Management

Development of social capital through effective relationship-building among organizational members is an important but under-explored area in strategic human resource management (HRM) research. The systematic generation of social capital through HR practices offers many opportunities to demonstrate how strategic HR can produce competitive advantage. The present paper establishes a framework for
building a relationship-based HR architecture and identifies the variables, processes, and practices involved in such an approach. We begin by discussing the relationship among social capital, strategic HRM, and leader-member exchange to demonstrate the importance of relationships for HRM. We then present a model of relationship-building through role-making to help understand how to generate effective
relationships. New concepts in role-making are introduced, and relational necessities of respect, trust, and obligation are discussed. These concepts are applied to specific HR functions. Propositions are offered as a base to extend and test hypotheses regarding the competitive contributions of research on relational theory within organizational contexts.

PurposeThe purpose of this paper is to compare the performance of neural networks (NNs) and support vector machines (SVMs) as text classifiers. SVMs are considered one of the best classifiers. NNs could be adopted as text classifiers if their performance is comparable to that of SVMs.Design/methodology/approachSeveral NNs are trained to classify the same set of text documents with SVMs and their effectiveness is measured. The performance of the two tools is then statistically compared.FindingsFor text classification (TC), the performance of NNs is statistically comparable to that of the SVMs even when a significantly reduced document size is used.Practical implicationsThis research finds not only that NNs are very viable TC tools with comparable performance to SVMs, but also that it does so using a much reduced size of document. The successful use of NNs in classifying reduced text documents would be its great advantage as a classification tool, compared to others, as it can bring g...