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Hierarchically superior bodies develop normative instructions to induce the diffusion of innovations, stimulating the adoption of management practices in supervised public bodies and seeking public administration efficiency increase. Despite this, the effectiveness of these normative instructions is unknown, as well as its inducing and lasting effects in the diffusion of these innovations, especially in Brazil. This study aims to understand the effects of normative induction.

The adoption of risk management, integrity & ethics and information security practices was evaluated over a decade (2009 to 2019), including the adoption behavior of more than 200 Brazilian federal agencies. Public open data were collected and analyzed with multinomial logistic regression.

The normative instructions’ effectiveness in propagating the evaluated practices is remarkable; however, its mere development by the superior bodies cannot be considered enough since the general adoption index can be considered good but not excellent. No evaluated practice reached a saturation level above 75%.

This paper contributes to bringing the international literature’s generic knowledge on the adoption of innovation to the specific Brazilian public administration context, providing insightful implications for policymakers, public managers and researchers.

This work is unique, as it systematically analyzes multiple innovation adoption and presents excellent opportunities for future researchers by reproducing all scripts and automation developed. Furthermore, all data are available and hosted on public platforms with detailed steps and documentation.

The use of open data from governmental sources allows enhanced transparency and the discovery of affecting variables while observing innovation adoption in the public administration.

The presence of normative instructions and their adoption rate is rarely measured in the Brazilian public administration.

This study aimed at analyzing the factors that induce the intention of contribution by participants in crowdsourcing initiatives.

This study is an explanatory investigation using a quantitative approach. In the second stage, an exploratory study was carried out. Data were obtained through online questionnaires available to the contributors of two platforms, and results were obtained from a regression analysis.

The results revealed a greater importance given by participants to intrinsic motivational factors (learning, fun and satisfaction) compared with the extrinsic motivational factor (acknowledgment). Monetary rewards proved irrelevant in this process, whereas attitude and self-efficacy proved good predictors of the intention of contribution in crowdsourcing initiatives.

No study, as far as the authors’ knowledge extends, has been undertaken to understand what motivations are more relevant in the context of crowdsourcing platforms using multiple theories.

Data science lacks a distinctive identity and a theory-informed approach, both for its own sake and to properly be applied conjointly to the social sciences. This paper’s purposes are twofold: to provide (1) data science an illustration of theory adoption, able to address explanation and support prediction/prescription capacities and (2) a rationale for identification of the key phenomena and properties of data science so that the data speak through a contextual understanding of reality, broader than has been usual.

A literature review and a derived conceptual research model for a push–pull approach (adapted for a data science study in the management field) are presented. A real location–allocation problem is solved through a specific algorithm and explained in the light of the adapted push–pull theory, serving as an instance for a data science theory-informed application in the management field.

This study advances knowledge on the definition of data science key phenomena as not just pure “data”, but interrelated data and datasets properties, as well as on the specific adaptation of the push-pull theory through its definition, dimensionality and interaction model, also illustrating how to apply the theory in a data science theory-informed research. The proposed model contributes to the theoretical strengthening of data science, still an incipient area, and the solution of the location-allocation problem suggests the applicability of the proposed approach to broad data science problems, alleviating the criticism on the lack of explanation and the focus on pattern recognition in data science practice and research.

The proposed algorithm requires the previous definition of a perimeter of interest. This aspect should be characterised as an antecedent to the model, which is a strong assumption. As for prescription, in this specific case, one has to take complementary actions, since theory, model and algorithm are not detached from in loco visits, market research or interviews with potential stakeholders.

This study offers a conceptual model for practical location–allocation problem analyses, based on the push–pull theoretical components. So, it suggests a proper definition for each component (the object, the perspective, the forces, its degrees and the nature of the movement). The proposed model has also an algorithm for computational implementation, which visually describes and explains components interaction, allowing further simulation (estimated forces degrees) for prediction.

First, this study identifies an overlap of push–pull theoretical approaches, which suggests theory adoption eventually as mere common sense, weakening further theoretical development. Second, this study elaborates a definition for the push–pull theory, a dimensionality and a relationship between its components. Third, a typical location–allocation problem is analysed in the light of the refactored theory, showing its adequacy for that class of problems. And fourth, this study suggests that the essence of a data science should be the study of contextual relationships among data, and that the context should be provided by the spatial, temporal, political, economic and social analytical interests.