INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026

Digital-Driven Design Thinking as a Catalyst for Enhancing

Operational Efficiency and Environmental Sustainability in Oil and

Gas Industry.

JACK Ikuroaa Daerego¹, Dr SALAU, Adeyemi Nurudeen², Dr OSAWE Cyril Onyepuemu³

ADELANWA, Saheed Ololade Adeyinka⁴and Asso. Prof. Rotimi A. Adedokun (FCA)^5.

¹MBA, MIVA Open University, Lagos

²Department of Business Administration, Faculty of Management Sciences, Lagos State University, Ojo,

Lagos State, Nigeria. ORCiD: 0000-0001-8837-4418

³Department of Public Administration, Lagos State University, Ojo, ORCiD: 0000-0001-6319-1500

⁴Department of Computer Sciences, Lagos State University of Science and Technology, Ikorodu, Lagos

State.

⁵Dept of Business Professions, Odessa College, Texas, United States of America

DOI: https://doi.org/10.51583/IJLTEMAS.2026.150500159

Received: 14 May 2026; Accepted: 19 May 2026; Published: 10 June 2026

ABSTRACT

Today, the Nigerian oil and gas industry is grappling with several issues, including operational inefficiency,

environmental sustainability, and the need for digital transformation. Traditional engineering solutions are

inadequate when dealing with the complex interplay between technological innovation, human-centred problem

solving and sustainable operational practices. This study was designed to investigate the application of digital-

driven DT within the Niger Delta region of Nigeria for the benefit of the operation of the oil and gas organizations

to minimize impact on the environment and enhance the organization's operation. The study employed a cross-

sectional survey research design, which was quantified. The total population and sample size were 420 and 205,

respectively, while the retrieved questionnaire and valid responses were 195 and 188, respectively. Pearson

correlation and multiple regression analyses were employed for data analysis. The results showed that digital

experimentation has a tremendous positive impact on operational efficiency, with predictive maintenance,

process optimization, and less downtime. Human-centred digital problem solving proved to be a very good

predictor for operational efficiency, since it supports adaptive decision-making and employee participation. The

research revealed that cross-functional digital collaboration has a positive impact on environmental sustainability

aspects, such as emissions monitoring, coordinated decision-making and resource optimization. The paper

applies digital-driven DT to the oil and gas industry and shows that for sustainable operational performance, it

is not enough to adopt the technology, but organisations must have collaborative and human-centred processes.

Keywords: Cross-Functional Digital Collaboration, Digital Transformation, Digital-Driven Design Thinking,

Human-Centred Problem Solving, Operational Efficiency, and Environmental Sustainability

INTRODUCTION

The Niger Delta region is a hub of oil and gas exploration, production, and export, and the oil and gas sector has

long been the pillar of Nigeria's economy. For decades, oil and gas revenue has been the main source of foreign

exchange earnings and fiscal support for national development. This is in line with the claim of Nwanyanwu and

Daibi (2025) that the oil revenue from the Niger Delta constitutes more than 85% of the Nigerian economy. In

this context, engineering practice in the Nigerian oil and gas industry was mostly dominated by traditional and

asset-based operational approaches that are used in the West and are adopted from multinational companies.

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These models focused on the importance of infrastructure, reliability of machinery, standardized operating

procedures, and production optimization as the key elements of an organization's performance. While these

approaches have been instrumental for generating national revenues and building industrial capacity, they lacked

the consideration for human centric innovation, adaptive problem solving, and systemic integration between

organizational functions within a very limited technical scope.

In recent years, however, Nigeria's oil and gas industry has faced several challenges, including structural,

operational, and environmental challenges, which have highlighted the inadequacy of conventional engineering

solutions. Persistent operational inefficiencies, frequent system downtime, ageing infrastructure and rising

maintenance costs have been a growing concern for companies to enhance their performance in the face of tight

budgets and regulations. (NNPC, 2021; IEA, 2022). Meanwhile, the Niger Delta has been experiencing growing

environmental issues. Over the years, the processes of oil exploration and production have left behind

environmental degradation, oil spills, ecosystem damage, gas flaring, and socio-economic impacts on host

communities (Nwachukwu, Ozobialu, Ebitimi, Nwosu & Nwokoro, 2025). These challenges have been the

subject of persistent public debate, litigation, and international interest. Regulatory requirements are hence

becoming increasingly stringent, notably with the implementation of Nigeria’s Petroleum Industry Act (PIA)

2021, which underscores environmental responsibilities, transparency, and sustainable operations.

Recent empirical studies show that these technologies can assist in predictive maintenance, real-time monitoring,

better decision making, and asset utilization, which can all lead to improved operational efficiency (Onwuka &

Emeke, 2025; Egbumokei, 2024; McKinsey & Company, 2023). Moreover, digital solutions facilitate better

monitoring of the environment, emissions reporting, and compliance documentation, helping companies to be

more proactive in managing sustainability demands.

Specifically, digital twin technologies have come to the forefront as a way of producing digital replicas of

physical assets or operational systems. These digital representations enable organizations to simulate operating

scenarios, discover inefficiencies, explore different configurations of processes and optimize resource utilization

while avoiding the disruption of live operations. Research and practical experience indicate that digital twin

applications can lead to more efficient operations and a better environmental footprint by lowering the amount

of waste, avoiding unnecessary downtime, and enabling environmentally responsible decisions (McKinsey &

Company, 2023; IEA, 2025). These developments have not been enough to overcome more profound

organizational issues in the sector, and digitalisation on its own has not been adequate. In particular, many of the

oil and gas companies are still facing challenges associated with poor human behaviours, weak inter-functional

cooperation and static decision-making cultures. These challenges hinder the effective application of digital

technologies and limit their ability to provide ongoing improvements in operational effectiveness and

environmental outcomes.

Human-centered design thinking has been integrated with digital tools and processes that leverage data to guide

decision-making, creating what is called "digital-driven design thinking," a method of problem-solving

(Egbumokei, 2024). This is not only about the deployment of technology, but also about comprehending the

needs of users, the conditions of their operations and the involvement of stakeholders in the innovation process.

Digital-driven Design Thinking provides a structured approach to finding solutions that address technical needs

while also taking into account the organizational context and sustainability goals in the Nigerian and Niger Delta

environment, where operational complexity is highly intertwined with environmental, social and community

needs.

This scenario underscores the urgent need for critical interventions in organisational transformation and

technology adoption within the Nigerian oil and gas sector. Despite growing investments in digital technologies,

challenges such as operational inefficiencies, equipment failures, environmental concerns, and sustainability

issues persist. Previous studies have predominantly focused on digital transformation from technological and

regulatory standpoints, with limited attention to integrative approaches that encompass human-centered and

collaborative innovation practices. Consequently, there remains insufficient empirical evidence on whether

Digital-Driven Design Thinking can simultaneously enhance operational efficiency and environmental

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sustainability in oil and gas organisations in Nigeria’s Niger Delta region, highlighting the need for further

empirical research.

CONCEPTUAL REVIEW

Digital-Driven Design Thinking

Currently, digital transformation serves as a strategic lever to sustainable business performance and introduces

new ways for companies to pursue their economic, environmental, and social objectives (Bindeeba,

Tukamushaba, & Bakashaba, 2025). Digital-driven design thinking is a process of organizational design that

uses both digital technologies and human-centred design principles to solve complex problems within an

organisation through a series of iterative experiments, collaborations and adaptive learning. Digital-driven design

thinking is not like traditional engineering models, which focus on linear problem solving, but it entails

technological ability, empathy-based innovation, and cross-functional integration (Brown, 2008; Martin, 2009;

Liedtka, 2018). In high-risk sectors like oil and gas, this allows businesses to explore the unknown, experiment

with solutions in the virtual realm, reimagine processes, and tackle challenges with real-world stakeholders and

environments (Bharadwaj et al., 2013; Deloitte, 2024). The oil and gas sector is undergoing a transformation in

its operations that is driven by digital technology. Digital technology is reshaping the oil and gas industry,

offering improved operational efficiency and fostering new business models (Elete, Nwulu, Erhueh, Akano, &

Aderamo, 2024).

In the context of the Nigerian oil and gas industry, where companies are increasingly adopting automation,

analytics, and remote monitoring technologies, the application of digital-driven design thinking provides a

structure to ensure that technology adoption leads to significant organizational results. The Human-centred

Problem Framing and Collaborative Experimentation (IEA, 2025; PwC Nigeria, 2024) are essential to the

successful deployment of technological solutions, such as digital transformation initiatives, as evidenced by

various studies. Therefore, digital-driven design thinking serves as a bridge between Digital Innovation and

Operational effectiveness.

Digital Experimentation

Digital experimentation includes simulations, digital twins and iterative testing to test operational solutions

before full-scale implementation. This dimension focuses on learning through repeated and rapid

experimentation to find inefficiencies, improve processes, and minimize risks in operations (Boschert & Rosen,

2016; McKinsey & Company, 2023). Digital experimentation has the ability to facilitate asset performance,

process redesign, and predictive maintenance in industrial settings; this can aid decision-making for operations

and minimize downtime (IEA, 2022; NNPC Ltd., 2023). Digital experimentation helps organizations experiment

with ideas in virtual environments, enhancing their adaptive capability and driving efficiencies within complex

systems. To further substantiate this position, the researcher formulated this hypothesis:

Ho₁: Digital experimentation is not significantly associated with operational efficiency in oil and gas firms

in the Niger Delta region of Nigeria.

Human-Centred Digital Problem Solving

Human-centred digital problem solving is an innovation methodology that puts users' needs, experiences, and

realities at the heart of the development of digital solutions. This dimension goes beyond technology itself,

focusing on the relationship between employees, engineers, and stakeholders with technology in real work

environments. Human-centred approaches are based on design thinking philosophy, which emphasizes empathy,

understanding the context, and iterative learning cycles that help organizations build technically feasible and

practically usable solutions (Brown & Wyatt, 2010; Liedtka, 2015). Human-centred digital problem solving

promotes the iterative refinement, experimentation and feedback of digital tools within organizational spaces.

This is becoming more significant in the age of digital transformation, as many tech projects fail to be adopted

when people are not taken into account as part of the project, or when the project does not account for the

organizational culture. For digital transformation projects to succeed, it is essential to have employees involved

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in identifying issues, experimenting with solutions, and co-creating implementation processes (Vial, 2019;

Deloitte, 2024). Human-centred digital problem solving therefore, increases the likelihood of successful

adoption by ensuring digital innovation addresses the challenges of day-to-day operations, minimising resistance

to change, and fostering collaboration between hierarchical and functional levels. In this regard, digital problem-

solving is especially important as a human centred approach to bridge the gap between technological proficiency

and sustainable performance of the organization, ensuring that the digital design thinking process results in real

efficiency improvements and sustainable outcomes. This study assumes this hypothesis as a way to explore the

relationship between human-centred digital problem-solving and operational efficiency.

Ho₂: Human-centred digital problem solving is not significantly related to operational efficiency in oil and

gas companies in the Niger Delta region of Nigeria.

Cross-Functional Digital Collaboration

Cross-functional digital collaboration is the coordinated interaction among different organisational units through

digital tools and services to enable shared decision-making, knowledge sharing, and innovation. In complex

industrial settings like the oil & gas industry, operational issues are never just a single department's problem but

involve the engineering, operations, sustainability, finance, and digital technology departments. Thus, effective

collaboration aided by digital tools is a key for organizations to synchronise their goals and meet the changing

demands of their operations (Bharadwaj et al., 2013; Majchrzak et al., 2015). Digital collaboration platforms

enable real-time information sharing and cooperation among various functional areas, helping to reduce

information silos and enhance the efficiency of collaboration. Companies that are good at cross-functional digital

collaboration are more likely to connect the dots between technology and business operations, leading to a more

robust system and faster response to emerging challenges (Deloitte, 2024; KPMG, 2023). Real-time data analysis

and integrated workflows are made possible by a unified digital platform and systems, allowing teams to make

a coordinated evaluation and improvement. To examine the relationship between cross-functional digital

collaboration and environmental sustainability, this hypothesis was tested.

Ho₃: There is no significant relationship between cross-functional digital collaboration and environmental

sustainability of oil and gas organisations in the Niger Delta region of Nigeria.

Operational Efficiency

In the dynamic world of industries, the pursuit of operational excellence is more critical than ever for companies

looking to maintain their competitive edge and sustainability (Arora, Ahmad, Kumar, & Singh, 2025).

Operational efficiency is an organization's ability to maximize its effectiveness by minimizing its resources used,

and waste by improving the use of resources and streamlining processes without compromising the quality of

the output. In many capital-intensive industries, such as oil and gas, the efficiency of the operation is a measure

of cost containment, and it is one of the most critical strategic indicators of the reliability, productivity, stability,

and organisation's resilience. Focusing on the industries that are highly dependent on the energy markets that are

subject to volatility, efficiency is a component of competitive advantage and sustainable performance (Slack,

Brandon-Jones, & Burgess, 2022; Heizer, Render, & Munson, 2020).

Digital transformation has changed the way operational efficiency is achieved in recent years. Advanced

analytics, automation technologies and integrated monitoring systems allow organizations to monitor

performance in real-time, predict equipment failures, and streamline decision-making processes over complex

operational networks. Reports indicate that digitally-enabled operational models lead to greater asset reliability,

reduced downtime, and better coordination of work activities, which in turn boosts the performance of the system

as a whole (McKinsey & Company, 2023; World Bank, 2024). To achieve a significant leap in efficiency,

productivity, or profitability, an organisation's substantial operational redesign of processes is essential (Salau,

Tella, Osawe, & Oladele, 2025).

In the modern context, operational efficiency is not just about the volume of production; it's about optimizing

the process, utilizing resources, and reducing waste. According to Skalli et al.. Reduction in the number of

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unplanned downtime incidents results in higher productivity, efficiency in operations, and lower maintenance

costs (2025).

Environmental Sustainability

Environmental sustainability is about organizational practices and decisions that help reduce negative

environmental impacts and ensure the long-term sustainability of industrial operations. Oil and gas sustainability

is not just about compliance; it's about managing resources responsibly, controlling emissions, monitoring the

environment, and integrating sustainable practices into operations. The increasing focus on environmental

responsibility in global energy markets is putting increasing pressure on organizations to ensure that energy

production is compatible with their broader sustainability goals (International Energy Agency [IEA], 2023).

In the Niger Delta region, environmental sustainability is a specific area of concern because of the legacy of oil

spills, gas flaring, ecosystem deterioration, and environmental effects at the community level. These challenges

have led to increased pressure and expectations from regulators and the general public for better institutional

arrangements and policy measures to address environmental governance issues (United Nations Environment

Programme [UNEP], 2024; Federal Government of Nigeria, 2023). Therefore, oil and gas companies must show

tangible efforts in sustainable practices, better monitoring systems, responsible operational practices, and

transparent reporting.

Theoretical Framework

These theories in combination offer a multi-dimensional account of the adoption, enactment and translation of

digitally-enabled practices of design to sustainable organizational outcomes.

Diffusion of Innovation Theory

The Diffusion of Innovation Theory was developed by Rogers (2003) in an attempt to explain the way new ideas,

technologies, and practices spread within organizations and also from one social system to another. The theory

highlights the importance of factors like perceived usefulness, compatibility with existing systems, and the

readiness of the organization in innovation adoption. The diffusion perspective can be used to imagine how the

digital experimentation tools, collaborative platforms, and human-centered innovation practices are being

introduced and integrated into oil and gas operations in the context of digitally-driven design thinking.

In complex industrial environments, the use of digital technologies does not necessarily lead to better

performance results. Instead, innovation diffusion proceeds slowly and takes place with experimentation,

learning and organizational adaptation. Research indicates that iterative experimentation and collaborative

processes, when used to adopt digital tools, are more effective for organizations to attain operational efficiency

and sustainability outcomes (IEA, 2023; Deloitte, 2024). Thus, it can be argued that the diffusion framework

helps explain how the innovative practices of digital-driven design thinking can be integrated over time to

improve process optimization, resource utilization efficiency, and environment monitoring.

In addition, Diffusion of Innovation Theory emphasizes the importance of organizational culture and

communication network in innovation outcomes. Cross-functional digital collaboration accelerates the

implementation of sustainable practices and helps overcome resistance to change, leading to a better

understanding of sustainable technologies and knowledge sharing. A diffusion theory is appropriate for

understanding the different ways in which organizations adopt digital-driven innovation strategies in the

Nigerian oil and gas context, where legacy operational structures can impede the uptake of technology.

Dynamic Capabilities Theory

Dynamic Capabilities Theory (DCT) was first introduced in 1997 by David Teece, Gary Pisano, and Amy Shuen,

and later developed by Teece (2007), which extends the analysis of the adoption of innovations to how

organizations reconfigure their internal resources and processes to respond to changes in their environment and

in technology. The theory suggests that the continuous success of an organization is closely related to its capacity

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to sense opportunities, latch on to innovations and reconfigure the organization in dynamic environments (Teece,

Pisano, & Shuen, 1997; Eisenhardt & Martin, 2000; Teece, 2007).

In this study, digital-driven DT is not only treated as a technology initiative but also as an adaptive organizational

capability. Digital experimentation, human-centred problem framing and cross-functional collaboration

empower oil and gas companies to redesign workflows, optimize resource use, and minimize operational

inefficiencies. Dynamic Capabilities Theory (DCT) describes how digital-enabled DT contributes to better

operational efficiency through the continuous learning process, iterative experimentation and flexible

organizations. It provides a solid theoretical basis for understanding why some companies succeed in making

digital innovation into measurable efficiency gains, and why other companies don't manage to integrate digital

innovation due to their lack of adaptive capacity or their highly structured nature.

Empirical Review

Arora, Ahmad, Kumar, & Singh, (2025) studied how operational excellence and Industry 4.0 technologies

collaborate for mutual benefit. The paper aims to reveal the transformative potential of these technologies for

streamlining operations, boosting productivity, reducing waste and fostering overall corporate success. The

results show the need to build a complete strategy that effectively integrates the most recent technologies with

the best practices of operational excellence for business growth in a competitive way and for greater

environmental and social sustainability in the modern business environment.

Eteyen (2024) analyses how technologies improve operational performance and identifies the extent to which

opportunities and challenges are associated with their implementation. With the aid of systematic content

analysis in reviewing the studies, the study found that AI and big data significantly enhance decision-making,

predictive maintenance, inventory management, and risk mitigation, thereby contributing to overall supply chain

efficiency. But the lack of infrastructure, lack of skilled people, and resistance to change within the organization

were also identified as challenges, and the opportunities for operational optimisation and increasing supply chain

resilience are limited.

Elete, Nwulu, Erhueh, Akano, & Aderamo, (2024) comprehensively examined the multifaceted impact of digital

technologies on the industry, focusing on key areas such as data analytics, the Internet of Things (IoT), artificial

intelligence (AI), and blockchain. It highlighted the historical background of digital transformation for the oil

and gas industry and identified the challenges of implementing new technologies. Then it examines some of the

digital solutions that have been deployed throughout the industry, such as advanced data analytics for managing

reservoirs. The research revealed the potential role of digital technologies to revolutionize the oil and gas

industry, and gave insights to stakeholders who want to use them in order to achieve sustainable growth and

competitive advantage.

Chukwudi (2024) established the effect of digitalization on the performance of manufacturing firms in Nigeria.

It also used the desk secondary data collection method of obtaining data. In conclusion, it was established that

digitalization significantly impacted firm performance in Nigeria's manufacturing sector, which enhanced

operational efficiency, improved decision-making processes, and spurred innovation within firms. It further

suggested that manufacturing companies in Nigeria should invest in digital infrastructure, develop digital talents,

and foster an organizational culture that encourages digital innovation. The policymakers should develop

supportive policies/regulations and incentives for manufacturing companies in Nigeria to adopt digitalization.

METHODOLOGY

The methodological decisions are made on the basis of the nature of the research objectives, which are to

investigate relationships between organisational constructs through quantitative analysis. This study is a

quantitative survey research with a cross-sectional descriptive correlational survey design. The cross-sectional

design of the study enables data to be collected only once, which is appropriate for the study of organizational

practices and perceptions in an evolving industry like the oil and gas industry. The population of the study

consists of technical and managerial staff of the selected oil and gas organization in the Niger Delta Region of

Nigeria. The people include engineers, operations managers, digital transformation professionals, sustainability

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managers, and supervisory personnel with direct involvement in operational decision-making and digital

innovation projects. The selection of this population is based on the study's emphasis on design thinking and

operational practices that are driven by digital technologies. From the staff records of the selected firms, the total

population available for this study is 420 employees. While the sample size was 205, based on the Taro Yemae

sample determination formulae.

Validity and Reliability

Content validity was established through expert review involving three academics and two industry

practitioners. Cronbach Alpha results:

Table 1

Variable

Alpha

0.84

0.86

0.82

0.88

0.85

Digital experimentation

Human-centred problem solving

Cross-functional collaboration

Operational efficiency

Environmental sustainability

Overall α = 0.85

DATA ANALYSIS AND PRESENTATION OF RESULTS

Table 2: Demographic Profile of Respondents

Demographic Variable

Category

Frequency (N)

Percentage (%)

Professional Role

Operational/Engineering Staff

Managerial/Strategic Staff

More than 5 years

117

71

62.2

37.8

70.2

29.8

100.0

Years of Experience

158

67

5 years and below

Engineering, Operations, Digital

Transformation, Sustainability

225

Functional Areas

Author’s Field Survey 2026

Multiple Regression Analysis of the Effect of Digital-Driven Design Thinking on Organizational

Performance Outcomes (Operational Efficiency and Environmental Sustainability)

Model Specification (Variable Structure)

Based on the study objectives:

Independent Variable (IV): Digital-Driven Design Thinking (DDDT)

Measured through: Digital Experimentation (DE), Human-Centred Digital Problem Solving (HCDPS), Cross-

Functional Digital Collaboration (CFDC)

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Dependent Variables (DVs): Operational Efficiency (OE) & Environmental Sustainability (ES):

Correlation Analysis, (Pearson Correlation Matrix)

Table 3: Correlation Matrix of Study Variables

Variables

DE

HCDPS

0.62

1

CFDC

0.58

0.65

1

OE

ES

1

0.71

0.69

0.66

1

0.64

0.67

0.70

0.73

1

HCDPS

CFDC

OE

0.62

0.58

0.71

0.64

0.65

0.69

0.67

0.66

0.70

ES

0.73

Source: Author’s Computation (2026)

Interpretation of Findings

The correlation matrix displays the relationships between the research variables: Digital Experimentation (DE),

human capital development practices (HCDPS), Cross-Functional Digital Collaboration (CFDC), organisational

effectiveness (OE), and employee satisfaction (ES). All the variables are positively and statistically significantly

associated with each other, meaning that they move in the same direction.

The strongest relationship exists between Digital Experimentation and Operational Efficiency (r=.71), indicating

that increased digital experimentation is associated with improvements in operational performance.

The independent variable with the highest correlation is OE with DE (r = 0.71), which means that the variable

organizational effectiveness has the most significant relationship with the dependent variable. This indicates that

the more effective the organization the more likely they are to report positive DE results.

Also, ES (r = 0.64) and HCDPS (r = 0.62) have a high positive correlation with DE, thus they are important

factors that affect the dependent variable. CFDC (r = 0.58) is positive and significant, but has the lowest

correlation with DE compared to the other predictors.

Independent variables are also quite positively related to each other. For instance, there are very strong positive

correlations between OE and ES (r = 0.73), HCDPS (r = 0.69), and CFDC (r = 0.66), which indicates that these

organizational factors are very interrelated and mutually reinforcing.

The intercorrelation between the two variables is highest for OE and ES (r = 0.73), which means that there is a

high correlation between organizational effectiveness and employee satisfaction.

In summary, the findings suggest that improving human capital development, financial decision-making

capacity, organizational effectiveness, and employee satisfaction are all likely to contribute positively to DE,

with organizational effectiveness showing the strongest influence.

Table 4: Ho₁: Digital experimentation is not significantly associated with operational efficiency in oil and

gas firms in the Niger Delta region of Nigeria. &

Ho₂: Human-centred digital problem solving is not significantly related to operational efficiency in oil and

gas companies in the Niger Delta region of Nigeria.

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Regression Results

Variable

β

t

Sig.

Digital experimentation

0.431 6.32 0.000

Human-centered digital problem solving 0.387 5.87 0.000

Cross-functional digital collaboration

0.412 6.11 0.000

Table 5: Model Summary

R²

0.654

Adjusted R²

0.648

F

p

86.72

< 0.05

From tables 4 & 5, the regression analysis indicates that the dimensions of Digital-Driven Design Thinking

significantly influence organizational performance outcomes among oil and gas firms in the Niger Delta region

of Nigeria. The model yielded an R² value of 0.654, signifying that approximately 65.4% of the variance in

operational efficiency and environmental sustainability is jointly explained by digital experimentation, human-

centred digital problem solving, and cross-functional digital collaboration. The adjusted R² value of 0.648 further

confirms the robustness of the model after accounting for the number of predictors included in the analysis. The

overall model is statistically significant (F = 86.72, p < 0.05), indicating that the combined effect of the

independent variables meaningfully explains organizational performance outcomes in the oil and gas sector.

The coefficient estimates reveal that digital experimentation has a significant positive effect on organizational

performance (β = 0.431, t = 6.32, p = 0.000). This implies that increased digital experimentation through

simulations, predictive analytics, and digital technologies is associated with enhanced operational efficiency and

sustainability outcomes. The coefficient value underscores its positive contribution to organizational

performance. Similarly, human-centered digital problem solving demonstrates a statistically significant positive

impact (β = 0.387, t = 5.87, p = 0.000). This suggests that involving employees and stakeholders in digital

innovation and problem-solving processes boosts organizational adaptability and supports effective operational

practices. The positive coefficient indicates that organizations prioritizing human-centered approaches are more

likely to achieve improved performance outcomes.

Additionally, cross-functional digital collaboration significantly and positively affects organizational

performance (β = 0.412, t = 6.11, p = 0.000). This highlights that enhanced collaboration and information sharing

across organizational units via digital platforms contribute substantially to operational coordination and

environmental sustainability. Among the predictors, digital experimentation has the highest standardized beta

coefficient (β = 0.431), indicating it has the strongest influence on organizational performance, followed by

cross-functional digital collaboration (β = 0.412) and human-centered digital problem solving (β = 0.387).

DISCUSSION OF FINDINGS

The results offer empirical evidence to support the claim that design thinking with a digital approach is important

in enhancing the efficiency and environmental sustainability of the operations in the oil and gas industry.

The first hypothesis indicated that in the digital experimentation, there is a strong relationship between the digital

experimentation and operational efficiency, confirming that decision-making based on simulation will improve

the reliability of the process and minimize downtime. This is consistent with the results of Boschert and Rosen

(2016) showing the benefits of digital twins in terms of system performance through predictive maintenance and

operational optimization. It also validates the findings of several studies that have highlighted the various ways

digital experimentation boosts operational efficiency by automating processes, enabling predictive analytics,

facilitating real-time monitoring, and optimizing processes in oil and gas operations. For instance, Elete et al.

(2024) observed that digital transformation technologies such as Artificial Intelligence (AI), Internet of Things

(IoT), and big data analytics improve operational efficiency by streamlining production and maintenance

activities in oil and gas firms. Similarly, Anaba, Kess-Momoh, and Ayodeji (2024) argued that digital

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experimentation enhances efficiency and reduces operational costs through predictive maintenance and data-

driven decision-making systems in oil and gas production. Furthermore, Cherepovitsyn and Tretyakov (2023)

found that the adoption of digital projects and experimentation strategies in oil and gas companies contributes

to competitiveness, operational reliability, and process optimization.

Additionally, in the second hypothesis, human-centred digital problem solving proved to be the best predictor

of operational efficiency. This discovery once again underlines the fact that technology cannot be the only

solution if it still needs to match people's processes.

Brown and Wyatt (2010) and Vial (2019) studies highlight the importance of employee engagement to increase

the likelihood of successful implementation and minimize resistance to change. User views are important in the

oil and gas industry, where there are complex operations to enhance decision quality and system adaptability.

It further corroborate the positions of Onwuka and Chukwu (2025) who explained that digital technologies

improve operational planning, maintenance, and production optimization when employees actively engage with

digital systems and decision-making processes, Haouel and Nemeslaki (2023) who further asserted that digital

transformation initiatives in oil and gas firms become more effective when organizations align digital

technologies with human capabilities, operational reliability, and organizational learning systems.

Chandrasekharan and Baser (2025) found that digital skills and human-centred decision-making boost

operational effectiveness in oil and energy trading companies, foster analytical insights, and facilitate

collaborative problem-solving and risk management.

Thirdly, the cross-functional digital collaboration had a significant impact on environmental sustainability. This

reinforces the notion that sustainability results are systemic and need integrated efforts on the part of

organizational units. Moving beyond this, cross-functional collaboration in the digital realm has been

demonstrated to support environmental sustainability in oil and gas organizations, by bringing together

operational, technological and sustainability functions. According to KPMG (2023) and IEA (2025), integrated

digital teams facilitate better emissions monitoring and regulatory compliance by sharing data platforms and

working together in decision-making.

Several study, such as, Bento (2018) who noted that collaborative work systems and integrated digital operations

improve coordination, innovation, and sustainable operational practices in the oil and gas industry, Adekunle

and Akhimien (2023) also also found that technology infrastructure, technology applications, and organizational

collaboration positively influence environmental and sustainable performance among oil and gas firms in

Nigeria, and Egbumokei et al. (2024) who emphasized that cross-functional digital technologies such as AI,

IoT, and analytics support environmental sustainability.

Overall, the results suggest that human-centred and experimental approaches are the main factors that facilitate

the use of digital-driven DT for operational efficiency, whereas sustainability impact requires deeper connected

working across functional areas. The results extend previous studies by providing clear evidence of the

importance of the organization of learning, collaboration, and decision-making in the success and failure of

digital transformation in the oil and gas industry.

CONCLUSION

The findings demonstrate that digital-driven design thinking functions as a strategic capability for enhancing

operational and sustainability outcomes within oil and gas organizations. Digital experimentation improves

process reliability and operational efficiency, while human-centered problem solving strengthens adaptive

decision-making. Cross-functional collaboration promotes environmental sustainability through integrated

organizational responses. Accordingly, sustainable performance in the oil and gas sector depends not only on

technology adoption but also on the integration of technological capabilities, human engagement, and

collaborative structures.

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Recommendations

The results suggest some concrete actions organizations and policymakers can take to enhance the effectiveness

of design thinking with the use of digital.

Firstly, organizations need to make digital experimentation a fundamental way of working. This includes

investments in simulation tools, digital twins, and predictive analytics, as well as making experimentation a core

component of everyday decision-making.

Secondly, more attention should be paid to human-centred design. Those who work with operational systems

every day have valuable knowledge that technology can't replace. Establish formal mechanisms of participation,

where employees can provide inputs to their organizations.

Thirdly, the collaboration between functional areas should be improved. This is more than just platforms; it's

about how to purposefully design the organisation. It is encouraged to have cooperation and collaboration

between departments in problem-solving projects, particularly environmental performance projects.

Contributions of Knowledge to the Study

This study advances Dynamic Capability Theory and Diffusion of Innovation Theory by incorporating

dimensions of digital-driven design thinking into sustainable operational performance within the context of an

emerging economy.

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