Normal view MARC view ISBD view

Reshaping Society through Analytics, Collaboration, and Decision Support : Role of Business Intelligence and Social Media

By: Iyer, Lakshmi S.
Contributor(s): Power, Daniel J.
Material type: TextTextSeries: eBooks on Demand.Annals of Information Systems: Publisher: Cham : Springer International Publishing, 2014Description: 1 online resource (270 p.).ISBN: 9783319115757.Subject(s): Business intelligenceGenre/Form: Electronic books.Additional physical formats: Print version:: Reshaping Society through Analytics, Collaboration, and Decision Support : Role of Business Intelligence and Social MediaDDC classification: 650 LOC classification: HD38.7 -- .R47 2015ebOnline resources: Click here to view this ebook.
Contents:
Contents; About the Editors; Chapter 1: Introduction; 1.1 About AIS SIG DSA; Chapter 2: Big Data Panel at SIGDSS Pre-ICIS Conference 2013: A Swiss-Army Knife? The Profile of a Data Scientist; References; Chapter 3: Creating a Data-Driven Global Society; 3.1 Introduction; 3.2 Data Expansion and Decision Support; 3.3 Looking at Today; 3.4 Looking Forward; 3.5 Conclusions and Commentary; References; Chapter 4: Agile Supply Chain Decision Support System; 4.1 Introduction; 4.2 Literature Review; 4.2.1 Supplier Selection for Agile SCM Via fuzzy MCDM; 4.2.2 Fuzzy AHP; 4.2.3 Fuzzy TOPSIS
4.3 Framework of the Proposed Agile Supply Chain DSS4.3.1 Identification of Evaluation Criteria and Decision Hierarchy; 4.3.2 Identification of Evaluation Criteria Weights; 4.3.3 Supplier Identification, Evaluation, and Selection; 4.4 Assessing Business Impact of Agile Supply Chain DSS; 4.4.1 Simple Supply Chain Configuration; 4.4.2 Bullwhip Effect with Autocorrelation Coefficient and Desired Service Level; 4.4.3 The Pareto Fronts with Various Weights of Agility Criterion; 4.4.4 The Pareto Fronts with Various Weights of Agility Sub-Criteria; 4.5 Conclusion; References
Chapter 5: Hawkes Point Processes for Social Media Analytics5.1 Introduction; 5.2 Point Processes; 5.3 Hawkes Point Processes; 5.4 Hawkes Process Modeling Applications; 5.4.1 Finance; 5.4.2 Healthcare and Bioinformatics; 5.4.3 Sociology, Criminology and Terrorism; 5.4.4 Social Network Analysis; 5.5 Hawkes Process Applications in Social Media; 5.6 Conclusion; References; Chapter 6: Using Academic Analytics to Predict Dropout Risk in E-Learning Courses; 6.1 Introduction; 6.2 Literature Review; 6.2.1 Factors Affecting Student Retention and Dropout
6.2.2 Data Analysis and Mining Techniques in Dropout Prediction6.3 Research Approach; 6.3.1 Constructs and Variables; 6.3.2 Evaluation Criteria; 6.4 Data Analysis and Predictive Modeling; 6.4.1 SIS Data Analysis; 6.4.2 CMS Data Analysis; 6.4.3 Computation of Dynamic Risk Scores; 6.4.4 Evaluation of the Risk Scoring Model; 6.4.5 Comparison of Pre-census and Post-census Dropout Accuracy; 6.4.6 Recommendation for Deployment of Predictive Models; 6.4.7 Recommender System Architecture; 6.5 Conclusion; References
Chapter 7: Membership Reconfiguration in Knowledge Sharing Network: A Simulation Study7.1 Introduction; 7.2 Literature Review; 7.2.1 Social Behavior Theories, Collaboration Climates, and Knowledge Sharing; 7.2.2 Knowledge Sharing Bottlenecks and BIS Metric; 7.3 Framework of the Proposed KMS; 7.3.1 Rearrangement Propositions of CoP Members; 7.3.2 Knowledge Sharing Data Sets; 7.3.3 Simulation Experiments Setups; 7.4 Experimental Results: BIS Improvement and Network Structure; 7.4.1 Comparison of BIS Improvements and CoP Types Distribution; 7.4.2 Improvement of Collaboration Network Structures
7.5 Conclusion
Summary: This volume explores emerging research and pedagogy in analytics, collaboration, and decision support with an emphasis on business intelligence and social media. In general, the chapters help understand where technology involvement in human decisions is headed. Reading the chapters can help understand the opportunities and threats associated with the use of information technology in decision making. Computing and information technologies are reshaping our global society, but they can potentially reshape it in negative as well as positive ways. Analytics, collaboration and computerized decisio
Tags from this library: No tags from this library for this title. Log in to add tags.
Item type Current location Call number URL Status Date due Barcode
Electronic Book UT Tyler Online
Online
HD38.7 -- .R47 2015eb (Browse shelf) http://uttyler.eblib.com/patron/FullRecord.aspx?p=1968112 Available EBL1968112

Contents; About the Editors; Chapter 1: Introduction; 1.1 About AIS SIG DSA; Chapter 2: Big Data Panel at SIGDSS Pre-ICIS Conference 2013: A Swiss-Army Knife? The Profile of a Data Scientist; References; Chapter 3: Creating a Data-Driven Global Society; 3.1 Introduction; 3.2 Data Expansion and Decision Support; 3.3 Looking at Today; 3.4 Looking Forward; 3.5 Conclusions and Commentary; References; Chapter 4: Agile Supply Chain Decision Support System; 4.1 Introduction; 4.2 Literature Review; 4.2.1 Supplier Selection for Agile SCM Via fuzzy MCDM; 4.2.2 Fuzzy AHP; 4.2.3 Fuzzy TOPSIS

4.3 Framework of the Proposed Agile Supply Chain DSS4.3.1 Identification of Evaluation Criteria and Decision Hierarchy; 4.3.2 Identification of Evaluation Criteria Weights; 4.3.3 Supplier Identification, Evaluation, and Selection; 4.4 Assessing Business Impact of Agile Supply Chain DSS; 4.4.1 Simple Supply Chain Configuration; 4.4.2 Bullwhip Effect with Autocorrelation Coefficient and Desired Service Level; 4.4.3 The Pareto Fronts with Various Weights of Agility Criterion; 4.4.4 The Pareto Fronts with Various Weights of Agility Sub-Criteria; 4.5 Conclusion; References

Chapter 5: Hawkes Point Processes for Social Media Analytics5.1 Introduction; 5.2 Point Processes; 5.3 Hawkes Point Processes; 5.4 Hawkes Process Modeling Applications; 5.4.1 Finance; 5.4.2 Healthcare and Bioinformatics; 5.4.3 Sociology, Criminology and Terrorism; 5.4.4 Social Network Analysis; 5.5 Hawkes Process Applications in Social Media; 5.6 Conclusion; References; Chapter 6: Using Academic Analytics to Predict Dropout Risk in E-Learning Courses; 6.1 Introduction; 6.2 Literature Review; 6.2.1 Factors Affecting Student Retention and Dropout

6.2.2 Data Analysis and Mining Techniques in Dropout Prediction6.3 Research Approach; 6.3.1 Constructs and Variables; 6.3.2 Evaluation Criteria; 6.4 Data Analysis and Predictive Modeling; 6.4.1 SIS Data Analysis; 6.4.2 CMS Data Analysis; 6.4.3 Computation of Dynamic Risk Scores; 6.4.4 Evaluation of the Risk Scoring Model; 6.4.5 Comparison of Pre-census and Post-census Dropout Accuracy; 6.4.6 Recommendation for Deployment of Predictive Models; 6.4.7 Recommender System Architecture; 6.5 Conclusion; References

Chapter 7: Membership Reconfiguration in Knowledge Sharing Network: A Simulation Study7.1 Introduction; 7.2 Literature Review; 7.2.1 Social Behavior Theories, Collaboration Climates, and Knowledge Sharing; 7.2.2 Knowledge Sharing Bottlenecks and BIS Metric; 7.3 Framework of the Proposed KMS; 7.3.1 Rearrangement Propositions of CoP Members; 7.3.2 Knowledge Sharing Data Sets; 7.3.3 Simulation Experiments Setups; 7.4 Experimental Results: BIS Improvement and Network Structure; 7.4.1 Comparison of BIS Improvements and CoP Types Distribution; 7.4.2 Improvement of Collaboration Network Structures

7.5 Conclusion

This volume explores emerging research and pedagogy in analytics, collaboration, and decision support with an emphasis on business intelligence and social media. In general, the chapters help understand where technology involvement in human decisions is headed. Reading the chapters can help understand the opportunities and threats associated with the use of information technology in decision making. Computing and information technologies are reshaping our global society, but they can potentially reshape it in negative as well as positive ways. Analytics, collaboration and computerized decisio

Description based upon print version of record.

Author notes provided by Syndetics

<p> Lakshmi Iyer is an Associate Professor and Director of Graduate Programs in the Information Systems and Supply Chain Management Department, Bryan School of Business and Economics at the University of North Carolina Greensboro (UNCG). She received her Ph.D. in Business Administration from the University of Georgia, Athens, GA and her M.S. in Industrial Engineering from the University of Alabama, Tuscaloosa. Her research interests are in the area of business analytics, knowledge management, emerging technologies & its impact on organizations and users, and diversity in computing. Her research work has been published in or forthcoming in Communications of the AIS , Journal of Association for Information Systems, European Journal of Information Systems, Communications of the ACM, Decision Support Systems, eService Journal, Journal of Electronic Commerce Research, International Journal of Business Intelligence Research, Information Systems Management, Journal of Global Information Technology and Management, and others. She is a Board member of Teradata University Network, Chair of the Special Interest Group in Decision Support and Analytics (formerly SIGDSS), and served as research track co-chair for BI Congress.</p> <p>Dr. Iyer is also involved in community engaged outreach and scholarship that furthers the role of women in IT (wiit.uncg.edu). She is a member of the American Association of University Women (AAUW) and received the Dr. Shirley Hall Award from AAUW Greensboro Branch in April 2011 for exemplary contribution to enrich STEM education for women. She is founder and Director of the "IT is for Girls" at UNCG, an outreach program for middle and high-school girls that aims to increase their awareness about education and career paths in computing. She has received funding from AAUW, National Center for Women in IT (NCWIT) and from foundations to offer STEM events for young women. Dr. Iyer serves as a co-chair of the Association of Information Systems'' (AIS) task force on Women in IS to enhance the outreach efforts of AIS to women in Information Systems (IS) based on systematic assessment of the current status of women in IS, globally, including students (both current and potential) and professionals in academia, corporate, and non-profit organizations with the intent to creating a nurturing, supporting environment conducive to enhancing the growth and success of women in IS field.</p> <p> Daniel J. Power is a Professor of Management and Information Systems at the College of Business Administration at the University of Northern Iowa, Cedar Falls, Iowa and the Editor of DSSResources.COM, the Web-based knowledge repository about computerized systems that support decision making, the editor of PlanningSkills.COM, and the editor of DSS News, a bi-weekly e-newsletter. Dan writes a regular column in Decision Support News. Also, Dan is a blogger on the Business Intelligence Network.</p> <p>Since 1982, Daniel Power has published more than 50 articles, book chapters and proceedings papers. His articles have appeared in leading journals including Decision Sciences, Decision Support Systems, Journal of Decision Systems, MIS Quarterly, Academy of Management Review, Communications of the Association for Information Systems and Information and Management. He is also co-author of a book titled Strategic Management Skills and he has authored four books on computerized decision support. His DSS Concepts book (2002) titled Decision Support Systems: Concepts and Resources for Managers is a broad ranging scholarly handbook on the fundamentals of building decision support systems. His expanded DSS Framework has received widespread interest. His latest book from Business Expert Press is titled Decision Support, Analytics, and Business Intelligence.</p> <p>Professor Power is the Editor-in-Chief of the Journal of the Midwest Association for Information Systems (JMWAIS), a member of two academic journal editorial boards, and was the founding section editor of the ISWorld pages on Decision Support Systems Research and was founding Chair of the Association for Information Systems Special Interest Group on Decision Support and Analytics (SIG DSA). Also, Professor Power was the founding President of the Midwest United States Chapter of the Association for Information Systems (MWAIS) and served as the at-large member of the Board of Directors.</p> <p>In 1982, Professor Power received a Ph.D. in Business Administration from the University of Wisconsin-Madison. He was on the faculty at the University of Maryland-College Park from 1982 to 1989. Dr. Power has been a visiting lecturer at universities in China, Denmark, Ireland, Israel, and Russia. Power has consulted with a number of organizations and in Summer 2003 he was a Visiting Faculty Research Fellow with the U. S. Air Force Research Lab Information Directorate (AFRL/IF).</p> <p>Dr. Power is a pioneer developer of computerized decision aiding and support systems. During 1975-77, he developed a computerized system called DECAID, Decision AID. In 1981-83, he reprogrammed and expanded the system for the Apple II PC.</p>

There are no comments for this item.

Log in to your account to post a comment.