1,720,991 research outputs found
Assimilation and differentiation: A multilevel perspective on organizational and network change
Full text linkThis paper builds on recently derived stochastic actor-oriented models (SAOMs) for the coevolution of one-mode and two-mode networks, and extends them to the analysis of how concurrent multilevel processes of (internal) organizational and (external) network change affect one another over time. New effects are presented that afford specification and identification of two apparently conflicting micro-relational mechanisms that jointly affect decisions to modify the portfolio of internal organizational activities. The first mechanism, assimilation, makes network partners more similar by facilitating the replication and diffusion of experience. The second mechanism, functional differentiation, operates to maintain and amplify differences between network partners by preventing or limiting internal organizational change. We illustrate the empirical value of the model in the context of data that we have collected on a regional community of hospital organizations connected by collaborative patient transfer relations observed over a period of seven years. We find that processes of social influence conveyed by network ties may lead both to similarity and differences among connected organizations. We discuss the implications of the results in the context of current research on interorganizational networks
Determinants of knowledge-sharing networks in primary care
Full text linkBackground. Around the world, health reforms are increasingly fostering collaboration and integration among primary care physicians with the aim of facilitating knowledge sharing and evidence-informed decision making. Although extant research on this topic is abundant, the evidence and results regarding social and organizational factors affecting the formation of knowledge-sharing networks in this setting are inconclusive.
Purposes. The aim of this article is to explore multiple theoretical mechanisms explaining the formation of knowledge-sharing networks among primary care physicians across relevant clinical areas.
Methodology/Approach. The data are collected from two local health authorities (LHAs) in the Italian National Health Service that are responsible for delivering primary care in two Italian regions. Exponential random graph models are used to test the hypotheses.
Findings. Our findings indicate that knowledge-sharing networks are highly correlated across clinical areas. In addition, knowledge-sharing networks are highly reciprocal and clustered. We also observe that formal models adopted to foster collaboration have remarkably different effects on the formation of knowledge networks, depending upon the diverse knowledge management approaches adopted in the surveyed LHAs.
Practice Implications. Primary care organizations need to develop and implement knowledge management practices in order to help physicians in identifying knowledge domain experts as well as to support connections through formal groupings and incentives
From network ties to network structures: exponential random graph models of interorganizational relations
No full textTheoretical accounts of network ties between organizations emphasize the interdependence of individual intentions, opportunities, and actions embedded in local configurations of network ties. These accounts are at odds with empirical models based on assumptions of independence between network ties. As a result, the relation between models for network ties and the observed network structure of interorganizational fields is problematic. Using original fieldwork and data that we have collected on collaborative network ties within a regional community of hospital organizations we estimate newly developed specifications of Exponential Random Graph Models (ERGM) that help to narrow the gap between theories and empirical models of interorganizational networks. After controlling for the main factors known to affect partner selection decisions, full models in which local dependencies between network ties are appropriately specified outperform restricted models in which such dependencies are left unspecified and only controlled for statistically. We use computational methods to show that networks based on empirical estimates produced by models accounting for local network dependencies reproduce with accuracy salient features of the global network structure that was actually observed. We show that models based on assumptions of independence between network ties do not. The results of the study suggest that mechanisms behind the formation of network ties between organizations are local, but their specification and identification depends on an accurate characterization of network structure. We discuss the implications of this view for current research on interorganizational networks, communities, and fields
A multilevel study of social networks and collective reactions to organizational change
Full text linkThe purpose of this study is to examine the micro-level dynamics underlying macro-level associations between organizational change and its outcomes, focusing in particular on the role of networks in shaping individual reactions to change. Drawing upon multilevel research on situational and individual antecedents of change, we first argue that the magnitude of change at the unit level has a non-linear effect on change recipients’ tendency to resist change, which in turn influences their adaptive behaviors. We argue, further, that the attitudinal and structural composition of the professional networks in which change recipients are embedded account for differences in their adaptive behaviors. Finally, we argue that individual adaptivity coalesces at the collective, i.e., unit level, and predicts the attainment of desired change goals. We find general support for our arguments in a longitudinal study using multi-source data on 170 physicians in 29 units of a large hospital that experienced a major restructuring intervention. Results confirm that multilevel mechanisms involving individuals and their social context fundamentally undergird macro-level outcomes of change. We discuss the theoretical and practical implications of bringing a network perspective to bear on issues of individual and collective reactions to organizational change
Relational event models for longitudinal network data with an application to interhospital patient transfers
Full text linkThe main objective of this paper is to introduce and illustrate relational event models, a new class of statistical models for the analysis of time-stamped data with complex temporal and relational dependencies. We outline the main differences between recently proposed relational event models and more conventional network models based on the graph-theoretic formalism typically adopted in empirical studies of social networks. Our main contribution involves the definition and implementation of a marked point process extension of currently available models. According to this approach, the sequence of events of interest is decomposed into two components: (a) event time, and (b) event destination. This decomposition transforms the problem of selection of event destination in relational event models into a conditional multinomial logistic regression problem. The main advantages of this formulation are the possibility of controlling for the effect of event-specific data and a significant reduction in the estimation time of currently available relational event models. We demonstrate the empirical value of the model in an analysis of interhospital patient transfer within a regional community of health care organizations. We conclude with a discussion of how the models we presented help to overcome some the limitations of statistical models for networks that are currently available
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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