Growth and Heat Resistance Kinetic Variation Among Various Isolates of Salmonella and its Application to Risk Assessment

The abilities of cells of a particular type of bacteria to leave lag phase and begin the process of dividing or surviving heat treatment can depend on the serotypes or strains of the bacteria. This article reports an investigation of serotype-specific differences in growth and heat resistance kinetics of clinical and food isolates of Salmonella. Growth kinetics at 19 degrees C and 37 degrees C were examined in brain heart infusion broth and heat resistance kinetics for 60 degrees C were examined in beef gravy using a submerged coil heating apparatus. Estimates of the parameters of the growth curves suggests a small between-serotype variance of the growth kinetics. However, for inactivation, the results suggest a significant between-serotype effect on the asymptotic D-values, with an estimated between-serotype CV of about 20%. In microbial risk assessment, predictive microbiology is used to estimate growth and inactivation of pathogens. Often the data used for estimating the growth or inactivation kinetics are based on measurements on a cocktail--a mixture of approximately equal proportions of several serotypes or strains of the pathogen being studied. The expected growth or inactivation rates derived from data using cocktails are biased, reflecting the characteristics of the fastest growing or most heat resistant serotype of the cocktail. In this article, an adjustment to decrease this possible bias in a risk assessment is offered. The article also presents discussion of the effect on estimating growth when stochastic assumptions are incorporated in the model. In particular, equations describing the variation of relative growth are derived, accounting for the stochastic variations of the division of cells. For small numbers of cells, the expected value of the relative growth is not an appropriate "representative" value for actual relative growths that might occur.     

Risk Modeling, Assessment, and Management of Lahar Flow Threat

The 1991 eruption of Mount Pinatubo in the Philippines is considered one of the most violent and destructive volcanic activities in the 20th century. Lahar is the Indonesian term for volcanic ash, and lahar flows resulting from the massive amount of volcanic materials deposited on the mountain's slope posed continued post-eruption threats to the surrounding areas, destroying lives, homes, agricultural products, and infrastructures. Risks of lahar flows were identified immediately after the eruption, with scientific data provided by the Philippine Institute of Volcanology, the U.S. Geological Survey, and other research institutions. However, competing political, economic, and social agendas subordinated the importance of scientific information to policy making. Using systemic risk analysis and management, this article addresses the issues of multiple objectives and the effective integration of scientific techniques into the decision-making process. It provides a modeling framework for identifying, prioritizing, and evaluating policies for managing risk. The major considerations are: (1) applying a holistic approach to risk analysis through hierarchical holographic modeling, (2) applying statistical methods to gain insight into the problem of uncertainty in risk assessment, (3) using multiobjective trade-off analysis to address the issue of multiple decisionmakers and stakeholders in the decision-making process, (4) using the conditional expected value of extreme events to complement and supplement the expected value in quantifying risk, and (5) assessing the impacts of multistage decisions. Numerical examples based on ex post data are formulated to illustrate applications to various problems. The resulting framework from this study can serve as a general baseline model for assessing and managing risks of natural disasters, which the Philippines' lead agency-the National Disaster Coordinating Council (NDCC)-and other related organizations can use for their decision-making processes.     

The State of the Field in UK Management Research: Reflections of the Research Assessment Exercise (RAE) Panel

This paper reviews the state of the field of the sub-disciplines within UK management research, based upon the submissions of 94 UK higher education institutions to the Business and Management Studies Panel in the UK's 2001 Research Assessment Exercise (RAE). It offers observations on the UK model of the assessment of quality in, and funding of, research conducted in publicly funded higher education institutions.     

Learning How and Learning What: Effects of Tacit and Codified Knowledge on Performance Improvement Following Technology Adoption

This paper examines effects of tacit and codified knowledge on performance improvement as organizations gain experience with a new technology. We draw from knowledge management and learning curve research to predict improvement rate heterogeneity across organizations. We first note that the same technology can present opportunities for improvement along more than one dimension, such as efficiency and breadth of use. We compare improvement for two dimensions: one in which the acquisition of codified knowledge leads to improvement and another in which improvement requires tacit knowledge. We hypothesize that improvement rates across organizations will be more heterogeneous for dimensions of performance that rely on tacit knowledge than for those that rely on codified knowledge (H1), and that group membership stability predicts improvement rates for dimensions relying on tacit knowledge (H2). We further hypothesize that when performance relies on codified knowledge, later adopters should improve more quickly than earlier adopters (H3). All three hypotheses are supported in a study of 15 hospitals learning to use a new surgical technology. Implications for theory and practice are discussed.     

PIECING TOGETHER SERVICE QUALITY: A FRAMEWORK FOR ROBUST SERVICE*

This paper addresses the issue of service design, specifically that of designing the service encounter for improved quality. We introduce a framework based on the three T's of task, treatment, and tangibles as a means of organizing the application of the diverse and growing body of service quality literature to encounter design. The framework is consistent with how successful service managers disaggregate the design problem. More importantly, we show that mutually supportive interrelationships between the three T's produce an opportunity for designing in a robustness to service failure. The framework is supported by case based evidence.     

Mixed Levels of Uncertainty in Complex Policy Models

The characterization and treatment of uncertainty poses special challenges when modeling indeterminate or complex coupled systems such as those involved in the interactions between human activity, climate and the ecosystem. Uncertainty about model structure may become as, or more important than, uncertainty about parameter values. When uncertainty grows so large that prediction or optimization no longer makes sense, it may still be possible to use the model as a behavioral test bed to examine the relative robustness of alternative observational and behavioral strategies. When models must be run into portions of their phase space that are not well understood, different submodels may become unreliable at different rates. A common example involves running a time stepped model far into the future. Several strategies can be used to deal with such situations. The probability of model failure can be reported as a function of time. Possible alternative surprises can be assigned probabilities, modeled separately, and combined. Finally, through the use of subjective judgments, one may be able to combine, and over time shift between models, moving from more detailed to progressively simpler order-of-magnitude models, and perhaps ultimately, on to simple bounding analysis.