Institutionalization or Decoupling? An Exploratory Analysis of the UN Global Compact LEAD Initiative

Institutionalization occurs when organizations adopt policies and practices in an attempt to increase their legitimacy and competitive position. This process assumes that an organization's formal policies (macro‐level) are translated into organizational practices (micro‐level). This translation, however, can fail to occur, resulting in organizational decoupling and de‐institutionalization. Drawing on an institutionalization typology based on different levels of legitimacy (macro) and taken‐for‐grantedness (micro), the article draws on data from an assessment of the United Nations Global Compact LEAD initiative, assessing the extent to which this initiative is being institutionalized in the member firms and examining the implications for the LEAD initiative and our thinking about institutionalization.     

Clarifying Types of Uncertainty: When Are Models Accurate,and Uncertainties Small?

Professor Aven has recently noted the importance of clarifying the meaning of terms such as "scientific uncertainty" for use in risk management and policy decisions, such as when to trigger application of the precautionary principle. This comment examines some fundamental conceptual challenges for efforts to define "accurate" models and "small" input uncertainties by showing that increasing uncertainty in model inputs may reduce uncertainty in model outputs; that even correct models with "small" input uncertainties need not yield accurate or useful predictions for quantities of interest in risk management (such as the duration of an epidemic); and that accurate predictive models need not be accurate causal models.     

Quantifying uncertainty in the biospheric carbon flux for England and Wales

Summary.  A crucial issue in the current global warming debate is the effect of vegetation and soils on carbon dioxide (CO₂) concentrations in the atmosphere. Vegetation can extract CO₂ through photosynthesis, but respiration, decay of soil organic matter and disturbance effects such as fire return it to the atmosphere. The balance of these processes is the net carbon flux. To estimate the biospheric carbon flux for England and Wales, we address the statistical problem of inference for the sum of multiple outputs from a complex deterministic computer code whose input parameters are uncertain. The code is a process model which simulates the carbon dynamics of vegetation and soils, including the amount of carbon that is stored as a result of photosynthesis and the amount that is returned to the atmosphere through respiration. The aggregation of outputs corresponding to multiple sites and types of vegetation in a region gives an estimate of the total carbon flux for that region over a period of time. Expert prior opinions are elicited for marginal uncertainty about the relevant input parameters and for correlations of inputs between sites. A Gaussian process model is used to build emulators of the multiple code outputs and Bayesian uncertainty analysis is then used to propagate uncertainty in the input parameters through to uncertainty on the aggregated output. Numerical results are presented for England and Wales in the year 2000. It is estimated that vegetation and soils in England and Wales constituted a net sink of 7.55 Mt C (1 Mt C = 10¹² g of carbon) in 2000, with standard deviation 0.56 Mt C resulting from the sources of uncertainty that are considered.     

Examples of the use of an equivalence theorem in constructing optimum experimental designs for random-effects nonlinear regression models

The problem considered is that of finding an optimum measurement schedule to estimate population parameters in a nonlinear model when the patient effects are random. The paper presents examples of the use of sensitivity functions, derived from the General Equivalence Theorem for D-optimality, in the construction of optimum population designs for such schedules. With independent observations, the theorem applies to the potential inclusion of a single observation. However, in population designs the observations are correlated and the theorem applies to the inclusion of an additional measurement schedule. In one example, three groups of patients of differing size are subject to distinct schedules. Numerical, as opposed to analytical, calculation of the sensitivity function is advocated. The required covariances of the observations are found by simulation.     

Becoming an American and Liking It as Functions of Social Distance and Severity of Initiation

This paper examines the effects of social distance among a sample of immigrants in the process of becoming United States citizens. Using the Bogardus Social Distance Scale, a positive relationship was found between the degree of social distance and the likelihood of becoming a citizen. Social distance also serves as an important indicator of severity of initiation; the findings suggest that severity of initiation had a positive influence on reported satisfaction with life in America for those who became U. S. citizens. For those who did not become citizens, however, social distance had an inverse affect on satisfaction.     

CALIBRATING MEASURES OF FAMILY ACTIVITIES BETWEEN LARGE- AND SMALL-SCALE DATA SETS

Two studies of working families are combined to demonstrate a strategy for producing reliable estimates from the combination of self-reported (large N) and observational (small N) data. Both studies examine where and how dual-career families spend time at home. The 500 Family Study is sociological and uses self-reported time diary data from a national sample; the CELF study is anthropological and uses observational scan sampling data from a regional sample of 32 families. The data are combined as if they constitute one sample, and an analytic solution for establishing the reliability of the resulting composite estimates of time use is provided. Merging the data sets provides validation for each study, neither of which is without potential methodological weaknesses. The advantages of combining data from the independent data collection methods are discussed, and selected substantive findings on families' activities are highlighted, illustrating similarities and differences between findings in the independent and combined data sets. Results show that working families spend significant time in a small spectrum of home spaces, particularly kitchens and living rooms, with leisure activities prevailing, but mothers, fathers, and children differ in where and how they spend their time. Overall, a template for merging data from different disciplines and methods is provided.     

Domestic Violence Service Providers’ Needs and Perceptions of Technology: A Qualitative Study

There is growing recognition of the need to better understand the intersections between the work of domestic violence service providers and technology. Professionals who work with clients impacted by domestic violence are increasingly using technologies across different aspects of their work, including communicating with other professionals and seeking information and resources via the Internet. The current study used qualitative data from two sources—individual interviews and two focus groups—to learn about domestic violence service providers’ needs and perceptions related to technology use. The results provide insights about technologies used currently, expected benefits of future technological advances, barriers to using technology, and participants’ self-rated levels of comfort with technology.