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Big data analytics and green finance as drivers of sustainable ESG performance

Shagufta Tariq Khan et al.

Journal of Science and Technology Policy Management2026https://doi.org/10.1108/jstpm-10-2025-0471article
AJG 1ABDC C
Weight
0.50

What the paper says

Purpose A quantitative research design was adopted using a Web-based survey targeting 290 Indian procurement experts across public and private sectors. The data were analyzed using SmartPLS 4.0, applying the Partial Least Squares–Structural Equation Modeling technique to test measurement reliability, structural relationships and moderating effects. Design/methodology/approach This study investigates the role of Big Data Analytical Capabilities (BDAC) in enhancing firms’ environmental, social and governance (ESG) performance, with a particular emphasis on the moderating effect of Green Finance (GF). It aims to uncover how data-driven capabilities and sustainable financial mechanisms jointly promote ESG outcomes among Indian procurement professionals. Findings The results reveal that BDAC significantly and positively influences all ESG dimensions, particularly Environmental Performance (EP) and Green Performance (GP). Although GF independently improves ESG outcomes, its effect is weaker than BDAC’s. Importantly, GF significantly moderates the relationship between BDAC and GP, but not with EP or Social Performance, highlighting a synergistic role in advancing environmentally driven outcomes. Practical implications The findings underscore that firms can strengthen ESG performance by simultaneously investing in big data analytics and green financing instruments. Managers and policymakers should encourage data-driven sustainability monitoring and facilitate access to green funds to support environmentally responsible procurement and corporate governance practices. Originality/value This study extends the Resource-Based View Natural Resource-Based View and Resource-Dependence Theory by integrating technological and financial resources as complementary drivers of sustainability. To the best of the authors’ knowledge, this study is among the first to empirically examine this interaction in the context of Indian procurement experts, a critical yet understudied domain.

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https://doi.org/https://doi.org/10.1108/jstpm-10-2025-0471

Or copy a formatted citation

@article{shagufta2026,
  title        = {{Big data analytics and green finance as drivers of sustainable ESG performance}},
  author       = {Shagufta Tariq Khan et al.},
  journal      = {Journal of Science and Technology Policy Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/jstpm-10-2025-0471},
}

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Evidence weight

0.50

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

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
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.