Senior marketers’ insights into the challenges of social media implementation in large organisations: assessing generic and electronic orientation models as potential solutions

The unique characteristics of social media (SM) have made it difficult to implement this tool within many large organisations. This paper seeks to identify the implementation challenges and evaluate alternative organisational orientations that may provide solutions. We aimed to reconcile theory with current practice by integrating the extant literature with data from three focus groups involving 27 senior marketing executives. The managerial discussions identified additional challenges to those previously discussed in the literature, which appear to result from SM’s unique characteristics. These include: interactivity, the integration of communication into distribution channels, collaborative media and information collection. Using both broad orientation models (market orientation and entrepreneurial orientation) and a specific digital orientation (e-marketing orientation), guidelines and research propositions for effective implementation are put forward.

Estimating the economic value of Mount Buffalo National Park with the travel cost and contingent valuation models

The Mount Buffalo National Park is the oldest national park in Victoria, Australia. There has been a rapid increase in the number of visitors to the park during the last decade and park management has been a concern, especially in the light of declining budgetary allocations and potential damage due to the increased visitor numbers. Policy options to increase park revenue remain unclear because of a lack of information on demand parameters and user costs. This study estimates the economic value of the park using the travel cost method (TCM) and the contingent valuation method (CVM). The TCM gives higher consumer surplus (CS) than the CVM. The CS shows that the economic value of the park is high and that there are opportunities to introduce innovative fee schemes to enhance its revenue. Present entry fee systems do not capture the economic value of the park.

The discovery of generalized causal models with mixed variables using MML criterion

One major difficulty frustrating the application of linear causal models is that they are not easily adapted to cope with discrete data. This is unfortunate since most real problems involve both continuous and discrete variables. In this paper, we consider a class of graphical models which allow both continuous and discrete variables, and propose the parameter estimation method and a structure discovery algorithm based on Minimum Message Length and parameter estimation. Experimental results are given to demonstrate the potential for the application of this method.

Mlearning for India : any potential?

The trend towards mLearning is attributed to the growth of knowledge based societies (UNESC0,2005). In this paper, we examine if there is a case for mLearning in India, a developing nation with certain unique contributory factors such as rapid diffusion of mobile communication technologies negating the need for fixed line infrastructure; and the rising demand for flexible learning approaches by the eager, upwardly mobile, middle class population. Our research is informed by learning theory of constructivism that seems to underlie flexible adult learning in modem contexts. A speculative ongoing debate is examined through the lens of critical discourse analysis, to present an outlook for India. We open a launching platform for empirical work in India that would enable building of relevant models by extrapolating findings from this initial research. More significantly, the stakeholders in mLearning such as mobile technology/service providers, education providers and organisations that foster staff development in particular may be beneficiaries from the findings of this preliminary research.

Are the potential benefits of a community-based participatory approach to public health research worth the potential cost?

Much of public health research is conducted in a community setting or is designed to target particular population groups. Community-based participatory research (CBPR) is gaining recognition as good practice in studies of this type(Flicker et al 2007). Its merit is based on the inclusion of the community as active participants at all stages of the research process (Goodman 2006). The focus on justice and equity in this approach is seen to contribute to a range of additional potential research benefits including increased relevance and sustainability of interventions arising from the research ( Blumenthal 2004; Wallestein 2006) However, it is widely acknowledged that adoption of a consciously CBPR approach requires additional expertise. time and resources from researchers and from communities (Tanjasiri et al 2002; Massaro & Claiborne 2001; Israel et al 1998). Adoption of CBPR is also limited by existing infrastructures which are supportive of more· traditional models of research. Changes to professional development programs, funding guidelines and criteria. grant review processes and ethics requirements are needed to support increased application of this approach (Israel et al 2001). As all research resources are limited, the potential additional benefits offered by CBPR over and above a more traditional research approach need to be weighed against the potential additional costs involved. Changes to research infrastructure are unlikely to occur until the costs and
benefits of a consciously CBPR approach as compared to a more traditional research approach can be demonstrated.

This is an exploratory paper that summarises the arguments put forward to date in relation to CBPR. A research case study and an evaluation framework are then used for a conceptual analysis of differences in the potential costs and benefits of the two approaches. Firstly, the paper describes the differences between traditional and consciously CBPR approaches. The reported benefits of CBPR are then outlined, followed by a discussion of the potential costs. Finally, the potential costs are compared to the potential benefits of using a CBPR approach, using a case study of existing research.

Regional-scale models for relating land cover to basin surface-water quality using remotely sensed data in a GIS

Plant-based management systems implementing deep-rooted, perennial vegetation have been identified as important in mitigating the spread of secondary dryland salinity due to its capacity to influence water table depth. The Glenelg Hopkins catchment is a highly modified watershed in the southwest region of Victoria, where dryland salinity management has been identified as a priority. Empirical relationships between the proportion of native vegetation and in-stream salinity were examined in the Glenelg Hopkins catchment using a linear regression approach. Whilst investigations of these relationships are not unique, this is the first comprehensive attempt to establish a link between land use and in-stream salinity in the study area. The results indicate that higher percentage land cover with native vegetation was negatively correlated with elevated in-stream salinity. This inverse correlation was consistent across the 3 years examined (1980, 1995, and 2002). Recognising the potential for erroneously inferring causal relationships, the methodology outlined here was both a time and cost-effective tool to inform management strategies at a regional scale, particularly in areas where processes may be operating at scales not easily addressed with on-site studies.

Parametric conditional frailty models for recurrent cardiovascular events in the lipid study

Background Analysis of recurrent event data is frequently needed in clinical and epidemiological studies. An important issue in such analysis is how to account for the dependence of the events in an individual and any unobserved heterogeneity of the event propensity across individuals.Methods We applied a number of conditional frailty and nonfrailty models in an analysis involving recurrent myocardial infarction events in the Long-Term Intervention with Pravastatin in Ischaemic Disease study. A multiple variable risk prediction model was developed for both males and females. Results A Weibull model with a gamma frailty term fitted the data better than other frailty models for each gender. Among nonfrailty models the stratified survival model fitted the data best for each gender. The relative risk estimated by the elapsed time model was close to that estimated by the gap time model. We found that a cholesterol-lowering drug, pravastatin (the intervention being tested in the trial) had significant protective effect against the occurrence of myocardial infarction in men (HR¼0.71, 95% CI0.60–0.83). However, the treatment effect was not significant in women due to smaller sample size (HR¼0.75, 95% CI 0.51–1.10). There were no significant interactions between the treatment effect and each recurrent MI event (p¼0.24 for men and p¼0.55 for women). The risk of developing an MI event for a male who had an MI event during follow-up was about 3.4 (95% CI 2.6–4.4) times the risk compared with those who did not have an MI event. The corresponding relative risk for a female was about 7.8 (95% CI 4.4–13.6). Limitations The number of female patients was relatively small compared with their male counterparts, which may result in low statistical power to find real differences in the effect of treatment and other potential risk factors.Conclusions The conditional frailty model suggested that after accounting for all the risk factors in the model, there was still unmeasured heterogeneity of the risk for myocardial infarction, indicating the effect of subject-specific risk factors. These risk prediction models can be used to classify cardiovascular disease patients into different risk categories and may be useful for the most effective targeting of preventive therapies for cardiovascular disease.

Protein kinase C isozymes as potential therapeutic targets in immune disorders

Background: Members of the protein kinase C (PKC) family are key signalling mediators in immune responses, and pharmacological inhibition of PKCs may be useful for treating immune-mediated diseases. Objective: To review and discuss the insights gained so far into various PKC isozymes and the therapeutic potential and challenges of developing PKC inhibitors for immune disorder therapy. Methods: A literature review of the role of PKCs in immune cell signalling and recent studies describing immune functions associated with PKC isozyme deficiency in relevant mouse disease models, followed by specific case studies of current and potential therapeutic strategies targeting PKCs. Results/conclusion: There is vast amount of data supporting PKC isozymes as attractive drug targets for certain immune disorders. Although the development of specific PKC isozyme inhibitors has been challenging, some progress has been made. It remains to be seen if broad-scale or isozyme-selective inhibition of PKC will have clinical efficacy.

Relevance of health literacy models for people with severe mental illness

People with severe mental illness experience elevated levels of impairment, morbidity and health-risk behaviours compared with the general population. Despite this, it is consistently reported that they do not visit health professionals, including preventative health professionals, as regularly as the general population. Their poor health suggests that current health promotion efforts have been largely ineffective in addressing their specific needs. Barriers that might explain this include lack of motivation, expense and lack of access. Health literacy is also a potentially important factor. As a part of a programme of work to develop appropriate and effective health promotion for this group, we have explored existing health-literacy models and their relevance to marginalized populations, in particular, people experiencing severe mental illness. A comprehensive search of the literature was undertaken. Models of health literacy identified were analyzed to determine the source population, underpinning theory/frameworks, supporting research evidence and to consider their potential generalisability. This paper presents an analysis of existing health-literacy models in the context of severe mental illness. We propose that because existing models of health literacy were developed through consultation with people experiencing challenges to specific health and social issues, for example, cancer, low income and limited education, this raises questions as to the applicability of these models to people experiencing severe and ongoing mental illness. Whilst such individuals were not actively excluded in the development of the existing models, we propose the development of an alternative model which considers this population's needs and limitations in accessing effective health-promotion campaigns/programs.

Potential of technology and a familiar context to enhance students' concept of rate of change

Students' concept image of rate of change may be incomplete or erroneous. This paper reports a pilot study, with secondary school students, which explores the potential of technology (JavaMathWorlds), depicting a familiar context of motion, to develop students' existing schema of informal understandings of rate of change to more formal mathematical representations. Students developed numerous 'models of' rate of change in a motion context which then transferred to serve as a 'model for' rate of change in other contexts.

Simulation models of obesity : a review of the literature and implications for research and policy

Simulation models (SMs) combine information from a variety of sources to provide a useful tool for examining how the effects of obesity unfold over time and impact population health. SMs can aid in the understanding of the complex interaction of the drivers of diet and activity and their relation to health outcomes. As emphasized in a recently released report of the Institute or Medicine, SMs can be especially useful for considering the potential impact of an array of policies that will be required to tackle the obesity problem. The purpose of this paper is to present an overview of existing SMs for obesity. First, a background section introduces the different types of models, explains how models are constructed, shows the utility of SMs and discusses their strengths and weaknesses. Using these typologies, we then briefly review extant obesity SMs. We categorize these models according to their focus: health and economic outcomes, trends in obesity as a function of past trends, physiologically based behavioural models, environmental contributors to obesity and policy interventions. Finally, we suggest directions for future research.

A role for glutathione in the pathophysiology of bipolar disorder and schizophrenia? Animal models and relevance to clinical practice.

The tripeptide, glutathione (glutamylcysteinylglycine) is the primary endogenous free radical scavenger in the human body. When glutathione (GSH) levels are reduced there is an increased potential for cellular oxidative stress, characterised by an increase and accruement of reactive oxygen species (ROS). Oxidative stress has been implicated in the pathology of schizophrenia and bipolar disorder. This could partly be caused by alterations in dopaminergic and glutamatergic activity that are implicated in these illnesses. Glutamate and dopamine are highly redox reactive molecules and produce ROS during normal neurotransmission. Alterations to these neurotransmitter pathways may therefore increase the oxidative burden in the brain. Furthermore, mitochondrial dysfunction, as a source of oxidative stress, has been documented in both schizophrenia and bipolar disorder. The combination of altered neurotransmission and this mitochondrial dysfunction leading to oxidative damage may ultimately contribute to illness symptoms. Animal models have been established to investigate the involvement of glutathione depletion in aspects of schizophrenia and bipolar disorder to further characterise the role of oxidative stress in psychopathology. Stemming from preclinical evidence, clinical studies have recently shown antioxidant precursor treatment to be effective in schizophrenia and bipolar disorder, providing a novel clinical angle to augment often suboptimal conventional treatments.

Applications of nutrient profiling : potential role in diet-related chronic disease prevention and the feasibility of a core nutrient-profiling system

Background/objectives: A number of different nutrient-profiling models have been proposed and several applications of nutrient profiling have been identified. This paper outlines the potential role of nutrient-profiling applications in the prevention of diet-related chronic disease (DRCD), and considers the feasibility of a core nutrient-profiling system, which could be modified for purpose, to underpin the multiple potential applications in a particular country.

Methods: The ‘Four ‘P’s of Marketing’ (Product, Promotion, Place and Price) are used as a framework for identifying and for classifying potential applications of nutrient profiling. A logic pathway is then presented that can be used to gauge the potential impact of nutrient-profiling interventions on changes in behaviour, changes in diet and, ultimately, changes in DRCD outcomes. The feasibility of a core nutrient-profiling system is assessed by examining the implications of different model design decisions and their suitability to different purposes.

Results and conclusions: There is substantial scope to use nutrient profiling as part of the policies for the prevention of DRCD. A core nutrient-profiling system underpinning the various applications is likely to reduce discrepancies and minimise the confusion for regulators, manufacturers and consumers. It seems feasible that common elements, such as a standard scoring method, a core set of nutrients and food components, and defined food categories, could be incorporated as part of a core system, with additional application-specific criteria applying. However, in developing and in implementing such a system, several country-specific contextual and technical factors would need to be balanced.

Modelling of potential food policy interventions in Fiji and Tonga and their impacts on noncommunicable disease mortality

Background: To compare the likely costs and benefits of a range of potential policy interventions in Fiji and Tonga targeted at diet-related noncommunicable diseases (NCDs), in order to support more evidence-based decision-making.

Method: A relatively simple and quick macro-simulation methodology was developed. Logic models were developed by local stakeholders and used to identify costs and dietary impacts of policy changes. Costs were confined to government costs, and excluded cost offsets. The best available evidence was combined with local data to model impacts on deaths from noncommunicable diseases over the lifetime of the target population. Given that the modelling necessarily entailed assumptions to compensate for gaps in data and evidence, use was made of probabilistic uncertainty analysis.

Results: Costs of implementing policy changes were generally low, with the exception of some requiring additional long-term staffing or construction activities. The most effective policy options in Fiji and Tonga targeted access to local produce and high-fat meats respectively, and were estimated to avert approximately 3% of diet-related NCD deaths in each population. Many policies had substantially lower benefits. Cost-effectiveness was higher for the low-cost policies. Similar policies produced markedly different results in the two countries.

Conclusion: Despite the crudeness of the method, the consistent modelling approach used across all the options, allowed reasonable comparisons to be made between the potential policy costs and impacts. This type of modelling can be used to support more evidence-based and informed decision-making about policy interventions and facilitate greater use of policy to achieve a reduction in NCDs.

Identification of nonlinear fMRI models using Auxiliary Particle Filter and kernel smoothing method

Hemodynamic models have a high potential in application to understanding the functional differences of the brain. However, full system identification with respect to model fitting to actual functional magnetic resonance imaging (fMRI) data is practically difficult and is still an active area of research. We present a simulation based Bayesian approach for nonlinear model based analysis of the fMRI data. The idea is to do a joint state and parameter estimation within a general filtering framework. One advantage of using Bayesian methods is that they provide a complete description of the posterior distribution, not just a single point estimate. We use an Auxiliary Particle Filter adjoined with a kernel smoothing approach to address this joint estimation problem.