Resumen de The use of sparse inverse covariance estimation for relationship detection and hypothesis generation in strategic management
Mei Li, Ying Lin, Shuai Huang, Craig Crossland
Research summary: This paper uses Sparse Inverse Covariance Estimation (SICE) to advance strategic management research, focusing on an application of exploratory SICE techniques to generate novel, testable hypotheses. We demonstrate how SICE can identify intrinsic relationships among variables, especially within large, high-dimension–low-observation datasets. We also discuss the strengths and limitations of SICE, as well as the most appropriate uses of these techniques. We conclude with a detailed illustration of SICE analysis using the High Performance Manufacturing dataset.
Managerial summary: Most academic research in strategic management is based on formal hypotheses, which are testable statements of the relationships linking two or more variables. Researchers currently use several approaches to generate hypotheses, but all have different shortcomings. In our study, we demonstrate how researchers can use a set of quantitative techniques known as Sparse Inverse Covariance Estimation (SICE) to generate new hypotheses from large datasets.
