Forecasting the Innovation Efficiency of Oil and Gas Industry: remarks about the future in 2030
Palavras-chave:
Previsão;, Análise envoltória de dados, Análise de dados em painel, Petróleo e gás; Estudos do futuro.Resumo
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.
Keywords: Forecasting; Data envelopment analysis; Panel data analysis; Oil & gas; Future studies.
RESUMO
Objetivo: Este artigo analisa de forma preditiva a eficiência dos operadores de petróleo e gás no seu processo de inovação para o ano de 2030.
Metodologia/abordagem: A pesquisa combinou duas etapas, análise envoltória de dados (DEA) e análise de dados em painel, para prever a eficiência da inovação até o ano 2030. O método de análise envoltória de dados (DEA) foi utilizado para medir a eficiência e sua evolução. O insumo foi o montante do investimento em pesquisa e desenvolvimento, e os resultados foram as vendas líquidas e o número de patentes. A análise de dados em painel foi utilizada para prever a eficiência.
Originalidade/relevância: Foram identificadas algumas contribuições e eficiência da inovação para as organizações. Este estudo fornece implicações teóricas e gerenciais para futuros estudos da indústria de petróleo e gás.
Resultados: Em relação às previsões, a receita líquida mais uma vez se destacou como principal preditor. Em média, a eficiência em 2030 passará de 0,66 (eficiência média entre 09-20) para 0,85, com grande heterogeneidade quando se observa o comportamento individual das empresas.
Contribuições teóricas/metodológicas: O futuro da indústria de petróleo e gás tornou-se o terreno para pesquisas com vários métodos para estudar a vida finita deste recurso, as alterações climáticas globais, a perspectiva de economias de baixo carbono e a transição da energia para fontes renováveis.
Contribuições sociais e para gestão: Essas identificações futuras podem ser utilizadas no planejamento estratégico das organizações para melhorar seu respectivo desempenho com base no que as empresas consideradas mais eficientes realizaram.
Palavras-chave: Previsão; Análise envoltória de dados; Análise de dados em painel; Petróleo e gás; Estudos do futuro.
RESUMEN
Objetivo: Este artículo analiza de forma predictiva la eficiencia de los operadores de petróleo y gas en su proceso de innovación para el año 2030.
Metodología/enfoque: La investigación combinó dos pasos, análisis envolvente de datos (DEA) y análisis de datos de panel, para predecir la eficiencia de la innovación hasta el año 2030. Se utilizó el método de análisis envolvente de datos (DEA) para medir la eficiencia y su evolución. El insumo fue el monto de inversión en investigación y desarrollo, y los resultados fueron las ventas netas y el número de patentes. Se utilizó análisis de datos de panel para predecir la eficiencia.
Originalidad/relevancia: Se identificaron algunos aportes y eficiencia de la innovación para las organizaciones. Este estudio proporciona implicaciones teóricas y de gestión para futuros estudios de la industria del petróleo y el gas..
Resultados: En cuanto a las previsiones, los ingresos netos volvieron a destacar como principal predictor. En promedio, la eficiencia en 2030 aumentará desde 0,66 (eficiencia promedio entre 09-20) a 0,85, con gran heterogeneidad al observar el comportamiento individual de las empresas.
Contribuiciones teóricas/metodológicas: El futuro de la industria del petróleo y el gas se ha convertido en terreno para la investigación con diversos métodos para estudiar la vida finita de este recurso, el cambio climático global, las perspectivas de economías bajas en carbono y la transición energética hacia fuentes renovables.
Contribuciones sociales y de gestión: Estas identificaciones futuras se pueden utilizar en la planificación estratégica de las organizaciones para mejorar su desempeño respectivo en función de lo que hayan logrado las empresas consideradas más eficientes.
Palabras clave: Previsión; Análisis Envolvente de Datos; Análisis de datos de paneles; Petroleo y Gas; Estudios futuros.
Referências
Adams, D., Adams, K., Ullah, S., & Ullah, F. (2019). Globalisation, governance, accountability and the natural resource ‘curse’: Implications for socio-economic growth of oil-rich developing countries. Resources Policy, 61, 128–140. https://doi.org/10.1016/J.RESOURPOL.2019.02.009
Ahmad, T., & Zhang, D. (2020). A critical review of comparative global historical energy consumption and future demand: The story told so far. Energy Reports, 6, 1973–1991. https://doi.org/10.1016/j.egyr.2020.07.020
AlNuaimi, B. K., Al Mazrouei, M., & Jabeen, F. (2020). Enablers of green business process management in the oil and gas sector. International Journal of Productivity and Performance Management, 69(8), 1671–1694. https://doi.org/10.1108/IJPPM-11-2019-0524
Amara, R. C., & Salancik, G. R. (1971). Forecasting: From conjectural art toward science. Technological Forecasting and Social Change, 3(C), 415–426. https://doi.org/10.1016/S0040-1625(71)80029-X
Arora, A., Belenzon, S., & Patacconi, A. (2018). The decline of science in corporate R&D. Strategic Management Journal, 39(1), 3–32. https://doi.org/10.1002/SMJ.2693
Arranz, N., Arroyabe, M. F., Li, J., & de Arroyabe, J. C. F. (2019). An integrated model of organisational innovation and firm performance: Generation, persistence and complementarity. Journal of Business Research, 105, 270–282. https://doi.org/10.1016/J.JBUSRES.2019.08.018
Aytekin, A., Ecer, F., Korucuk, S., & Karamaşa, Ç. (2022). Global innovation efficiency assessment of EU member and candidate countries via DEA-EATWIOS multi-criteria methodology. Technology in Society, 68, 101896. https://doi.org/10.1016/J.TECHSOC.2022.101896
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
BP. (2022). Statistical Review of World Energy 2022. https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2022-full-report.pdf
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chiu, Y. ho, Huang, C. wei, & Chen, Y. C. (2012). The R&D value-chain efficiency measurement for high-tech industries in China. Asia Pacific Journal of Management, 29(4), 989–1006. https://doi.org/10.1007/S10490-010-9219-3/TABLES/5
Chun, D., Chung, Y., & Bang, S. (2015). Impact of firm size and industry type on R&D efficiency throughout innovation and commercialisation stages: evidence from Korean manufacturing firms. Technology Analysis and Strategic Management, 27(8), 895–909. https://doi.org/10.1080/09537325.2015.1024645
Cooper, W. W., Seiford, L. M., & Tone, K. (2000). Data Envelopment Analysis. Data Envelopment Analysis. https://doi.org/10.1007/B109347
Correia, A., Lopes, C., Costa e Silva, E., Monteiro, M., & Lopes, R. B. (2020). A multi-model methodology for forecasting sales and returns of liquefied petroleum gas cylinders. Neural Computing and Applications, 32(16), 12643–12669. https://doi.org/10.1007/S00521-020-04713-0/FIGURES/22
De Hoyos, R. E., & Sarafidis, V. (2006). Testing for Cross-Sectional Dependence in Panel-Data Models. Https://Doi.Org/10.1177/1536867X0600600403, 6(4), 482–496. https://doi.org/10.1177/1536867X0600600403
Diaz-Fernandez, M., Bornay-Barrachina, M., & Lopez-Cabrales, A. (2017). HRM practices and innovation performance: a panel-data approach. International Journal of Manpower, 38(3), 354–372. https://doi.org/10.1108/IJM-02-2015-0028/FULL/PDF
Dong, Y., Wei, Z., Liu, T., & Xing, X. (2020). The Impact of R&D Intensity on the Innovation Performance of Artificial Intelligence Enterprises-Based on the Moderating Effect of Patent Portfolio. Sustainability 2021, Vol. 13, Page 328, 13(1), 328. https://doi.org/10.3390/SU13010328
Dziallas, M., & Blind, K. (2019). Innovation indicators throughout the innovation process: An extensive literature analysis. Technovation, 80–81, 3–29. https://doi.org/10.1016/J.TECHNOVATION.2018.05.005
Emrouznejad, A., & Yang, G. liang. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4–8. https://doi.org/10.1016/J.SEPS.2017.01.008
Faraji, A. (2021). Neuro-fuzzy system based model for prediction of project performance in downstream sector of petroleum industry in Iran. Journal of Engineering, Design and Technology, 19(6), 1268–1290. https://doi.org/10.1108/JEDT-06-2020-0241/FULL/PDF
Fergnani, A. (2022). Corporate Foresight: A New Frontier for Strategy and Management. Https://Doi.Org/10.5465/Amp.2018.0178, 36(2), 820–844. https://doi.org/10.5465/AMP.2018.0178
Friedlingstein, P., Jones, M. W., O’Sullivan, M., Andrew, R. M., Hauck, J., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Le Quéré, C., DBakker, O. C. E., Canadell1, J. G., Ciais1, P., Jackson, R. B., Anthoni1, P., Barbero, L., Bastos, A., Bastrikov, V., Becker, M., … Zaehle, S. (2019). Global carbon budget 2019. Earth System Science Data, 11(4), 1783–1838. https://doi.org/10.5194/ESSD-11-1783-2019
Gokhberg, L., Kuzminov, I., Khabirova, E., & Thurner, T. (2020). Advanced text-mining for trend analysis of Russia’s Extractive Industries. Futures, 115, 102476. https://doi.org/10.1016/J.FUTURES.2019.102476
Gould, W. (2022, July 6). Chow tests. https://www.stata.com/support/faqs/statistics/chow-tests/
Guan, J., Zuo, K., Chen, K., & Yam, R. C. M. (2016). Does country-level R&D efficiency benefit from the collaboration network structure? Research Policy, 45(4), 770–784. https://doi.org/10.1016/J.RESPOL.2016.01.003
Han, E. J., & Sohn, S. Y. (2017). Firms’ Negative Perceptions on Patents, Technology Management Strategies, and Subsequent Performance. Sustainability 2017, Vol. 9, Page 440, 9(3), 440. https://doi.org/10.3390/SU9030440
Hashimoto, A., & Haneda, S. (2008). Measuring the change in R&D efficiency of the Japanese pharmaceutical industry. Research Policy, 37(10), 1829–1836. https://doi.org/10.1016/J.RESPOL.2008.08.004
Hausman, J. A. (2015). Specification tests in econometrics. Applied Econometrics, 38(2), 112–134. https://doi.org/10.2307/1913827
Hunt, J. D., Nascimento, A., Nascimento, N., Vieira, L. W., & Romero, O. J. (2022). Possible pathways for oil and gas companies in a sustainable future: From the perspective of a hydrogen economy. Renewable and Sustainable Energy Reviews, 160, 112291. https://doi.org/10.1016/J.RSER.2022.112291
Hussinger, K., & Pacher, S. (2019). Information ambiguity, patents and the market value of innovative assets. Research Policy, 48(3), 665–675. https://doi.org/10.1016/J.RESPOL.2018.10.022
Iden, J., Methlie, L. B., & Christensen, G. E. (2017). The nature of strategic foresight research: A systematic literature review. Technological Forecasting and Social Change, 116, 87–97. https://doi.org/10.1016/J.TECHFORE.2016.11.002
IEA. (2023). Oil Market Report - August 2023. https://www.iea.org/reports/oil-market-report-august-2023
Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74. https://doi.org/10.1016/S0304-4076(03)00092-7
IPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu and B. Zhou (eds.) Cambridge University Press. In Press. https://www.ipcc.ch/report/ar6/wg1/
Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 90(1), 1–44. https://doi.org/10.1016/S0304-4076(98)00023-2
King, L. C., & van den Bergh, J. C. J. M. (2018). Implications of net energy-return-on-investment for a low-carbon energy transition. Nature Energy 2018 3:4, 3(4), 334–340. https://doi.org/10.1038/s41560-018-0116-1
Kostopoulos, K., Papalexandris, A., Papachroni, M., & Ioannou, G. (2011). Absorptive capacity, innovation, and financial performance. Journal of Business Research, 64(12), 1335–1343. https://doi.org/10.1016/J.JBUSRES.2010.12.005
Ma, X., Liu, Z., Gao, Y., & Liang, N. (2020). Innovation Efficiency Evaluation of Listed Companies Based on the DEA Method. Procedia Computer Science, 174, 382–386. https://doi.org/10.1016/J.PROCS.2020.06.103
Maaouane, M., Zouggar, S., Krajačić, G., & Zahboune, H. (2021). Modelling industry energy demand using multiple linear regression analysis based on consumed quantity of goods. Energy, 225, 120270. https://doi.org/10.1016/J.ENERGY.2021.120270
Nindl, E., Confraria, H., Rentocchini, F., Napolitano, F., Georgakaki, A., Ince, E., Fako, P., Hernández Guevara, H., Gavigan, J., Tübke, A., Pinero-Mira, P., Rueda-Cantuche, J. M., Banacloche-Sánchez, S., de Prato, G., & Calza, E. (2023, December 14). The 2023 EU Industrial R&D Investment Scoreboard. European Commission. https://iri.jrc.ec.europa.eu/scoreboard
Oliveira, M. S. de, Lizot, M., Siqueira, H., Afonso, P., & Trojan, F. (2023). Efficiency Analysis of Oil Refineries Using DEA Window Analysis, Cluster Analysis, and Malmquist Productivity Index. Sustainability 2023, Vol. 15, Page 13611, 15(18), 13611. https://doi.org/10.3390/SU151813611
Pickl, M. J. (2019). The renewable energy strategies of oil majors – From oil to energy? Energy Strategy Reviews, 26, 100370. https://doi.org/10.1016/J.ESR.2019.100370
Plank, J., & Doblinger, C. (2018). The firm-level innovation impact of public R&D funding: Evidence from the German renewable energy sector. Energy Policy, 113, 430–438. https://doi.org/10.1016/J.ENPOL.2017.11.031
Ponta, L., Puliga, G., & Manzini, R. (2021). A measure of innovation performance: the Innovation Patent Index. Management Decision, 59(13), 73–98. https://doi.org/10.1108/MD-05-2020-0545/FULL/PDF
Quintana-García, C., & Benavides-Velasco, C. A. (2004). Cooperation, competition, and innovative capability: a panel data of European dedicated biotechnology firms. Technovation, 24(12), 927–938. https://doi.org/10.1016/S0166-4972(03)00060-9
Ribeiro, C. G., Inacio Junior, E., Li, Y., Furtado, A., & Gardim, N. (2019). The influence of user-supplier relationship on innovation dynamics of Oil & Gas industry. Https://Doi.Org/10.1080/09537325.2019.1641193, 32(2), 119–132. https://doi.org/10.1080/09537325.2019.1641193
Rohrbeck, R., Battistella, C., & Huizingh, E. (2015). Corporate foresight: An emerging field with a rich tradition. Technological Forecasting and Social Change, 101, 1–9. https://doi.org/10.1016/J.TECHFORE.2015.11.002
Roper, S., & Hewitt-Dundas, N. (2015). Knowledge stocks, knowledge flows and innovation: Evidence from matched patents and innovation panel data. Research Policy, 44(7), 1327–1340. https://doi.org/10.1016/J.RESPOL.2015.03.003
Santha kumar, R., & Kaliyaperumal, K. (2015). A scientometric analysis of mobile technology publications. Scientometrics 2015 105:2, 105(2), 921–939. https://doi.org/10.1007/S11192-015-1710-7
Shuen, A., Feiler, P. F., & Teece, D. J. (2014). Dynamic capabilities in the upstream oil and gas sector: Managing next generation competition. Energy Strategy Reviews, 3(C), 5–13. https://doi.org/10.1016/J.ESR.2014.05.002
Skea, J., van Diemen, R., Portugal-Pereira, J., & Khourdajie, A. Al. (2021). Outlooks, explorations and normative scenarios: Approaches to global energy futures compared. Technological Forecasting and Social Change, 168, 120736. https://doi.org/10.1016/J.TECHFORE.2021.120736
Srivastava, M. K., Gnyawali, D. R., & Hatfield, D. E. (2015). Behavioral implications of absorptive capacity: The role of technological effort and technological capability in leveraging alliance network technological resources. Technological Forecasting and Social Change, 92, 346–358. https://doi.org/10.1016/J.TECHFORE.2015.01.010
Stornelli, A., Ozcan, S., & Simms, C. (2021). Advanced manufacturing technology adoption and innovation: A systematic literature review on barriers, enablers, and innovation types. Research Policy, 50(6), 104229. https://doi.org/10.1016/J.RESPOL.2021.104229
Wang, Q., Hang, Y., Sun, L., & Zhao, Z. (2016). Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach. Technological Forecasting and Social Change, 112, 254–261. https://doi.org/10.1016/J.TECHFORE.2016.04.019
Wooldridge, J. M. (2013). Introductory Econometrics: A Modern Approach (Fifth edition). South-Western Cengage Learning. https://www.google.com.br/books/edition/Introductory_Econometrics/4TZnpwAACAAJ?hl=pt-BR
Wu, L., Wei, Y., & Wang, C. (2021). Disentangling the effects of business groups in the innovation-export relationship. Research Policy, 50(1), 104093. https://doi.org/10.1016/J.RESPOL.2020.104093
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