Uncovering the genome of leading indicators from lagging indicators and normative documents: A proof-of-concept study

Introduction: This research implements the steps of developing or identifying leading indicators (LIs) delineated in a previously published conceptual model to test its practicality on case study data. Concomitant objectives are (a) to systematically review extant literature of ‘LIs development and identification’ to develop an analytical framework for identifying LIs; and to identify LIs from case study incident reports and normative documents using the framework. Method: To empirically validate the conceptual model, a two staged data analysis process was adopted: (1) a theoretical work stage, where pertinent literature was studied through systematic literature review using Scopus and Web of Science databases and a detailed framework analysis; and (2) practical work stage, where an inductively developed analytical framework and insights gained from the theoretical work stage were applied to real-life case study data and their apposite normative documents. Random sampling was used to select 12 different case studies of accidents from a private database of 97 case studies. In total, 2,423 LIs were identified from extant literature and through framework analysis using the bespoke analytical framework generated, a total of 484 LIs were identified from a combination of selected case study materials and their relevant normative documents. All these 484 newly developed LIs were contrasted with a compilation of the previously published 2,423 LIs in the literature. Results: Consequently, a total of 232 LIs out of 484 were recognized as entirely new and novel. These LIs were then thematically grouped into 19 clusters for brevity. A novel analytical framework for identifying new LIs was inductively developed. The framework enables identification of LIs from a qualitative dataset and classify them into eight types of LIs. Practical Applications: This novel research constitutes the first attempt to identify and validate LIs via the use of an analytical framework and real-life case study data.