New Directions in Compensation Research: Synergies, Risk, and Survival

We describe and use two theoretical frameworks, the resource-based view of the firm and institutional theory, as lenses for examining three promising areas of compensation research. First, we examine the nature of the relationship between pay and effectiveness. Does pay typically have a main effect or, instead, does the relationship depend on other human resource activities and organization characteristics? If the latter is true, then there are synergies between pay and these other factors and thus, conclusions drawn from main effects models may be misleading. Second, we discuss a relatively neglected issue in pay research, the concept of risk as it applies to investments in pay programs. Although firms and researchers tend to focus on expected returns from compensation interventions, analysis of the risk, or variability, associated with these returns may be essential for effective decision-making. Finally ,pay program survival, which has been virtually ignored in systematic pay research, is investigated. Survival appears to have important consequences for estimating pay plan risk and returns, and is also integral to the discussion of pay synergies. Based upon our two theoretical frameworks, we suggest specific research directions for pay program synergies, risk, and survival

Peeling Back the Onion Competitive Advantage Through People: Test of a Causal Model

Proponents of the resource-based view (RBV) of the firm have identified human resource management (HRM) and human capital as organizational resources that can contribute to sustainable competitive success. A number of empirical studies have documented the relationship between systems of human resource policies and practices and firm performance. The mechanisms by which HRM leads to firm performance, however, remain largely unexplored. In this study, we explore the pathways leading from HRM to firm performance. Specifically, we use structural equation modeling to test a model positing a set of causal relationships between high performance work systems (HPWS), employee retention, workforce productivity and firm market value. Within a set of manufacturing firms, results indicate the primary impact of HPWS on productivity and market value is through its influence on employee retention

Toward a Strategic Human Resource Management Model of High Reliability Organization Performance

In this article, we extend strategic human resource management (SHRM) thinking to theory and research on high reliability organizations (HROs) using a behavioral approach. After considering the viability of reliability as an organizational performance indicator, we identify a set of eight reliability-oriented employee behaviors (ROEBs) likely to foster organizational reliability and suggest that they are especially valuable to reliability seeking organizations that operate under “trying conditions”. We then develop a reliability-enhancing human resource strategy (REHRS) likely to facilitate the manifestation of these ROEBs. We conclude that the behavioral approach offers SHRM scholars an opportunity to explain how people contribute to specific organizational goals in specific contexts and, in turn, to identify human resource strategies that extend the general high performance human resource strategy (HPHRS) in new and important ways

Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera

Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation. The performance of a thermal error compensation system depends on the accuracy and robustness of the thermal error model and the quality of the inputs to the model. The location of temperature measurement must provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results. In this paper, a new intelligent compensation system for reducing thermal errors of machine tools using data obtained from a thermal imaging camera is introduced. Different groups of key temperature points were identified from thermal images using a novel schema based on a Grey model GM (0, N) and Fuzzy c-means (FCM) clustering method. An Adaptive Neuro-Fuzzy Inference System with Fuzzy c-means clustering (FCM-ANFIS) was employed to design the thermal prediction model. In order to optimise the approach, a parametric study was carried out by changing the number of inputs and number of membership functions to the FCM-ANFIS model, and comparing the relative robustness of the designs. According to the results, the FCM-ANFIS model with four inputs and six membership functions achieves the best performance in terms of the accuracy of its predictive ability. The residual value of the model is smaller than ± 2 μm, which represents a 95% reduction in the thermally-induced error on the machine. Finally, the proposed method is shown to compare favourably against an Artificial Neural Network (ANN) model