Abstract
Purpose The purpose of this paper is to empirically test the relations among different knowledge management (KM) factors, such as human capital (HC), processes and information systems (IS) on organizational sustainable competitive advantage (CA), within the SMEs context. Design/methodology/approach Structured questionnaires were distributed to CEOs and managers of Portuguese organizations through an electronic survey. Partial least squares software was utilized to analyze the data. Findings The measurement model results identify and validate the dimensions of HC, processes and IS representing the KM construct. The structural model results demonstrate that HC and processes have a direct and significant impact on organizational CA, on the customer and financial dimensions, respectively. IS indirectly and significantly influence organizational CA, mediated by HC and processes. Research limitations/implications The sample size includes mostly service business and SMEs. Other organizations sectors, such as industry, should be analyzed in order to develop a comparative cross-sectorial study. Practical implications This study establishes suggestions for managers to make legitimate decisions concerning investments on knowledge assets and organizational capabilities that can foster business growth and sustainable CA within a SMEs context. Originality/value The authors propose a mediation mechanism showing that the relationship between IS and sustainable CA is not direct, but it is mediated by HC and processes. This mechanism points out some critical issues for the strategic knowledge and intellectual capital assets, as a source of organizational CA.

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Formal concept analysis (FCA) is largely applied in different areas. However, in some FCA applications the volume of information that needs to be processed can become unfeasible. Thus, the demand for new approaches and algorithms that enable processing large amounts of information is increasing substantially. This article presents a new algorithm for extracting proper implications from high-dimensional contexts. The proposed algorithm, called ImplicPBDD, was based on the Proplm algorithm, and uses a data structure called binary decision diagram (BDD) to simplify the representation of the formal context and enhance the extraction of proper implications. In order to analyze the performance of the ImplicPBDD algorithm, we performed tests using synthetic contexts varying the number of objects, attributes and context density. The experiments show that ImplicPBDD has a better performance-up to 80% faster-than its original algorithm, regardless of the number of attributes, objects and densities

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