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This study aims to conceive and develop a pricing model for the Ijara contingent convertible contract (ICCC, hereafter), considering the possibility that the lessee may default. The ICCC model grants the lessor the option of converting the unpaid amount into equity or recovering the leased equipment and selling it at market price in case of financial distress.

The ICCC is consistent with the profit-sharing approach and the new risk management techniques, which are compatible with Islamic philosophy. Relying on real options theory and the contingent claim approach, a closed-form solution of the firm’s assets is developed in a dynamic environment, where the rate of return is generated by a Cox-Ingersoll-Ross stochastic process.

Examining the numerical analysis reveals the impact of the firm value, the conversion or sell decision and the conversion ratio and volatility on the ICCC value. The value of the ICCC can increase substantially as the value of the firm approaches the conversion threshold. The conversion ratio as well as the asset market price play equally an important role in the decision to convert or sell.

This paper develops a pricing model for a contingent Ijara contract, which incorporates a conversion option to mitigate the lessee’s credit risk during periods of economic instability. The ICCC is a cooperative strategy that would be advantageous to all parties, including the lessor and lessee. In the event of a conversion, businesses may be able to continue operating thanks to this financial innovation, and the lessor may profit from the company’s recovery by freeing up more resources for the use of more profitable ventures.

The present research investigates the effects of adopting Industry 4.0 technologies (ADT) on the Pakistani food sector’s sustainable performance (SP). Specifically, it focuses on the roles of green supply chain collaboration (GSCC), circular economy practices (CEP) and technological readiness (TR) as mediators and environmental dynamism (EDY) as a moderator.

This study is based on the natural resource-based view (NRBV) theory to determine the 14.0 adoption toward sustainable performance with mediators and moderators. The authors conducted an online questionnaire from 318 employees of the food manufacturing industries in Lahore, Pakistan, and applied the PLS-SEM approach to test the relationships of variables.

The findings of this study reveal that adopting Industry 4.0 technologies significantly influences sustainable performance through GSCC, CEP and TR in the Pakistani food sector. Moreover, the relation between industry 4.0 technologies, GSCC, CEP and TR is positively moderated by environmental dynamism.

The findings have marked implications for the literature on adopting I4.0 on sustainable performance in the Pakistani food sector. This research is based on data collected from a single country, and industry is the limitation of this study.

The present study provides conclusive evidence of the influence of the adoption of Industry 4.0 on sustainable performance through GSCC, CEP and TR in the Pakistani food sector. This study is the first to investigate the moderating role of environmental dynamism among industry 4.0 technologies, GSCC, CEP and TR.

In this research, we seek to understand the effects of artificial intelligence (AI) and knowledge management (KM) processes in enhancing proactive green innovation (PGI) within oil and gas organizations. It also aims to investigate the moderator role of trust and sustainability in these relationships.

This paper employs a quantitative analysis. Surveys have been gathered from the middle-line managers of twenty-four oil and gas government organizations to evaluate the perceptions of the managers towards AI, KM processes, trust, sustainability measures and proactive measures toward green innovation. Analytical and statistical tools that were employed in this study, including structural equation modeling with SmartPLSv3.9, have been used to analyze the data and to examine the measurement and structural models of this study.

The study results reveal a significant and positive impact of AI utilization, KM processes and PGI within oil and gas organizations. Furthermore, trust and sustainability turn out to be viable moderators affecting, and influencing the strength and direction of AI, KM and PGI relationships. In particular, higher levels of trust and more substantial sustainability commitments enhance the positive impact of AI and KM on green innovation outcomes.

Understanding the impact of AI, KM, trust and sustainability offers valuable insights for organizational leaders and policymakers seeking to promote proactive green innovation within the oil and gas industry. Thus, organizations can increase the efficiency of sustainable product development, process improvement and environmental management by using robust AI technologies and effective KM systems. Furthermore, fostering trust among stakeholders and embedding sustainability principles into organizational culture can amplify the effectiveness of AI and KM initiatives in driving green innovation outcomes.

This study extends the current knowledge by assessing the effect of AI and KM on proactive green innovation while accounting for trust and sustainability as moderators. Utilizing quantitative methods offers a nuanced understanding of the complex interactions between these variables, thereby advancing theoretical knowledge in the fields of innovation management, sustainability and organizational behavior. Additionally, the identification of specific mechanisms and contextual factors enriches practical insights for organizational practitioners striving for a practical understanding of the dynamics of the complexities of sustainable innovation in an AI-driven era.