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

Authors

DOI:

https://doi.org/10.20397/2177-6652/2024.v24i4.2828

Keywords:

Forecasting, Data envelopment analysis, Panel data analysis, Oil & gas, Future studies

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.

Author Biography

Murilo Alvarenga Oliveira, Universidade Federal Fluminense - PPGA

Visiting Professor - Gustavson School of Business | UVicPost-Doctoral Program in Business Administration - FEA | USPAssociate Professor - PPGA UFF

References

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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Published

2024-10-17

How to Cite

Oliveira, M. A., Spers, R. G., Atílio, L. A., & Gomes, A. L. (2024). Forecasting the innovation efficiency of Oil and Gas industry: remarks about the future in 2030. Journal of Management & Technology, 24(4), 39–66. https://doi.org/10.20397/2177-6652/2024.v24i4.2828

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ARTIGO