Microfoundations of strategic management: Toward micro–macro research in the resource-based theory

Over the last few years, a new stream of research has emerged in the field of strategic management which focuses on the analysis of its microfoundations. This line of research analyzes strategic topics examining their foundations rooted in individual actions and interactions. The main purpose of this paper is to examine this emerging literature of microfoundations, indicating its usefulness and main characteristics. Through a systematic literature review, this paper contributes to the field by identifying the main areas studied, the benefits and potential of this approach, and some limitations and criticisms. Moreover, the paper studies how the integration of micro and macro aspects in strategy research may be carried out, examining several works that use a multilevel approach. Some methodological approaches that may help to effect this integration are indicated. These aspects will be analyzed in relation to the resource-based theory.

Algorithm for the detection of outliers based on the theory of rough sets

Outliers are objects that show abnormal behavior with respect to their context or that have unexpected values in some of their parameters. In decision-making processes, information quality is of the utmost importance. In specific applications, an outlying data element may represent an important deviation in a production process or a damaged sensor. Therefore, the ability to detect these elements could make the difference between making a correct and an incorrect decision. This task is complicated by the large sizes of typical databases. Due to their importance in search processes in large volumes of data, researchers pay special attention to the development of efficient outlier detection techniques. This article presents a computationally efficient algorithm for the detection of outliers in large volumes of information. This proposal is based on an extension of the mathematical framework upon which the basic theory of detection of outliers, founded on Rough Set Theory, has been constructed. From this starting point, current problems are analyzed; a detection method is proposed, along with a computational algorithm that allows the performance of outlier detection tasks with an almost-linear complexity. To illustrate its viability, the results of the application of the outlier-detection algorithm to the concrete example of a large database are presented.