Computer-assisted construct classification of organizational performance concerning different stakeholder groups
This work addresses the challenge of categorizing literature for researchers in business and management, but it is incremental as it builds on existing classification methods with contextual enhancements.
The paper tackles the problem of classifying organizational performance constructs in business and management research articles into a three-level categorization based on stakeholder groups and measurement subcategories, finding that using contextual information from surrounding sentences and external references improves classification accuracy for disaggregate-level labels with limited training data.
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.