A ‘Rhizomic’ Model of Organizational Change and Transformation: Perspective from a Metaphysics of Change

We are not good at thinking movement. Our instinctive skills favour the fixed and the static, the separate and the self-contained. Taxonomies, hierarchies, systems and structures represent the instinctive vocabulary of institutionalized thought in its determined subordinating of flux, movement, change and transformation. Our dominant models of change in general and organizational change in particular are, therefore, paradoxically couched in the language of stasis and equilibrium. This paper seeks to offer an alternative model of change which, it is claimed, affords a better understanding of the inherent dynamic complexities and intrinsic indeterminacy of organization transformational processes.     

Comparison of Six Dose-Response Models for Use with Food-Borne Pathogens

Food-related illness in the United States is estimated to affect over six million people per year and cost the economy several billion dollars. These illnesses and costs could be reduced if minimum infectious doses were established and used as the basis of regulations and monitoring. However, standard methodologies for dose-response assessment are not yet formulated for microbial risk assessment. The objective of this study was to compare dose-response models for food-borne pathogens and determine which models were most appropriate for a range of pathogens. The statistical models proposed in the literature and chosen for comparison purposes were log-normal, log-logistic, exponential, -Poisson and Weibull-Gamma. These were fit to four data sets also taken from published literature, Shigella flexneri, Shigella dysenteriae,Campylobacter jejuni, and Salmonella typhosa, using the method of maximum likelihood. The Weibull-gamma, the only model with three parameters, was also the only model capable of fitting all the data sets examined using the maximum likelihood estimation for comparisons. Infectious doses were also calculated using each model. Within any given data set, the infectious dose estimated to affect one percent of the population ranged from one order of magnitude to as much as nine orders of magnitude, illustrating the differences in extrapolation of the dose response models. More data are needed to compare models and examine extrapolation from high to low doses for food-borne pathogens.     

Combining Probability Distributions From Experts in Risk Analysis

This paper concerns the combination of experts' probability distributions in risk analysis, discussing a variety of combination methods and attempting to highlight the important conceptual and practical issues to be considered in designing a combination process in practice. The role of experts is important because their judgments can provide valuable information, particularly in view of the limited availability of hard data regarding many important uncertainties in risk analysis. Because uncertainties are represented in terms of probability distributions in probabilistic risk analysis (PRA), we consider expert information in terms of probability distributions. The motivation for the use of multiple experts is simply the desire to obtain as much information as possible. Combining experts' probability distributions summarizes the accumulated information for risk analysts and decision-makers. Procedures for combining probability distributions are often compartmentalized as mathematical aggregation methods or behavioral approaches, and we discuss both categories. However, an overall aggregation process could involve both mathematical and behavioral aspects, and no single process is best in all circumstances. An understanding of the pros and cons of different methods and the key issues to consider is valuable in the design of a combination process for a specific PRA. The output, a combined probability distribution, can ideally be viewed as representing a summary of the current state of expert opinion regarding the uncertainty of interest.     

The Effects of Learning,Forgetting, and Relearning on Decision Rule Performance in Multiproject Scheduling

Product development occurs in multiproject environments where preemption is often allowed so that critical projects can be addressed immediately. Because product development is characterized by time-based competition, there is pressure to make decisions quickly using heuristics methods that yield fast project completion. Preemption heuristics are needed both to choose activities for preemption and then to determine which resources to use to restart preempted activities. Past research involving preemption has ignored any completion time penalty due to the forgetting experienced by project personnel during preemption and the resulting relearning time required to regain lost proficiency. The purpose of this research is to determine the impact of learning, forgetting, and relearning (LFR) on project completion time when preemption is allowed. We present a model for the LFR cycle in multiproject development environments. We test a number of priority rules for activity scheduling, activity preemption, and resource assignment subsequent to preemption, subject to the existence of the LFR cycle, for which a single type of knowledge worker resource is assigned among multiple projects. The results of the simulation experiments clearly demonstrate that LFR effects are significant. The tests of different scheduling, preemption, and resource reassignment rules show that the choice of rule is crucial in mitigating the completion time penalty effects of the LFR cycle, while maintaining high levels of resource utilization. Specifically, the worst performing rules tested for each performance measure are those that attempt to maintain high resource utilization. The best performing rules are based on activity criticality and resource learning.     

Utilizing fuzzy compatibility of skill sets for team selection in multi-phase projects

In this paper, a model is developed for the selection of personnel for a multiple phase project which takes into account the match between the skills possessed by each individual, the skills needed for each phase, and rather flexible budget considerations. The algorithm uses the fuzzy construct of compatibility to measure the fit of a person’s skill set to the goal set for each project phase. Based on the individual fuzzy compatibility measures, the team is formed from combined levels of compatibility and acceptable levels of quality defined for the goal set. 1 and 2 present the background information necessary to an understanding of project management phases and compatibility of skills. The development of the model and subsequent algorithm in 3 and 4, respectively rely on fuzzy measures of compatibility. Finally, an application is presented in Section 4 with conclusions stated in Section 5.     

A Contrast of HRM and TQM Approaches to Performance Management: Some Evidence*

Using a survey, this paper provides information about the current state of performance management (appraisal) from a sample of UK‐based EFQM‐affiliated organizations. It particularly focuses on several critical issues of performance management in the context of TQM including: the effectiveness of TQM programmes; the rationale for performance management; degree of internal consistency between TQM assumptions and performance management systems; and the relationship among performance management, effectiveness of TQM programmes, employee satisfaction and overall organization performance. Although the fundamental precepts advocated by founders of TQM appear to be in conflict with performance management practices, however, the article argues that, rather than being contradictory, both can add value to the operations of the other in the interest of the organization as a whole. More precisely, the paper explains how a successful TQM strategy requires a rethinking and changing the organization's performance management system, otherwise it is highly likely to result in a disaster. To conclude, the survey evidence is used, combined with previous literature, to discuss the implications of these results for designing a contextually appropriate performance management for TQM and in the interest of the future research on TQM and HRM.     

POLICIES AND IMPACT: AN ANALYSIS OF VILLAGE‐LEVEL MICROFINANCE INSTITUTIONS

This paper uses variation in policies and institutional characteristics to evaluate the impacts of village‐level microfinance institutions in rural Thailand. To identify impacts, we use policies related to the successful/unsuccessful provision of services as exogenous variation in effective financial intermediation. We find that institutions, particularly those with good policies, can promote asset growth, consumption smoothing and occupational mobility, and can decrease moneylender reliance. Specifically, cash‐lending institutions—production credit groups and especially women's groups—are successful in providing intermediation and its benefits to members, while buffalo banks and rice banks are not. The policies identified as important to intermediation and benefits: the provision of savings services, especially pledged savings accounts; emergency services; and training and advice. Surprisingly, much publicized policies such as joint liability, default consequences, or repayment frequency had no measured impacts. (JEL: 012, 016)