The interplay of agency, culture and networks in field evolution

We examine organizational field change instigated by activists. Contrary to existing views emphasizing incumbent resistance, we suggest that collaboration between incumbents and challenger movements may emerge when a movement's cultural and relational fabric becomes moderately structured, creating threats and market opportunities but remaining permeable to external influence. We also elucidate how lead incumbents' attempts at movement cooptation may be deflected through distributed brokerage. The resulting confluence of cultural and relational "structuration" between movement and field accelerates the pace but dilutes the radicalness of institutional innovation, ensuring ongoing, incremental field change. Overall, this article contributes to the emergent literature on field dynamics by uncovering the evolution and outcomes of collaborative work at the intersection of social movements and incumbent fields

The ethical desirability of moral bioenhancement: A review of reasons

Background: The debate on the ethical aspects of moral bioenhancement focuses on the desirability of using biomedical as opposed to traditional means to achieve moral betterment. The aim of this paper is to systematically review the ethical reasons presented in the literature for and against moral bioenhancement. Discussion: A review was performed and resulted in the inclusion of 85 articles. We classified the arguments used in those articles in the following six clusters: (1) why we (don't) need moral bioenhancement, (2) it will (not) be possible to reach consensus on what moral bioenhancement should involve, (3) the feasibility of moral bioenhancement and the status of current scientific research, (4) means and processes of arriving at moral improvement matter ethically, (5) arguments related to the freedom, identity and autonomy of the individual, and (6) arguments related to social/group effects and dynamics. We discuss each argument separately, and assess the debate as a whole. First, there is little discussion on what distinguishes moral bioenhancement from treatment of pathological deficiencies in morality. Furthermore, remarkably little attention has been paid so far to the safety, risks and side-effects of moral enhancement, including the risk of identity changes. Finally, many authors overestimate the scientific as well as the practical feasibility of the interventions they discuss, rendering the debate too speculative. Summary: Based on our discussion of the arguments used in the debate on moral enhancement, and our assessment of this debate, we advocate a shift in focus. Instead of speculating about non-realistic hypothetical scenarios such as the genetic engineering of morality, or morally enhancing 'the whole of humanity', we call for a more focused debate on realistic options of biomedical treatment of moral pathologies and the concrete moral questions these treatments raise

The impact of social networks on knowledge transfer in long-term care facilities: Protocol for a study

<p>Abstract</p> <p>Background</p> <p>Social networks are theorized as significant influences in the innovation adoption and behavior change processes. Our understanding of how social networks operate within healthcare settings is limited. As a result, our ability to design optimal interventions that employ social networks as a method of fostering planned behavior change is also limited. Through this proposed project, we expect to contribute new knowledge about factors influencing uptake of knowledge translation interventions.</p> <p>Objectives</p> <p>Our specific aims include: To collect social network data among staff in two long-term care (LTC) facilities; to characterize social networks in these units; and to describe how social networks influence uptake and use of feedback reports.</p> <p>Methods and design</p> <p>In this prospective study, we will collect data on social networks in nursing units in two LTC facilities, and use social network analysis techniques to characterize and describe the networks. These data will be combined with data from a funded project to explore the impact of social networks on uptake and use of feedback reports. In this parent study, feedback reports using standardized resident assessment data are distributed on a monthly basis. Surveys are administered to assess report uptake. In the proposed project, we will collect data on social networks, analyzing the data using graphical and quantitative techniques. We will combine the social network data with survey data to assess the influence of social networks on uptake of feedback reports.</p> <p>Discussion</p> <p>This study will contribute to understanding mechanisms for knowledge sharing among staff on units to permit more efficient and effective intervention design. A growing number of studies in the social network literature suggest that social networks can be studied not only as influences on knowledge translation, but also as possible mechanisms for fostering knowledge translation. This study will contribute to building theory to design such interventions.</p

Logit models and logistic regressions for social networks: III. Valued relations

social networks, p* models, autologistic models, pseudo-likelihood estimation,

Applying Social Network Analysis and Centrality Measures to Improve Information Flow Analysis

In software development projects, documents are very important for sharing requirements and other information among employees. However, information can be transported in different ways. Conversations, meetings, workshops and emails convey and impart information as well. Especially large companies struggle in dealing with unclear and incorrect information flows. These information flows can be improved by means of information flow analysis and flow patterns. One technique to analyze information flows is the FLOW method. It supports visualization and analysis of information flows to detect lacks and anomalies and thereby improves information flows. An analyst gathers information transported in the company. Afterwards, information flows are visualized and analyzed based on patterns and personal experience. Nevertheless, analysis based on individual knowledge is error-prone. Hence, we improve the FLOW method with the help of social network analysis applying centrality measures to the FLOW method and to support the FLOW analyst