Computer-assisted construct classification of organizational performance concerning different stakeholder groups

TL;DR

This paper presents a computer-assisted method for classifying organizational performance constructs in research articles into a detailed three-level taxonomy, enhancing literature categorization and understanding.

Contribution

It introduces a novel three-level classification framework for organizational performance constructs and demonstrates improved accuracy using contextual features.

Findings

Contextual features improve classification accuracy

Multi-level taxonomy captures detailed construct distinctions

Method aids research synthesis in management literature

Abstract

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.