This article presents a state-of-the-art system to extract and synthesize causal statements from company reports into a directed causal graph. The extracted information is organized by its relevance to different stakeholder group benefits (customers, employees, investors, and the community/environment). The presented method of synthesizing extracted data into a knowledge graph comprises a framework that can be used for similar tasks in other domains, e.g., medical information. The current work addresses the problem of finding, organizing, and synthesizing a view of the cause-and-effect relationships based on textual data in order to inform and even prescribe the best actions that may affect target business outcomes related to the benefits for different stakeholders (customers, employees, investors, and the community/environment).

Using natural language processing tools, we investigate the semantic differences in medical guidelines for three decision problems: breast cancer screening, lower back pain and hypertension management. The recommendation differences may cause undue variability in patient treatments and outcomes. Therefore, having a better understanding of their causes can contribute to a discussion on possible remedies. We show that these differences in recommendations are highly correlated with the knowledge brought to the problem by different medical societies, as reflected in the conceptual vocabularies used by the different groups of authors. While this article is a case study using three sets of guidelines, the proposed methodology is broadly applicable. Technically, our method combines word embeddings and a novel graph-based similarity model for comparing collections of documents. For our main case study, we use the CDC summaries of the recommendations (very short documents) and full (long) texts of guidelines represented as bags of concepts. For the other case studies, we compare the full text of guidelines with their abstracts and tables, summarizing the differences between recommendations. The proposed approach is evaluated using different language models and different distance measures. In all the experiments, the results are highly statistically significant. We discuss the significance of the results, their possible extensions, and connections to other domains of knowledge. We conclude that automated methods, although not perfect, can be applicable to conceptual comparisons of different medical guidelines and can enable their analysis at scale.

The number of research articles in business and management has dramatically increased along with terminology, constructs, and measures. Proper classification of organizational performance constructs from research articles plays an important role in categorizing the literature and understanding to whom its research implications may be relevant. In this work, we classify constructs (i.e., concepts and terminology used to capture different aspects of organizational performance) in research articles into a three-level categorization: (a) performance and non-performance categories (Level 0); (b) for performance constructs, stakeholder group-level of performance concerning investors, customers, employees, and the society (community and natural environment) (Level 1); and (c) for each stakeholder group-level, subcategories of different ways of measurement (Level 2). We observed that increasing contextual information with features extracted from surrounding sentences and external references improves classification of disaggregate-level labels, given limited training data. Our research has implications for computer-assisted construct identification and classification - an essential step for research synthesis.