A study on big data analytics and innovation: From technological and business cycle perspectives

Uthayasankar Sivarajah, Sachin Kumar, Vinod Kumar, Sheshadri Chatterjee and Jing Li

Technological Forecasting and Social Change, 2024, vol. 202, issue C

Abstract: In today's rapidly changing business landscape, organizations increasingly invest in different technologies to enhance their innovation capabilities. Among the technological investment, a notable development is the applications of big data analytics (BDA), which plays a pivotal role in supporting firms' decision-making processes. Big data technologies are important factors that could help both exploratory and exploitative innovation, which could affect the efforts to combat climate change and ease the shift to green energy. However, studies that comprehensively examine BDA's impact on innovation capability and technological cycle remain scarce. This study therefore investigates the impact of BDA on innovation capability, technological cycle, and firm performance. It develops a conceptual model, validated using CB-SEM, through responses from 356 firms. It is found that both innovation capability and firm performance are significantly influenced by big data technology. This study highlights that BDA helps to address the pressing challenges of climate change mitigation and the transition to cleaner and more sustainable energy sources. However, our results are based on managerial perceptions in a single country. To enhance generalizability, future studies could employ a more objective approach and explore different contexts. Multidimensional constructs, moderating factors, and rival models could also be considered in future studies.

Keywords: Big data analytics; Technological cycle; Technological innovation; Firm performance; Business cycle; Incremental change (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:202:y:2024:i:c:s0040162524001240

DOI: 10.1016/j.techfore.2024.123328

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