Municipal benchmarking:organisational learning and network performance in the public sector

The main purpose of this dissertation is to assess the relation between municipal benchmarking and organisational learning with a specific emphasis on benchlearning and performance within municipalities and between groups of municipalities in the building and housing sector in the Netherlands. The first and main conclusion is that this relation exists, but that the relative success of different approaches to dimensions of change and organisational learning are a key explanatory factor for differences in the success of benchlearning. Seven other important conclusions could be derived from the empirical research. First, a combination of interpretative approaches at the group level with a mixture of hierarchical and network strategies, positively influences benchlearning. Second, interaction among professionals at the inter-organisational level strengthens benchlearning. Third, stimulating supporting factors can be seen as a more important strategy to strengthen benchlearning than pulling down barriers. Fourth, in order to facilitate benchlearning, intrinsic motivation and communication skills matter, and are supported by a high level of cooperation (i.e., team work), a flat organisational structure and interactions between individuals. Fifth, benchlearning is facilitated by a strategy that is based on a balanced use of episodic (emergent) and systemic (deliberate) forms of power. Sixth, high levels of benchlearning will be facilitated by an analyser or prospector strategic stance. Prospectors and analysers reach a different learning outcome than defenders and reactors. Whereas analysers and prospectors are willing to change policies when it is perceived as necessary, the strategic stances of defenders and reactors result in narrow process improvements (i.e., single-loop learning). Seventh, performance improvement is influenced by functional perceptions towards performance, and these perceptions ultimately influence the elements adopted. This research shows that efforts aimed at benchlearning and ultimately improved service delivery, should be directed to a multi-level and multi-dimensional approach addressing the context, content and process of dimensions of change and organisational learning.

Green operations initiatives in the automotive industry:an environmental reports analysis and benchmarking study

Purpose: The purpose of this paper is to focus on investigating and benchmarking green operations initiatives in the automotive industry documented in the environmental reports of selected companies. The investigation roadmaps the main environmental initiatives taken by the world's three major car manufacturers and benchmarks them against each other. The categorisation of green operations initiatives that is provided in the paper can also help companies in other sectors to evaluate their green practices. Design/methodology/approach: The first part of the paper is based on existing literature on the topic of green and sustainable operations and the "unsustainable" context of automotive production. The second part relates to the roadmap and benchmarking of green operations initiatives based on an analysis of secondary data from the automotive industry. Findings: The findings show that the world's three major car manufacturers are pursuing various environmental initiatives involving the following green operations practices: green buildings, eco-design, green supply chains, green manufacturing, reverse logistics and innovation. Research limitations/implications: The limitations of this paper start from its selection of the companies, which was made using production volume and country of origin as the principal criteria. There is ample evidence that other, smaller, companies are pursuing more sophisticated and original environmental initiatives. Also, there might be a gap between what companies say they do in their environmental reports and what they actually do. Practical implications: This paper helps practitioners in the automotive industry to benchmark themselves against the major volume manufacturers in three different continents. Practitioners from other industries will also find it valuable to discover how the automotive industry is pursuing environmental initiatives beyond manufacturing, apart from the green operations practices covering broadly all the activities of operations function. Originality/value: The originality of the paper is in its up-to-date analysis of environmental reports of automotive companies. The paper offers value for researchers and practitioners due to its contribution to the green operations literature. For instance, the inclusion of green buildings as part of green operations practices has so far been neglected by most researchers and authors in the field of green and sustainable operations. © Emerald Group Publishing Limited.

Self-assessment benchmarking as a quality management tool in primary healthcare

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Innovation and performance:benchmarking Australian firms

This article empirically analyses the link between innovation and performance using a sample of large Australian firms, with a specific aim of developing benchmarking tools. Innovation is measured by firms' investment in R&D and applications for patents, trademarks and designs. An innovation index is constructed to provide one method of benchmarking. The index incorporates a firm's innovative activities into a single figure after accounting for firm size. The index provides a ranking of the most innovative firms in Australia. A second method of benchmarking uses a stochastic production frontier. This type of analysis identifies the firms which are located closest to a ‘best practice innovation frontier’.

Benchmarking of distributed computing engines spark and GraphLab for big data analytics

In this paper we evaluate and compare two representativeand popular distributed processing engines for large scalebig data analytics, Spark and graph based engine GraphLab. Wedesign a benchmark suite including representative algorithmsand datasets to compare the performances of the computingengines, from performance aspects of running time, memory andCPU usage, network and I/O overhead. The benchmark suite istested on both local computer cluster and virtual machines oncloud. By varying the number of computers and memory weexamine the scalability of the computing engines with increasingcomputing resources (such as CPU and memory). We also runcross-evaluation of generic and graph based analytic algorithmsover graph processing and generic platforms to identify thepotential performance degradation if only one processing engineis available. It is observed that both computing engines showgood scalability with increase of computing resources. WhileGraphLab largely outperforms Spark for graph algorithms, ithas close running time performance as Spark for non-graphalgorithms. Additionally the running time with Spark for graphalgorithms over cloud virtual machines is observed to increaseby almost 100% compared to over local computer clusters.