Reshaping Society through Analytics, Collaboration, and Decision Support: Role of Business Intelligence and Social Media (Annals of Information Systems, Volume 18)
Format: PDF / Kindle (mobi) / ePub
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 decision support are powerful decision aiding and decision making tools that have enormous potential to impact crisis decision making, regulation of financial systems, healthcare decision making and many more important decision domains.
Many information technologies can potentially support, assist and even decide for human decision makers. Despite the potential, some researchers think that we know the answers to how these technologies will change society. The "Wisdom of Crowds" or "Big Data" become the topic of the day and are soon replaced with new marketing terms. In many ways, mobile technology is just another form factor to adapt decision support capabilities too and experiment with new capabilities. The cloud is a nebulous metaphor that adds to the mystery of information technology. Wireless technology enables the ubiquitous presence of analytics and decision support. With new networking capabilities, collaboration is possible anywhere and everywhere using voice, video and text. Documents can be widely shared and massive numbers of documents can be carried on a small tablet computer. Recent developments in technologies impact the processes organizations use to make decisions. In addition, academics are looking for ways to enhance their pedagogy to train students to be more adept in understanding how emerging technology will be used effectively for decision making in organizations.
The chapters are based on papers originally reviewed at the Special Interest Group on Decision Support Systems (SIGDSS) Workshop at the 2013 International Conference on Information Systems (ICIS 2013). Ultimately this volume endeavors to find a balance between systematizing what we know, so we can teach our findings from prior research better, and stimulating excitement to move the field in new directions.
students drop out by day 10 – the institution’s census date. However, it is important to note that once the model is built, an institution (including staff and instructors) could customize the model for use on any selected course dates. For example, this model could be customized to 82 R. Bukralia et al. Table 6.7 Descriptive statistics of the 3-Day CMS dataset numeric variables. n = 592 No. Total logins Course level Total time spent in minutes Credit hours Days since last login Course status
risk of dropout (higher likelihood of course completion). All 205 records were individually reviewed to examine the baseline risk score against course status. Accuracy_Baseline_Score provides information about whether the prediction was correct or not using the following rule: =IF(OR(AND(Baseline_Risk_Score>=50,Course_ Status=0),AND(Baseline_Risk_Score<50,Course_Status=1)),"Correct", "Incorrect") The above rule explains that the variable Accuracy_Baseline_Score was coded as “Correct” if
(Cowie and Lehnert 1996). In this paper, we focus on the knowledge-based IE approaches that are suitable for contexts where assumptions and relationships among the different domain concepts already exist in the form of domain expertise, although they may or may not exist in an explicitly structured format (e.g., the financial service example described above). By encapsulating the domain expertise in the form of domain ontologies, IE can be performed to leverage this domain expertise to discover
Cimiano, P., Racioppa, S., & Siegel, M. (2006). Ontology-based information extraction with SOBA. In Proceedings of the international conference on language resources and evaluation. Genoa, Italy Celjuska, D., & Vargas-Vera, M. (2004). Ontosophie: A semi-automatic system for ontology population from text. In Proceedings international conference on natural language processing ICON 2004. UK: Knowledge Media Institute, The Open University. Chen, Y.-J. (2010). Development of a method for
-EDICATION errors with electronic prescribing (Ep): Two views of the same picture. BMC Health Services Research, 10 n 3ETIA 0 3ETIA - +RISHNAN 2 3AMBAMURTHY 6 4HE EFFECTS OF THE ASSIMILATION AND USE of it applications on financial performance in healthcare organizations. Journal of the Association for Information Systems, 12 n 3HARKEY 3 (UDAK 3 (ORN 3 $ "ARRETT 2 3PECTOR 7 ,IMCANGCO 2 %XPLORATORY study of nursing home