The influence of big data analytics capabilities on the performance of manufacturing firms

Abstract

Rapid advances in technologies and complementary improvements in the Industrial Internet of Things have contributed to the exponential growth in data availability. This has resulted in a new level of dynamism and complexities manufacturing organisations must overcome to maintain performance levels, competitiveness and create value. Big data research has been primarily positioned as a conduit through which manufacturing organisations can improve performance through higher-order analytical techniques, which contribute to improved data-driven decision-making. Despite all the benefits that big data can have for manufacturing organisational performance, research on how big data should be implemented in these dynamic, continuously evolving environments is limited. This research is theoretically positioned within the organisation's resource base view and dynamic capabilities. Organisational information technology capabilities (management, infrastructure and expert skills) are the foundational cornerstone of big data analytics capabilities influence performance in manufacturing organisations. This research has a particular focus on the impact process-orientated dynamic capabilities have on organisational performance. This is of significant importance to manufacturing organisations which consists of many distinct interrelated concurrent processes. Research into this construct is limited, and the findings could prove beneficial to organisations in overcoming current and future challenges that may impact the performance of the broader organisation. This study analysed 165 online survey responses from South African manufacturing sector respondents. The research employed a higher-order formative-reflective PLS-SEM model. The findings of all three research hypothesis questions found that big data analytics capabilities positively influenced manufacturing organisations' performance and process-orientated dynamic capabilities. The study highlighted notable insights for manufacturing and academia through dynamic and process-orientated capabilities to extract value.