From Big Data Analytics to Organizational Agility: What Is the Mechanism?

Abstract

In the age of digitalization, big data analytics capabilities are considered one of the most critical organizational resources. Many organizations make considerable investments in these resources with an intention to improve their agility. However, the mechanism to reap agility from big data analytics still requires extensive empirical research and analysis. This study extends the big data analytics model by examining the mediating effects of entrepreneurial orientation between big data analytics capabilities and organizational agility. Partial least squares-structured equation modeling (PLS-SEM) was used to analyze the responses collected from 104 firms in Jordan. Results demonstrate that entrepreneurial orientation explains the relationship between big data analytics capabilities and agility. This finding contributes to the management literature by showing that big data analytics capabilities may enhance firm entrepreneurial orientation. While much of the prior research conceptualized the entrepreneurial orientation of the firm as a static characteristic, the current study argues that big data analytic capabilities play a key role in developing organizational agility through its role in improving entrepreneurial orientation, which subsequently creates value for firms, their customers, and the other stakeholders. Finally, challenges and future scope pertaining to this study are discussed. Recommendations for future studies on this promising topic include the use of longitudinal designs and mixed methods (quantitative with qualitative) approaches to provide researchers with new insights.

Navigating Production Automation as a Service: Unveiling Drivers, Benefits, and Challenges in Manufacturing Companies

“…These identified benefits, including cost-effectiveness, enhanced flexibility, scalability, productivity, efficiency, and improved product quality, provide additional information that supports PAaaS implementation in manufacturing [9,19,20,23,24,28]. While previous studies have acknowledged cost-effectiveness and increased flexibility through the adoption of production automation [22,30], this study further contributes to the theory by providing empirical evidence of these benefits, thus establishing a more comprehensive theoretical framework supported by real-life experiences.…”

“…Cloud-based platforms offer access to real-time production data, facilitating precise decision-making [29]. Predictive analytics enable manufacturers to adjust production according to future demand, maintaining a consistent production level while responding to changes [30].…”

The impact of big data analytics capabilities on green supply chain performance: is green supply chain innovation the missing link?

“…Kabil (2021) defined BDA as a set of systematic steps taken by a firm or a BD analyst to examine a large set of datasets characterised by diversity and large size to get new information or knowledge that enables firms to better understand the hidden patterns and relationships that can help them improve decision-making. BDA can be considered one of the most important organisational resources for any manufacturing firm that uses smart applications related to digitisation and the Fourth Industrial Revolution (Al-Darras and Tanova, 2022). Consequently, BDA requires strong human, financial, and infrastructure capabilities to benefit from these data (Adrian et al.…”