A brazilian study on data-driven public service readiness

Data is an engine for public sector digital transformation, whose potential is to improve social well-being and combat population's poverty. This article aims to propose steps to improve agencies' readiness to transform their operation model into a data-driven public service (DDPS). Theref...

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Main Authors: Melo, Adriane Medeiros, Mariano, Ari Melo
Format: Conference Proceeding
Language:English
Subjects:
Applied computing
Brazil
Computational modeling
Data models
Data-driven Government
Digital transformation
Engines
Information systems
Mathematical models
PLS-SEM
Transforms
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Mariano, Ari Melo
description Data is an engine for public sector digital transformation, whose potential is to improve social well-being and combat population's poverty. This article aims to propose steps to improve agencies' readiness to transform their operation model into a data-driven public service (DDPS). Therefore, explanatory research with a quantitative approach was used through structural equations to validate the model, based on data collected from a questionnaire applied to 101 Brazilian public servants. The results revealed that Operational Capacity factor (37.7 \%) has the greatest impact on readiness for the transition to DDPS, when using partial least squares structural equation modeling (PLS-SEM).
doi_str_mv 10.23919/CISTI58278.2023.10211899
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subjects Applied computing
Brazil
Computational modeling
Data models
Data-driven Government
Digital transformation
Engines
Information systems
Mathematical models
PLS-SEM
Transforms
title A brazilian study on data-driven public service readiness
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