Laura J. Black

A Dynamic Theory of Expertise and Occupational Boundaries in New Technology Implementation: Building on Barley's Study of CT Scanning:

In this paper, we develop a theory to explain why the implementation of new technologies often disrupts occupational roles in ways that delay the expected benefits. To explore these disruptions, we construct a dynamic model grounded in ethnographic data from Barleys widely cited (1986) study of computed tomography (CT) as implemented in two hospitals. Using modeling, we formalize the recursive relationship between the activity of CT scanning and the types and accumulations of knowledge used by doctors and technologists. We find that a balance of expertise across occupational boundaries in operating the technology creates a pattern in which the benefits of the new technology are likely to be realized most rapidly. By operationalizing the dynamics between knowledge and social action, we specify more clearly the recursive relationship between structuring and structure. *

Learning from our GWAS mistakes: from experimental design to scientific method.

Many public and private genome-wide association studies that we have analyzed include flaws in design, with avoidable confounding appearing as a norm rather than the exception. Rather than recognizing flawed research design and addressing that, a category of quality-control statistical methods has arisen to treat only the symptoms. Reflecting more deeply, we examine elements of current genomic research in light of the traditional scientific method and find that hypotheses are often detached from data collection, experimental design, and causal theories. Association studies independent of causal theories, along with multiple testing errors, too often drive health care and public policy decisions. In an era of large-scale biological research, we ask questions about the role of statistical analyses in advancing coherent theories of diseases and their mechanisms. We advocate for reinterpretation of the scientific method in the context of large-scale data analysis opportunities and for renewed appreciation of falsifiable hypotheses, so that we can learn more from our best mistakes.

Modeling intergovernmental collaboration: a system dynamics approach

Describes the development and testing of a system-dynamics model of collaboration, trust-building and knowledge sharing in a complex, inter-governmental information system project. The model-building and testing activity was an exploration of the feasibility of applying these modeling methods to a complex inter-organizational process about which only qualitative data were available. The process to be modeled was the subject of qualitative field research studying knowledge and information sharing in inter-organizational networks. This research had produced a large volume of observational and interview data and analyses about the technology project. In the course of collecting and analyzing data from this project, the researchers noted evidence of what appeared to be important feedback effects. The feedback loops appeared to influence the collaboration and knowledge sharing, critical parts of how the information system design and construction progressed. These observations led to conversations with colleagues who had extensive experience in dynamic modeling. All agreed that applying dynamic modeling methods to this process had considerable potential to yield valuable insights into collaboration. As a novel application of the methods, it could yield new modeling insights as well. The modeling experience supported both propositions and was judged a success that will lead to continued exploration of these questions.

Assessing executability in large complex programs

Why is it, when we execute very large aerospace development programs according to project management best practices, we do not reliably achieve program success? Standard project management tools used on programs include static tools such as PERT charts, critical path analysis, and earned-value analysis. These tools, however, are insufficient for representing all the dependencies that exist, or for recognizing the unintended consequences that often result from actions taken to get a program “back on track.” Also, standard project management tools provide only limited visibility into emerging short-term and long-term dynamics during development that affect a programs ability to meet its requirements adequately within the expected cost and schedule constraints, i.e., a programs ability to be executable. This paper reports on research undertaken to enhance the governments capability for managing large, complex programs. This research will produce a dynamic model adaptable to multiple large space-system development programs. However, the rigor of the modeling process has underscored the need for theoretical constructs that describe management of large, complex programs. To that end, we have sought sources to support an emerging theory that can be translated into a dynamic model that adequately represents both best and actual practices in program management. This theory is developed by creating internally consistent causal relations affecting capabilities, cost, quality, and schedule and their associated accumulations, over time.