Designing a Big Data Policy-Making Model Influencing the Growth of Digital Economy Startups
Keywords:
Policymaking, Big Data, Digital Economy Growth, StartupAbstract
Objective: This study aimed to design and expert-validate a policy-making model for big data that influences the growth of digital economy startups.
Methodology: A sequential qualitative design was employed. In the model-design phase, a systematic grounded theory approach (open, axial, and selective coding) was applied using semi-structured interviews. Participants included (i) relevant faculty members and researchers, (ii) startup managers and entrepreneurs, (iii) governmental experts and policy-makers, and (iv) data analysts and IT specialists, selected through snowball sampling until theoretical saturation (n = 21). Credibility and dependability were supported via expert review, member checking, and within-subject agreement (0.79). In the validation phase, a three-round Delphi technique was conducted using an expert checklist and descriptive analysis in SPSS with purposive sampling (n = 17). Test–retest reliability of the checklist was 0.89.
Findings: The qualitative analysis identified 510 initial codes; after removing 389 duplicates, 121 final indicators were retained. These indicators were organized into 27 subcategories and 11 main categories, and then positioned within a six-component paradigm model: causal conditions, contextual conditions, intervening conditions, the central phenomenon, strategies, and outcomes. In Round 3 of the Delphi, Kendall’s coefficients indicated high consensus across dimensions: causal (0.854), contextual (0.902), intervening (0.929), strategies (0.898), and outcomes (0.918). Round-3 rankings showed the highest mean scores for “data infrastructure and access” (causal), “macroeconomic dynamics and risk” (contextual), “implementation financing” (intervening), “model and data operations” (strategies), and “economic value growth” (outcomes).
Conclusion: The final model provides an integrated policy-making roadmap for leveraging big data to support startup-driven digital economy growth, emphasizing data infrastructure and governance, ecosystem capability-building, cross-sector alignment and execution capacity, and outcome-oriented measurement and accountability mechanisms.
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