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This chapter presents six examples of organization-related social network mining: 1) interorganizational and sentiment networks in the Deepwater BP Oil Spill events, 2) intraorganizational interdepartmental networks in the Savannah College of Art and Design (SCAD), 3) who-to-whom email networks across the organizational hierarchy the Ford Motor Com...
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... In an interorganizational study, organizations listed on the White House's web pages about the Gulf oil leak were network analyzed based on an include list run with related news stories in daily Gulf-area newspapers using a weekly time interval [10]. The hypothesis was supported that the more central BP was in the interorganizational network, the more negative was the sentiment in the news stories. ...
This research highlights a kind of semantic network analysis based on an include list. We analyze the networks only among words on the list as they appear in a series of text corpora word pairs for an organization. The example uses documents about Facebook over a 12-month period, dividing them into 12 time-based files. In each time slice we map networks among key publics and measure the centrality of each from one time period to the next. The network of publics becomes more complex across time. Publics fluctuate in centrality. We describe other kinds of semantic network analysis for business applications using include lists.
This paper introduces a method for creating scales of constructs based on word bigram cooccurrences in natural language text. Instead of using a stop-word list to drop less useful words, we use a start-word list that enables computing the cooccurrences of only these “smart words.” In this way, we can create scales to measure communication constructs by first listing the key terms in the conceptual definition, then expanding the terms by looking up synonyms in dictionaries such as WordNet. Following this, we compute the cooccurrence network among these words with a sliding window. Next, we extract the first dimension through principal components analysis and identify the words that load most highly on the first factor. For these words, we sum the frequencies, which produces the final index for the construct. This operationalization yields index scales that have high construct validity, which contributes to external validity. Extending the procedures of classic psychometric index construction into the natural language domain avoids the biases of data based on fixed-choice questionnaires. To demonstrate the procedures for construct operationalization, we use a dataset of news stories about the BP Deepwater Horizon Gulf Oil Spill, scaling environmental uncertainty and organizational responses to it including innovation, strategic planning, and changes in organizational structure.
Environmental governance is of growing concern in a world that is more interconnected and interdependent than ever and threatened by climate change and the depletion or contamination of natural resources that are often shared by multiple stakeholders. Governance at the local, national, and international level requires representatives of diverse stakeholder groups with often competing ideologies and interests to make decisions collaboratively (Buck et al. 2001; Folke et al. 2005; Wondolleck and Yaffee 2000). This collaborative decision-making often takes place in formalized groups who are given the responsibility for developing governmental regulations that determine how natural resources will be used and protected.
