Forecasting the innovation efficiency of Oil and Gas industry: remarks about the future in 2030

Purpose: This paper predictively analyzes the efficiency of oil and gas operators in their innovation process for the year 2030.Design/methodology/approach: The research design combined two steps, data envelopment analysis (DEA) and panel data analysis, to forecast innovation efficiency by the year 2030. The data envelopment analysis (DEA) method was used to measure efficiency and its evolution. The input was the amount of investment in research and development, and the outputs were net sales and the number of patents. Panel data analysis was used to predict efficiency.Originality/relevance: Some contributions and innovation efficiency for organizations were identified. This study provides theoretical and managerial implications for future oil and gas industry studies.Findings: Regarding forecasts, net revenues once again stood out as the primary predictor. On average, efficiency in 2030 will rise from 0.66 (average efficiency between 09-20) to 0.85, with wide heterogeneity when observing the individual behavior of firms.Theoretical/ /methodological contributions: The future of the O&G industry has become the ground for research with various methods to study the finite life of this resource, global climate change, the prospect of low-carbon economies, and the transition of energy to renewable sources.Social and management implications: These future identifications can be used in organizations' strategic planning to improve their respective performance based on what firms considered the most efficient have accomplished.

Authors

Institutions

Publication Details

Journal
Revista Gestão & Tecnologia
Published
2024-10-17
DOI
https://doi.org/10.20397/2177-6652/2024.v24i4.2828
Primary Topic
Arctic and Russian Policy Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Forecasting the innovation efficiency of Oil and Gas industry: remarks about the future in 2030

Renata Giovinazzo Spers, Luccas Assis Attílio, Murilo Alvarenga Oliveira
Revista Gestão & Tecnologia
Arctic and Russian Policy Studies
article

Forecasting the innovation efficiency of Oil and Gas industry: remarks about the future in 2030

Renata Giovinazzo Spers, Luccas Assis Attílio, Murilo Alvarenga Oliveira
article en

Abstract

Purpose: This paper predictively analyzes the efficiency of oil and gas operators in their innovation process for the year 2030.Design/methodology/approach: The research design combined two steps, data envelopment analysis (DEA) and panel data analysis, to forecast innovation efficiency by the year 2030. The data envelopment analysis (DEA) method was used to measure efficiency and its evolution. The input was the amount of investment in research and development, and the outputs were net sales and the number of patents. Panel data analysis was used to predict efficiency.Originality/relevance: Some contributions and innovation efficiency for organizations were identified. This study provides theoretical and managerial implications for future oil and gas industry studies.Findings: Regarding forecasts, net revenues once again stood out as the primary predictor. On average, efficiency in 2030 will rise from 0.66 (average efficiency between 09-20) to 0.85, with wide heterogeneity when observing the individual behavior of firms.Theoretical/ /methodological contributions: The future of the O&G industry has become the ground for research with various methods to study the finite life of this resource, global climate change, the prospect of low-carbon economies, and the transition of energy to renewable sources.Social and management implications: These future identifications can be used in organizations' strategic planning to improve their respective performance based on what firms considered the most efficient have accomplished.

Revista Gestão & TecnologiaVol. 24(4)
Universidade Federal de Ouro Preto (BR), Universidade Federal Fluminense (BR), Fundace (BR)
Openalex Percentile: Top 53%
Arctic and Russian Policy Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.