Effect of Evidence-Based Policymaking on Public Service Delivery Efficiency: Evidence from the Indian Technology Industry Policy
DOI:
https://doi.org/10.66348/jpa.26.v1.n1.a25Keywords:
Evidence-Based Policymaking; Data Analytics Capability; Policy Implementation Quality; Public Service Delivery Efficiency; Indian Technology Industry; PLS-SEMAbstract
This study examines the relationships among Evidence-Based Policymaking (EBPM), Data Analytics Capability (DAC), Policy Implementation Quality (PIQ), and Public Service Delivery Efficiency (PSDE) in the context of the Indian technology industry. Drawing on a quantitative, cross-sectional survey of 380 policy stakeholders, including government officials, industry professionals, and consultants, the study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed conceptual model. Results indicate that both EBPM and DAC have significant positive effects on PSDE, with PIQ partially mediating these relationships. The findings highlight the critical role of effective policy implementation in translating evidence-based and data-driven practices into enhanced service delivery outcomes. The study contributes theoretically by integrating EBPM, DAC, and implementation quality into a unified model and empirically validating their interrelationships. Practically, it underscores the importance of investing in evidence-based practices, analytical capabilities, and high-quality implementation mechanisms to improve governance performance, transparency, and citizen satisfaction. Limitations include the cross-sectional design and sector-specific focus, suggesting future research could explore longitudinal approaches and multi-sector comparisons.
Received: 2026-04-09 | Revised: 2026-06-02 | Accepted: 2026-06-23 | Published: 2026-06-30
Declarations
Ethics and Guidelines: The study was conducted in accordance with the Declaration of Helsinki. The ethical approval specifically covers the research procedures and data collection activities conducted for this study.
Consent to participate: Written informed consent was obtained from all participants prior to data collection. Consent was obtained using a standardized written form, signed by each participant before participation in the survey.
Consent to publish: The authors have provided consent to publish.
Competing interests: The authors declare no competing interests.
Data availability statement: Data will be made available on reasonable request from the corresponding author.
Funding: This research received no external funding.
Clinical Trial Number: Not Applicable.
Declaration of using generative AI: During the preparation of this work the author(s) used ChatGPT in order to correct the grammatical errors. After using this tool/service, the author(s) reviewed and edited the content as needed and take full responsibility for the content of the published article.
Author Contributions: Conceptualization, A.A.J.; methodology, A.A.J.; formal analysis, A.A.J.; writing—original draft preparation, A.A.J.; writing—review and editing, A.A.J. All authors have read and agreed to the published version of the manuscript.
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Data Availability Statement
Data will be made available on reasonable request from the corresponding author.
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Copyright (c) 2026 Abdullah Al Jubayed (Author)
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