Inertia in Japanese organizations: knowledge management routines and failure to innovate

This paper argues that features of Japanese organizations, previously held to be the foundations of innovation, change and flexibility, can equally be significant barriers to change, innovation and adaptation in turbulent economic environments. This paper draws on two in-depth case studies of Japanese organizations. It shows how, in both cases, these firms displayed specific weaknesses in the ways in which they integrate and bundle knowledge, in particular around their research and development (R&D) functions. Despite the adoption of strategies of technological innovation and internationalization, the data suggest that the pursuit of both strategies is beset by barriers of inertia. Embedded internal network connections and knowledge-sharing routines between central R&D and other divisions are inappropriate for the revised strategy. Existing external connections, with preferred suppliers and customers within keiretsu structures, and close relationships with existing R&D partners retard these firms' strategic flexibility. With a limited variety of latent routines, knowledge, capabilities and agency to draw on when needed, these firms have limited organizational responsiveness and high levels of path-dependency.

Serial persistence in individual real estate returns in the UK

Purpose – The purpose of this paper is to examine individual level property returns to see whether there is evidence of persistence in performance, i.e. a greater than expected probability of well (badly) performing properties continuing to perform well (badly) in subsequent periods. Design/methodology/approach – The same methodology originally used in Young and Graff is applied, making the results directly comparable with those for the US and Australian markets. However, it uses a much larger database covering all UK commercial property data available in the Investment Property Databank (IPD) for the years 1981 to 2002 – as many as 216,758 individual property returns. Findings – While the results of this study mimic the US and Australian results of greater persistence in the extreme first and fourth quartiles, they also evidence persistence in the moderate second and third quartiles, a notable departure from previous studies. Likewise patterns across property type, location, time, and holding period are remarkably similar. Research limitations/implications – The findings suggest that performance persistence is not a feature unique to particular markets, but instead may characterize most advanced real estate investment markets. Originality/value – As well as extending previous research geographically, the paper explores possible reasons for such persistence, consideration of which leads to the conjecture that behaviors in the practice of institutional-grade commercial real estate investment management may themselves be deeply rooted and persistent, and perhaps influenced for good or ill by agency effects. - See more at: http://www.emeraldinsight.com/journals.htm?articleid=1602884&show=abstract#sthash.hc2pCmC6.dpuf

Linking pesticide exposure and spatial dynamics: An individual-based model of wood mouse (Apodemus sylvaticus) populations in agricultural landscapes

The wood mouse is a common and abundant species in agricultural landscape and is a focal species in pesticide risk assessment. Empirical studies on the ecology of the wood mouse have provided sufficient information for the species to be modelled mechanistically. An individual-based model was constructed to explicitly represent the locations and movement patterns of individual mice. This together with the schedule of pesticide application allows prediction of the risk to the population from pesticide exposure. The model included life-history traits of wood mice as well as typical landscape dynamics in agricultural farmland in the UK. The model obtains a good fit to the available population data and is fit for risk assessment purposes. It can help identify spatio-temporal situations with the largest potential risk of exposure and enables extrapolation from individual-level endpoints to population-level effects. Largest risk of exposure to pesticides was found when good crop growth in the “sink” fields coincided with high “source” population densities in the hedgerows. Keywords: Population dynamics, Pesticides, Ecological risk assessment, Habitat choice, Agent-based model, NetLogo

A novel approach to national technological accumulation and absorptive capacity: aggregating Cohen and Levinthal

The paper develops a more precise specification and understanding of the process of national-level knowledge accumulation and absorptive capabilities by applying the reasoning and evidence from the firm-level analysis pioneered by Cohen and Levinthal (1989, 1990). In doing so, we acknowledge that significant cross-border effects due to the role of both inward and outward FDI exist and that assimilation of foreign knowledge is not only confined to catching-up economies but is also carried out by countries at the frontier-sharing phase. We postulate a non-linear relationship between national absorptive capacity and the technological gap, due to the effects of the cumulative nature of the learning process and the increase in complexity of external knowledge as the country approaches the technological frontier. We argue that national absorptive capacity and the accumulation of knowledge stock are simultaneously determined. This implies that different phases of technological development require different strategies. During the catching-up phase, knowledge accumulation occurs predominately through the absorption of trade and/or inward FDI-related R&D spillovers. At the pre-frontier-sharing phase onwards, increases in the knowledge base occur largely through independent knowledge creation and actively accessing foreign-located technological spillovers, inter alia through outward FDI-related R&D, joint ventures and strategic alliances.

Beta event-related desynchronization as an index of individual differences in processing human facial expression: further investigations of autistic traits in typically developing adults

The human mirror neuron system (hMNS) has been associated with various forms of social cognition and affective processing including vicarious experience. It has also been proposed that a faulty hMNS may underlie some of the deficits seen in the autism spectrum disorders (ASDs). In the present study we set out to investigate whether emotional facial expressions could modulate a putative EEG index of hMNS activation (mu suppression) and if so, would this differ according to the individual level of autistic traits [high versus low Autism Spectrum Quotient (AQ) score]. Participants were presented with 3 s films of actors opening and closing their hands (classic hMNS mu-suppression protocol) while simultaneously wearing happy, angry, or neutral expressions. Mu-suppression was measured in the alpha and low beta bands. The low AQ group displayed greater low beta event-related desynchronization (ERD) to both angry and neutral expressions. The high AQ group displayed greater low beta ERD to angry than to happy expressions. There was also significantly more low beta ERD to happy faces for the low than for the high AQ group. In conclusion, an interesting interaction between AQ group and emotional expression revealed that hMNS activation can be modulated by emotional facial expressions and that this is differentiated according to individual differences in the level of autistic traits. The EEG index of hMNS activation (mu suppression) seems to be a sensitive measure of the variability in facial processing in typically developing individuals with high and low self-reported traits of autism.

Using an individual-based model to select among alternative foraging strategies of woodpigeons: data support a memory-based model with a flocking mechanism

Population modelling is increasingly recognised as a useful tool for pesticide risk assessment. For vertebrates that may ingest pesticides with their food, such as woodpigeon (Columba palumbus), population models that simulate foraging behaviour explicitly can help predicting both exposure and population-level impact. Optimal foraging theory is often assumed to explain the individual-level decisions driving distributions of individuals in the field, but it may not adequately predict spatial and temporal characteristics of woodpigeon foraging because of the woodpigeons’ excellent memory, ability to fly long distances, and distinctive flocking behaviour. Here we present an individual-based model (IBM) of the woodpigeon. We used the model to predict distributions of foraging woodpigeons that use one of six alternative foraging strategies: optimal foraging, memory-based foraging and random foraging, each with or without flocking mechanisms. We used pattern-oriented modelling to determine which of the foraging strategies is best able to reproduce observed data patterns. Data used for model evaluation were gathered during a long-term woodpigeon study conducted between 1961 and 2004 and a radiotracking study conducted in 2003 and 2004, both in the UK, and are summarised here as three complex patterns: the distributions of foraging birds between vegetation types during the year, the number of fields visited daily by individuals, and the proportion of fields revisited by them on subsequent days. The model with a memory-based foraging strategy and a flocking mechanism was the only one to reproduce these three data patterns, and the optimal foraging model produced poor matches to all of them. The random foraging strategy reproduced two of the three patterns but was not able to guarantee population persistence. We conclude that with the memory-based foraging strategy including a flocking mechanism our model is realistic enough to estimate the potential exposure of woodpigeons to pesticides. We discuss how exposure can be linked to our model, and how the model could be used for risk assessment of pesticides, for example predicting exposure and effects in heterogeneous landscapes planted seasonally with a variety of crops, while accounting for differences in land use between landscapes.

An energy budget agent-based model of earthworm populations and its application to study the effects of pesticides

Earthworms are important organisms in soil communities and so are used as model organisms in environmental risk assessments of chemicals. However current risk assessments of soil invertebrates are based on short-term laboratory studies, of limited ecological relevance, supplemented if necessary by site-specific field trials, which sometimes are challenging to apply across the whole agricultural landscape. Here, we investigate whether population responses to environmental stressors and pesticide exposure can be accurately predicted by combining energy budget and agent-based models (ABMs), based on knowledge of how individuals respond to their local circumstances. A simple energy budget model was implemented within each earthworm Eisenia fetida in the ABM, based on a priori parameter estimates. From broadly accepted physiological principles, simple algorithms specify how energy acquisition and expenditure drive life cycle processes. Each individual allocates energy between maintenance, growth and/or reproduction under varying conditions of food density, soil temperature and soil moisture. When simulating published experiments, good model fits were obtained to experimental data on individual growth, reproduction and starvation. Using the energy budget model as a platform we developed methods to identify which of the physiological parameters in the energy budget model (rates of ingestion, maintenance, growth or reproduction) are primarily affected by pesticide applications, producing four hypotheses about how toxicity acts. We tested these hypotheses by comparing model outputs with published toxicity data on the effects of copper oxychloride and chlorpyrifos on E. fetida. Both growth and reproduction were directly affected in experiments in which sufficient food was provided, whilst maintenance was targeted under food limitation. Although we only incorporate toxic effects at the individual level we show how ABMs can readily extrapolate to larger scales by providing good model fits to field population data. The ability of the presented model to fit the available field and laboratory data for E. fetida demonstrates the promise of the agent-based approach in ecology, by showing how biological knowledge can be used to make ecological inferences. Further work is required to extend the approach to populations of more ecologically relevant species studied at the field scale. Such a model could help extrapolate from laboratory to field conditions and from one set of field conditions to another or from species to species.

The climate change–infectious disease nexus: is it time for climate change syndemics?

Conceptualizing climate as a distinct variable limits our understanding of the synergies and interactions between climate change and the range of abiotic and biotic factors, which influence animal health. Frameworks such as eco-epidemiology and the epi-systems approach, while more holistic, view climate and climate change as one of many discreet drivers of disease. Here, I argue for a new paradigmatic framework: climate-change syndemics. Climate-change syndemics begins from the assumption that climate change is one of many potential influences on infectious disease processes, but crucially is unlikely to act independently or in isolation; and as such, it is the inter-relationship between factors that take primacy in explorations of infectious disease and climate change. Equally importantly, as climate change will impact a wide range of diseases, the frame of analysis is at the collective rather than individual level (for both human and animal infectious disease) across populations.

Allocating community-level payments for ecosystem services initial experiences from a REDD pilot in Tanzania

Payments for ecosystem services (PES) typically reward landowners for managing their land to provide ecosystem services that would not otherwise be provided. REDD—Reduced Emissions from Deforestation and Forest Degradation—is a form of PES aimed at decreasing carbon emissions from forest conversion and extraction in lower-income countries. A key challenge for REDD occurs when it is implemented at the community rather than the individual landowner level. Whilst achieving this community-level reduction relies on individuals changing their interaction with the forest, incentives are not aligned explicitly at the individual level. Rather, payments are made to the community as a single entity in exchange for verified reduced forest loss, as per a PES scheme. In this paper, we explore how community level REDD has been implemented in one multiple-village pilot in Tanzania. Our findings suggest that considerable attention has been paid to monitoring, reporting, verification, and equity. Though no explicit mechanism ensures individual compliance with the group PES, the development of village level institutions, “social fencing,” and a shared future through equal REDD payments factor into community decisions that influence the level of community compliance that the program will eventually achieve. However, few villages allocate funds for explicit enforcement efforts to protect the forest from illegal activities undertaken by outsiders.

Modelling nitrogen fluxes from the land surface to the coastal zone in European systems: the perspective of the INCA project

This paper describes the results and conclusions of the INCA (Integrated Nitrogen Model for European CAtchments) project and sets the findings in the context of the ELOISE (European Land-Ocean Interaction Studies) programme. The INCA project was concerned with the development of a generic model of the major factors and processes controlling nitrogen dynamics in European river systems, thereby providing a tool (a) to aid the scientific understanding of nitrogen transport and retention in catchments and (b) for river-basin management and policy-making. The findings of the study highlight the heterogeneity of the factors and processes controlling nitrogen dynamics in freshwater systems. Nonetheless, the INCA model was able to simulate the in-stream nitrogen concentrations and fluxes observed at annual and seasonal timescales in Arctic, Continental and Maritime-Temperate regimes. This result suggests that the data requirements and structural complexity of the INCA model are appropriate to simulate nitrogen fluxes across a wide range of European freshwater environments. This is a major requirement for the production of coupled fiver-estuary-coastal shelf models for the management of our aquatic environment. With regard to river-basin management, to achieve an efficient reduction in nutrient fluxes from the land to the estuarine and coastal zone, the model simulations suggest that management options must be adaptable to the prevailing environmental and socio-economic factors in individual catchments: 'Blanket approaches' to environmental policy appear too simple. (c) 2004 Elsevier B.V. All rights reserved.

Extraction of tidal channel networks from aerial photographs alone and combined with laser altimetry

Tidal channel networks play an important role in the intertidal zone, exerting substantial control over the hydrodynamics and sediment transport of the region and hence over the evolution of the salt marshes and tidal flats. The study of the morphodynamics of tidal channels is currently an active area of research, and a number of theories have been proposed which require for their validation measurement of channels over extensive areas. Remotely sensed data provide a suitable means for such channel mapping. The paper describes a technique that may be adapted to extract tidal channels from either aerial photographs or LiDAR data separately, or from both types of data used together in a fusion approach. Application of the technique to channel extraction from LiDAR data has been described previously. However, aerial photographs of intertidal zones are much more commonly available than LiDAR data, and most LiDAR flights now involve acquisition of multispectral images to complement the LiDAR data. In view of this, the paper investigates the use of multispectral data for semiautomatic identification of tidal channels, firstly from only aerial photographs or linescanner data, and secondly from fused linescanner and LiDAR data sets. A multi-level, knowledge-based approach is employed. The algorithm based on aerial photography can achieve a useful channel extraction, though may fail to detect some of the smaller channels, partly because the spectral response of parts of the non-channel areas may be similar to that of the channels. The algorithm for channel extraction from fused LiDAR and spectral data gives an increased accuracy, though only slightly higher than that obtained using LiDAR data alone. The results illustrate the difficulty of developing a fully automated method, and justify the semi-automatic approach adopted.

Remote sensing of tidal networks and their relation to vegetation

The study of the morphology of tidal networks and their relation to salt marsh vegetation is currently an active area of research, and a number of theories have been developed which require validation using extensive observations. Conventional methods of measuring networks and associated vegetation can be cumbersome and subjective. Recent advances in remote sensing techniques mean that these can now often reduce measurement effort whilst at the same time increasing measurement scale. The status of remote sensing of tidal networks and their relation to vegetation is reviewed. The measurement of network planforms and their associated variables is possible to sufficient resolution using digital aerial photography and airborne scanning laser altimetry (LiDAR), with LiDAR also being able to measure channel depths. A multi-level knowledge-based technique is described to extract networks from LiDAR in a semi-automated fashion. This allows objective and detailed geomorphological information on networks to be obtained over large areas of the inter-tidal zone. It is illustrated using LIDAR data of the River Ems, Germany, the Venice lagoon, and Carnforth Marsh, Morecambe Bay, UK. Examples of geomorphological variables of networks extracted from LiDAR data are given. Associated marsh vegetation can be classified into its component species using airborne hyperspectral and satellite multispectral data. Other potential applications of remote sensing for network studies include determining spatial relationships between networks and vegetation, measuring marsh platform vegetation roughness, in-channel velocities and sediment processes, studying salt pans, and for marsh restoration schemes.

Uses of airborne scanning laser altimetry for river flood modelling and coastal geomorphology

Two ongoing projects at ESSC that involve the development of new techniques for extracting information from airborne LiDAR data and combining this information with environmental models will be discussed. The first project in conjunction with Bristol University is aiming to improve 2-D river flood flow models by using remote sensing to provide distributed data for model calibration and validation. Airborne LiDAR can provide such models with a dense and accurate floodplain topography together with vegetation heights for parameterisation of model friction. The vegetation height data can be used to specify a friction factor at each node of a model’s finite element mesh. A LiDAR range image segmenter has been developed which converts a LiDAR image into separate raster maps of surface topography and vegetation height for use in the model. Satellite and airborne SAR data have been used to measure flood extent remotely in order to validate the modelled flood extent. Methods have also been developed for improving the models by decomposing the model’s finite element mesh to reflect floodplain features such as hedges and trees having different frictional properties to their surroundings. Originally developed for rural floodplains, the segmenter is currently being extended to provide DEMs and friction parameter maps for urban floods, by fusing the LiDAR data with digital map data. The second project is concerned with the extraction of tidal channel networks from LiDAR. These networks are important features of the inter-tidal zone, and play a key role in tidal propagation and in the evolution of salt-marshes and tidal flats. The study of their morphology is currently an active area of research, and a number of theories related to networks have been developed which require validation using dense and extensive observations of network forms and cross-sections. The conventional method of measuring networks is cumbersome and subjective, involving manual digitisation of aerial photographs in conjunction with field measurement of channel depths and widths for selected parts of the network. A semi-automatic technique has been developed to extract networks from LiDAR data of the inter-tidal zone. A multi-level knowledge-based approach has been implemented, whereby low level algorithms first extract channel fragments based mainly on image properties then a high level processing stage improves the network using domain knowledge. The approach adopted at low level uses multi-scale edge detection to detect channel edges, then associates adjacent anti-parallel edges together to form channels. The higher level processing includes a channel repair mechanism.

Optimality analysis of Th1/Th2 immune responses during microparasite-macroparasite co-infection, with epidemiological feedbacks

Individuals are typically co-infected by a diverse community of microparasites (e.g. viruses or protozoa) and macroparasites (e.g. helminths). Vertebrates respond to these parasites differently, typically mounting T helper type 1 (Th1) responses against microparasites and Th2 responses against macroparasites. These two responses may be antagonistic such that hosts face a 'decision' of how to allocate potentially limiting resources. Such decisions at the individual host level will influence parasite abundance at the population level which, in turn, will feed back upon the individual level. We take a first step towards a complete theoretical framework by placing an analysis of optimal immune responses under microparasite-macroparasite co-infection within an epidemiological framework. We show that the optimal immune allocation is quantitatively sensitive to the shape of the trade-off curve and qualitatively sensitive to life-history traits of the host, microparasite and macroparasite. This model represents an important first step in placing optimality models of the immune response to co-infection into an epidemiological framework. Ultimately, however, a more complete framework is needed to bring together the optimal strategy at the individual level and the population-level consequences of those responses, before we can truly understand the evolution of host immune responses under parasite co-infection.

The joint effects of larval density and C-14-cypermethrin on the life history and population growth rate of the midge Chironomus riparius

1. Chemical effects on organisms are typically assessed using individual-level endpoints or sometimes population growth rate (PGR), but such measurements are generally made at low population densities. In contrast most natural populations are subject to density dependence and fluctuate around the environmental carrying capacity as a result of individual competition for resources. As ecotoxicology aims to make reliable population projections of chemical impacts in the field, an understanding of how high-density or resource-limited populations respond to environmental chemicals is essential. 2. Our objective was to determine the joint effects of population density and chemical stress on the life history and PGR of an important ecotoxicological indicator species, Chironomus riparius, under controlled laboratory conditions. Populations were fed the same ration but initiated at different densities and exposed to a solvent control and three concentrations of C-14-cypermethrin in a sediment-water test system for 67 days at 20 +/- 1 degreesC. 3. Density had a negative effect on all the measured life-history traits, and PGR declined with increasing density in the controls. Exposure to C-14-cypermethrin had a direct negative effect on juvenile survival, presumably within the first 24 h because the chemical rapidly dissipated from the water column. Reductions in the initial larval densities resulted in an increase in the available resources for the survivors. Subsequently, exposed populations emerged sooner and started producing offspring earlier than the controls. C-14-cypermethrin had no effect on estimated fecundity and adult body weight but interacted with density to reduce the time to first emergence and first reproduction. As a result, PGR increased with cypermethrin concentration when populations were initiated at high densities. 4. Synthesis and applications. The results showed that the effects of C-14-cypermethrin were buffered at high density, so that the joint effects of density and chemical stress on PGR were less than additive. Low levels of chemical stressors may increase carrying capacity by reducing juvenile competition for resources. More and perhaps fitter adults may be produced, similar to the effects of predators and culling; however, toxicant exposure may result in survivors that are less tolerant to changing conditions. If less than additive effects are typical in the field, standard regulatory tests carried out at low density may overestimate the effects of environmental chemicals. Further studies over a wide range of chemical stressors and organisms with contrasting life histories are needed to make general recommendations.