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The aim of the paper is to rank and analyze the key strategies to sustainable finance adoption in the manufacturing sector using Fuzzy Delphi method (FDM), Interpretive Structural Modeling (ISM) and MICMAC (impact matrix cross-reference multiplication applied to a classification) analysis.

The study develops a novel framework to identify and analyze the mutual relationships among set of sustainable policies using extensive literature survey and experts opinion. Initially, the study found 14 strategies to implement sustainable finance with the help of vast literature. Then, the list of identified factors were screened through Fuzzy Delphi Method (FDM). Based on driving and dependence power, the final list of factors are divided into three categories.

The study findings reveal that “environmental rules and practices”, “financial incentives, tax reduction and subsidy”, have strongest driving power for promoting sustainable financial system in Pakistani manufacturing sector. Furthermore, “environmental awareness” and “long term vision” are found to be highly influenced by other corresponding elements in a system.

The ISM approach assists professionals, academics, and managers in identifying and ranking policies in implementing green business techniques. The hierarchical representation of ISM results provides a roadmap for decision-makers to navigate and prioritize factors effectively, facilitating the implementation of strategies that contribute to sustainable growth within organizations.

The study results provide interesting clues regarding green finance policies that provide the foundations, incentives, protections or other provisions that support the ecological conservancy’s mission. Specifically, the findings guide that government must offer research grants to private enterprises, research and development institutions, and universities to promote environmental protection and develop transformative technologies such as waste recycling, renewable energy, carbon capture, and power consumption.

The exploration of strategies for sustainable finance adoption with the help of mixed methodological approach and classification of these strategies on the basis of importance level is a new attempt in the field of manufacturing sector.

Islamic banking has undergone significant adaption since its inception. This study aims to investigate why and how Islamic banks adapt their services, using participatory financing as evidence.

A qualitative study is designed, using working capital financing and commodity operations financing in Pakistan as analytical units. The data for each analytical unit is analyzed using a qualitative content analysis, while the findings are synthesized using a cross-case synthesis method.

Findings suggest that participatory financing has undergone extensive adaptation in the Islamic banking industry of Pakistan, in the wake of resolving constraints to participatory financing and increasing its viability. Consequently, participatory finance has emerged as an attractive and viable option in Pakistan. These findings suggest that unlike in the past, where Islamic banks used to buffer themselves from the environment and ignore the market demands, they have learned to respond effectively to the market demands and the challenges posed by the environment.

Findings suggest that the adaptation strategy is more effective than the migration strategy, because it enables the financial service systems to reduce the underlying risks by avoiding emergent threats and eradicating the inherent weaknesses.

The extant literature provides a generalized view on the adaptation process that Islamic banks undergo to comply with their environment. However, it is limited in terms of conceptualizing the adaptations and innovations in their products and the underlying structural variations. The present study fills this gap.

The purpose of this paper is to propose a new grey prediction model, GOFHGM (1,1), which combines generalised fractal derivative and particle swarm optimisation algorithms. The aim is to address the limitations of traditional grey prediction models in order selection and improve prediction accuracy.

The paper introduces the concept of generalised fractal derivative and applies it to the order optimisation of grey prediction models. The particle swarm optimisation algorithm is also adopted to find the optimal combination of orders. Three cases are empirically studied to compare the performance of GOFHGM(1,1) with traditional grey prediction models.

The study finds that the GOFHGM(1,1) model outperforms traditional grey prediction models in terms of prediction accuracy. Evaluation indexes such as mean squared error (MSE) and mean absolute error (MAE) are used to evaluate the model.

The research study may have limitations in terms of the scope and generalisability of the findings. Further research is needed to explore the applicability of GOFHGM(1,1) in different fields and to improve the model’s performance.

The study contributes to the field by introducing a new grey prediction model that combines generalised fractal derivative and particle swarm optimisation algorithms. This integration enhances the accuracy and reliability of grey predictions and strengthens their applicability in various predictive applications.

The purpose of this paper is to analyse and classify the literature that contributed to three questions, namely, what are the benefits of big data analytics (BDA) in the field of supply chain management (SCM) and logistics, what are the challenges in BDA applications in the field of SCM and logistics and what are the determinants of successful applications of BDA in the field of SCM and logistics.

This paper conducts a systematic literature review (SLR) to analyse the findings of 44 selected papers published in the period from 2016 to 2020, in the area of BDA and its impact on SCM. The designed protocol is composed of 14 steps in total, following Tranfeld (2003). The selected research papers are categorized into four themes.

This paper identifies sets of benefits to be gained from the use of BDA in SCM, including benefits in data analytics capabilities, operational efficiency of logistical operations and supply chain/logistics sustainability and agility. It also documents challenges to be addressed in this application, and determinants of successful implementation.

The scope of the paper is limited to the related literature published until the beginning of Corona Virus (COVID) pandemic. Therefore, it does not cover the literature published since the COVID pandemic.

This paper contributes to the academic research by providing a roadmap for future empirical work into this field of study by summarising the findings of the recent work conducted to investigate the uses of BDA in SCM and logistics. Specifically, this paper culminates in a summary of the most relevant benefits, challenges and determinants discussed in recent research. As the field of BDA remains a newly established field with little practical application in SCM and logistics, this paper contributes by highlighting the most important developments in contemporary literature practical applications.