Keywords

5.1 Introduction

Many industrial sectors are increasingly concerned about supply chain sustainability-related risk factors. However, there are limited quantitative methods for evaluating sustainability risk using a triple bottom line (economic, social and environmental) [1]. Evidence of sustainable risk management is sparse in many emerging and developing nations, and most developing countries experience more challenges in adopting the sustainability risk framework due to complex supply chains [2, 3]. Sustainability risk management must be understood to ensure global supply chains are more equitable, as low-income countries suffer more from sustainability than high-income countries do [4]. Sustainable risk perception is ambiguous and varies among industrial sectors, as does stakeholder involvement [5–8]. Many industry stakeholders worry about supply chain sustainability hazards (e.g. [2, 9, 10].

The oil and gas business has a limited quantitative approach to analysing supply chain sustainability risk using the triple bottom line (economic, social, and environmental). Despite calls to improve energy transition and switch to more sustainable ways, it is evident, especially for low-income developing nations, that the SDG ambition will be a problem [4]. Mismanaging sustainability risks can damage a company’s brand and operations when stakeholders anticipate social, ecological, and economic sustainability [1, 11]. Economic development and social activities that rely on the oil and gas industry require sustainabe risk management. Developing countries with weak laws and a lack of political will need to balance economic growth with environmental sustainability. Thus, sustainability initiatives can be side-lined for short-term productivity and job advantages (Omanga et al. 2014). Previous studies on sustainability risk perception have generated varied results, [2] confirmed that endogenous environmental threats are regarded as the most relevant across industries, and the interconnectedness between numerous sustainability-related risks is relatively high. In other studies, technological and institutional risks were biggest sustainability threats for telecom corporations [12]. Corruption, inflation, and supply-and-demand volatility are agro-food supply chain concerns [10]. However, some studies have not investigate the technological and institutional issues affecting agro-food sustainability [13]. Given the segment’s role in economic development, what are the oil and gas industry's most significant sustainability risks in the oil and gas industries. Researchers have stressed on supplier and logistics management for healthy O&G supply chains [14]. Others say that MNCs have established sustainability plans for their operations, but few have involved (tier-one) suppliers. MNCs rarely engage their suppliers, despite the higher occurrence of environmental and social breaches that can jeopardise their operations and reputation [15]. Hence understanding the risk perception of decision-making is crucial. Subjective judgments regarding risky events are essential to risk perception. This affects the risks people care about and how they tackle them. Risk perception is vital to how many stakeholders know and comprehend dangers and how they feel about them [16]. Discordant risk perceptions among stakeholders [17]. This study quantifed the risk perception of oil and gas stakeholder risk perceptions. Based on literature and interviews, a questionnaire was devised to assess stakeholders’ perceptions of each risk by evaluating their risk anxiety to drive successful proactive risk mitigation measures. This evaluates stakeholder risk perception and reveals differences.

5.2 Literature and Theory Development

Sustainability risk management (SRM) is an extension of enterprise risk management that aims to maximise environmental, social, and economic performance for a company’s survival [18]. Therefore, it involves controlling the identification, assessment, and response strategies accessed by different industries [10, 12, 19]. However, few studies have examined this from the context of an oil and gas. For instance [20] discussed SRM within the oil and gas sector and discovered that the three most common hurdles in sustainability risk identification and prioritisation include its inherent difficulty to quantify and frequent subjectivity based on stakeholder perceptions—rather than the result of objective criteria such as regulatory requirements, cost or revenue potential and timescale of impacts. As Sustainability impacts are also inherently longer-term focused, researchers have highlighted the importance of risk culture in driving effective SRM practices [21]. Building on this mechanism and drawing on stakeholder theory, the previous researchers have developed a conceptualisation of sustainability risks, laying the basis for future investigations in this respective field [3]. As a result, we integrated sustainability risk management by identifying the effects of sustainability issues on stakeholders, actively including sustainability in objectives across the levels of the organisational hierarchy, and developing concrete support to identifying, assess, and manage sustainability risks. Companies can enhance their competitiveness while providing leadership during the sustainability transition [4]. Risk perception studies examine people's judgments when asked to characterise and evaluate hazardous activities and technologies. Previous scholars have acknowledged that risk perception can aid risk analysis and policymaking by providing a basis for understanding and anticipating public responses to hazards, thereby improving the communication of risk information among lay people, technical experts, and decision-makers [22]. Therefore, comparing different stakeholders’ perceptions of each risk by measuring the levels of their concern about risks can be used to make the critical hypothesis for this research as shown in Fig. 5.1.

H1:

stakeholder risk perception positively impacts sustainability risk management in the oil and gas supply chain.

5.2.1 The conceptual model

See (Fig. 5.1).

Fig. 5.1
A block diagram has the following flow, proactive, ability to control S R, accommodating S R, risk averse, and threat. Stakeholder risk perception. Sustainability risk management. Sustainability risk with 3 factors and risk mitigation strategy with 3 factors.

Conceptualisiing stakeholder risk perception in oil and gas supply chains

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5.3 Methodology

The questionnaire was developed from a literature review and pilot survey from academics and experts from the oil and gas supply chain. These questions used closed-end Likert scales to measure stakeholder risk perception for each criterion. A survey of 265 stakeholders working within different oil and gas companies in Nigeria was conducted; subsequently, analyses were conducted between top management (30), middle management (86), supervisors (72), operations (67), and others (10) to assess and analyse several dimensions of sustainability-related risk. The questionnaire was sent to targeted stakeholders using an exponential non-discriminate snowballing technique over a 3 month period. The initial process involved determining the severity, frequency, and ease of detection for each internal stakeholder category used to develop the latent model variables that connected all lower-order constructs. However, given that the model was a reflective formative higher-order model, six of the latent lower-order constructs were formatted as higher-order constructs. A hierarchical component model was established for each sustainability risk factor and mitigation strategy category. However, some measurement items did not reach the threshold and were subsequently deleted or retained because of their relevance to theory in the lower-order format. Using redundancy analysis, the test for collinearity, VIF, and convergent validity was approximately 0.7 for most variables. Cronbach Alpha, composite reliability, and AVE were all suitable except for environmental risk factors highlighted below in Table 5.2. Subsequently, the path model is analysed and discussed.

5.4 Result and Discussion

Initially failure mode effect analysis (FMEA) was used to measure the severity and frequency (SRM), which was compared among stakeholders to determine any significant differences in patterns. These categories are shown the Table 5.1. The result shows that the top three sustainability-related risk factors revealed by the survey are unfair wages, excess working hours, working life (SRM6), man-made disasters (SRM1), and unhealthy and unsafe work (SRM5) for the overall internal stakeholders.

Table 5.1 Sustainability risk factors
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Table 5.2 Construct validity and reliability
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However, for different stakeholder groups, top management perceives that economic risk factors, including Unfair wages, excessive working hours, Work-life balance, industrial actions, strikes and boycotts (SRM6, SRM9), were the primary concerns based on the response. In contrast, middle management and supervisors focus predominately on the social risk factors: unfair wages, excessive working hours, Work-life balance, and unhealthy and unsafe working environments (SRM6, SRM5). However, operator staff members were more concerned about man-made disasters (SRM6). The analysis used FMEA to identify the severity, frequency, and mitigation strategies. At the same time, top management considers Industrial action, strikes, boycotts (SRM9), and the role of Bribery, false claims, and corruption (SRM8) as significant risk factors that should be prioritised. Middle management and supervisors were more concerned about unfair wages, excessive working hours, no work-life balance (SRM6) and an unhealthy and unsafe working environment (SRM5). However, given the high correlation between the risk factors, any attempt to address the critical risk priority would have a ripple effect on other risk factors that are ultimately correlated.

The results revealed that socioeconomic factors are perceived as more significant risk factors by stakeholders contributing to the sustainability of the oil and gas supply chain than environmental risks. This can be attributed environment’s significant behaviour when the impact is influenced by the availability of resources or energy from the environment or alters ecosystems (Stern 2000). As a result, perceptions are influenced by the by-products of human needs for physical comfort, mobility, labour, enjoyment, power, and personal security, and humanity has developed to satisfy these desires and will have a more significant impact on human decision-making in developing nations where there is often a scarcity. The study also identified the proposed response strategies as the most important factors to consider when addressing the various risks associated with the supply chain. The high interconnectedness of various sustainability-related risks has revealed the need for effective risk management strategies. It also calls for integrated risk management frameworks to help companies develop sustainable strategies. This preliminary study provides academics and practitioners with an exemplar of sustainability risk management from a developing country's oil and gas perspective. The result of this study is beneficial for practitioners, mainly does who can use this study as guidance on how to identify and select the critical sustainability risks and plan mitigating strategies accordingly as well as decision-makers to reassess stakeholders’ varying judgments when considering sustainability risk assessment.

The relationships between the path constructs were tested, as shown in Fig. 5.2. The estimation results are shown in Tables 5.2 and 5.3. Following parameter estimation, bootstrapping was performed to confirm the robustness of the findings. Thus 5000 bootstrap samples were built by re-sampling with replacements from the original sample. The summary results for bootstrapping are provided in Table 5.2. After verifying the hypotheses, we ran a confirmatory factor analysis to determine convergent and discriminant validity [23]. Table 5.2 provides standardisation coefficients for factor loadings, scale composite reliability, and overall variance average (AVE), which were significantly higher than the lower value specified (ideally be above α = 0.7, SCR = 0.7, and AVE = 0.5) except for environmental risk mitigation strategy where the item loading composite reliability and AVE were below. However, given its theoretical relevance, it was not eliminated because stakeholders often perceive their inability to respond to environmental factors, which may explain the outlier compared to other sustainability variables. The theoretical constructs framework has convergent validity for the higher order construction used to formulate the sustainability risk management properties, and the independent variable stakeholder risk perception was accepted.

Fig. 5.2
A model connects S R P with S R P underscore 1, 4, and 5. It leads to R S M which is connected to eco R M S, eco risk, E N V risk, E N V R M S, S O C risk, and S O C R M S via various values.

PLS model

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We can therefore conclude that constructs of our theoretical framework possess convergent validity higher-order latent variables to determine the discriminant validity of the model, as shown in Table 5.3.

Table 5.3 Discriminant validity
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The model result is shown in Table 5.4, which analysed the loading of the key hypotheses that stakeholder risk perception positively impacts sustainability risk management (p > 0.001). Standard root means squared residual (SRMR) was used to access the model fit (SRMR = 0.056); a value below 0.08 is considered a good fit in the covariance-based structural equation. The path was positively between the latent variables SRP and RSM, and the R2 value was 13 per cent (R2 = 0.13) in RSM was accounted for by this model.

Table 5.4 Model fit
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Consequently, it can be concluded that stakeholder risk perception would significantly impact sustainability risk management which implies that SRP contributes uniquely to the decision of how different categories of stakeholders prioritise response to sustainability events.

5.5 Conclusion

The purpose of this research was to provide a means to investigate the measurement of latent variables to evaluate the theoretical relationship underlying the diverse perceptions and prioritisation of sustainability risk factors in Nigeria’s oil and gas supply chain. In the model, stakeholder risk perception highly predicts sustainability risk management. Consequently, the more significant the risk perception, the greater the impact on SRM. However, some of the evaluation items did not adequately address the requirements. These included environmental risk mitigation strategy which did not accurately represent the item due to the alleged inability of stakeholders to respond effectively to natural and man-made disasters.

In addition, the dependent variable could only be explained at a rate of 13%, indicating that additional antecedent factors are needed to investigate the model. The result highlighted the similarity between the top and middle management, supervisors, and operation staff’s perceived sustainability risk factors. It further indicated more significant concern for unfair wages, excessive working hours, working life, illness, and a safe working environment.

A major drawback of this study is the absence of crucial antecedent variables that influence sustainability risk management. The survey results show that more predictive variables are required to account for the variance in RSM. In addition, the study’s design did not account for the importance of external stakeholders in collaborative decision-making. In addition, despite their uniquie cultureal influence, this was universally relevant within the setting of developing countries, which may limit the generalisability of the study. In the subsequent phase, we plan to investigate additional variables that may influence the risk perceptions of oil and gas supply chain stakeholders.