Experimental Research Methods in Business and Organizations
The author provides a survey of the literature illustrating applied experimental research methods in cross-sections of business and organization types. The advantages and disadvantages of the experimental research methods are discussed for each of the examples provided which run the gamut from depression-era agricultural economics to research conducted for the National Science Institute. While the article focuses on business research methods, the range of examples from multiple disciplines serves to demonstrate the adaptability of various methods to distinct contexts, the importance of thoughtfully developed research questions, and perceptions in the field regarding scientific rigor. The article is intended to guide students in their exploration of the breadth and depth of experimental research methods and to convey a sense of the challenges of applied scientific inquiry.
Key words: Experimental research, quasi-experimental research, open innovation, market research, operations management, organization development, scientific inquiry.
Innovation, Entrepreneurship, and Predictive Technology
Predictive technologies focus on using more computing power and technology to develop systems that predict routes, behavior, inventory, patterns, etc. Computer technology has improved over time. When we consider that the average SmartPhone has more computing power than all of NASA's first Apollo missions, we get the idea of access. If we look at Moore's law, every 18 months, the industry changes drastically (unexpected and planned changes). This allows for the development of predictive technologies to roll out in all sorts of products: coffee makers, the home (lighting, music, heating predicted when you are almost home), transportation, etc.
Harnessing Unstructured Data in Radiology: NLP, RadLex & AIM
When it comes to the harnessing of unstructured data in radiology, it is very important to consider how much value that data will provide. In many cases, there is information in that data that can be valuable to the case and the patient, but only if the data is located and used correctly. Using Natural Language Processing (NLP) can help collect and process unstructured data from radiology reports, but there are difficulties with the accuracy of NLP in many cases, and that poses a big concern from a patient safety standpoint.