Shaping strategic action through the rhetorical construction and exploitation of ambiguity

This paper extends existing understandings of how actors' constructions of ambiguity shape the emergent process of strategic action. We theoretically elaborate the role of rhetoric in exploiting strategic ambiguity, based on analysis of a longitudinal case study of an internationalization strategy within a business school. Our data show that actors use rhetoric to construct three types of strategic ambiguity: protective ambiguity that appeals to common values in order to protect particular interests, invitational ambiguity that appeals to common values in order to invite participation in particular actions, and adaptive ambiguity that enables the temporary adoption of specific values in order to appeal to a particular audience at one point in time. These rhetorical constructions of ambiguity follow a processual pattern that shapes the emergent process of strategic action. Our findings show that (1) the strategic actions that emerge are shaped by the way actors construct and exploit ambiguity, (2) the ambiguity intrinsic to the action is analytically distinct from ambiguity that is constructed and exploited by actors, and (3) ambiguity construction shifts over time to accommodate the emerging pattern of actions.

Dynamic client portfolios as sources of ambidexterity:exploration and exploitation within and across client relationships

This paper explains how dynamic client portfolios can be a source of ambidexterity (i.e., exploration and exploitation) for knowledge-intensive firms (KIFs). Drawing from a unique qualitative dataset of firms in the global reinsurance market, we show how different types of client relationships underpin a dynamic client portfolio and become a source of ambidexterity for a KIF. We develop a process model to show how KIFs attain knowledge by segmenting their client portfolios, use that knowledge to explore and exploit within and across their client relationships, and dynamically adjust their client portfolios over time. Our study contributes to the literature on external sources of ambidexterity and dynamic management of client knowledge within KIFs.

A study of the exploitation of the properties of natural rubber in tyres

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Application of Naïve Bayesian sequential analysis to primary care optometry

The objective of this study was to investigate the effects of circularity, comorbidity, prevalence and presentation variation on the accuracy of differential diagnoses made in optometric primary care using a modified form of naïve Bayesian sequential analysis. No such investigation has ever been reported before. Data were collected for 1422 cases seen over one year. Positive test outcomes were recorded for case history (ethnicity, age, symptoms and ocular and medical history) and clinical signs in relation to each diagnosis. For this reason only positive likelihood ratios were used for this modified form of Bayesian analysis that was carried out with Laplacian correction and Chi-square filtration. Accuracy was expressed as the percentage of cases for which the diagnoses made by the clinician appeared at the top of a list generated by Bayesian analysis. Preliminary analyses were carried out on 10 diagnoses and 15 test outcomes. Accuracy of 100% was achieved in the absence of presentation variation but dropped by 6% when variation existed. Circularity artificially elevated accuracy by 0.5%. Surprisingly, removal of Chi-square filtering increased accuracy by 0.4%. Decision tree analysis showed that accuracy was influenced primarily by prevalence followed by presentation variation and comorbidity. Analysis of 35 diagnoses and 105 test outcomes followed. This explored the use of positive likelihood ratios, derived from the case history, to recommend signs to look for. Accuracy of 72% was achieved when all clinical signs were entered. The drop in accuracy, compared to the preliminary analysis, was attributed to the fact that some diagnoses lacked strong diagnostic signs; the accuracy increased by 1% when only recommended signs were entered. Chi-square filtering improved recommended test selection. Decision tree analysis showed that accuracy again influenced primarily by prevalence, followed by comorbidity and presentation variation. Future work will explore the use of likelihood ratios based on positive and negative test findings prior to considering naïve Bayesian analysis as a form of artificial intelligence in optometric practice.

Distributed sequential task allocation in foraging swarms

When designing a practical swarm robotics system, self-organized task allocation is key to make best use of resources. Current research in this area focuses on task allocation which is either distributed (tasks must be performed at different locations) or sequential (tasks are complex and must be split into simpler sub-tasks and processed in order). In practice, however, swarms will need to deal with tasks which are both distributed and sequential. In this paper, a classic foraging problem is extended to incorporate both distributed and sequential tasks. The problem is analysed theoretically, absolute limits on performance are derived, and a set of conditions for a successful algorithm are established. It is shown empirically that an algorithm which meets these conditions, by causing emergent cooperation between robots can achieve consistently high performance under a wide range of settings without the need for communication. © 2013 IEEE.

Improving brain drug targeting through exploitation of the nose-to-brain route:a physiological and pharmacokinetic perspective

With an ageing population and increasing prevalence of central-nervous system (CNS) disorders new approaches are required to sustain the development and successful delivery of therapeutics into the brain and CNS. CNS drug delivery is challenging due to the impermeable nature of the brain microvascular endothelial cells that form the blood-brain barrier (BBB) and which prevent the entry of a wide range of therapeutics into the brain. This review examines the role intranasal delivery may play in achieving direct brain delivery, for small molecular weight drugs, macromolecular therapeutics and cell-based therapeutics, by exploitation of the olfactory and trigeminal nerve pathways. This approach is thought to deliver drugs into the brain and CNS through bypassing the BBB. Details of the mechanism of transfer of administrated therapeutics, the pathways that lead to brain deposition, with a specific focus on therapeutic pharmacokinetics, and examples of successful CNS delivery will be explored. © 2014 Bentham Science Publishers.

Projected sequential Gaussian processes:a C++ tool for interpolation of large datasets with heterogeneous noise

Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed. © 2010 Elsevier Ltd.

The evolution, development and exploitation of SIALON materials

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