been an increase in risk as more hacks and unauthorized access prevail. This has contributed to the need for systems to ensure that there are no hacks to information systems and programs to ensure data cannot be changed in any way. Blockchain has addressed the issue by providing that all information stored in the organization is not altered.
Although blockchain technology has been associated with significant skepticism, especially with its first application n bitcoin, the technology has gained considerable attention (Tapscott & Tapscott, 2016). Blockchain is applicable in the financial sector and in other industries to coordinate the movements of goods, keep track of health records, and manage original content. The practical application of Blockchain technology has also raised concerns within the academic community due to trust and cryptographic factors (Beck et al., 2016).
The Integration of New Technologies 
The world today is subject to rapid technological changes. Thus organizations, including startups and small enterprises, are required to react based on these changes. This is because innovative technologies change the way goods, services, and, in some cases, whole industries operate. Thus, when companies fail to respond to these technological and environmental changes, the chances are high that they will fail. This has been witnessed among large corporations such as Blackberry, Nokia, and Kodak, which were leaders in the industry for a long time but failed to uphold this position because their competitors adopted new and superior technologies (Hussein, 2020). Therefore, it is essential to understand how small enterprises and startups would benefit from adopting new technologies. 
One of the most critical aspects of technology adoption is the management’s willingness to adopt the technology and make changes to its current business model and operations. This is known as the primary phase, where management is responsible for deciding whether or not to adopt a technology. This is an essential aspect of the technology adoption process (Ebers & Maurer, 2016). 
After the preliminary decision to adopt technology has been determined, secondary adoption begins. Secondary adoption refers to the adoption of the technology by the user and is determined by the nature of the organization. Secondary adoption can take any of the following approaches: the management can mandate technology adoption across the organization at once, provide the needed resources to facilitate the adoption process or target specific pilot projects in the organization, observe the process and outcomes to determine whether implementing the project in the entire organization would be beneficial.  
Benefits of Blockchain Technology for SMEs 
           Blockchain technology is expected to result in significant benefits for small enterprises and startups. Unlike larger firms, small enterprises and startups often struggle with access to resources. Therefore, this implies that they have to use resources sparingly to avoid wastage. One of the advantages of Blockchain is that it eliminates intermediary transaction costs, thus reducing operational costs. The reduction of transaction costs positions small enterprises and startups better to compete well with larger companies. 
           A significant challenge faced by small enterprises and startups is internalization. The success of small enterprises and startups requires additional resources and trusted relationships. The challenge of loss of resources makes small enterprises and startups hesitant to do business with actors who lack credible trading records (Ilbiz & Durst, 2019). This hesitation could limit small enterprises and startups from taking advantage of potentially profitable opportunities. However, intelligent contracts resolve these challenge as it provides small enterprises and startups with the opportunity to do business with untrusted parties. Smart contracts create a platform where peers do not need to trust one another to transact. Small enterprises and startups can set their terms and conditions to execute business transactions. Smart contracts allow money to be held in the Blockchain until the goods are delivered, thus eliminating risk for small enterprises and startups. 
           Additionally, when blockchains are used to facilitate online documentation, the chances of human errors are eliminated. This is because data recorded in the Blockchain cannot be altered. Once a transaction is made between peers, it is validated by nodes, thus making it impossible to alter these records in the Blockchain. Therefore, this implies that peers have to be certain about data posted in the Blockchain. According to Wong et al. (2020), small enterprises and startups rely on less formal business processes, unlike larger enterprises. Thus, they are more prone to making mistakes during their data management processes. Blockchain presents an opportunity to resolve this challenge through the unalterable data recording system.
           Another significant benefit of Blockchain is that it eliminates the reliance of peers on a central authority due to its decentralized nature (Wong et al., 2020). Additionally, Carson et al. (2018) highlighted that the potential benefits of blockchain increase with the size of the network. This implies that as the Blockchain grows, it becomes stronger to external attacks. For startups and small businesses to reap the benefits of blockchain technology, they need to be part of a more robust and more extensive network. Ilbiz & Durst (2019) advised that before startups and small businesses invest in blockchain technology, they need to consider whether their network capacity is sufficient to saturate distributed nodes. 
Problem Statement
Blockchain implementation in startups and small businesses has been reported as a prevailing challenge among various small-scale companies. Generally, the process of introducing a new technology in the market can be very lengthy. While some organizations would prefer to adopt a technology when it is new, others prefer to wait while observing the performance of the technology in other organizations. Additionally, some organizational factors hinder the adoption of blockchain technology, especially among startups and small enterprises. In support, Lohmer & Lasch (2020) investigated existing barriers in blockchain technology. The research results noted that obstacles to Blockchain adoption are still existent through lack of organizational awareness, skepticism regarding trust, legal and regulatory uncertainties, and lack of standards.
This is evident through different startups and small companies not implementing the Blockchain, and some of which have implemented had challenges implementing the strategy. To determine how to enhance the performance, there needs to be a clear understanding of Blockchain implementation in smaller firms. Factors that determine the effectiveness of the process have to be specified in terms of the business’s ability. This provides a foundation to assess why the implementation of Blockchain becomes a challenge to business organizations and what factors hinder the use of Blockchain in such startups and small businesses.
Aim of the Study
Although blockchain technology has received significant praise regarding its capabilities to enhance business efficiencies, small enterprises and startups’ adoption rate is significantly slow (Mathivathanan et al., 2021). There are two types of people in technology adoption early and late adopters. Organizations in different sectors are considering adopting the technology to reap its benefits in terms of improved business efficiencies or to wait until the integration of the technology becomes more cost-effective, and its benefits are more promising (Choi et al., 2020). 
The primary purpose of the thesis and this study is to explore the unprecedented factors and drawbacks that make blockchain adoption difficult among startups and small firms. Regardless of its usefulness, the adoption rate among small enterprises and startups has been significantly slow (Kouhizadeh et al., 2021). Choi et al. (2020) highlighted that the adoption rate might be lower due to lack of scalability, difficulty to integrate with legacy systems, and mainly due to lack of awareness. 
To successfully implement blockchain technology, it is essential to understand the factors that hinder its adoption. Understanding these limitations will be possible to develop solutions that would significantly improve the technology’s rate of acceptance and adoption. The primary purpose of the present research is to unveil the factors that hinder blockchain adoption among startups and small organizations. This study also investigates organizational adoption methods in the decision-making process by contributing to the benefits of adopting the Blockchain in upcoming businesses. This research will be focused on bringing blockchain technology a step closer to practicality. The study will attempt to understand the organizational factors that favor blockchain adoptions and identify the main barriers limiting startups and small enterprises from adopting the technology. A significant portion of research studies on obstacles to blockchain adoption focuses on supply chains, making them prominent and unique. It addresses how startups and small businesses can leverage the benefits of blockchain technology. Blockchain can further improve traceability and transparency to impact every sector using this technology designed to operate without a central authority. 
Significance of the Research 
From the discussion presented, it is evident that Blockchain offers significant opportunities to elevate the state of startups and small businesses. However, regardless of these opportunities, the blockchain adoption rate among startups and small businesses remains very low. Thus, the primary purpose of this research is to identify the underlying challenges of blockchain adoption among small enterprises and startups despite the many benefits that the technology presents in strengthening their competitive advantage. Understanding these factors is essential because it will provide recommendations that can be used to resolve these challenges and make it easier for startups and small businesses to adopt and reap the benefits of blockchain technology. Additionally, findings from the research will enable startups and small enterprises to understand how blockchain technology can allow them to enhance efficiency and the factors that could limit them from successfully implementing the technology. 
Hypothesis
Research Variables in the study will be the independent variables such as perceptions of a firm regarding risks of early blockchain adoption, availability, IT investment, the size of the firm, and the security involved. The dependent variable will be blockchain adoption. The dependent and independent variables will be measure based on a 5-point Likert Scale. The research will rely on correlation and regression to determine the relationship between the dependent and independent variables. 
Research Questions 
Article: Blockchain Technology and the Sustainable Supply Chain: Theoretically Exploring Adoption Barriers (Kouhizadeh et al., 2021). 
Research Questions:  
“Why has blockchain technology not been implemented in supply chains considerably for sustainability purposes? Can the barriers be examined theoretically and placed within TOE and force field frameworks? What are the levels of importance and relationships amongst the barriers?” (Kouhizadeh et al., 2021).
How do participants feel or be able to change to new technology and the impact they fear?
Participants: Academics and practitioners knowledgeable in Blockchain and sustainable supply chains.
Findings: The research attempts to help better understanding the current Blockchain adoption decision factors and their existing technological barriers. The study highlights the significance of organizational involvement in reducing barriers to blockchain adoption and helps decide the drawbacks by making Blockchain a success. 
Limitations for the adoption process
One of the significant limitations in adopting Blockchain is limited knowledge on the nature and applications of Blockchain in organizations. This is contributed by the low level of awareness and understanding, particularly for small organizations and startups. With such challenges, resistance among stakeholders becomes an issue. The majority of the shareholders not supporting implementing the projects such resistance results in long adoption processes, which consume much time and resources for the organization. Also, regulation and governance become an issue in implementing the adoption of the Blockchain in the organization. The organization must comply with the outlined laws and regulations to ensure the blockchain adoption process is legal. The company is free from potential legal liabilities associated with non-compliance. In addition to the limitations is a high cost associated with the adoption process. The small organization must organize resources and expertise to ensure maximum efficiency in the adoption process (Xu et al., 2020).
 
Chapter 2. Literature Review
Introduction
The primary purpose of the research is to explore the barriers to blockchain adoption among small enterprises and startups. Therefore, the first scope for the present research is that the study will be limited to startups and small enterprises. Additionally, the data collection process will also be limited to owners, employees, and managers working at small enterprises and startups. The COVID-19 protocols will determine the research recruitment process to social media platforms. To maintain a reasonable scope, the research will be limited to the organization in the United States. To quantify the opinions from participants, and due to time and resource constraints, the scope will be limited to a quantitative research methodology.
The findings from the research are expected to provide recommendations on how blockchain technology can be easily integrated among startups and small enterprises to enhance operational efficiency and effectiveness. This section provides a detailed analysis of different concepts developed by other scholars about the topic of study. The literature produced in this section aims to determine the research gap that needs to be filled by engaging in additional research activities to solve the problem raised in the topic. As discussed in this section, there have been different research findings on blockchain adoption in small organizations. Each scholar is trying to solve a specific area of the adoption process. This research starts with a comprehensive analysis of two theories: Resource dependency theory and diffusion of innovation theory. The two theories provide a basis for the development of other innovation adoption concepts and policies. Understanding the theory enhances applying the triangular approach in deriving blockchain adoption on startups and small organizations. However, the blockchain adoption process is based on certain assumptions that determine its effectiveness in attaining its objectives and goals.
Research Scope
The primary purpose of the research is to explore the barriers to blockchain adoption among small enterprises and startups. Therefore, the first scope for the present research is that the study will be limited to startups and small enterprises. Additionally, the data collection process will also be limited to owners, employees, and managers working at small enterprises and startups. The COVID-19 protocols will define the research recruitment process to social media platforms. To maintain a reasonable scope, the research will be limited to the organization in the United States. Also, to quantify the opinions from participants, and due to time and resource constraints, the scope will be limited to a quantitative research methodology.
The findings from the research are expected to provide recommendations on how blockchain technology can be easily integrated among startups and small enterprises to enhance operational efficiency and transparency.
Literature Search Strategy
The research will use a systematic literature review technique for the literature section based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The research will prior research studies based on the research questions and hypothesis will be developed. Empirical evidence will be identified from different databases; these will be reviewed and assessed to find the most suitable and relevant scholarly materials relevant to the present research. Existing studies were reviewed based on the questions posed in this current review, and the findings obtained are presented in a manner that ensured that research questions are answered comprehensively. The systematic review will be done in three phases: selecting reputable databases, literature search, screening process, and analysis.
Phase 1: Selecting Reputable Databases.
Electronic databases will be used in the review to retrieve appropriate articles relevant to the topic under investigation. The databases used in this present review will be selected based on their ability to provide as many scientific publications as possible. Accordingly, six reputable databases will be considered to aid in the literature survey process. These include Elsevier, EMBASE, PubMed, EBSCOhost, ProQuest, Web of Science, Emerald, and Scopus. These databases will be used to search for relevant publications on the subject under review. The research will mainly focus on scholarly works published between January 2019 and May 2021. This ensures that the information included in the literature survey is up-to-date and relevant for the research process.
Phase 2: Literature Search
After determining reputable databases to use, a literature search will be conducted across all the databases to identify the most appropriate scholarly materials. Search terms or keywords will be formulated to assist in the literature search process. The keywords developed in the review to find relevant scientific studies will include: “blockchain adoption,” “Blockchain adoption AND small enterprises, “Blockchain adoption AND startups,” and “blockchain adoption AND barriers.” Keywords with Boolean operators “AND” and “OR” will be used during the search process.
Boolean operators will help connect the keywords to find the relevant publications. Using the identified keywords helped retrieve the most pertinent articles relating to the topic of this review. The keywords and Boolean operators used in the search process will develop a pool of scholarly materials that can efficiently support the findings from the research. However, it is expected the literature search will result in a large number of academic materials. Thus, it is essential to develop inclusion and exclusion criteria that will limit the number of studies to be included and guarantee that the studies included are quality are related to the topic in discussion.
Inclusion Criteria: The literature review will only include scholarly works published between 2019 and 2021. The research will also have peer-reviewed journals as they are known to be credible sources of information.
Exclusion criteria: Research books, conference proceedings, organization reports, magazines, web articles, theses, periodicals, research papers, and doctoral dissertation papers are excluded. Articles published later than three years and published in another language other than English will also be excluded. Further, non-full text articles or those with only abstracts were excluded from the review.
Theoretical Approach
Theories used for the research are the Diffusion of Innovation Theory (DIT) and the Resource Dependency Theory (RDT). These two theories explain the different processes used in adopting a project in an organization and the various factors considered in implementing the strategy.
Diffusion of innovation theory (dit).
The diffusion theory was developed by EM in 1962 and is among the oldest theories in social science. The theory initially originated from communication and was used to explain how a product or an idea gained momentum over time and was adopted by people within a social system. The result of the theory is the ability of people to adopt the behavior and make it part of the social system. The main underlying idea behind this theory is that people would perceive this idea or product as innovative. However, the adoption of a new idea or does not coincide with a social system. Instead, it is procedural where some people are more apt about adopting the innovation than others. The theory asserts that people who are more likely to adopt technology at its infancy stage portray different character traits than those who embrace it later. Therefore, when innovation is advertised to a target market, it is essential to consider the character traits …
FACTORS INFLUENCING THE ADOPTION OF CLOUD COMPUTING
DRIVEN BY BIG DATA TECHNOLOGY: A QUANTITATIVE STUDY
by
Naser Chowdhury
CHRISTOPHER LUCARELLI, PhD, Faculty Mentor and Chair
GARY ROBINSON, PhD, Committee Member
ADOLFO GORRIARAN, PhD, Committee Member
Rhonda Capron, EdD, Dean
School of Business and Technology
A Dissertation Presented in Partial Fulfillment
Of the Requirements for the Degree
Doctor of Philosophy
Capella University
July 2018
ProQuest Number:
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In the unlikely event that the author did not send a complete manuscript
and there are missing pages, these will be noted. Also, if material had to be removed,
a note will indicate the deletion.
ProQuest
Published by ProQuest LLC ( ). Copyright of the Dissertation is held by the Author.
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This work is protected against unauthorized copying under Title 17, United States Code
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ProQuest LLC.
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© Naser Chowdhury, 2018
Abstract
A renewed interest in cloud computing adoption has occurred in academic and industry
settings because emerging technologies have strong links to cloud computing and Big
Data technology. Big Data technology is driving cloud computing adoption in large
business organizations. For cloud computing adoption to increase, cloud computing must
transition from low-level technology to high-level business solutions. The purpose of this
study was to develop a predictive model for cloud computing adoption that included Big
Data technology-related variables, along with other variables from two widely used
technology adoption theories: technology acceptance model (TAM), and technology-
organization-environment (TOE). The inclusion of Big Data technology-related variables
extended the cloud computing’s mix theory adoption approach. The six independent
variables were perceived usefulness, perceived ease of use, security effectiveness, the
cost-effectiveness, intention to use Big Data technology, and the need for Big Data
technology. Data collected from 182 U.S. IT professionals or managers were analyzed
using binary logistic regression. The results showed that the model involving six
independent variables was statistically significant for predicting cloud computing
adoption with 92.1% accuracy. Independently, perceived usefulness was the only
predictor variable that can increase cloud computing adoption. These results indicate that
cloud computing may grow if it can be leveraged into the emerging Big Data technology
trends to make cloud computing more useful for its users.

iv
Dedication
This dissertation is dedicated to my mother, Mariam Chowdhury, and to the
memory of my late father, Mohammed U. Chowdhury, who taught me to dream big, and
to my lovely wife, Marowa Chowdhury, who has been supportive throughout my
academic journey. Furthermore, this dissertation is dedicated to our children Eman N.
Chowdhury, Siyam N. Chowdhury and Salat N. Chowdhury, who were supportive and
understanding throughout my dissertation journey. Distinct thanks to my sister, Farhana
Chowdhury, and brother, Naim Chowdhury for their support and encouragement. Special
thanks to all my relatives and friends who made this journey a reality.

v
Acknowledgments
I wish to express my sincere thanks to my entire committee for their support
throughout my dissertation journey, especially Dr. Christopher Lucarelli, mentor and
chair of my dissertation committee. Thanks for your supervision, time, and patience that
made it possible for me to complete this journey. I also thank committee members Dr.
Adolfo Gorriaran and Dr. Gary Robinson for all their support, and feedback. Special
thanks to Dr. Tsun Chow, Dr. Bill Dafnis, Dr. Richard Livingood, Dr. Charlene Dunfee,
Dr. Jonathan Gehrz, and Johannah Bomster for their guidance and valuable feedback for
this dissertation.

vi
Table of Contents
List of Tables …………………………………………………………………………………………… ix
List of Figures …………………………………………………………………………………………….x
CHAPTER 1. INTRODUCTION …………………………………………………………………………….1
Background of the Problem ………………………………………………………………………….1
Statement of the Problem ……………………………………………………………………………..3
Purpose of the Study ……………………………………………………………………………………4
Significance of the Study ……………………………………………………………………………..5
Research Question ………………………………………………………………………………………6
Research Hypotheses …………………………………………………………………………………..6
Definition of Terms……………………………………………………………………………………..7
Research Design………………………………………………………………………………………..11
Assumptions and Limitations ……………………………………………………………………..14
Organization of the Remainder of the Study …………………………………………………15
CHAPTER 2. LITERATURE REVIEW …………………………………………………………………16
Methods of Searching ………………………………………………………………………………..16
Theoretical Foundation of Big Data Integrated Cloud Computing Adoption
Model ……………………………………………………………………………………………17
An Overview of Cloud Computing ………………………………………………………………21
Cloud Computing in Recent Time ……………………………………………………………….26
Current State of Cloud Computing Adoption ………………………………………………..28
Changing Landscape of Cloud Computing ……………………………………………………31
vii
Computing Environment Essential for Cloud Computing and Big Data
Technology ……………………………………………………………………………………33
Big Data Technology Overview ………………………………………………………………….37
Big Data Technology Trends ………………………………………………………………………40
An Integrated Solution Involving Big Data Technology and Cloud Computing ..48
Architecture of Big Data Technology Integrated Cloud Computing Solutions …..53
Analytical Tools for Big Data Technology ……………………………………………………54
Predictive Analytics Supported by Big Data Technology in Organizations……….54
Impact of Big Data and the Need for Big Data Technology in Organizations ……55
Summary ………………………………………………………………………………………………….56
CHAPTER 3. METHODOLOGY ………………………………………………………………………….57
Purpose of the Study ………………………………………………………………………………….57
Research Questions and Hypotheses ……………………………………………………………57
Research Design………………………………………………………………………………………..59
Target Population and Sample …………………………………………………………………….60
Power Analysis …………………………………………………………………………………………61
Procedures ………………………………………………………………………………………………..62
Data Collection …………………………………………………………………………………………63
Data Analysis ……………………………………………………………………………………………63
Instruments ……………………………………………………………………………………………….67
Ethical Considerations ……………………………………………………………………………….68
Summary ………………………………………………………………………………………………….69
CHAPTER 4. RESULTS ………………………………………………………………………………………71
viii
Background ………………………………………………………………………………………………71
Description of the Sample …………………………………………………………………………..72
Summary of Results …………………………………………………………………………………..74
Details of Analysis and Results …………………………………………………………………..75
Hypothesis Testing…………………………………………………………………………………….77
Summary ………………………………………………………………………………………………….80
CHAPTER 5. DISCUSSION, IMPLICATIONS, RECOMMENDATIONS ………………..82
Summary of the Results ……………………………………………………………………………..82
Discussion of the Results ……………………………………………………………………………83
Limitations ……………………………………………………………………………………………….86
Implications for Practice …………………………………………………………………………….87
Recommendations for Further Research ……………………………………………………….88
Conclusion ……………………………………………………………………………………………….90
REFERENCES ……………………………………………………………………………………………………92
APPENDIX A. SURVEY INSTRUMENT ……………………………………………………………100

ix
List of Tables
Table 1. Descriptive Statistics for Categorical Variables …………………………………………. 72
Table 2. Descriptive Statistics for the Continuous Variables ……………………………………. 74
Table 3. Cronbach’s Alpha of Reliability Coefficients for the Composite Scores ……….. 76
Table 4. Logistic Regression with the Six Independent Variables Predicting Cloud
Computing Adoption ……………………………………………………………………………….. 77
Table 5. Summary of Hypotheses Testing Results ………………………………………………….. 81

x
List of Figures
Figure 1. Cloud computing logical diagram.. …………………………………………………………. 23
Figure 2. PaaS Architecture. ………………………………………………………………………………… 24
Figure 3. Security for the SaaS stack. ……………………………………………………………………. 30
Figure 4. Hadoop 1.x and Hadoop 2.x. ………………………………………………………………….. 38
Figure 5. MapReduce. …………………………………………………………………………………………. 39
Figure 6. The Hadoop ecosystem………………………………………………………………………….. 39
Figure 7. Big Data infrastructure taxonomy. ………………………………………………………….. 42
Figure 8. A hierarchical framework of efficient machine learning for Big Data
processing. ……………………………………………………………………………………………… 45
Figure 9. Spark.. …………………………………………………………………………………………………. 47
Figure 10. A simple instance of large-scale data stream processing service. The example
service consists of Apache Kafka (data ingestion layer). Apache Storm (data
analytics layer), and Apache Cassandra Systems (data storage layer). ……………. 48
Figure 11. Cloud computing usage in Big Data.. …………………………………………………….. 50
Figure 12. Comparison of several Big Data cloud platform. …………………………………….. 52
Figure 13. A high-level architecture of large-scale data processing service. The Big Data
analytics architectures have three layers-data ingestion, analytics, and storage –
and the first two layers communicate with various databases during execution. . 53

1
CHAPTER 1. INTRODUCTION
Cloud computing and Big Data technologies have the potential to disrupt
industries and businesses through predictive analytics, machine learning, and artificial
intelligence (AI) applications, bringing massive competitive advantages for businesses
(Hashim, Hassan, & Hashim, 2015). Therefore, when scholar-practitioner Liu (2013)
claimed that Big Data technology was the driver of cloud computing adoption in large
business organizations, academic research was needed to accept or reject that claim. To
examine Liu’s recommendation that cloud computing adoption can be increased by
increasing usefulness of the cloud computing through cloud-powered business enhancing
service offerings such as Big Data analytics and business intelligence, a predictive model
for cloud computing adoption, was created. This model integrates Big Data technology-
related variables with other cloud computing adoption variables adapted from the
technology adoption model (TAM), and technology-organization-environment (TOE),
and was tested with survey data collected from U.S. IT professionals or managers by
using binary logistic regression for data analysis.
Background of the Problem
The technology industry is experiencing massive innovation in cloud computing,
Big Data, social media, mobile devices, IoT devices, AI, machine learning, and deep
learning algorithms. To understand the effect of Big Data on cloud computing adoption,
and the broader implication of Big Data technology and cloud computing on societies and
organizations, it was essential to investigate the technological advancement and changes
in the current business landscape. Srinivasan (2017) stated that for business organizations
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to be relevant in Big Data era, they must create innovative solutions at every level of their
organizations.
Hashim et al. (2015) found that only four previous studies focused on IT
professionals’ perspective of cloud computing adoption, and they recommended
additional research involving IT professionals’ perspective of cloud computing adoption.
When emerging technologies are researched, designed, and implemented in silos, they
offer advancement in industry, field, or application. However, to understand the holistic
effects of these technologies, it is necessary to go beyond the individual technology
adoption theories of technology acceptance model (TAM), diffusion of innovation (DOI)
theory and technology-organization-environment (TOE) framework. Additional research
was needed to move from a single technology adoption model, theory, or framework to a
more complex model with multiple emerging technologies involved.
Cloud computing service providers previously have focused on servers and
storage (Liu, 2013). Many service providers were missing opportunities to increase cloud
computing service growth by not focusing on increasing business capability building
products and services powered by cloud computing (Liu, 2013). Liu (2013) classified
essential elements of cloud computing adoption into three major categories: hurdles,
motivators, and new innovative products and service offering.
The variables that were identified as hurdles were cost, security, and the need for
training (Liu, 2013). These variables are comparable to the cost-effectiveness, security
effectiveness, and perceived ease of use in the current model.
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Three variables that were identified as motivators in the model presented by Liu
(2013) were rapid provisioning, reduced overhead, and service diversities. These
variables are comparable to perceived usefulness of cloud computing adoption theories.
Liu (2013) suggested Big Data technology integrated cloud computing products
and services as the innovative products and service offering. This suggestion of Big Data
technology integrated cloud computing products and services are comparable to the
variable perceived usefulness of cloud computing adoption theories adapted from TAM.
Variables from TAM and TOE were considered to give Liu’s cloud computing adoption
related variables a theoretical structure. This study included an examination of six
independent variables and one dependent variable. The six independent variables were,
perceived usefulness, perceived ease of use, security effectiveness, the cost-effectiveness,
intention to use Big Data technology, and the need for Big Data technology (Hood-Clark,
2016; Optiz, Langkau, Schmidt, & Kolbe, 2012; Ross, 2010). The dependent variable,
use of cloud computing, was adopted from the study of Ross (2010).
Statement of the Problem
The literature review revealed that perceived ease of use, perceived usefulness,
security, complexity, compatibilities were the most widely measured cloud computing
adoption factors for IT professionals; and TAM, DOI, and TOE were the most commonly
used technology adoption theories and frameworks (Hashim et al., 2015). However, there
has not been any research to test the validity of claims made by Liu (2013) that Big Data
was driving cloud computing adoption by increasing the perceived usefulness of cloud
computing. A new cloud computing adoption model needs to be developed to address
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claims made by Liu, which integrates variables from TAM, TOE, and Big Data
technology variables to determine the impact of Big Data technology on cloud computing
adoption.
Purpose of the Study
Hemalatha, Kokila, and Krithika (2016) noticed critical changes in the technology
landscape, especially in the area of Big Data analytics. Big Data analytics is changing
from insight generation from social data, videos, and tweets to more complex scenarios,
where data are generated by intelligent systems (e.g., smart meters, smart sensors, and in-
vehicle infotainment) integrated with human-generated data. Intelligent systems are
generating some of the biggest volume, fastest streaming and most complex Big Data
(Hemalatha et al., 2016). These intelligent systems and the Internet of Things (IoT)
devices distribute data through the cloud’s infrastructure. Hence, the cloud is becoming
not only the processing hub of the Big Data, but also the data sources, analytics, and
distribution platform of Big Data. Consequently, in the 21st-century environment, to base
cloud computing adoption on standard technology adoption theories and framework alone
is inadequate (Hemalatha et al., 2016).
A new study is needed to determine the factors that are driving cloud computing
adoption from the perspective of IT professionals or managers. To achieve that goal and
to bring the Big Data technology into cloud computing adoption model, this study relied
on the scholarly article written by Liu (2013), “Big Data Drives Cloud Adoption in
Enterprise” as the anchor literature for this study. The purpose of the study was to
examine claims made by Liu by developing a new cloud computing adoption model and
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testing that model on IT professionals or managers to determine the impact of Big Data
technology on cloud computing adoption.
Significance of the Study
This research has broad practical implications because this research aims to fill
the gap in the current literature related to cloud computing adoption driven by Big Data
Technology. At the age of AI, IoT and other emerging technologies, it is more critical
than before to investigate, how much of cloud computing adoption is driven by Big Data
technology (Liu, 2013), directly or indirectly. Despite knowing cloud computing
technologies are being adopted in many industries, and cloud computing technologies are
becoming the platform of innovation and growth; business executives need to understand
what factors drive others in the industry to adopt cloud computing (Nasir & Niazi, 2011;
Ho, 2015). Service providers that implement cloud computing and Big Data technology
solutions to business organizations need to understand what makes their customers drive
to adopt cloud computing solutions. Therefore, when researchers (Hemalatha et al., 2016)
claimed that Big Data technologies driven by smart IoT devices are producing a
significant amount of complex data in cloud computing infrastructure, it was needed to
verify those claims with a new predictive cloud computing adoption model. That
predictive cloud computing adoption model was tested with data from U.S. IT
professionals or managers, who are most often directly involved in planning and
implementation of cloud computing and Big Data technologies in their organizations.
For academic researchers, this research opened new possibilities of technology
adoption research involving the integration of multiple technologies and multiple
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technology adoption theories. Before this research, it was very difficult if not impossible
to verify the claims of Liu (2013) and Hemalatha et al. (2016) that Big Data technology
was driving cloud computing adoption. This new model provides the theoretical
underpinning necessary to investigate the predictive cloud computing adoption model
integrating Big Data technology-related variables with other cloud computing adoption
variables adapted from technology acceptance model (TAM), and technology-
organization-environment (TOE). This research, added new knowledge to cloud
computing adoption research involving Big Data technology, and IT professionals or
managers.
Research Question
To what extent, if any, is cloud computing adoption predicted by perceived ease
of use, perceived usefulness, security effectiveness, the cost-effectiveness, intention to
use Big Data technology, and the need for Big Data technology?
Research Hypotheses
H01: Cloud computing adoption cannot be predicted by perceived ease of use,
perceived usefulness, security effectiveness, the cost-effectiveness, intention to use Big
Data technology, and the need for Big Data technology.
HA1: Cloud computing adoption can be predicted by perceived ease of use,
perceived usefulness, security effectiveness, the cost-effectiveness, intention to use Big
Data technology, and the need for Big Data technology.
There are also six additional sub-hypotheses for this study:
H02: Cloud computing adoption cannot be predicted by perceived ease of use.
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HA2: Cloud computing adoption can be predicted by perceived ease of use.
H03: Cloud computing adoption cannot be predicted by perceived usefulness.
HA3: Cloud computing adoption can be predicted by perceived usefulness.
H04: Cloud computing adoption cannot be predicted by intention to use Big Data
technology.
HA4: Cloud computing adoption can be predicted by intention to use Big Data
technology.
H05: Cloud computing adoption cannot be predicted by the need for Big Data
technology.
HA5: Cloud computing adoption can be predicted by the need for Big Data
technology.
H06: Cloud computing adoption cannot be predicted by security effectiveness.
HA6: Cloud computing adoption can be predicted by security effectiveness.
H07: Cloud computing adoption cannot be predicted by the cost-effectiveness.
HA7: Cloud computing adoption can be predicted by the cost-effectiveness.
Definition of Terms
Big Data. De Mauro, Greco, and Grimaldi (2015) defined Big Data technology
based on a study that involved multiple studies describing Big Data, Big Data analytics,
and Big Data technology. The researchers used those terms interchangeably. De Mauro et
al. wrote, “Big Data represents the Information assets characterized by such a High
Volume, Velocity, and Variety to require specific Technology and Analytical Methods
for its transformation into Value” (p. 103).
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Cloud computing. After extensive research on the definition of cloud computing,
Madhavaiah, Bashir, and Shafi (2012) determined the following definition:
Cloud computing is an information technology-based business model, provided as
a service over the Internet, where both hardware and software computing services
are delivered on-demand to customers in a self-service fashion, independent of
device and location within high levels of quality, in a dynamically scalable,
rapidly provisioned, shared and virtualized way and with minimal service
provider interaction. (p. 172)
The Cost-Effectiveness. According to Ross (2010), the cost-effectiveness in the
context of cloud computing is the ability to pay computing resources as a utility service
and on a needed basis. Cloud provides an opportunity to avoid the significant initial cost
of purchasing computing hardware and infrastructure cost. Liu (2013) emphasized the
cost-effectiveness alone might be insufficient to increase cloud computing adoption;
hence, it was included in this study to investigate whether Liu’s hypothesis holds true.
Lian, Yen, and Wang (2014) also used cost as an independent variable in their cloud
adoption-related research.
Deep learning. Gualtieri et al. (2017) described Forrester’s definition of deep
learning as “A rapidly evolving set of technologies and algorithms used by researchers,
data scientists, and/or developers to build, train, and test artificial neural networks for use
in predictive models to probabilistically predict outcomes and/or identify complex
patterns in data” (p. 2).
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Infrastructure as a Service (IaaS). Computing, storage, and operating systems are
bundled together and delivered as a service in IaaS model (e.g., Amazon EC2, S3, and
Rackspace). This service model’s pay-per-use model benefits the user by not requiring
payment for hardware and software in advance (Oliveira, Thomas, & Espadanal, 2014).
Intention to use Big Data technology. Intention to use measures a person’s
intention to use or not use Big Data technology (Optiz et al., 2012).
Machine learning. Alpaydin (2010) defined machine learning as “programming
computers to optimize a performance criterion using example data or experience” (p. 2).
Perceived Ease of Use. According to Optiz et al. (2012), perceived ease of use is
the degree to which a user expects the use of a target technology or a system to be free of
effort.
Perceived Usefulness. Perceived ease of use is defined as the prospective user’s
subjective probability that by using certain application or technology will increase that
users’ job performance within the organizational perspective (Optiz et al., 2012).
Platform as a service (PaaS). In this service model, service providers offer a
solution in which technologies get integrated into software development in the cloud
(e.g., Google AppEngine, and Microsoft Azure) and this service model brings design,
testing, maintenance, and hosting of software development under one provider (Oliveira
et al., 2014).
Security Effectiveness. The term security effectiveness of cloud computing is
derived from all aspects of cloud security, from data integrity, networking, to each
delivery model of cloud computing, namely SaaS, PaaS, and IaaS (Ross, 2012).
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Independent variable security effectiveness was adopted from Ross’s study. Liu also
stated that security effectiveness was vital in every cloud adoption decision; therefore,
this variable was included in the current study. Security effectiveness was an independent
variable, similar to the TOE framework’s data security (Lian, et al. 2014).
Software as a service (SaaS). Describing SaaS, Oliveira et al. (2014) wrote,
Users access the applications centrally hosted in the cloud using a thin client
(such as a web browser or a mobile application) instead of installing software on
their computer (e.g., Joyent and Salesforce CRM). The benefits of this model of
cloud service include centralized configuration and hosting, software release
updates without requiring reinstallation, and accelerated feature delivery. (p. 498)
The Need for Big Data Technology. The need for Big Data technology refers to an
organization’s needs for a variety of Big Data …




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