The shift to sustainable energy systems offers complicated decision-making tasks, which demand the trade-off between several conflicting requirements in the conditions of uncertainty. Classical methods of fuzzy sets (FSs) usually have difficulty dealing with the multidimensional, asymmetrical, and imprecise data that is typical of real-world energy planning conditions. In this study, a hybrid neutrosophic picture fuzzy soft set (NPFSS) framework is proposed that combines the four-dimensional uncertainty modeling (truth, falsity, indeterminacy and refusal) and parameterized soft set (SfSs) theory. As a way of dealing with complex relationships among information, we construct two special forms of aggregation operators, the neutrosophic picture fuzzy soft Einstein weighted average (NPFSEWA) and the neutrosophic picture fuzzy soft Einstein weighted geometric (NPFSEWG), with reference to Einstein’s laws of operation. The suggested methodology has a better ability to represent expert hesitation and address conflicting criteria than the current fuzzy techniques. When applied to the selection of a sustainable energy source, our framework tackle all the challenges of uncertainty for choosing the best option, which has been tested in detail using mean absolute error (MAE) analysis and correlation measures as well as reliability checking. The results validate the robustness and effectiveness of the proposed approach in handling intricate multi-criteria decision-making (MCDM) situations involving uncertainty.

This study proposes a UK energy transition policy framework aligned with SDG 7 (Affordable and Clean Energy), examining the exogenous between green finance (GF), the digital economy (DE), and the renewable energy transition (RET). Using principal component analysis, it constructs digital economy and energy transition indices. A Multivariate-QQR approach evaluates the parameters' impact, supplemented by sensitivity analysis KRLS, Quantile's comparison plots for validation. Results show GF has limited or negative influence on RET at lower quantiles but becomes significantly positive at higher levels, promoting large-scale clean energy investments. DE positively impacts RET, especially at mid-to-high quantiles, reflecting inter-quantile dependence. RET outcomes improve when both GF and DE are moderately to highly developed. KRLS regression confirms these relationships, showing that a 1% increase in GF and DE yields average RET improvements of 0.111% and 0.487%, respectively. Granger causality tests reveal GF drives RET, while DE and RET are bidirectionally linked; no causality is observed between GF and DE. The study supports a policy approach integrating GF and DE to accelerate the UK's energy transition and achieve sustainable development goals. It offers critical insights for policymakers to align financial and digital strategies with clean energy objectives.

This study examines the impact of responsible leadership (RL) on employee outcomes, specifically customer stewardship behavior (CSB) and service sabotage, within the hospitality sector. Grounded in conservation of resources (COR) theory, we propose that RL, as a key contextual resource, fosters organizational virtuousness (OV), which serves as a resource passageway that transmits the positive effects of RL to employees. Furthermore, we explore the moderating role of role clarity in these relationships, focusing on how it influences the indirect effects of RL on employee outcomes through OV. A field study conducted in Morocco, involving employees and their supervisors in the hospitality industry, validated the hypothesized model. The findings reveal that RL positively influences CSB and reduces service sabotage, both directly and indirectly through OV. Role clarity moderates these relationships, amplifying the positive effects of OV on CSB when role clarity is high, and strengthening the negative relationship between OV and service sabotage when role clarity is low. This research highlights RL's role in promoting positive employee behaviors and reducing negative ones through OV, with role clarity enhancing these effects. Practical implications include adopting RL training programs, fostering OV, and ensuring high role clarity to improve employee engagement, customer satisfaction, and organizational performance, especially in service industries like hospitality.

Under the background that sustainable development has become a global consensus, corporate environmental innovation has become a key way to coordinate economic growth and environmental protection. This paper aims to explore how firms can respond to this trend through effective environmental innovation strategies. Firstly, this paper analyzes the multiple driving mechanism of the combination of external regulation pressure and internal transformation demand of enterprises formed by the goal of sustainable development. On this basis, the paper systematically expounds the pedigree of strategic choice of enterprise environmental innovation from three core strategic dimensions: innovation level, market orientation and cooperation mode. Furthermore, the research deeply analyzes the specific practice path of enterprise environmental innovation, including promoting the green transformation of production process, realizing the green innovation of products and services, and building the internal and external collaborative guarantee mechanism. This study provides a theoretical framework and practical guidance for enterprises to transform environmental challenges into competitive advantages, and has reference value for promoting green and low-carbon development.

ABSTRACT While the majority of extant studies often view corporate social responsibility (CSR) as a singular concept and examine its impact on firm performance, the distinct effects of internal and external CSR on firms' sustainable performance (SP)—environmental performance (EP) and financial performance (FP) remain underexplored in business strategy and sustainability literature. To address these gaps, this study adopts stakeholder theory to examine the impact of internal and external CSR on firms' EP and FP, as well as the moderating roles of media coverage and internal control quality (ICQ). Using a dataset of 602 Chinese‐listed firms from 2012 to 2020, our analysis indicates that both internal and external CSR strategies positively influence EP and FP. Furthermore, media coverage and ICQ enhance these effects, highlighting their critical role in maximizing the strategic impact of CSR on SP outcomes. These findings remain robust across various alternative measures and endogeneity tests, including propensity score matching analysis, Heckman two‐stage model, and a two‐year lagged analysis. Overall, this study contributes to the business strategy and sustainability literature by offering insights into how different CSR strategies and governance mechanisms affect SP. The findings suggest that policymakers should design regulatory frameworks and incentive schemes that encourage balanced investments in internal and external CSR, strengthen monitoring mechanisms such as media transparency and internal control systems, and promote multi‐stakeholder collaboration to enhance firms' SP outcomes.

Purpose This study aims to examine how Industry 5.0 capabilities influence firms’ achievement of sustainable development goals (SDGs) by investigating the mediating role of opportunity recognition and the moderating role of social pressure. Drawing on dynamic capability theory, the study explains how and when technological advancements translate into sustainable outcomes in emerging economies. Design/methodology/approach Data were collected using a two-wave time-lagged survey from 487 managers across manufacturing firms in China. The proposed model was tested using partial least squares structural equation modeling with SmartPLS 4. Findings The results reveal that Industry 5.0 has a significant positive effect on both opportunity recognition and SDG achievement. Opportunity recognition partially mediates the relationship between Industry 5.0 and SDGs, highlighting its role as a key sensing capability. Furthermore, social pressure positively moderates the relationship between opportunity recognition and SDGs, strengthening the impact of recognized opportunities on sustainable outcomes. Originality/value This study contributes to the literature by providing a process-based explanation of how Industry 5.0 drives sustainability outcomes through the lens of dynamic capability theory. Specifically, it identifies opportunity recognition as a critical sensing capability and highlights social pressure as an important boundary condition. The findings offer practical insights for managers in China’s rapidly evolving digital economy on leveraging Industry 5.0 technologies to systematically identify sustainability opportunities and achieve SDGs.

Circular supply networks represent a broad and well researched field, with significant study dedicated to the challenges of their establishment. However, research on the impacts within government managed circular supply chains remains limited. Consequently, there is still a lack of understanding regarding how digital technologies can help overcome these specific challenges in the public sector’s transition to circularity. In this work, we develop a multi level decision model based on five distinct pathways to handle complex decision-making scenarios with high accuracy. We employ spherical fuzzy sets (SpFS), which offer greater tolerance and adaptability in capturing vague information compared to intuitionistic and Pythagorean fuzzy sets. Within the SpFS framework, the three-dimensional membership functions enable decision-makers to improve judgment under uncertainty. Digital circular supply chain decision systems particularly in transportation can be structured using the five-way decision (5WD) concept with spherical fuzzy (SpF) information. The proposed model classifies criteria across multiple levels and evaluates their positions using decision-theoretic fuzzy rough sets (DTRFS) and the spherical weighted arithmetic mean (SpWAM). This study advocates the use of SpF data alongside stable ranking and structured classification algorithms to support decision-making in complex, ambiguous situations. A numerical example is provided to illustrate the applicability of the algorithm, tested across five distinct decision scenarios to validate the model’s accuracy. Statistical assessments confirm the strong performance of the proposed framework.

Multidimensional decision-making has substituted traditional decision-making due to the increased risk and complexity involved in the decision processes and cognitive behaviors. Moreover, uncertainty management is necessary in the decision-making processes that involve the degree of confidence of the experts. Conflict assessment and resolution are paramount to the smooth functioning of the industrial ecosystem in such an automated dynamic environment. This research aims to create a multi-attribute decision-making (MADM) model in a hybrid fuzzy frame to evaluate and resolve conflicts in Industry 4.0. The MADM model dwells on three primary points, i.e., (i) how to efficiently manage ambiguity and interrelationships in MADM issues; (ii) how to encompass the mindset of the decision maker in all areas concerned; and (iii) how to demonstrate results in terms of acceptance and rejection rather than ranking issues when more than one factor is involved. The test data of a fermatean fuzzy set (FFS) with rough relations, which addresses upper and lower approximations, demonstrates the possible uncertainty of the information. A fermatean fuzzy rough set (FFRS) is initially defined within the model. Subsequently, an FFRS incorporating the operator’s confidence level is delineated. This demonstrates the importance of FFRS in MADM contexts and suggests that they require further examination of their data processing regulations. Furthermore, we evaluate the accuracy and validity of the results by employing mean absolute errors, cosine similarity of the operators, and Spearman rank correlation. To illustrate the accuracy and validity of our method in the MADM context, we performed a comparative analysis. Finally, a practical illustration of the selection of Industry 4.0 technologies within the healthcare sector exemplifies the efficacy and potential of this innovative approach for future applications of MADM. The intricate multi-stakeholder conflicts and data uncertainties presented by Industry 4.0 environments, especially regarding healthcare technology implementation, will be examined using the research framework illustrated in Fig. 1.

The advancement of Industry 5.0 brings unprecedented challenges and complexities in transportation and mobility systems, demanding robust and adaptive risk assessment frameworks. Traditional rough set theory examines how decision-makers respond to risks based on psychological behaviors and preferences, while threeway decision (3-WD) theory provides a structured approach to managing uncertainty through acceptance, rejection, or deferment actions. However, current methods often struggle with incomplete, multiscale data and vague failure information. In response, this study introduces a novel data-driven 3-WD model that integrates rough set theory (RST) with circular Pythagorean fuzzy sets (Cir-PyFSs) to handle missing utility values and ambiguous information in high-stakes environments. Cir-PyFSs capture membership, non-membership, and radius information, offering enhanced flexibility and precision in modeling uncertainty. The proposed model incorporates decision-theoretic rough sets (DTRSs) with a relative loss function framework, effectively addressing unexpected uncertainties and cost-sensitive decisions. To demonstrate its applicability, we present a multi-attribute decision-making (MADM) model based on newly developed operators within Cir-PyFSs settings. Our approach is applied to a real-world transportation and mobility scenario, evaluating the risks and impacts associated with Industry 5.0 adoption. The findings reveal that the proposed model provides a powerful and practical tool for advanced risk assessment, enabling Industry leaders and policymakers to make informed, resilient decisions in rapidly evolving industrial landscapes.

Corporate social responsibility (CSR) effectiveness depends not only on disclosure volume but also on whether firms address the CSR themes that customers genuinely care about. Existing CSR assessments primarily rely on aggregated external ratings or firm-reported information, which obscures firm-customer misalignment at the level of specific CSR themes and provides limited actionable guidance. This study proposes CSRMatchNet, an intelligent semantic decision support system that evaluates firm-customer CSR congruence at the level of individual CSR themes. CSRMatchNet directly derives CSR themes from large-scale firm-authored CSR disclosures and customer-generated CSR discourse, without relying on predefined taxonomies or external ratings. The system embeds firm and customer texts into a shared semantic space using a pretrained multilingual Sentence-BERT model and applies a lightweight residual adapter trained with covariance alignment (CORAL) and multi-kernel maximum mean discrepancy (MMD) to align cross-source distributions. Unsupervised clustering is used to identify recurring CSR themes, and theme-level firm-customer alignment is quantified based on semantic divergence between firm and customer representations. To enhance decision relevance, alignment scores are weighted by customer salience to construct an Effective CSR (ECSR) measure, capturing the realized market-facing effectiveness of CSR efforts rather than disclosure intensity alone. An empirical application to major Chinese firms demonstrates that CSRMatchNet produces fine-grained CSR diagnostics that distinguish shared priorities, CSR gaps, over-communicated themes, and low-priority themes. External validation is performed using the CSR Market Performance Index (CMPI) to evaluate whether firm-level ECSR signals exhibit meaningful external relevance and interpretability. Overall, CSRMatchNet advances CSR analytics by providing an AI-enabled, transparent, and actionable decision support framework for aligning CSR strategies with customer expectations.

The paper suggests a hybrid concept in reducing cyber risks within digital banking through T-spherical fuzzy sets (T-SpFS) and a machine learning (ML) algorithm known as three-way decision (TWD). The suggested approach for addressing the uncertainty of cyber risks relies on assessments made by specialists using T-SpFS. ML methods with the help of this framework benefit us in better classifying our risks through experience in evaluating them by experts and changing to new cyber threats. In its case study research, the T-spherical fuzzy three-way classification (T-SpF TWD) framework ranks four FinTech platforms as accepted, rejected, and uncertain through its spherical fuzzy approach. The evaluation shows that the digital payment app, I-1, is the most reliable position, while the robo-advisory service, I-4, is the least reliable. The proposed method of analyzing digital banking sustainability provides superior results in comparison with the traditional techniques, as the former employs traditional techniques to address uncertainty and provide valuable findings. The study provides a comprehensive, step-by-step model for safeguarding bank cyber systems against threats and enhancing their resilience to digital attacks. The step-by-step process is shown in Figure 1.

The purpose of this paper is to analyze whether, how and when direct manager phubbing, defined as the snubbing of someone in a social or work setting by paying attention to one’s mobile phone rather than engaging in communication with the individual directly, influences two deviant employee outcomes - task withdrawal and procrastination. We specifically investigate the mediating role of moral disengagement and the moderating role of employee age and level within the organization. We utilized affective events theory (AET) and moral disengagement theory (MDT) to underpin our hypotheses. We gathered data from 251 service employees in organizations using a three-stage time-lagged research design. We found a direct positive relationship between direct manager phubbing and both task withdrawal and employee procrastination and that moral disengagement acts as a mediator of these relationships. We additionally found that age moderates both the direct relationship between direct manager phubbing and moral disengagement and the indirect relationship between direct manager phubbing and both negative employee outcomes via moral disengagement. These relationships were stronger in the case of younger employees. The findings for the moderating role of employee level indicates that the higher the level of the employee the weaker the direct and indirect relationships are. The study findings highlight important practical implications for organizations and the detrimental impact of direct manager phubbing for two negative employee outcomes. It also highlights that younger and lower-level employees are more susceptible to the negative impacts of direct manager phubbing.

Corporate social responsibility (CSR) is widely recognized for its role in advancing sustainability within emerging economies like China. However, the specific mechanisms and contextual factors that shape the impact of internal CSR (ICSR) and external CSR (ECSR) on firms' environmental performance (EP) remain insufficiently understood. This study addresses this gap by leveraging stakeholder theory to explore the distinct effects of ICSR and ECSR on EP, while examining how these relationships are moderated by industry competition and media scrutiny. Using a comprehensive dataset of 815 Chinese firms spanning 2008 to 2020, we reveal that both ICSR and ECSR positively enhance EP, with ECSR exerting a comparatively stronger influence. Furthermore, our findings show that industry competition attenuates the positive effects of ICSR and ECSR, whereas media scrutiny amplifies them. These insights contribute to the environmental management and business strategy literature by clarifying how and when diverse CSR approaches drive firms' environmental outcomes, offering practical implications for organizations navigating competitive and media-intensive landscapes.

The study inquires the influence of environmental, social, and governance (ESG) investment and oxy-combustion technology inclusion on seawater pollution reduction, ocean ecosystem preservation and climate commitment acquisition in terms of net-zero carbon emission. The study explored ten major cities of Guangdong province of China that are located along the coasts based on empirical data from 2001 to 2024. This research tested historical data, and the findings evidenced that (a) ESG investment has a significant influence on seawater pollution reduction. (b) ESG investment has a significant role in ensuring net-zero carbon emissions in fulfilling climate commitments. (c) It is found that the use of oxy-combustion system technology increases efficiency and diminishes pollutant emissions of seawater. For that, gross tree cover area around beaches and green agricultural yield in Guangdong province needs to increase. The results showed that city-economic density, infrastructure level, green innovation, and public awareness of seawater pollution play a significant role. The recommendations for policymakers to advance the scale and reachability of investment flows effectively for seawater ecological improvement. A city-level pollution reduction benchmark is missing in Guangdong province, requiring more work from practitioners.

Using job demands resources theory, our paper develops and tests a novel research model that explores the detrimental effect of organizational citizenship behavior directed towards customers (OCB-C) on knowledge sabotage and procrastination. We utilize compassion fatigue as a mediator and moral identity as a moderator in the abovementioned association. Data came from hotel and restaurant employees. The results suggest that compassion fatigue mediates the effect of OCB-C on knowledge sabotage and procrastination. The results suggest that moral identity is a moderator of the effect of compassion fatigue on said employee outcomes. Our results denote the importance of moral identity as a moderator and provide practical insights into the enhancement of employee well-being and organizational performance.

This study conducts empirical research to analyze the relationships among green bonds, the digital economy index, and the energy transition in the Japanese economy. The analysis uses principal component analysis to evaluate the digital economy index, which includes fixed broadband subscriptions, mobile cellular subscriptions, internet accessibility, fixed telephone subscriptions, and the energy transition index, consisting of clean energy investments, energy efficiency, carbon emissions, and carbon intensity. This study uses the multivariate-QQR method to analyze the impact of parameters on energy transition in Japan and transforms the bivariate-QQR model into a multivariate framework. The study additionally utilized sensitivity analysis, kernel regularized least squares (KRLS) quantile regression, and Granger causality in quantiles (GCQ) to validate the benchmark assessments. The study's findings revealed that green bond issuance has significantly facilitated the transition toward renewable energy sources. We determine positive effects on the energy transition index in the digital economy index context, particularly within the middle to higher quantiles. These results emphasize a robust positive correlation between green bond issuance, digital economic growth, and Japan's transition to sustainable energy sources, primarily within middle and higher quantile segments. Additionally, the KRLS quantile regression and GCQ studies further supported the benchmark results. Furthermore, our findings suggest that Japan can strengthen its efforts to combat climate change and promote sustainable development by supporting green finance and digital development to achieve its sustainable development goals.

We survey 1208 payers through online questionnaires to examine the impact of mental accounting on consumers' vulnerability to payment biases in Pakistan. Using a mental accounting perspective, we evaluate the direct effects of current and future payment methods on consumer spending behaviour. Furthermore, it scrutinises the interaction role of Digital Financial Literacy (DFL) on the link between current payment methods and spending behaviour and government support between future payment methods and spending behaviour. Survey-based questionnaires were used to gather data through purposive sampling. Smart-PLS 4 results show that current and future payment methods significantly affect spending behaviour, with digital payments having a more pronounced impact than cash payments. Our findings also disclose that DFL profoundly moderates the relationship, while government support moderates only between future digital payments and spending behaviour. This study presents a comprehensive model that serves as a guideline for policymakers aiming to promote a cashless society and contributes to a better understanding of prudent spending behaviour.

Incorporating state-of-the-art technology into supply chain management has become a core focus for improving operational sustainability and efficiency. However, despite aggregate investment in disruptive technologies such as blockchain technology, there are still gaps in our understanding of their specific impact on sustainable supply chain performance, especially in developing economies. This study aims to establish a research model and conduct an empirical analysis to comprehend blockchain technology adoption's transformational potential in the supply chain environment. The required data was collected from the managers of the manufacturing organizations via a structured questionnaire (n = 268). Structural equation modeling, bootstrapping, and importance-performance map analysis have been performed to test the proposed hypotheses. The results provide significant insights into how organizational compatibility interacts with blockchain technology adoption and explain the mechanism of how blockchain technology adoption affects sustainable supply chain performance through supply chain ambidexterity and survivability. However, the results did not demonstrate a direct impact of blockchain technology adoption on sustainable supply chain performance. The findings reveal the theoretical advances of industrial supply chain practitioners and scholars and their practical significance in positively contributing to strategic decision-making.