Aim/Purpose: In the United States, the Centers for Disease Control and Prevention (CDC) tracks the disease activity using data collected from medical practices on a weekly basis. Collection of data by CDC from medical practices on a weekly basis leads to a lag time of approximately 2 weeks before any viable action can be planned. The 2-week delay problem was addressed in the study by creating machine learning models to predict flu outbreak. Background: The 2-week delay problem was addressed in the study by correlation of the flu trends identified from Twitter data and official flu data from the Centers for Disease Control and Prevention (CDC) in combination with creating a machine learning model using both data sources to predict flu outbreak. Methodology: A quantitative correlational study was performed using a quasi-experimental design. Flu trends from the CDC portal and tweets with mention of flu and influenza from the state of Georgia were used over a period of 22 weeks from December 29, 2019 to May 30, 2020 for this study. Contribution: This research contributed to the body of knowledge by using a simple bag-of-word method for sentiment analysis followed by the combination of CDC and Twitter data to generate a flu prediction model with higher accuracy than using CDC data only. Findings: The study found that (a) there is no correlation between official flu data from CDC and tweets with mention of flu and (b) there is an improvement in the performance of a flu forecasting model based on a machine learning algorithm using both official flu data from CDC and tweets with mention of flu. Recommendations for Practitioners: In this study, it was found that there was no correlation between the official flu data from the CDC and the count of tweets with mention of flu, which is why tweets alone should be used with caution to predict a flu out-break. Based on the findings of this study, social media data can be used as an additional variable to improve the accuracy of flu prediction models. It is also found that fourth order polynomial and support vector regression models offered the best accuracy of flu prediction models. Recommendation's for Researchers: Open-source data, such as Twitter feed, can be mined for useful intelligence benefiting society. Machine learning-based prediction models can be improved by adding open-source data to the primary data set. Impact on Society: Key implication of this study for practitioners in the field were to use social media postings to identify neighborhoods and geographic locations affected by seasonal outbreak, such as influenza, which would help reduce the spread of the disease and ultimately lead to containment. Based on the findings of this study, social media data will help health authorities in detecting seasonal outbreaks earlier than just using official CDC channels of disease and illness reporting from physicians and labs thus, empowering health officials to plan their responses swiftly and allocate their resources optimally for the most affected areas. Future Research: A future researcher could use more complex deep learning algorithms, such as Artificial Neural Networks and Recurrent Neural Networks, to evaluate the accuracy of flu outbreak prediction models as compared to the regression models used in this study. A future researcher could apply other sentiment analysis techniques, such as natural language processing and deep learning techniques, to identify context-sensitive emotion, concept extraction, and sarcasm detection for the identification of self-reporting flu tweets. A future researcher could expand the scope by continuously collecting tweets on a public cloud and applying big data applications, such as Hadoop and MapReduce, to perform predictions using several months of historical data or even years for a larger geographical area. *** NOTE: This Proceedings paper was revised and published in the journal Issues in Informing Science and Information Technology, 16, 63-81 At the bottom of this page, click DOWNLOAD PDF to download the published paper. ***
Aim/Purpose: In the United States, the Centers for Disease Control and Prevention (CDC) tracks the disease activity using data collected from medical practice's on a weekly basis. Collection of data by CDC from medical practices on a weekly basis leads to a lag time of approximately 2 weeks before any viable action can be planned. The 2-week delay problem was addressed in the study by creating machine learning models to predict flu outbreak. Background: The 2-week delay problem was addressed in the study by correlation of the flu trends identified from Twitter data and official flu data from the Centers for Disease Control and Prevention (CDC) in combination with creating a machine learning model using both data sources to predict flu outbreak. Methodology: A quantitative correlational study was performed using a quasi-experimental design. Flu trends from the CDC portal and tweets with mention of flu and influenza from the state of Georgia were used over a period of 22 weeks from December 29, 2019 to May 30, 2020 for this study. Contribution: This research contributed to the body of knowledge by using a simple bag-of-word method for sentiment analysis followed by the combination of CDC and Twitter data to generate a flu prediction model with higher accuracy than using CDC data only. Findings: The study found that (a) there is no correlation between official flu data from CDC and tweets with mention of flu and (b) there is an improvement in the performance of a flu forecasting model based on a machine learning algorithm using both official flu data from CDC and tweets with mention of flu. Recommendations for Practitioners: In this study, it was found that there was no correlation between the official flu data from the CDC and the count of tweets with mention of flu, which is why tweets alone should be used with caution to predict a flu out-break. Based on the findings of this study, social media data can be used as an additional variable to improve the accuracy of flu prediction models. It is also found that fourth order polynomial and support vector regression models offered the best accuracy of flu prediction models. Recommendations for Researchers: Open-source data, such as Twitter feed, can be mined for useful intelligence benefiting society. Machine learning-based prediction models can be improved by adding open-source data to the primary data set. Impact on Society: Key implication of this study for practitioners in the field were to use social media postings to identify neighborhoods and geographic locations affected by seasonal outbreak, such as influenza, which would help reduce the spread of the disease and ultimately lead to containment. Based on the findings of this study, social media data will help health authorities in detecting seasonal outbreaks earlier than just using official CDC channels of disease and illness reporting from physicians and labs thus, empowering health officials to plan their responses swiftly and allocate their resources optimally for the most affected areas. Future Research: A future researcher could use more complex deep learning algorithms, such as Artificial Neural Networks and Recurrent Neural Networks, to evaluate the accuracy of flu outbreak prediction models as compared to the regression models used in this study. A future researcher could apply other sentiment analysis techniques, such as natural language processing and deep learning techniques, to identify context-sensitive emotion, concept extraction, and sarcasm detection for the identification of self-reporting flu tweets. A future researcher could expand the scope by continuously collecting tweets on a public cloud and applying big data applications, such as Hadoop and MapReduce, to perform predictions using several months of historical data or even years for a larger geographical area.
Aim/Purpose: Board of Directors seek to use their big data as a competitive advantage. Still, scholars note the complexities of corporate governance in practice related to information security risk management (ISRM) effectiveness. Background: While the interest in ISRM and its relationship to organizational success has grown, the scholarly literature is unclear about the effects of Chief Technology Officers (CTOs) leadership styles, the alignment of the governance of big data, and ISRM effectiveness in organizations in the West-ern United States. Methodology: The research method selected for this study was a quantitative, correlational research design. Data from 139 participant survey responses from Chief Technology Officers (CTOs) in the Western United States were analyzed using 3 regression models to test for mediation following Baron and Kenny’s methodology. Contribution: Previous scholarship has established the importance of leadership styles, big data governance, and ISRM effectiveness, but not in a combined understanding of the relationship between all three variables. The researchers’ primary objective was to contribute valuable knowledge to the practical field of computer science by empirically validating the relationships between the CTOs leadership styles, the alignment of the governance of big data, and ISRM effectiveness. Findings: The results of the first regression model between CTOs leadership styles and ISRM effectiveness were statistically significant. The second regression model results between CTOs leadership styles and the alignment of the governance of big data were not statistically significant. The results of the third regression model between CTOs leadership styles, the alignment of the governance of big data, and ISRM effectiveness were statistically significant. The alignment of the governance of big data was a significant predictor in the model. At the same time, the predictive strength of all 3 CTOs leadership styles was diminished between the first regression model and the third regression model. The regression models indicated that the alignment of the governance of big data was a partial mediator of the relationship between CTOs leadership styles and ISRM effectiveness. Recommendations for Practitioners: With big data growing at an exponential rate, this research may be useful in helping other practitioners think about how to test mediation with other interconnected variables related to the alignment of the governance of big data. Overall, the alignment of governance of big data being a partial mediator of the relationship between CTOs leadership styles and ISRM effectiveness suggests the significant role that the alignment of the governance of big data plays within an organization. Recommendations for Researchers: While this exact study has not been previously conducted with these three variables with CTOs in the Western United States, overall, these results are in agreement with the literature that information security governance does not significantly mediate the relationship between IT leadership styles and ISRM. However, some of the overall findings did vary from the literature, including the predictive relationship between transactional leadership and ISRM effectiveness. With the finding of partial mediation indicated in this study, this also suggests that the alignment of the governance of big data provides a partial intervention between CTOs leadership styles and ISRM effectiveness. Impact on Society: Big data breaches are increasing year after year, exposing sensitive information that can lead to harm to citizens. This study supports the broader scholarly consensus that to achieve ISRM effectiveness, better alignment of governance policies is essential. This research highlights the importance of higher-level governance as it relates to ISRM effectiveness, implying that ineffective governance could negatively impact both leadership and ISRM effectiveness, which could potentially cause reputational harm. Future Research: This study raised questions about CTO leadership styles, the specific governance structures involved related to the alignment of big data and ISRM effectiveness. While the research around these variables independently is mature, there is an overall lack of mediation studies as it relates to the impact of the alignment of the governance of big data. With the lack of alignment around a universal framework, evolving frameworks could be tested in future research to see if similar results are obtained. *** NOTE: This Proceedings paper was revised and published in the journal Issues in Informing Science and Information Technology, 16, 41-61. At the bottom of this page, click DOWNLOAD PDF to download the published paper. ***
Aim/Purpose: Board of Directors seek to use their big data as a competitive advantage. Still, scholars note the complexities of corporate governance in practice related to information security risk management (ISRM) effectiveness. Background: While the interest in ISRM and its relationship to organizational success has grown, the scholarly literature is unclear about the effects of Chief Technology Officers (CTOs) leadership styles, the alignment of the governance of big data, and ISRM effectiveness in organizations in the West-ern United States. Methodology: The research method selected for this study was a quantitative, correlational research design. Data from 139 participant survey responses from Chief Technology Officers (CTOs) in the Western United States were analyzed using 3 regression models to test for mediation following Baron and Kenny’s methodology. Contribution: Previous scholarship has established the importance of leadership styles, big data governance, and ISRM effectiveness, but not in a combined understanding of the relationship between all three variables. The researchers’ primary objective was to contribute valuable knowledge to the practical field of computer science by empirically validating the relationships between the CTOs leadership styles, the alignment of the governance of big data, and ISRM effectiveness. Findings: The results of the first regression model between CTOs leadership styles and ISRM effectiveness were statistically significant. The second regression model results between CTOs leadership styles and the alignment of the governance of big data were not statistically significant. The results of the third regression model between CTOs leadership styles, the alignment of the governance of big data, and ISRM effectiveness were statistically significant. The alignment of the governance of big data was a significant predictor in the model. At the same time, the predictive strength of all 3 CTOs leadership styles was diminished between the first regression model and the third regression model. The regression models indicated that the alignment of the governance of big data was a partial mediator of the relationship between CTOs leadership styles and ISRM effectiveness. Recommendations for Practitioners: With big data growing at an exponential rate, this research may be useful in helping other practitioners think about how to test mediation with other interconnected variables related to the alignment of the governance of big data. Overall, the alignment of governance of big data being a partial mediator of the relationship between CTOs leadership styles and ISRM effectiveness suggests the significant role that the alignment of the governance of big data plays within an organization. Recommendations for Researchers: While this exact study has not been previously conducted with these three variables with CTOs in the Western United States, overall, these results are in agreement with the literature that information security governance does not significantly mediate the relationship between IT leadership styles and ISRM. However, some of the overall findings did vary from the literature, including the predictive relationship between transactional leadership and ISRM effectiveness. With the finding of partial mediation indicated in this study, this also suggests that the alignment of the governance of big data provides a partial intervention between CTOs leadership styles and ISRM effectiveness. Impact on Society: Big data breaches are increasing year after year, exposing sensitive information that can lead to harm to citizens. This study supports the broader scholarly consensus that to achieve ISRM effectiveness, better alignment of governance policies is essential. This research highlights the importance of higher-level governance as it relates to ISRM effectiveness, implying that ineffective governance could negatively impact both leadership and ISRM effectiveness, which could potentially cause reputational harm. Future Research: This study raised questions about CTO leadership styles, the specific governance structures involved related to the alignment of big data and ISRM effectiveness. While the research around these variables independently is mature, there is an overall lack of mediation studies as it relates to the impact of the alignment of the governance of big data. With the lack of alignment around a universal framework, evolving frameworks could be tested in future research to see if similar results are obtained.
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