This study examines the integration and use of big data in Performance Management Systems (PMS), along with the key factors driving this process. Additionally, this study aims to understand the impact of big data on enhancing PMS processes, decision-making and operational efficiency.
A qualitative case study was conducted on an Indonesian transportation company that uses the Global Positioning System (GPS). Data were collected through 34 in-depth interviews with key organisational members of the company and other external parties, as well as through documentary reviews. Thematic analysis, viewed through the lens of Socio-Technical System (STS) theory, was used to analyse the data.
The findings of this study showed that integrating GPS-derived big data into PMS enhances operational efficiency, control and decision-making. The key drivers of adoption include the regulatory compliance, stakeholder expectations, financial constraints and the need to mitigate business risks. Effective integration spans technical, organisational and cognitive domains, involving elements such as IS infrastructure, collaborative processes, personnel and organisational culture. This integration enables alignment of big data capabilities with organisational performance goals, fostering a data-driven culture within the organization.
This study provides managers with insights on how to incorporate big data into PMS, foster data-driven cultures and develop corporate data strategies.
The findings offer practical implications for organisations and managers on effectively integrating big data into their PMS.
This study contributes to the limited literature on big data integration in PMS, specifically within the context of public transportation. By using the STS theory, this study provides a more comprehensive perspective on the role of big data in performance measurement and control, offering insights for both practitioners and policymakers.
