Analyzing Small Businesses' Adoption of Big Data Security Analytics

Analyzing Small Businesses’ Adoption of Big Data Security Analytics by Henry Mathias MCA, Anna University, 1995 BSc, Bharathidasan University, 1992 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Management Walden University May 2019 Abstract Despite the increased cost of data breaches due to advanced, persistent threats from malicious sources, the adoption of big data security analytics among U.S. small businesses has been slow. Anchored in a diffusion of innovation theory, the purpose of this correlational study was to examine ways to increase the adoption of big data security analytics among small businesses in the United States by examining the relationship between small business leaders’ perceptions of big data security analytics and their adoption. The research questions were developed to determine how to increase the adoption of big data security analytics, which can be measured as a function of the user’s perceived attributes of innovation represented by the independent variables: relative advantage, compatibility, complexity, observability, and trialability. The study included a cross-sectional survey distributed online to a convenience sample of 165 small businesses. Pearson correlations and multiple linear regression were used to statistically understand relationships between variables. There were no significant positive correlations between relative advantage, compatibility, and the dependent variable adoption; however, there were significant negative correlations between complexity, trialability, and the adoption. There was also a significant positive correlation between observability and the adoption. The implications for positive social change include an increase in knowledge, skill sets, and jobs for employees and increased confidentiality, integrity, and availability of systems and data for small businesses. Social benefits include improved decision making for small businesses and increased secure transactions between systems by detecting and eliminating advanced, persistent threats.Despite the increased cost of data breaches due to advanced, persistent threats from malicious sources, the adoption of big data security analytics among U.S. small businesses has been slow. Anchored in a diffusion of innovation theory, the purpose of this correlational study was to examine ways to increase the adoption of big data security analytics among small businesses in the United States by examining the relationship between small business leaders’ perceptions of big data security analytics and their adoption. The research questions were developed to determine how to increase the adoption of big data security analytics, which can be measured as a function of the user’s perceived attributes of innovation represented by the independent variables: relative advantage, compatibility, complexity, observability, and trialability. The study included a cross-sectional survey distributed online to a convenience sample of 165 small businesses. Pearson correlations and multiple linear regression were used to statistically understand relationships between variables. There were no significant positive correlations between relative advantage, compatibility, and the dependent variable adoption; however, there were significant negative correlations between complexity, trialability, and the adoption. There was also a significant positive correlation between observability and the adoption. The implications for positive social change include an increase in knowledge, skill sets, and jobs for employees and increased confidentiality, integrity, and availability of systems and data for small businesses. Social benefits include improved decision making for small businesses and increased secure transactions between systems by detecting and eliminating advanced, persistent threats. Analyzing Small Businesses’ Adoption of Big Data Security Analytics by Henry Mathias MCA, Anna University, 1995 BSc, Bharathidasan University, 1992 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Management Walden University May 2019 Dedication I thank the Lord Jesus Christ for the great privilege to begin and complete my doctoral dissertation. I dedicate this work first to the only immortal and invisible Christ who enabled me to pursue and achieve my academic dreams. I would also dedicate this work to my father, Mathias Moves, who was my constant inspiration and motivation in all my academic dreams and successes. Finally, I dedicate this dissertation to my wife, Christina Henry, who supported me all through these tough years of this doctoral journey. I would not have accomplished all these milestones without the constant support and encouragement from my family. Acknowledgments I would like to thank my Dissertation Chair, Dr. David Gould, for his outstanding guidance, continuous availability, and his constant motivation for me to succeed in this doctoral journey. His enthusiasm and genuine interest in making me successful, kept me energized and focused throughout my dissertation phase. I also would like to thank my Committee Member, Dr. Anthony Lolas, for his excellent input and guidance in the field of big data and quantitative methodologies. My special thanks to Dr. Thomas Butkiewicz, as my University Research Reviewer and for all his valuable contribution to my doctoral success. Above all, I thank my God and the Lord Jesus Christ for helping me to complete this lifelong lofty dream. I am exceedingly grateful to everyone who has helped me on this doctoral journey without whose assistance, I would not have completed my doctoral program. Every small help and guidance has enabled me to complete this monumental and substantial task of completing the dissertation. I thank God for this wonderful journey and for angels who helped me to achieve this great milestone in life.