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Achieving a zero-carbon city requires a long-term strategic perspective. The authors propose a decision-making model which would take into account the economic, environmental and social impacts for prioritizing the zero-carbon measures for sustainable urban transportation.

An integrated intuitionistic fuzzy gained and lost dominance score (IF-GLDS) model is introduced based on intuitionistic fuzzy Yager weighted aggregation (IFYWA) operators and proposed weight-determining IF-SPC procedure. In addition, a weighting tool is presented to obtain the weights of decision experts. Further, the feasibility and efficacy of developed IF-SPC-GLDS model is implemented on a multi-criteria investment company selection problem under IFS context.

The results of the developed model, “introducing zero-emission zones” should be considered as the first measure to implement. The preference of this initiative offers sustainable transport in India to achieve a zero-carbon transport by having the greatest impact on the modal shift from cars to sustainable mobility modes with a lower operational and implementation cost as well as having greater public support. The developed model utilized can be relocated to other smart cities which aim to achieve a zero-carbon transport. Sensitivity and comparative analyses are discussed to reveal the robustness of obtained result. The outcomes show the feasibility of the developed methodology which yields second company as the suitable choice, when compared to and validated using the other MCDA methods from the literature, including TOPSIS, COPRAS, WASPAS and CoCoSo with intuitionistic fuzzy information.

A new intuitionistic fuzzy symmetry point of criterion (IF-SPC) approach is presented to find weights of criteria under IFSs setting. Then, an IF-GLDS model is introduced using IFYWA operators to rank the options in the realistic multi-criteria decision analysis (MCDA) procedure. For this purpose, the IFYWA operators and their properties are developed to combine the IFNs. These operators can offer a flexible way to deal with the realistic MCDA problems with IFS context.

The study aims at evaluating the most appropriate transport project which is one of the critical concerns of transport infrastructure scheduling. This process will be applied considering a set of criteria and discussed alternatives with sustainable perspectives.

In this paper, a complex proportional assessment (COPRAS) framework is discussed to handle the sustainable transport investment project (STIP) assessment problem within a single-valued neutrosophic set (SVNSs). To form the procedure more useful in handling with uncertain features, a SVNS is applied as a valuable procedure to handle uncertainty. First, a new discrimination measure for SVNSs is introduced and discussed some elegant properties to determine the significance degree or weight values of criteria with the sustainabality perspectives. Second, an integrated approach is introduced based on the discrimination measure and the COPRAS method on SVNSs and named as SVN-COPRAS.

A case study of an STIP evaluation problem is used to confirm the practicality and effectiveness of the SVN-COPRAS framework. Lastly, comparative discussion and sensitivity investigation are illustrated to prove the strength and solidity of the proposed framework.

The SVNSs enrich the essence of linguistic information when a decision expert (DE) vacillates among different linguistic values (LVs) to measure a sustainable transport project alternative problem. The utilization of SVNSs provides a more stable procedure to describe DEs' evaluations. So, an elegant methodology is developed to incorporate the DEs' awareness and experience for electing the desired STIPs. The introduced methodology has higher operability than the single-valued neutrosophic set technique for order preference by similarity to an ideal solution (SVN-TOPSIS) procedure during the larger numbers of attribute(s) or option(s). For the SVN-COPRAS methodology, there is no need to estimate the single-valued neutrosophic ideal solution (SVN-IS) and single-valued neutrosophic anti-ideal solution (SVNA-IS). The outcomes are calculated with handling the realistic data, which elucidates that the introduced model can tackle more intricate and realistic multi-criteria decision-making issues.

This paper develops a decision-analysis model to prioritize and select the site to establish a new hospital over different indicators such as cost, market conditions, environmental factors, government factors, locations and demographics. In this way, an integrated model is proposed under the intuitionistic fuzzy information (IFI), the standard deviation (SD), the rank-sum (RS) and the measurement of alternatives and ranking using the compromise solution (MARCOS) approach for ranking hospital sites (HSs).

The IF-SD-RS model is presented to obtain the combined weight with the objective and subjective weights of diverse sub-criteria and indicators for ranking sites to establish the hospital. The IF-MARCOS model is discussed to prioritize the various sites to establish the hospital over several crucial indicators and sub-criteria.

The authors implement the developed model on a case study of HSs assessment for the construction of new hospital. In this regard, inclusive set of 6 key indicators and 18 sub-criteria are considered for the evaluation of HSs. This study distinguished that HS (h₂) with combined utility function 0.737 achieves highest rank compared to the other three sites for the given information. Sensitivity analysis is discussed with different parameter values of sub-criteria to examine how changes in weight parameter ratings of the sub-criteria affect the prioritization of the options. Finally, comparative discussion is made with the diverse extant models to show the reasonability of the developed method.

This study aims to develop an original hybrid weighting tool called the IF-SD-RS model with the integration of IF-SD and IF-RS approaches to find the indicators' weights for prioritizing HSs. The developed integrated weighting model provides objective weight by IF-SD and subjective weight with the IF-RS model. The model presented in the paper deals with a consistent multi-attribute decision analysis (MADA) concerning the relations between indicators and sub-criteria for choosing the appropriate options using the developed IF-SD-RS-MARCOS model.

Reverse logistics (RL) is a type of supply chain management that moves goods from the end customer to the original manufacturer for reuse, remanufacturing and disposal purposes. Owing to growing environmental legislations and the development of new technologies in marketing, RL has attracted more significance among experts and academicians. Outsourcing RL practices to third-party reverse logistics provider (3PRLP) has been identified as one of the most important management strategies due to complexity of RL operations and the lack of available resource. Current sustainability trends have made 3PRLP assessment and selection process more complex. In order to select the 3PRLP, the existence of several aspects of sustainability motivates the experts to establish a new multi-criteria decision analysis (MCDA) approach.

With the growing complexity and high uncertainty of decision environments, the preference values of 3PRLPs are not always expressed with real numbers. As the generalized version of fuzzy set, intuitionistic fuzzy set and Fermatean fuzzy set, the theory of q-rung orthopair fuzzy set (q-ROFS) is used to permit decision experts (DEs) to their assessments in a larger space and to better cope with uncertain information. Given that the combined compromise solution (CoCoSo) is an innovative MCDA approach with higher degree of stability and reliability than several existing methods.

To exhibit the potentiality and applicability of the presented framework, a case study of S3PRLPs assessment is taken from q-rung orthopair fuzzy perspective. The assessment process consists of three sustainability aspects namely economic, environment and social dimensions related with a total of 14 criteria. Further, sensitivity and comparative analyses are made to display the solidity and strength of the presented approach. The results of this study approve that the presented methodology is more stable and efficient in comparison with other methods.

Thus, the objective of the study is to develop a hybrid decision-making methodology by combining CoCoSo method and discrimination measure with q-ROFS for selecting an appropriate sustainable 3PRLP (S3PRLP) candidate under uncertain environment. In the proposed method, a novel procedure is proposed to obtain the weights of DEs within q-ROFS context. To calculate the criteria weights, a new formula is presented based on discrimination measure, which provides more realistic weights. In this respect, a new discrimination measure is proposed for q-ROFSs.

This study aims to empirically investigate the impact of adopting big data and data analytics (BD&A) on audit quality (AQ).

A questionnaire was distributed among audit practitioners working at audit firms in Egypt and 205 responses were collected. Partial least square structural equation modeling (PLS-SEM) was used to analyze and test research hypotheses.

The results reveal that BD&A has a direct significant positive effect on the audit process (AP) and auditor competence (AC). However, an insignificant impact of BD&A is found on audit fees (AF). In addition, the results indicate that BD&A has significant positive direct and indirect impacts on AQ.

The results of this study will benefit several auditing stakeholders, such as audit firms, audit regulators, novice financial auditors and academic scholars.

This research is one of the earliest to empirically address the role of BD&A in enhancing AQ. It incorporates AP, AC and AF as mediators into a single model to explain the impact of BD&A on AQ. Also, it attempts to provide empirical evidence from a developing country with a less-regulated audit environment.

This research examines the complex relationship between usage of Chat Generative Pre-Trained Transformer (ChatGPT) amongst student and their creativity, learning and assessment using empirical data collected from postgraduate students. In addition, the study explores the student’s intrinsic motivation for usage to understand student categories. This research seeks to provide further insights into this artificial intelligence tool in enhancing the educational ecosystem for all stakeholders concerned.

The target population of this research – the students of post-graduation in diverse fields of science and management. A five-point Likert scale-structured questionnaire adapted from earlier literature relevant to the research questions was adopted for data collection. The data were collected for two months, resulted in 403 usable responses. Ethical considerations of assurance of confidentiality to the participants were strictly adhered to. Structured equation modelling (SEM) was employed to explore the relationships between the constructs of the study for the assessment of latent relationships. SmartPLS 4 was used to explore these relationships.

Usage has a negative impact on a student’s creativity, but increased usage of ChatGPT encourages a student’s adoption due to its perceived usability. Pedagogical applications of ChatGPT aid students as a learning tool but require controlled usage under supervision.

This study is innovative in the context of postgraduate students, where very little evidence of creativity exists. Through this research, the authors illuminate how ChatGPT use affects academic performance, benefiting educators as a tool but for evaluation and assessment, policymakers and students. The findings of the study provide implications that help to create effective digital education strategies for stakeholders.

Motivated by the conflicting evidence on the effect of financial development on environmental quality, this study investigates the moderating role of institutional quality in the link between financial development and environmental quality using a robust proxy in Malaysia from 1984 to 2017.

Ecological footprint is used to measure environmental quality, while financial development is proxied using three measures (domestic credit provided by the private sector, domestic credit provided by the financial sector and domestic credit provided by the banking sector). An index of institutional quality is generated from voice and accountability, government effectiveness, regulatory quality, rule of law and control of corruption. Autoregressive Distributed Lag Bounds Test, Fully Modified Ordinary Least Square and Canonical Cointegrating Regression were used as the estimation techniques.

The results show that financial development, institutional quality, economic growth and foreign direct investment improve environmental quality in the short run, whereas trade openness and natural resources worsen it. In the long run, financial development, institutional quality, economic growth, trade openness and natural resources deteriorate the environment. Furthermore, findings from the interactive term suggest that institutions and financial development complement each other to affect the environment in the short run. However, institutions and financial development perform a substitutability role in influencing the environment in the long run.

The outcome of this study suggests that there are time lags in the relationship between institutional quality, financial development and ecological footprint in Malaysia. Furthermore, the study offers important policy implications to policymakers in Malaysia and other developing countries on how to mitigate environmental degradation.

This study contributes to the body of knowledge on the moderating role of institutional quality in the relationship between financial development and ecological footprint in Malaysia. It examines the direct and indirect effects of financial development on environmental degradation through institutional quality, which have received less attention in the context of Malaysia. The findings from this study are robust to different proxies and estimation techniques.

Sustainable strategic management (SSM) is an integrated concept that entails sustainability in the approaches and processes of strategic management. Banking being a rapidly growing industry in service sector, it is reasonably important to study the SSM and its integrated performance. This paper aims to study the sustainable-strategic (SS) factors of Indian banks that affects their endurance and prioritizes the banks based on their sustainable and strategic performance.

This study appertains a sustainable balanced scorecard (SBSC) framework to determine the SS factors and an integrated multicriteria decision making (MCDM) method to weigh the criteria and prioritize the banks' performance.

The criterion weightage signifies sustainability, followed by financial and internal business perspective to be a crucial dimension for the performance of banks. The banks, State Bank of India, HDFC Bank Ltd and Canara Bank are ranked top-most on the SS performance.

The insights from the study on SS factors and banks' performance can be further used by policy-makers and researchers to understand the sustainability assessing factors and focus on making policies and further studies to enhance the performance in this regard.

Banking is one of the potentially growing industry in service sector. It being a major part of economy's sustainable growth, it is essential to assess the SS factors that enhance their sustainable performance. There is dearth of study in this purview, for developing countries like India. Thus, this study critically analyses the strategic sustainability of Indian banking industry to ascertain the SS factors and prioritize the performance of banks based on criterion weightage and bank's SS operations.

This study aims to find the best location for constructing green energy facilities in India and reducing CO₂ emissions. Incorporating green energy is a priority for many countries under the Paris Agreement. This task is challenging due to factors that affect implementation, and making the wrong decision wastes resources. India’s goals are net-zero emissions by 2070 and 50% renewable electricity by 2030. Other developing nations should emulate India’s renewable energy strategy. India ranks fourth in renewable energy and wind power, and fifth in solar power capacity. This research aims to identify the best locations in India for implementing green energy projects.

To identify the optimal green energy implementation sites in India, this research uses the hybrid multicriteria decision analysis (MCDA) in an uncertain environment. This research uses the Delphi method to identify the most suitable green energy implementation sites in India. It adapts the elements for this investigation. In addition, the utilization of the Fermatean fuzzy weighted aggregated sum product assessment technique is implemented to effectively prioritize the factors that impact the selection of these sites. This study used the MEREC method (method based on the removal effects of criteria) to identify the most suitable areas in India for implementing green energy. The highest accuracy is attained through the amalgamation of these hybrid methods.

Following the computation data by hybrid MCDA in uncertainty environment, NP Kunta in Andhra Pradesh emerges as the recommended green energy site among the 11 considered. Also among the factors political strategies and objectives hold the highest priority among them.

This study is pioneering in its efforts to provide a comprehensive perspective on the development and management of green energy operations in India. The study proves advantageous for diverse sites in the successful adoption and management of green energy. The study is additionally valuable in informing policy development aimed at promoting the use of green energy by employees through the utilization of MCDA methods in uncertain environments.

Logistics service provider (LSP) selection involves multiple criteria, alternatives and decision makers. Group decision-making involves vagueness and uncertainty. This paper aims to propose a novel fuzzy method for assessing and selecting agile, resilient and sustainable LSP, taking care of the inconsistency and uncertainty in subjective group ratings.

Eighteen agile, resilient, operational, economic, environmental and social sustainability criteria were identified from the literature and discussion with experts. Interval-valued Fermatean fuzzy (IVFF) sets are more flexible and accurate for handling complex uncertainty, impreciseness and inconsistency in group ratings. The IVFF PIvot Pairwise RElative Criteria Importance Assessment Simplified (IVFF-PIPRECIAS) and IVFF weighted aggregated sum product assessment (IVFF-WASPAS) methods are applied to determine criteria weights and LSP evaluation, respectively.

Collaboration and partnership, range of services, capacity flexibility, geographic coverage, cost of service and environmental safeguard are found to have a greater influence on the LSP selection, as per this study. The LSP (L3) with the highest score (0.949) is the best agile, resilient and sustainable LSP in the manufacturing industry.

Hybrid IVFF-based PIPRECIAS and WASPAS methods are proposed for the selection of agile, resilient and sustainable LSP in the manufacturing industry.

The model can help supply chain managers in the manufacturing industry to easily adopt the hybrid model for agile, resilient and sustainable LSP selection.

The paper also contributes to the social sustainability of logistics workers.

To the best of the authors’ knowledge, IVFF-PIPRECIAS and IVFF-WASPAS methods are applied for the first time to select the best agile, resilient and sustainable LSP in a developing economy context.