Advances in research methods for information systems research: data mining, data envelopment analysis, value focused thinking / edited by Kweku-Muata Osei-Bryson and Ojelanki Ngwenyama

Includes index.

Chapter1. Introduction -- Chapter2. Logical Foundations of Social Science Research -- Chapter3. Overview on Decision Tree Induction -- Chaptyer4. An Approach for Using Data Mining to Support Theory Development -- Chapter5. Application of a Hybrid Induction-Based Approach for Exploring Cumulative Abnormal Returns -- Chapter6. Ethnographic Decision Tree Modeling: An Exploration Cumulative Abnormal Returns -- Chapter7. Using Association Rules Mining to Facilitate Qualitative Data Analysis in Theory Building -- Chapter8. Overview on Multivariate Adaptive Regression Splines -- Chapter9. Reexamining the Impact of Information Technology Investment on Productivity Using Regression Tree and MARS -- Chapter10. Overview on Cluster Analysis -- Chapter11. Overview on Data Envelopment Analysis -- Chapter12. Exploring the ICT Utilization using Data Envelopment Analysis -- Chapter13. A DEA-centric Decision Support System for Monitoring Efficiency-Based Performance -- Chapter14. Overview on the Value Focused Thinking Methodology -- Chapter15. A Hybrid VFT-GQM Method for Developing Performance Criteria and Measures .

Advances in social science research methodologies and data analytic methods are changing the way research in information systems is conducted. New developments in statistical software technologies for data mining (DM) such as regression splines or decision tree induction can be used to assist researchers in systematic post-positivist theory testing and development. Established management science techniques like data envelopment analysis (DEA), and value focused thinking (VFT) can be used in combination with traditional statistical analysis and data mining techniques to more effectively explore behavioral questions in information systems research.