Conceptualization and scale development for big data-based learning organization capability.

Abstract

Introduction: In today's competitive business landscape, organizations must enhance learning and adaptability to gain a strategic edge. While big data significantly influences organizational learning, a comprehensive tool to measure this capability has been lacking in the literature. This study aims to develop a valid and reliable scale to assess big data-based learning organization capability.

Methods: A two-phase research design was employed. In the first phase, Exploratory Factor Analysis (EFA) was conducted on data collected from 232 managers, identifying 22 items across three underlying factors. In the second phase, Confirmatory Factor Analysis (CFA) was applied to an independent sample (n = 128) to validate the scale's structure and its alignment with the theoretical model.

Results: The EFA results revealed a clear three-factor structure, and the CFA confirmed the model's fit to the data, demonstrating good psychometric properties. The final BD-LOC scale shows high internal consistency and construct validity.

Discussion: The BD-LOC scale provides organizations with a valuable tool to assess their big data-driven learning capabilities. It supports strategic decision-making, fosters innovation, and enhances operational efficiency. This study fills a significant gap in the literature and contributes to the effective implementation of digital transformation strategies in organizations.