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In order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number.

Firstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model.

The results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers.

The decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers.

The paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.

Professional and corporate codes of ethics (Codes) are commonly used, but they are not consistently effective. Research has indicated the importance of values included in Codes, but there is little research exploring how to improve the effectiveness of Code values. There are proven pedagogies that can be used in ethics training, notably, the values-focused approach known as value self-confrontation (VSC). VSC comes from the field of psychology and has been researched for over 50 years. This theory-based methodology is effective at increasing the importance of targeted values and positively changing attitudes and behaviors. Based on our thematic review of extant VSC literature, we develop a simplified VSC implementation strategy and instrument called code value self-confrontation (CVSC). CVSC involves a self-confrontation between a participant’s personal values and the values of the organization. This confrontation can create value dissonance in the participant, which can increase the importance given to the values of the organization. VSC has been effective at positively impacting pro-organization behaviors as well as societal issues such as equality, race relations, and environmentalism. By increasing the importance of ethical values, organizations can be rewarded with behavioral changes that translate into more ethical work behaviors and decision-making.

Despite the unparalleled importance of value within healthcare, value-based models remain underutilised in the procurement of medical devices. Research is needed to understand what factors incentivise standard, low-priced device purchasing as opposed to value-adding devices with potentially higher overall health outcomes. Framed in agency theory, we examine the conditions under which different actors involved in purchasing decisions select premium-priced, value-adding medical devices over low-priced, standard medical devices.

We conducted 2 × 2 × 2 between-subjects scenario-based vignette experiments on three UK-based online samples of managers (n = 599), medical professionals (n = 279) and purchasing managers (n = 449) with subjects randomly assigned to three treatments: (1) cost-saving incentives, (2) risk-sharing contracts and (3) stronger (versus weaker) clinical evidence.

Our analysis demonstrates the harmful effects of intra-organisational cost-saving incentives on value-based purchasing (VBP) adoption; the positive impact of inter-organisational risk-sharing contracts, especially when medical professionals are involved in decision-making; and the challenge of leveraging clinical evidence to support value claims.

Our results demonstrate the need to align incentives in a context with multiple intra- and inter-organisational agency relationships at play, as well as the difficulty of reducing information asymmetry when information is not easily interpretable to all decision-makers. Overall, the intra-organisational agency factors strongly influenced the choices for the inter-organisational agency relationship.

We contribute to VBP in healthcare by examining the role of intra- and inter-organisational agency relationships and incentives concerning VBP (non-) adoption. We also examine how the impact of such mechanisms differs between medical and purchasing (management) professionals.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

The purpose of this study is to identify and prioritize capabilities and practices to ensure a resilient supply chain during an unexpected disruption. In addition, this study ranks maturity factors that influence the main capabilities identified.

This paper is conducted in three stages. First, capabilities and practices are extracted through a literature review. Second, capabilities and practices are ranked using the analytical hierarchical process method. Third, a gray technique for order preference by similarity to ideal solution method is used to rank maturity factors influencing capabilities.

The findings indicate that responsiveness, readiness, flexibility and adaptability are the most important capabilities for supply chain resilience. Also, commitment and communication are the highest maturity factors influencing resilience capabilities.

The findings provide a hierarchical vision of capabilities and practices for industries to increase resilience. Limitations of the paper are related to capabilities, practices and number of experts consulted.

This paper highlights the importance of high-maturity practices in resilience capability adoption. The findings of this study will encourage decisions-makers to increase maturity practices to build resilience against disruption.

The paper reveals that developing powerful capabilities, good practices and a high level of maturity improve supply chain resilience.

In the accounting field, sustainability accounting (SA) has evolved as a valuable tool that links improvements in environmental, social and governance issues to financial performance. This study aims to examine the structure and evolution of SA research, map the state of knowledge and analyse the literature trends and gaps.

The study adopts a bibliometric review technique with data sourced from the Scopus database. A total of 7,049 extant literature spanning from 1982 to 2022 was analysed using the VOSviewer software.

The authors find a significant growth in the number of publications on SA research, primarily driven by collaboration among researchers from Europe and America. The analysis highlights emerging themes, structure and discusses in detail the changing phases of SA research over the past four decades while highlighting key events that have impacted the development of SA research. Furthermore, the dominant theories used by extant studies are discussed and potential avenues for future research are provided. The authors draw the attention of the research community to the dominant authors, the most cited articles, prominent publication outlets and countries advancing research in this field.

This study advances knowledge on SA research by providing a retrospective assessment of the state of knowledge in the field while highlighting avenues for future research.

Milling is a flexible creation process for the manufacturing of dies and aeronautical parts. While machining thin-walled parts, heat generation during machining essentially affects the accuracy. The workpiece temperature (WT), as well as the responses like material removal rate (MRR) and surface roughness (SR) for input parameters like cutting speed (CS), feed rate (F), depth-of-cut (DOC), step over (SO) and tool diameter (TD), becomes critical for sustaining the accuracy of the thin walls.

Response surface methodology was used to make 46 tests. To convert the multi-character problem into a single-character problem, the weightage was assessed using the entropy approach and the grey relational coefficient (GRC) was determined. To investigate the connection among input parameters and single-objective (GRC), a fuzzy mathematical modelling technique was used. The optimal performance of process parameters was estimated by grey relational entropy grade (GREG)-fuzzy and genetic algorithm (GA) optimization.

SR was found to be a significant process parameter, with CS, feed and DOC, respectively. Similarly, F, DOC and TD were found to be significant process parameters with MRR, respectively, and F, DOC, SO and TD were found to be significant process parameters with WT, respectively. GREG-fuzzy-GA found more suitable for minimizing the WT with the constraint s of SR and MRR and provide maximum desirability of 0.665. The projected and experimental values have a good agreement, with a standard error of 5.85%, and so the responses predicted by the suggested method are better optimized.

The GREG-fuzzy-GA is a new hybrid technique for analysing Inconel625 behaviour during machining in a 2.5D milling process.

This paper aims to explore current trends in how companies disclose climate-related risks and opportunities in their financial statements. As part of the authors’ analysis, they examine: whether forward-looking assumptions and judgements are typically considered in reporting climate-related risks/opportunities; whether there are differences in the reporting practices of firms in carbon-intensive industries versus non-carbon-intensive industries; and whether negative media reports have an influence on the levels of disclosure a firm makes.

The authors chose content analysis as their methodology and examined the financial statements published by firms listed on the UK’s FTSE 100 between 2016 and 2020. This analysis is framed by Suchman’s three dimensions of legitimacy, being pragmatic, cognitive and moral.

Climate-related disclosures in the notes and financial accounts of these firms did increase over the period. Yet, overall, the level the disclosures was inadequate and the quality was inconsistent. From this, the authors conclude that pragmatic legitimacy is not a particularly strong driving factor in compelling organisations to disclose climate-related information. The firms in carbon-intensive industries do provide greater levels of disclosure, including both qualitative and quantitative (monetary) content, which is consistent with cognitive legitimacy. However, from a moral legitimacy perspective, this study finds that firms did not adapt responsively to negative media coverage as a way of reflecting their accountability to broader public norms and values. Overall, this analysis suggests that regulatory enforcement and a systematic reporting framework with adequate guidance is going to be critical to developing transparent climate-related reporting in future.

This paper contributes to existing studies on climate-related disclosures, which have mainly examined the ‘front-half’ of annual reports. Conversely, this study aims to shed light on these practices in the “back-half” of these reports, exploring the underlying reasons for reporting climate-related risks and opportunities in financial accounts. The authors’ insights into the current disclosure practices make a theoretical contribution to the literature. Practitioners can also draw on these insights to improve how they report on climate-related risks and opportunities in their financial statements.

Taking a co-creation perspective and integrating knowledge-based and resource-based perspectives, the authors examine the role of customer participation in organizational performance and project success. The authors also investigate the mediating role of knowledge integration and the moderating role of requirement risk for these relationships in uncertain contexts.

The authors undertook two studies. The first study was carried out in 2018 in which the authors drew on survey data from 150 information technology (IT) sector employees and examined the mediating role of knowledge integration in the relationship of customer participation with organizational performance and project success. In the second study undertaken in 2020, the authors drew on data from 92 IT and telecom sector employees and examined the moderating role of requirement risk in the relationship between customer participation and knowledge integration. Study 2 was conducted during the COVID-19 pandemic when employees were largely working from home and were more sensitive to risks and uncertainty about the scope and system requirements. Both studies were survey-based, and analysis was carried out using structural equation modeling.

The authors’ two-study examination indicated that knowledge integration positively mediates the relationship of customer participation with organizational performance and project success during the co-creation process. Furthermore, the authors demonstrate that when requirement risks are high, customer participation relationship with knowledge integration is weaker.

The authors show that integrating customer knowledge is critical to project success and organizational performance. By identifying risk uncertainties and environmental contingencies, the authors highlight the constraints of customer participation for knowledge integration, organizational performance and project success. The authors provide some key study findings based on survey data obtained from project teams during two periods (normal and pandemic).

This study aims to address the critical challenge of implementing lean manufacturing (LM) in emerging economies, where sustainability complexities on the production floor hinder production efficiency and the transition towards a circular economy (CE). Addressing a gap in existing research, the paper introduces a path analysis model to systematically identify, prioritize and overcome LM implementation barriers, aiming to enhance performance through strategic removal.

The authors used a mixed-method approach, combining empirical survey data with literature reviews to pinpoint key LM barriers. Using the grey-based Decision-Making Trial and Evaluation Laboratory (DEMATEL) along with the Network Knowledge (NK) method, they mapped causal relationships and barrier intensities. This formed the basis for developing a path simulation algorithm, integrating heuristic considerations for practical decision-making.

This analysis reveals that the primary barriers to LM adoption is the negative perception and inadequate understanding of lean tools and CE principles. The study provides a strategic framework for managers, offering new insights into barrier prioritization and overcoming strategies to facilitate successful LM adoption.

This research provides a strategic pathway for overcoming LM implementation barriers, empowering managers in emerging economies to enhance sustainability and competitive advantage through LM and CE integration. It emphasizes the significance of structured barrier management in the manufacturing sector.

This research pioneers a systematic exploration of LM implementation barriers in the CE context, making a significant contribution to the literature. It identifies, evaluates barriers and proposes a practical model for overcoming them, enriching sustainable manufacturing practices in emerging markets.