The sweet spot in sustainability: a framework for corporate assessment in sugar manufacturing

The assessment of corporate sustainability has become an increasingly important topic, both within academia and in industry. For manufacturing companies to conform to their commitments to sustainable development, a standard and reliable measurement framework is required. There is, however, a lack of sector-specific and empirical research in many areas, including the sugar industry. This paper presents an empirically developed framework for the assessment of corporate sustainability within the Thai sugar industry. Multiple case studies were conducted, and a survey using questionnaires was also employed to enhance the power of generalisation. The developed framework is an accurate and reliable measurement instrument of corporate sustainability, and guidelines to assess qualitative criteria are put forward. The proposed framework can be used for a company’s self-assessment and for guiding practitioners in performance improvement and policy decision-maki

Physician privacy concerns when disclosing patient data for public health purposes during a pandemic influenza outbreak

Background: Privacy concerns by providers have been a barrier to disclosing patient information for public health\ud purposes. This is the case even for mandated notifiable disease reporting. In the context of a pandemic it has been\ud argued that the public good should supersede an individual’s right to privacy. The precise nature of these provider\ud privacy concerns, and whether they are diluted in the context of a pandemic are not known. Our objective was to\ud understand the privacy barriers which could potentially influence family physicians’ reporting of patient-level\ud surveillance data to public health agencies during the Fall 2009 pandemic H1N1 influenza outbreak.\ud Methods: Thirty seven family doctors participated in a series of five focus groups between October 29-31 2009.\ud They also completed a survey about the data they were willing to disclose to public health units. Descriptive\ud statistics were used to summarize the amount of patient detail the participants were willing to disclose, factors that\ud would facilitate data disclosure, and the consensus on those factors. The analysis of the qualitative data was based\ud on grounded theory.\ud Results: The family doctors were reluctant to disclose patient data to public health units. This was due to concerns\ud about the extent to which public health agencies are dependable to protect health information (trusting beliefs),\ud and the possibility of loss due to disclosing health information (risk beliefs). We identified six specific actions that\ud public health units can take which would affect these beliefs, and potentially increase the willingness to disclose\ud patient information for public health purposes.\ud Conclusions: The uncertainty surrounding a pandemic of a new strain of influenza has not changed the privacy\ud concerns of physicians about disclosing patient data. It is important to address these concerns to ensure reliable\ud reporting during future outbreaks.University of Ottawa Open Access Author Fun

Ranking Sports Teams and the Inverse Equal Paths Problem

The problem of rank aggregation has been studied in contexts varying from sports, to multi-criteria decision making, to machine learning, to academic citations, to ranking web pages, and to descriptive decision theory. Rank aggregation is the mapping of inputs that rank subsets of a set of objects into a consistent ranking that represents in some meaningful way the various inputs. In the ranking of sports competitors, or academic citations or ranking of web pages the inputs are in the form of pairwise comparisons. We present here a new paradigm using an optimization framework that addresses major shortcomings in current models of aggregate ranking. Ranking methods are often criticized for being subjective and ignoring some factors or emphasizing others. In the ranking scheme here subjective considerations can be easily incorporated while their contributions to the overall ranking are made explicit. The inverse equal paths problem is introduced here, and is shown to be tightly linked to the problem of aggregate ranking “optimally”. This framework is useful in making an optimization framework available and by introducing specific performance measures for the quality of the aggregate ranking as per its deviations from the input rankings provided. Presented as inverse equal paths problem we devise for the aggregate ranking problem polynomial time combinatorial algorithms for convex penalty functions of the deviations; and show the NP-hardness of some forms of nonlinear penalty functions. Interestingly, the algorithmic setup of the problem is that of a network flow problem. We compare the equal paths scheme here to the eigenvector method, Google PageRank for ranking web sites, and the academic citation method for ranking academic papers