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The impact of predictive and prescriptive big data analytics on sustainable product development through technological innovation in manufacturing SMEs

Naga Bharadwaj Bhavikatta

Production & Manufacturing Research2026https://doi.org/10.1080/21693277.2026.2618335article
AJG 1
Weight
0.37

What the paper says

This research investigates the influence of prescriptive (PRBDA) and predictive big data analytics (PBDA) on sustainable product development performance (SPDP) among manufacturing SMEs, with technological innovation (TI) serving as a mediator. Grounded in Information Technology (IT) and Dynamic Capabilities Theory (DCT), the research explores how data-driven capabilities enhance sustainability through innovation. Using a quantitative design, a Likert-scale survey was distributed to 260 SME respondents. Data were analyzed using SPSS and PROCESS macro (Model 4), ensuring rigor through validity, reliability, and mediation tests. Results indicate that both PRBDA and PBDA significantly impact SPDP, with TI providing partial mediation. Notably, PRBDA shows a stronger indirect effect, highlighting its role in transforming insights into actionable initiatives. Theoretically, this extends DCT to SME sustainability. Practically, it guides leaders to integrate analytics-driven innovation, while policy implications advocate for institutional support to bolster green and digital transitions in the SME sector.

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https://doi.org/https://doi.org/10.1080/21693277.2026.2618335

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@article{naga2026,
  title        = {{The impact of predictive and prescriptive big data analytics on sustainable product development through technological innovation in manufacturing SMEs}},
  author       = {Naga Bharadwaj Bhavikatta},
  journal      = {Production & Manufacturing Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/21693277.2026.2618335},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.