Guest editors’ introduction: is the HRM process important? Past, current, and future challenges

Ten years ago, Bowen and Ostroff (2004) criticized the one-sided focus on the content-based approach, where researchers take into account the inherent virtues (or vices) associated with the content of HR practices to explain performance. They explicitly highlight the role of the psychological processes through which employees attach meaning to HRM. In this first article of the special section entitled “Is the HRM Process Important?” we present an overview of past, current, and future challenges. For past challenges, we attempt to categorize the various research streams that originated from the seminal piece. To outline current challenges, we present the results of a content analysis of the original 15 articles put forward for the special section. In addition, we provide the overview of a caucus focused on this theme that was held at the Academy of Management annual meeting in Boston in 2012. In conclusion, we discuss future challenges relating to the HRM process approach and review the contributions that have been selected—against a competitive field—for this special issue

The 6P's of sales resource management

This paper will explore a data-driven approach called Sales Resource Management (SRM) that can provide real insight into sales management. The DSMT (Diagnosis, Strategy, Metrics and Tools) framework can be used to solve field sales management challenges. This paper focus on the 6P's strategy of SRM and illustrates how to use them to solve the CAPS (Concentration, Attrition, Performance and Spend) challenges. © 2010 IEEE.

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support Vector Machines (SVMs) are widely used classifiers for detecting physiological patterns in Human-Computer Interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the application of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables, and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.