A Surge toward a Sustainable Future: Organizational Change and Transformational Vision by an Oil and Gas Company

Jaber, Tahrir

doi:10.1590/1982-7849rac2021200031.en

Tahrir Jaber * Corresponding Author

UiT The Arctic University of Norway, School of Business and Economics, Tromso, Norway.

Authorship Tahrir Jaber* UiT The Arctic University of Norway, School of Business and Economics Breivangvegen 23, 9010 Tromsø, Norway. E-mail address: tahrir.jaber@uit.no https://orcid.org/0000-0002-0428-1810

https://orcid.org/0000-0002-0428-1810

* Corresponding Author

Editor-in-chief: Wesley Mendes-Da-Silva (Fundação Getulio Vargas, EAESP, Brazil)

Associate Editor: Elin Oftedal (The Artic University of Norway, Norway)

Reviewers: Two reviewers chose not to disclose their identities.

Peer Review Report: The disclosure of the Peer Review Report was not authorized by its reviewers.

Authorship

Tahrir Jaber* UiT The Arctic University of Norway, School of Business and Economics Breivangvegen 23, 9010 Tromsø, Norway. E-mail address: tahrir.jaber@uit.no https://orcid.org/0000-0002-0428-1810

Conflict of Interests

The authors have stated that there is no conflict of interest.

Authors' Contributions

1^st author: conceptualization (lead); writing - original draft (lead); project administration (lead); validation (lead).

Literature	Outcomes variables	Moderator	Mediator
Gabzdylova, Raffensperger, and Castka (2009)Gabzdylova, B., Raffensperger, J. F., & Castka, P. (2009). Sustainability in the New Zealand wine industry: drivers, stakeholders and practices. Journal of Cleaner Production, 17(11), 992-998. https://doi.org/10.1016/j.jclepro.2009.02.015 https://doi.org/10.1016/j.jclepro.2009.0...	Personal values, preferences, satisfaction (i.e., enjoyment of the work itself), product quality, and customers' demand.	Size of the involved companies	Sustainability drivers
Bjørner, Hansen, and Russell (2004)Bjørner, T. B., Hansen, L. G., & Russell, C. S. (2004). Environmental labeling and consumers’ choice - An empirical analysis of the effect of the Nordic Swan. Journal of Environmental Economics and Management, 47(3), 411-434. https://doi.org/10.1016/j.jeem.2003.06.002 https://doi.org/10.1016/j.jeem.2003.06.0... ; Haigh and Jones (2006)Haigh, M., & Jones, M. T. (2006). The drivers of corporate social responsibility: A critical review. The Business Review, 245-251. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.528.6764&rep=rep1&type=pdf http://citeseerx.ist.psu.edu/viewdoc/dow... ; Marshall, Cordano, and Silverman (2005)Marshall, R. S., Cordano, M., & Silverman, M. (2005). Exploring individual and institutional drivers of proactive environmentalism in the US Wine industry. Business Strategy and the Environment, 14(2), 92-109. https://doi.org/10.1002/bse.433 https://doi.org/10.1002/bse.433... ; Tullberg (2005)Tullberg, J. (2005). Reflections upon the responsive approach to corporate social responsibility. Business Ethics: A European Review, 14(3), 261-276. http://doi.org/10.1111/j.1467-8608.2005.00408.x http://doi.org/10.1111/j.1467-8608.2005....	Managerial attitudes, employees' demands, organizational culture, internal pressure on business managers, and social development activities.	-------------------	Sustainability drivers
Luthra, Govindan, and Mangla (2017)Luthra, S., Govindan, K., & Mangla, S. K. (2017). Structural model for sustainable consumption and production adoption-A grey-DEMATEL based approach. Resources, Conservation and Recycling, 125, 198-207. https://doi.org/10.1016/j.resconrec.2017.02.018 https://doi.org/10.1016/j.resconrec.2017...	Management support, governmental policies and regulations, gaining the market edge, and improving the overall performance.	-------------------	Drivers to sustainable consumption and production adoption
Thomas-Seale, Kirkman-Brown, Attallah, Espino, and Shepherd (2018)Thomas-Seale, L. E. J., Kirkman-Brown, J. C., Attallah, M. M., Espino, D. M., & Shepherd, D. E. T. (2018). The barriers to the progression of additive manufacture: Perspectives from UK industry. International Journal of Production Economics, 198, 104-118. https://doi.org/10.1016/j.ijpe.2018.02.003 https://doi.org/10.1016/j.ijpe.2018.02.0...	Education, cost, software, materials, mechanical properties, validation, and finishing.	Industrial manufacturing companies	Barriers to the progression of technologies government and policy makers are interested in
Trianni, Cagno, and Neri (2017)Trianni, A., Cagno, E., & Neri, A. (2017). Modelling barriers to the adoption of industrial sustainability measures. Journal of Cleaner Production, 168, 1482-1504. https://doi.org/10.1016/j.jclepro.2017.07.244 https://doi.org/10.1016/j.jclepro.2017.0...	Economic barrier and resistance to change (lack of information and other priorities).	Manufacturing firms	Barriers that hinder sustainable implementations
Aboelmaged (2018)Aboelmaged, M. (2018). The drivers of sustainable manufacturing practices in Egyptian SMEs and their impact on competitive capabilities: A PLS-SEM model. Journal of Cleaner Production, 175, 207-221. https://doi.org/10.1016/j.jclepro.2017.12.053 https://doi.org/10.1016/j.jclepro.2017.1...	Organizational drivers, environmental pressure, and competitive capabilities.	Small and medium-sized industries	Drivers to sustainable manufacturing practices
	More sustainability awareness.	Experience of project managers	-------------------
Hind (2009)Hind, P. (2009). Developing leaders for sustainable business. Corporate governance: The International Journal Of Business In Society, 9(1), 7-20. http://doi.org/10.1108/14720700910936029 http://doi.org/10.1108/14720700910936029...	Responsible leadership (integrity, open-minded, ethical behavior, care for people, and managing responsibility outside the organization).	Leadership sustainable skills	-------------------
Robinson (2006)Robinson, H. S. (2006). STEPS: a knowledge management maturity roadmap for corporate sustainability. Business Process Management Journal, 12(6), 793-808. https://doi.org/10.1108/14637150610710936 https://doi.org/10.1108/1463715061071093...	Environmental issues, social issues, and financial issues.	Managers' knowledge toward promoting sustainability in companies	-------------------

	Factor
	1	2	3	4
Cronbach's alpha	0.932	0.873	0.876	0.69
Regulative pillar 2			0.787
Regulative pillar 3			0.979
Normative pillar 1		0.541
Normative pillar 2		0.605
Normative pillar 3		0.994
Normative pillar 4		0.720
Cognitive pillar 1	0.635
Cognitive pillar 3	0.954
Cognitive pillar 4	0.919
Transition 1				0.857
Transition 2				0.581

	Chi-square	df	p-value	Invariant?
Overall model					Step 1. Provide chi-square and df for unconstrained and constrained models, and provide the number of groups. The thresholds will be updated automatically.
unconstrained	149.8	114
Fully constrained	205.3	131
Number of groups		2
Difference	55.5	17	0.000	NO	Groups are different at the model level. Check path differences.

	Selection	Barriers	Drivers	Transition	Cognitive	Regulative	Normative
Selection	1.00
Barriers	0.38^ Correlation is significant at the 0.01 level (2-tailed).	1.00
Drivers	0.03	0.04	1.00
Transition	0.32^ Correlation is significant at the 0.01 level (2-tailed).	0.16	0.32^ Correlation is significant at the 0.01 level (2-tailed).	1.00
Cognitive	0.03	0.06	0.42^ Correlation is significant at the 0.01 level (2-tailed).	0.74^ Correlation is significant at the 0.01 level (2-tailed).	1.00
Regulative	0.38^ Correlation is significant at the 0.01 level (2-tailed).	0.31^ Correlation is significant at the 0.01 level (2-tailed).	0.35^ Correlation is significant at the 0.01 level (2-tailed).	0.77^ Correlation is significant at the 0.01 level (2-tailed).	0.72^ Correlation is significant at the 0.01 level (2-tailed).	1.00
Normative	0.34^ Correlation is significant at the 0.01 level (2-tailed).	0.23^* * Correlation is significant at the 0.05 level (2-tailed).	0.28^ Correlation is significant at the 0.01 level (2-tailed).	0.74^ Correlation is significant at the 0.01 level (2-tailed).	0.82^ Correlation is significant at the 0.01 level (2-tailed).	0.75^ Correlation is significant at the 0.01 level (2-tailed).	1.00

	CR	AVE	MSV	MaxR(H)	Barriers	Normative	Regulative	Cognitive	Transition	Drivers	Selection
Barriers	0.789	0.555	0.106	0.790	0.745
Normative	0.903	0.757	0.689	0.914	0.156	0.870
Regulative	0.881	0.788	0.448	0.891	0.296	0.583	0.888
Cognitive	0.935	0.829	0.689	0.964	0.011	0.830	0.531	0.910
Transition	0.713	0.554	0.448	0.719	0.128	0.590	0.669	0.607	0.744
Drivers	0.828	0.708	0.127	0.863	0.061	0.092	0.357	0.163	0.259	0.841
Selection	0.740	0.417	0.113	0.742	0.325	0.297	0.336	0.000	0.282	0.028	0.645

		Unstandardized coefficients		Standardized coefficients			Collinearity statistics
	Model	B	Std. error	Beta	t	Sig.	Tolerance	VIF
1	(Constant)	2.463	0.419		5.881	0.000
	Barriers	0.074	0.052	0.130	1.428	0.157	0.810	1.234
	Drivers	0.036	0.082	0.041	.443	0.659	0.797	1.255
	Cognitive	-0.396	0.073	-0.897	-5.419	0.000	0.245	4.081
	Regulative	0.303	0.091	0.459	3.347	0.001	0.357	2.799
	Normative	0.471	0.110	0.693	4.279	0.000	0.256	3.904

		Unstandardized coefficients		Standardized coefficients			Collinearity statistics
	Model	B	Std. error	Beta	t	Sig.	Tolerance	VIF
1	(Constant)	2.067	0.491		4.213	0.000
	Barriers	-0.042	0.061	-0.047	-0.685	0.495	0.810	1.234
	Drivers	0.007	0.096	0.005	0.073	0.942	0.797	1.255
	Cognitive	0.164	0.086	0.240	1.912	0.059	0.245	4.081
	Regulative	0.457	0.106	0.448	4.307	0.000	0.357	2.799
	Normative	0.225	0.129	0.214	1.745	0.085	0.256	3.904

Direct/indirect path		Unstandardized estimate	Lower	Upper	p-value	Standardized estimate
H2	Normative --> Drivers --> Selection	-0.011	-0.090	0.020	0.429	-0.016
H3	Normative --> Barriers --> Selection	0.032	-0.001	0.091	0.112	0.047
H1b	Normative --> Selection --> Transition	0.234	0.104	0.401	0.001	0.223^* *** p < 0.001, P < 0.01, * P < 0.05, ┼ P < 0.1.
H2	Cognitive --> Drivers --> Selection	0.012	-0.027	0.073	0.523	0.027
H3	Cognitive --> Barriers --> Selection	-0.034	-0.081	0.003	0.131	-0.076
H1c	Cognitive --> Selection --> Transition	-0.202	-0.301	-0.101	0.001	-0.296^* *** p < 0.001, P < 0.01, * P < 0.05, ┼ P < 0.1.
H2	Regulative --> Drivers --> Selection	0.007	-0.010	0.054	0.348	0.011
H3	Regulative --> Barriers --> Selection	0.042	-0.001	0.100	0.105	0.063
H1a	Regulative --> Selection --> Transition	0.161	0.067	0.301	0.001	0.158^* *** p < 0.001, P < 0.01, * P < 0.05, ┼ P < 0.1.

\begin{matrix} Y = β_{0} + β_{1} * Barriers + β_{2} * Drivers + β_{3} * Cognitive + β_{4} * Regulative + β_{5} * Normative + ε \\ Innovation Selection = 2.5 + 0.07 * Barriers + 0.04 * Drivers - 0.4 * Cognitive + 0.3 * Regulative + 0.47 * Normative + ε \\ Sustainable Transition = 2.07 - 0.04 * Barriers + 0.01 * Drivers + 0.16 * Cognitive + 0.46 * Regulative + 0.23 * Normative + ε \end{matrix}

Variables	Items
Regulative pillar	Your management team supports renewable energy activities There are incentives for sustainable activities at X Company* X Company has policies to enhance its sustainable development practices* Your management team has clear goals to make X a sustainable company Top management plays an important role in making X a sustainable company
Normative pillar	Employees want to contribute to a variety of sustainable projects in my unit Individual initiatives toward sustainability are respected in my unit* Sustainable activities are seen as the way toward future opportunities in my unit* Operating sustainability is a goal in my unit*** In my unit, we believe that we have a personal responsibility/commitment toward society/the environment
Cognitive pillar	My unit has a good understanding of sustainability* My unit has a good understanding of sustainable technology My unit builds knowledge on becoming more sustainable* My unit is always looking for additional ways to improve sustainability***
Sustainable transition	X Company has established environmental targets to introduce a shift toward sustainability* Sustainability will become considerably more important to X Company in the future* X Company has implemented sustainability goals into its overall strategy This is the right time for X Company to introduce clean activities into its business practices
Innovation selection	The project should be within our core strategy* It should be covered by our competence* It should represent an interesting market* It should serve the interest of our top manager* It should achieve high return on investment (ROI) It should achieve a positive environmental profile It should achieve a positive social profile
Drivers	Customer expectations 'Green' strategy Internal requirements Corporate culture Knowledge of sustainability Use of new technology* Development of new technology* Return on investment of 'green' technology Demand from investors Reputation of the firm
Barriers	Lack of financial funds Lack of competence and capabilities* Lack of employee motivation* Lack of technology*** Lack of support from top management Lack of return on investment (ROI) Lack of perceived importance (ex.: Giving priority to other activities)

Hypothesis	Quantitative analysis	Comment
Hypothesis 1a	Supported	The regulative pillar increases the effect of innovation selection on sustainable transition. This confirms that the case company has new policies and laws to invest in new sustainable activities.
Hypothesis 1b	Supported	The normative pillar increases the effect of innovation selection on sustainable transition. This confirms that the employees are engaged in the sustainable shift that is happening in the company and they are willing to introduce new sustainable projects to the top management team.
Hypothesis 1c	Not supported	The cognitive pillar dampens the effect of innovation selection on sustainable transition. This is due to the negative effect on this factor. This means that lack of sustainability knowledge increases the intention to select more RE activities. This shows that employees might need to find new knowledge to shift the company toward sustainability.
Hypothesis 2	Not supported	The sustainable drivers have no effect on the company's innovation selection. This confirms that the company itself was interested in adopting new sustainable practices into its business.
Hypothesis 3	Not supported	The sustainable barriers have no effect on the company's innovation selection. This confirms that the barriers could not prevent the company from adopting new sustainable activities.
Hypothesis 4a	Not supported	Experienced managers weaken the relationship between the regulative pillar and sustainable transition. This shows that experienced managers have some difficulties in shifting the company toward sustainability.
Hypothesis 4b	Supported	Experienced managers strengthen the relationships between the normative pillar and sustainable transition. This shows that the employees have a self-interest to shift the company toward sustainability.
Hypothesis 4c	Supported	Experienced managers strengthen the relationship between the cognitive pillar and sustainable transition. This shows that the employees understand what would satisfy external audiences and understand the importance of the sustainable change.
Hypothesis 4d	Supported	Experienced managers strengthen the relationship between the regulative pillar and innovation selection. This shows that experienced managers are willing to select appropriate sustainable projects into their companies.
Hypothesis 4e	Supported	Experienced managers strengthen the relationships between the normative pillar and innovation selection. This shows that the employees have a self-interest to introduce new sustainable projects to their top management teams.
Hypothesis 4f	Not supported	Experienced managers weaken the relationship between the cognitive pillar and innovation selection. This shows that the sustainable shift still needs time to be accepted and understood by everyone.

[1] * Corresponding Author

Brasil

Brasil

A Surge toward a Sustainable Future: Organizational Change and Transformational Vision by an Oil and Gas Company

O Despontar de um Futuro Sustentável: Mudança Organizacional e Visão Transformadora em uma Empresa de Gás e Petróleo

ABSTRACT

Context:

Objective:

Method:

Results:

Conclusion: