Technological capability, relational capability and firms’ performance The role of learning capability

Purpose – The purpose of this paper is to empirically evaluate the mediating role of learning capability on the relationship between technological capability, relational capability and small and medium enterprises (SMEs) performance in developing economy of Africa. Design/methodology/approach – A quantitative survey design was employed to collect the data from owner/manager of manufacturing SMEs in Nigeria. Partial least square structural equation model was used in the evaluation of both the measurement and structural models to determine the reliability and validity of the measurement and test the hypotheses, respectively. Findings – The statistical result indicates a positive relationship between technological capability, learning capability and SMEs performance. Equally, relational capability significantly and positively relates to SMEs learning capability. However, relational capability negatively relates to SMEs performance, while technological capability also negatively relates to learning capability. Furthermore, learning capability mediates the negative relationship of relational capability and SMEs performance to significant positive relationship, while it does not mediate the relationship of technological capability and performance. Research limitations/implications – The analysis of this study is restricted to only resource-based view and dynamic capability theory. Data of the study were collected once a time on a self-reported technique. The study contributed significantly to the body literature on technological and relational capabilities and performance. It also demonstrated the need for SMEs manager to recognize and appreciate the roles of these strategic capabilities in achieving sustainable competitive position. Practical implications – Through relational capability SMEs develops efficient collaborative relationship to acquire new techniques, knowledge. This is specifically, essential for SMEs firms from less developing and emerging economies as they are lagging behind at the global competitive platform, and that the possession of specific advantage locally may not be adequately enough to help penetrate the global markets. Similarly, technological capability enable firms to identify acquire and apply new external knowledge to develop operational competencies which may lead to the attainment of superior performance. Social implications – Government policies and programs designed to support technological development and innovation must be adjusted to consider the peculiar nature of SMEs firms in terms of technology and innovativeness that enhances competitive position and performance. Originality/value – This study empirically examined the relationship of technological and relational capabilities and the SMEs learning capability and performance

Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

Semantics, Modelling, and the Problem of Representation of Meaning -- a Brief Survey of Recent Literature

Over the past 50 years many have debated what representation should be used to capture the meaning of natural language utterances. Recently new needs of such representations have been raised in research. Here I survey some of the interesting representations suggested to answer for these new needs.Comment: 15 pages, no figure