Adapted customer relationship management implementation framework:facilitating value creation in nursing homes

This paper proposes a framework to support Customer Relationship Management (CRM) implementation in nursing homes. The work extends research by Cheng et al. (2005) who conducted in-depth questionnaires to identify critical features (termed value-characteristics), which are areas identified as adding the most value if implemented. Although Cheng et al. did proposed an implementation framework, summary of, and inconsistent inclusion of value-characteristics, limits the practical use of this contribution during implementation. In this paper we adapt the original framework to correct perceived deficiencies. We link the value characteristics to operational, analytical, strategic and/or collaborative CRM solution types, to allow consideration in context of practical implementation solutions. The outcome of this paper shows that, practically, a 'one solution meets all characteristic' approach to CRM implementation within nursing homes is inappropriate. Our framework, however, supports implementers in identifying how value can be gained when implementing a specific CRM solution within nursing homes; which subsequently support project management and expectation management.

A conceptual framework for aligning business and information systems from organisational learning perspective

The three decades of on-going executives’ concerns of how to achieve successful alignment between business and information technology shows the complexity of such a vital process. Most of the challenges of alignment are related to knowledge and organisational change and several researchers have introduced a number of mechanisms to address some of these challenges. However, these mechanisms pay less attention to multi-level effects, which results in a limited un-derstanding of alignment across levels. Therefore, we reviewed these challenges from a multi-level learning perspective and found that business and IT alignment is related to the balance of exploitation and exploration strategies with the intellec-tual content of individual, group and organisational levels.

A Lagrangian model of air-mass photochemistry and mixing using a trajectory ensemble: the Cambridge Tropospheric Trajectory model of Chemistry And Transport (CiTTyCAT) version 4.2

A Lagrangian model of photochemistry and mixing is described (CiTTyCAT, stemming from the Cambridge Tropospheric Trajectory model of Chemistry And Transport), which is suitable for transport and chemistry studies throughout the troposphere. Over the last five years, the model has been developed in parallel at several different institutions and here those developments have been incorporated into one "community" model and documented for the first time. The key photochemical developments include a new scheme for biogenic volatile organic compounds and updated emissions schemes. The key physical development is to evolve composition following an ensemble of trajectories within neighbouring air-masses, including a simple scheme for mixing between them via an evolving "background profile", both within the boundary layer and free troposphere. The model runs along trajectories pre-calculated using winds and temperature from meteorological analyses. In addition, boundary layer height and precipitation rates, output from the analysis model, are interpolated to trajectory points and used as inputs to the mixing and wet deposition schemes. The model is most suitable in regimes when the effects of small-scale turbulent mixing are slow relative to advection by the resolved winds so that coherent air-masses form with distinct composition and strong gradients between them. Such air-masses can persist for many days while stretching, folding and thinning. Lagrangian models offer a useful framework for picking apart the processes of air-mass evolution over inter-continental distances, without being hindered by the numerical diffusion inherent to global Eulerian models. The model, including different box and trajectory modes, is described and some output for each of the modes is presented for evaluation. The model is available for download from a Subversion-controlled repository by contacting the corresponding authors.

Generalised Bayesian cloud detection for satellite imagery. part 2: technique and validation for day-time imagery

Numerical Weather Prediction (NWP) fields are used to assist the detection of cloud in satellite imagery. Simulated observations based on NWP are used within a framework based on Bayes' theorem to calculate a physically-based probability of each pixel with an imaged scene being clear or cloudy. Different thresholds can be set on the probabilities to create application-specific cloud masks. Here, the technique is shown to be suitable for daytime applications over land and sea, using visible and near-infrared imagery, in addition to thermal infrared. We use a validation dataset of difficult cloud detection targets for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) achieving true skill scores of 89% and 73% for ocean and land, respectively using the Bayesian technique, compared to 90% and 70%, respectively for the threshold-based techniques associated with the validation dataset.

A three-dimensional open-framework indium selenide: [C7H10N][In9Se14]

An open-framework indium selenide, [C7H10N][In9Se14], has been prepared under solvothermal conditions in the presence of 3,5-dimethylpyridine, and characterized by single crystal diffraction, thermogravimetry, elemental analysis, FTIR spectroscopy and UV-Vis diffuse reflectance. The crystal structure of [C7H10N][In9Se14] contains an unusual building unit, in which corner-linked and edge-linked InSe45- tetrahedra coexist. The presence of one-dimensional circular channels, of ca. 6 Å diameter, results in approximately 25% of solvent accessible void space.

Beyond urban legends : an emerging framework of urban ecology, as illustrated by the Baltimore Ecosystem Study

The emerging discipline of urban ecology is shifting focus from ecological processes embedded within cities to integrative studies of large urban areas as biophysical-social complexes. Yet this discipline lacks a theory. Results from the Baltimore Ecosystem Study, part of the Long Term Ecological Research Network, expose new assumptions and test existing assumptions about urban ecosystems. The findings suggest a broader range of structural and functional relationships than is often assumed for urban ecological systems. We address the relationships between social status and awareness of environmental problems, and between race and environmental hazard. We present patterns of species diversity, riparian function, and stream nitrate loading. In addition, we probe the suitability of land-use models, the diversity of soils, and the potential for urban carbon sequestration. Finally, we illustrate lags between social patterns and vegetation, the biogeochemistry of lawns, ecosystem nutrient retention, and social-biophysical feedbacks. These results suggest a framework for a theory of urban ecosystems.

CASCADE: An Agent Based Framework For Modeling The Dynamics Of Smart Electricity Systems

The Complex Adaptive Systems, Cognitive Agents and Distributed Energy (CASCADE) project is developing a framework based on Agent Based Modelling (ABM). The CASCADE Framework can be used both to gain policy and industry relevant insights into the smart grid concept itself and as a platform to design and test distributed ICT solutions for smart grid based business entities. ABM is used to capture the behaviors of diff erent social, economic and technical actors, which may be defi ned at various levels of abstraction. It is applied to understanding their interactions and can be adapted to include learning processes and emergent patterns. CASCADE models ‘prosumer’ agents (i.e., producers and/or consumers of energy) and ‘aggregator’ agents (e.g., traders of energy in both wholesale and retail markets) at various scales, from large generators and Energy Service Companies down to individual people and devices. The CASCADE Framework is formed of three main subdivisions that link models of electricity supply and demand, the electricity market and power fl ow. It can also model the variability of renewable energy generation caused by the weather, which is an important issue for grid balancing and the profi tability of energy suppliers. The development of CASCADE has already yielded some interesting early fi ndings, demonstrating that it is possible for a mediating agent (aggregator) to achieve stable demandfl attening across groups of domestic households fi tted with smart energy control and communication devices, where direct wholesale price signals had previously been found to produce characteristic complex system instability. In another example, it has demonstrated how large changes in supply mix can be caused even by small changes in demand profi le. Ongoing and planned refi nements to the Framework will support investigation of demand response at various scales, the integration of the power sector with transport and heat sectors, novel technology adoption and diffusion work, evolution of new smart grid business models, and complex power grid engineering and market interactions.

A risk-based framework for assessing the effectiveness of stratospheric aerosol geoengineering

Geoengineering by stratospheric aerosol injection has been proposed as a policy response to warming from human emissions of greenhouse gases, but it may produce unequal regional impacts. We present a simple, intuitive risk-based framework for classifying these impacts according to whether geoengineering increases or decreases the risk of substantial climate change, with further classification by the level of existing risk from climate change from increasing carbon dioxide concentrations. This framework is applied to two climate model simulations of geoengineering counterbalancing the surface warming produced by a quadrupling of carbon dioxide concentrations, with one using a layer of sulphate aerosol in the lower stratosphere, and the other a reduction in total solar irradiance. The solar dimming model simulation shows less regional inequality of impacts compared with the aerosol geoengineering simulation. In the solar dimming simulation, 10% of the Earth’s surface area, containing 10% of its population and 11% of its gross domestic product, experiences greater risk of substantial precipitation changes under geoengineering than under enhanced carbon dioxide concentrations. In the aerosol geoengineering simulation the increased risk of substantial precipitation change is experienced by 42% of Earth’s surface area, containing 36% of its population and 60% of its gross domestic product.

Adjustments in the forcing-feedback framework for understanding climate change

The traditional forcing-feedback framework has provided an indispensable basis for discussing global climate changes. However, as analysis of model behavior has become more detailed, shortcomings and ambiguities in the framework have become more evident and physical effects unaccounted for by the traditional framework have become interesting. In particular, the new concept of adjustments, which are responses to forcings that are not mediated by the global mean temperature, has emerged. This concept, related to the older ones of climate efficacy and stratospheric adjustment, is a more physical way of capturing unique responses to specific forcings. We present a pedagogical review of the adjustment concept, why it is important, and how it can be used. The concept is particularly useful for aerosols, where it helps to organize what has become a complex array of forcing mechanisms. It also helps clarify issues around cloud and hydrological response, transient vs. equilibrium climate change, and geoengineering.

A land cover map of Latin America and the Caribbean in the framework of the SERENA project

Land cover maps at different resolutions and mapping extents contribute to modeling and support decision making processes. Because land cover affects and is affected by climate change, it is listed among the 13 terrestrial essential climate variables. This paper describes the generation of a land cover map for Latin America and the Caribbean (LAC) for the year 2008. It was developed in the framework of the project Latin American Network for Monitoring and Studying of Natural Resources (SERENA), which has been developed within the GOFC-GOLD Latin American network of remote sensing and forest fires (RedLaTIF). The SERENA land cover map for LAC integrates: 1) the local expertise of SERENA network members to generate the training and validation data, 2) a methodology for land cover mapping based on decision trees using MODIS time series, and 3) class membership estimates to account for pixel heterogeneity issues. The discrete SERENA land cover product, derived from class memberships, yields an overall accuracy of 84% and includes an additional layer representing the estimated per-pixel confidence. The study demonstrates in detail the use of class memberships to better estimate the area of scarce classes with a scattered spatial distribution. The land cover map is already available as a printed wall map and will be released in digital format in the near future. The SERENA land cover map was produced with a legend and classification strategy similar to that used by the North American Land Change Monitoring System (NALCMS) to generate a land cover map of the North American continent, that will allow to combine both maps to generate consistent data across America facilitating continental monitoring and modeling

Towards improving the framework for probabilistic forecast evaluation

The evaluation of forecast performance plays a central role both in the interpretation and use of forecast systems and in their development. Different evaluation measures (scores) are available, often quantifying different characteristics of forecast performance. The properties of several proper scores for probabilistic forecast evaluation are contrasted and then used to interpret decadal probability hindcasts of global mean temperature. The Continuous Ranked Probability Score (CRPS), Proper Linear (PL) score, and IJ Good’s logarithmic score (also referred to as Ignorance) are compared; although information from all three may be useful, the logarithmic score has an immediate interpretation and is not insensitive to forecast busts. Neither CRPS nor PL is local; this is shown to produce counter intuitive evaluations by CRPS. Benchmark forecasts from empirical models like Dynamic Climatology place the scores in context. Comparing scores for forecast systems based on physical models (in this case HadCM3, from the CMIP5 decadal archive) against such benchmarks is more informative than internal comparison systems based on similar physical simulation models with each other. It is shown that a forecast system based on HadCM3 out performs Dynamic Climatology in decadal global mean temperature hindcasts; Dynamic Climatology previously outperformed a forecast system based upon HadGEM2 and reasons for these results are suggested. Forecasts of aggregate data (5-year means of global mean temperature) are, of course, narrower than forecasts of annual averages due to the suppression of variance; while the average “distance” between the forecasts and a target may be expected to decrease, little if any discernible improvement in probabilistic skill is achieved.

A framework for designing multi-functional cover crops

Cover crops are sown to provide a number of ecosystem services including nutrient management, mitigation of diffuse pollution, improving soil structure and organic matter content, weed suppression, nitrogen fixation and provision of resources for biodiversity. Although the decision to sow a cover crop may be driven by a desire to achieve just one of these objectives, the diversity of cover crops species and mixtures available means that there is potential to combine a number of ecosystem services within the same crop and growing season. Designing multi-functional cover crops would potentially help to reconcile the often conflicting agronomic and environmental agendas and contribute to the optimal use of land. We present a framework for integrating multiple ecosystem services delivered by cover crops that aims to design a mixture of species with complementary growth habit and functionality. The optimal number and identity of species will depend on the services included in the analysis, the functional space represented by the available species pool and the community dynamics of the crop in terms of dominance and co-existence. Experience from a project that applied the framework to fertility building leys in organic systems demonstrated its potential and emphasised the importance of the initial choice of species to include in the analysis

Three-dimensional gallium sulphide open framework

The synthesis and crystal structure of four gallium sulphide open frameworks, built from supertetrahedral clusters, are described. The structures of [C4NH12]6[Ga10S18][C4NH12]6[Ga10S18](1) and [C4NH12]12[Ga20S35.5(S3)0.5O](2) contain supertetrahedral T3 clusters, while in the isostructural compounds [C4NH12]16[Ga10S18M4Ga16S33][C4NH12]16[Ga10S18M4Ga16S33] (M=CoM=Co(3), Zn (4)), T3 and T4 clusters alternate. These materials exhibit three-dimensional frameworks, with topologies consisting of two interpenetrating diamond lattices, and contain over 50% of solvent accessible void space. UV–Vis diffuse reflectance measurements indicate that these compounds are semiconducting, with band gaps over the range 3.4–4.1 eV.

The dark side of brand attachment: a conceptual framework of brand attachment's detrimental outcomes

Brand attachment recently has received great attention among practitioners and academics alike. Scholars consider brand attachment a key requisite in consumer-brand relationships that create favourable consumer behaviours such as positive brand attitudes and brand loyalty. Few studies, however, examine the detrimental outcomes of brand attachment. In this paper, we develop a conceptual framework that explores how brand attachment may explain detrimental consumer behaviours, such as oppositional brand loyalty and antibrand actions. We investigate consumers' trash-talking and schadenfreude in brand communities and their subsequent outcomes. Our framework reveals that the link between brand attachment and oppositional brand loyalty is driven by consumers' social identity and sense of rivalry. Furthermore, we put forward that brand attachment leads to anti-brand actions when relationships deteriorate. We identify two factors behind the deterioration: (1) companies' opportunism activities, and (2) incongruity between consumers' values and the brand's values. Theoretical and managerial implications are discussed arising from our emerging 'dark side' brand attachment framework.