A Critical Review of the Literature and Practice of Competency Modelling

Competency models are commonly practiced today in many organizations as they lead to significant human resource development that provides organizations with a competitive edge. Because of their immense importance, measurement and modelling of competencies has become an important research field. However, despite the extensive research, there are large research gaps regarding the empirical knowledge and applicability of competency models. This article presents a critical review of competency modelling literature and practice from the major perspectives (including applied, academic, and professional) in an attempt to shed additional light on the advantages and practices of competency modelling, as well as outlining current challenges in such a vibrant domain. The intention, in this article, was to build a coherent argument with an objective of illustrating the effective use, as well as deficiencies in this domain based on aggregated experiences of many authors across many years and settings. The author explicitly acknowledges that the approach for this critical review has many limitations, since it is experience-based rather than empirically based. Yet, it is believed that this article may provide a framework that can lead to a solid investigation of competency modelling with more rigor than they have been afforded to date.     Keywords: Competency Modelling, Behavioural Paradigm, Job Analysis, Performance, NViv

HOW CAN PD PROCESS MODELLING BE MADE MORE USEFUL? AN EXPLORATION OF FACTORS WHICH INFLUENCE MODELLING UTILITY

In what sense is PD process modelling useful? and how can the utility of modelling be improved? In this paper, we approach these questions through an analysis of PD process modelling ‘utility’ – which in broad terms we consider to be the degree to which a model-based approach or modelling intervention benefits practice. We view the utility of modelling as a composite characteristic which depends both on the properties of models and on the way they are applied. The paper draws upon established principles of cybernetic systems in an attempt to explain the role played by process modelling in operating and improving PD processes. We use this framework to identify eight key factors which influence the utility of modelling in the context of use. Further, we indicate how these factors can be interpreted to identify opportunities to improve modelling utility.International Design Conference - DESIGN 201

Advances and visions in large-scale hydrological modelling: findings from the 11th Workshop on Large-Scale Hydrological Modelling

Large-scale hydrological modelling has become increasingly wide-spread during the last decade. An annual workshop series on large-scale hydrological modelling has provided, since 1997, a forum to the German-speaking community for discussing recent developments and achievements in this research area. In this paper we present the findings from the 2007 workshop which focused on advances and visions in large-scale hydrological modelling. We identify the state of the art, difficulties and research perspectives with respect to the themes "sensitivity of model results", "integrated modelling" and "coupling of processes in hydrosphere, atmosphere and biosphere". Some achievements in large-scale hydrological modelling during the last ten years are presented together with a selection of remaining challenges for the future

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Decision-focussed resource modelling for design decision support

Resource management including resource allocation, levelling, configuration and monitoring has been recognised as critical to design decision making. It has received increasing research interests in recent years. Different definitions, models and systems have been developed and published in literature. One common issue with existing research is that the resource modelling has focussed on the information view of resources. A few acknowledged the importance of resource capability to design management, but none has addressed the evaluation analysis of resource fitness to effectively support design decisions. This paper proposes a decision-focused resource model framework that addresses the combination of resource evaluation with resource information from multiple perspectives. A resource management system constructed on the resource model framework can provide functions for design engineers to efficiently search and retrieve the best fit resources (based on the evaluation results) to meet decision requirements. Thus, the system has the potential to provide improved decision making performance compared with existing resource management systems

Freshwater ecosystem services in mining regions : modelling options for policy development support

The ecosystem services (ES) approach offers an integrated perspective of social-ecological systems, suitable for holistic assessments of mining impacts. Yet for ES models to be policy-relevant, methodological consensus in mining contexts is needed. We review articles assessing ES in mining areas focusing on freshwater components and policy support potential. Twenty-six articles were analysed concerning (i) methodological complexity (data types, number of parameters, processes and ecosystem-human integration level) and (ii) potential applicability for policy development (communication of uncertainties, scenario simulation, stakeholder participation and management recommendations). Articles illustrate mining impacts on ES through valuation exercises mostly. However, the lack of ground-and surface-water measurements, as well as insufficient representation of the connectivity among soil, water and humans, leave room for improvements. Inclusion of mining-specific environmental stressors models, increasing resolution of topographies, determination of baseline ES patterns and inclusion of multi-stakeholder perspectives are advantageous for policy support. We argue that achieving more holistic assessments exhorts practitioners to aim for high social-ecological connectivity using mechanistic models where possible and using inductive methods only where necessary. Due to data constraints, cause-effect networks might be the most feasible and best solution. Thus, a policy-oriented framework is proposed, in which data science is directed to environmental modelling for analysis of mining impacts on water ES

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Modelling iteration in engineering design

This paper examines design iteration and its modelling in the simulation of New Product Development (NPD) processes. A framework comprising six perspectives of iteration is proposed and it is argued that the importance of each perspective depends upon domain-specific factors. Key challenges of modelling iteration in process simulation frameworks such as the Design Structure Matrix are discussed, and we argue that no single model or framework can fully capture the iterative dynamics of an NPD process. To conclude, we propose that consideration of iteration and its representation could help identify the most appropriate modelling framework for a given process and modelling objective, thereby improving the fidelity of design process simulation models and increasing their utility