Human resource management in India: strategy, performance and complementarity

This study seeks to explore which types of HR practice are associated with better organisational performance (OP). Whilst the core finding—that specific HR practices lead to better organisational outcomes may not be surprising—we also found an absence of complementarity. Normally, the absence of complementarities would suggest limitations in institutional supports; on the one hand, however, institutional shortfalls are not unique to India and may be encountered in many emerging market settings. In contrast, the great internal diversity of the Indian setting, with strong variations recognised amongst institutions, along with enforcement capabilities, might suggest that these tendencies are particularly pronounced. We also found a strong link between the intrinsic rewards and performance—an unexpected result in a low-income country, where wages are generally low. We suggest that this may reflect the nature of the labour market and the limited (and possibly proportionately shrinking) pool of good jobs, making exit a difficult option for all but the best qualified. Whilst this puts employees in a poor bargaining position in bidding-up pay (making pay rises seem unfeasible), the intrinsic attributes of the job become more important

The dimensions of personality in humans and other animals: A comparative and evolutionary perspective

This paper considers the structure and proximate mechanisms of personality in humans and other animals. Significant similarities were found between personality structures and mechanisms across species in at least two broad traits: Extraversion and Neuroticism. The factor space tapped by these personality dimensions is viewed as a general integrative framework for comparative and evolutionary studies of personality in humans and other animals. Most probably, the cross-species similarities between the most broad personality dimensions like Extraversion and Neuroticism as well as other Big Five factors reflect conservative evolution: constrains on evolution imposed by physiological, genetic and cognitive mechanisms. Lower-order factors, which are more species- and situation-specific, would be adaptive, reflecting correlated selection on and trade-offs between many traits

Comparative psychometrics: establishing what differs is central to understanding what evolves

Cognitive abilities cannot be measured directly. What we can measure is individual variation in task performance. In this paper, we first make the case for why we should be interested in mapping individual differences in task performance on to particular cognitive abilities: we suggest that it is crucial for examining the causes and consequences of variation both within and between species. As a case study, we examine whether multiple measures of inhibitory control for non-human animals do indeed produce correlated task performance; however, no clear pattern emerges that would support the notion of a common cognitive ability underpinning individual differences in performance. We advocate a psychometric approach involving a three-step programme to make theoretical and empirical progress: first, we need tasks that reveal signature limits in performance. Second, we need to assess the reliability of individual differences in task performance. Third, multi-trait multi-method test batteries will be instrumental in validating cognitive abilities. Together, these steps will help us to establish what varies between individuals that could impact their fitness and ultimately shape the course of the evolution of animal minds. Finally, we propose executive functions, including working memory, inhibitory control and attentional shifting, as a sensible starting point for this endeavour

Reliability and validity in comparative studies of software prediction models

Empirical studies on software prediction models do not converge with respect to the question "which prediction model is best?" The reason for this lack of convergence is poorly understood. In this simulation study, we have examined a frequently used research procedure comprising three main ingredients: a single data sample, an accuracy indicator, and cross validation. Typically, these empirical studies compare a machine learning model with a regression model. In our study, we use simulation and compare a machine learning and a regression model. The results suggest that it is the research procedure itself that is unreliable. This lack of reliability may strongly contribute to the lack of convergence. Our findings thus cast some doubt on the conclusions of any study of competing software prediction models that used this research procedure as a basis of model comparison. Thus, we need to develop more reliable research procedures before we can have confidence in the conclusions of comparative studies of software prediction models

Structure Selection of Polynomial NARX Models using Two Dimensional (2D) Particle Swarms

The present study applies a novel two-dimensional learning framework (2D-UPSO) based on particle swarms for structure selection of polynomial nonlinear auto-regressive with exogenous inputs (NARX) models. This learning approach explicitly incorporates the information about the cardinality (i.e., the number of terms) into the structure selection process. Initially, the effectiveness of the proposed approach was compared against the classical genetic algorithm (GA) based approach and it was demonstrated that the 2D-UPSO is superior. Further, since the performance of any meta-heuristic search algorithm is critically dependent on the choice of the fitness function, the efficacy of the proposed approach was investigated using two distinct information theoretic criteria such as Akaike and Bayesian information criterion. The robustness of this approach against various levels of measurement noise is also studied. Simulation results on various nonlinear systems demonstrate that the proposed algorithm could accurately determine the structure of the polynomial NARX model even under the influence of measurement noise

An investigation of machine learning based prediction systems

Traditionally, researchers have used either o�f-the-shelf models such as COCOMO, or developed local models using statistical techniques such as stepwise regression, to obtain software eff�ort estimates. More recently, attention has turned to a variety of machine learning methods such as artifcial neural networks (ANNs), case-based reasoning (CBR) and rule induction (RI). This paper outlines some comparative research into the use of these three machine learning methods to build software e�ort prediction systems. We briefly describe each method and then apply the techniques to a dataset of 81 software projects derived from a Canadian software house in the late 1980s. We compare the prediction systems in terms of three factors: accuracy, explanatory value and configurability. We show that ANN methods have superior accuracy and that RI methods are least accurate. However, this view is somewhat counteracted by problems with explanatory value and configurability. For example, we found that considerable eff�ort was required to configure the ANN and that this compared very unfavourably with the other techniques, particularly CBR and least squares regression (LSR). We suggest that further work be carried out, both to further explore interaction between the enduser and the prediction system, and also to facilitate configuration, particularly of ANNs