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This article reviews the extensive history of dynamic performance research, with the goal of providing a clear picture of where the field has been, where it is now, and where it needs to go. Past research has established that job performance does indeed change, but the implications of this dynamism and the predictability of performance trends remain unresolved. Theories are available to help explain dynamic performance, and although far from providing an unambiguous understanding of the phenomenon, they offer direction for future theoretical development. Dynamic performance research does suffer from a number of methodological difficulties, but new techniques have emerged that present even more opportunities to advance knowledge in this area. From this review, I propose research questions to bridge the theoretical and methodological gaps of this area. Answering these questions can advance both research involving job performance prediction and our understanding of the effects of human resource interventions.

We discuss recent methodological advances in the NK modeling in the Strategy literature and analyze issues related to its current use including different implementation algorithms, relative versus absolute performance, establishing significance of simulation results and long- versus short-term performance measurements. To facilitate cross-pollination of ideas, we point to advances and extensions of the model developed in other fields that could be effectively utilized to answer Strategy-related questions. These include modeling the strength of interaction, varying the importance of decision elements, utilizing alternative functional forms, incorporating endogeneity in N and K parameters and embedding the NK model in a broader dynamic framework.

Pew and Mavor (1998) called for an integrative representation of human behavior for use in models of individual combatants and organizations. Models with integrated representation of behavior have only been achieved at rudimentary levels according to those performing the studies (e.g. Pew & Mavor, 1998; Tulving, 2002) and those building the models (e.g. Warwick et al., 2002). This chapter will address aspects of cognitive performance that are important to incorporate into models of combat based on acceptance of theory, strength of empirical data, or for other reasons such as to bridge gaps where incomplete knowledge exists about cognitive behavior and performance. As a starting point, this chapter will assess which of Pew and Mavor’s recommendations are still appropriate as determined by a review of selected literature on cognition and its representation. We will also provide some review and extensions of key literature on cognition and modeling and suggest a way ahead to close the remaining gaps. Different aspects of cognition are described with recent findings, and most are followed by an example of how they have been represented in computer models or a discussion of challenges to their representation in modeling.

In recent times, there has been a lot of research focused on performance measurement (PM) in project-based sectors. However, there are very few studies that were reported on the significance of PM in the construction sector. Keeping track of an organization in achieving organizations goals and objectives seems an important way. One of the major challenges faced by the industry is unavailable of an appropriate PM system for assessing organizational performance. Most of the PM approaches consider the traditional project triangle assessment of project success. Based on the limitations identified in existing PM models, the purpose of this paper is to develop a comprehensive PM model, i.e. Modified Project Quarter Back Rating (MPQR) applicable for construction projects.

A detailed list of performance areas as a method for PM is analyzed in the construction industry context. Also, industry-specific professionals conducted semi-structured interviews to assess whether these performance areas are sufficient to measure and understand the PM systems.

The research finding focuses on developing the MPQR model that considers both financial and non-financial areas for performance assessment to provide a holistic assessment of project performance.

MPQR model provides an opportunity to set the benchmark for overall performance for construction organizations.

The findings of the study are expected to provide guidelines to construction professionals for implementing the performance model that will improve performance in the construction industry.

The success of construction projects can be indicated by measuring their performance. For effective project performance (PP), the successful execution of a construction projects is very important. A systematic review of the literature on performance areas and performance assessment models was undertaken. The purpose of this paper is to develop a mathematical formulation for construction PP areas to suit the Indian context by modifying the current project quarterback rating (PQR) model.

Based on the literature, the PQR model has not been validated for suitability in the Indian context. To validate the PQR model and modify the same for the Indian context, a survey instrument was used to collect data on performance areas and a multivariate data analysis technique was carried out to develop a modified model. Delphi technique was used to assign the weights for each performance metric in performance areas.

This study concluded the importance of three additional performance areas, namely, productivity, stakeholder satisfaction and environment for assessing PP for Indian construction projects. It also identified the interrelationship between the performance areas and the PP.

The developed modified PQR model (MPQR) will guide the concerned stakeholders to take corrective actions for improving the performance of construction projects.

The MPQR proposed in this paper covers ten areas and is a comprehensive single score that can be used to benchmark and compare performance over different projects to achieve continuous improvement.

Improving the performance of health care organizations is now perceived as essential in order to better address the needs of the populations and respect their ability to pay for the services. There is no consensus on what is performance. It is increasingly considered as the optimal execution of four functions that every organization must achieve in order to survive and develop: reach goals; adapt to its environment; produce goods or services and maintain values; and a satisfying organizational climate. There is also no consensus on strategies to improve this performance. The paper aims to discuss these issues.

This paper intends to analyze the performance of primary health care organizations from the perspective of Kauffman’s model. It mainly aims to understand the often contradictory, paradoxical and unexpected results that emerge from studies on this topic.

To do so, the first section briefly presents Kauffman’s model and lays forward its principal components. The second section presents three studies on the performance of primary organizations and brings out the contradictory, paradoxical and unexpected results they obtained. The third section explains these results in the light of Kauffman’s model.

Kauffman’s model helps give meaning to the results of researches on performance of primary health care organizations that were qualified as paradoxical or unexpected. The performance of primary health care organizations then cannot be understood by only taking into account the characteristics of these organizations. The complexity of the environments in which they operate must simultaneously be taken into account. This paper brings original development of an integrated view of the performance of organizations, their own characteristics and those of the local environment in which they operated.

The purpose of this paper is to assess the performance of competing methods and model selection, which are non‐trivial issues given the financial implications. Researchers have adopted various methods including statistical models and machine learning methods such as neural networks to assist decision making in direct marketing. However, due to the different performance criteria and validation techniques currently in practice, comparing different methods is often not straightforward.

This study compares the performance of neural networks with that of classification and regression tree, latent class models and logistic regression using three criteria – simple error rate, area under the receiver operating characteristic curve (AUROC), and cumulative lift – and two validation methods, i.e. bootstrap and stratified k‐fold cross‐validation. Systematic experiments are conducted to compare their performance.

The results suggest that these methods vary in performance across different criteria and validation methods. Overall, neural networks outperform the others in AUROC value and cumulative lifts, and the stratified ten‐fold cross‐validation produces more accurate results than bootstrap validation.

To select predictive models to support direct marketing decisions, researchers need to adopt appropriate performance criteria and validation procedures.

The study addresses the key issues in model selection, i.e. performance criteria and validation methods, and conducts systematic analyses to generate the findings and practical implications.

Our paper contrasts and validates the relevance of Miles and Snow (1978) and Robinson and Pearce (1988) strategic models, and their causal relationships with performance. The empirical study was carried out on a sample of 189 enterprises from the Spanish hospitality sector. The results confirm the relevance of both models, and the importance of the different strategies as a source to explain performance. The analysis uses structural equation models and variance analysis (ANOVA) methodologies.