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This study aims to fill a knowledge gap about the best ways to execute waste management regulations that support sustainability and environmental responsibility. However, the study seeks to investigate and assess how well waste management systems support sustainability and environmentally friendly behaviors.

Since the quantitative technique was used to discover and generate hypotheses about relationships between variables, it was chosen for the entirety of the study. The data collection from ready-made garments (RMG) in Bangladesh was carried out using a survey strategy that involved the distribution of questionnaires using a cluster sampling approach. The partial least squares structural equation model (PLS-SEM) was used for quantitative analysis with SmartPLS.

However, the results show the importance of waste management, green practice methods and sustainability for the company's goal of maintainable performance. On the other hand, PLS-SEM demonstrated that based on the provided t-statistics and p-values, all of the routes in the structural model are statistically significant at the level of p < 0.05. These routes also appeared to have far broader and more powerful effects. Principles of the circular economy are highlighted, as are the importance of holistic methods, awareness and education, stakeholder participation and fusing these concepts. Furthermore, it seems to affect significantly both short-term and long-term success due to its distinctiveness in sustained performance.

Much time will be required to assess waste management methods and their long-term effects. A healthcare manager may adapt research findings to practice, ensuring the study enhances management and patient care.

This research addresses a significant literature gap by providing managers and policymakers with concrete ideas on integrating sustainability into operational and strategic frameworks. In today's competitive environment, integrating stakeholder engagement and green practices into core company operations is an innovative way to achieve sustainable excellence.

This study aims to investigate which configurations of organizational-level and group-level management controls support an identity fit for management accountants in the management accounting profession. It aims to complement recent qualitative management accounting research. This stream just begun to use role and identity theory to investigate role expectations, conflicts and coping strategies of management accountants when they struggle with their work identity.

Based on configuration theory, this study uses a fuzzy-set qualitative comparative analysis to indicate all possible configurations of formal and informal management controls that improve management accountants’ sense of their identity in an organization. The analyses are based on the results of a cross-sectional survey of 277 management accountants from Germany, Austria, Switzerland and Liechtenstein.

The results show that a strong group culture and high psychological safety at the group level are relevant conditions for a high identity fit. Further, the configurations differ regarding the career stages of management accountants.

This study contributes to work identity research of management accountants and to research on formal and informal control configurations as a control package. It is of particular importance for various professions that are affected by role change, as from the findings on management accountants’ identity fit, implications can also be made for other organizational functions that need to engage in identity work.

This study aims to use regression Least-Square Support Vector Machine (LS-SVM) as a probabilistic model to determine the factor of safety (FS) and probability of failure (PF) of anisotropic soil slopes.

This research uses machine learning (ML) techniques to predict soil slope failure. Due to the lack of analytical solutions for measuring FS and PF, it is more convenient to use surrogate models like probabilistic modeling, which is suitable for performing repetitive calculations to compute the effect of uncertainty on the anisotropic soil slope stability. The study first uses the Limit Equilibrium Method (LEM) based on a probabilistic evaluation over the Latin Hypercube Sampling (LHS) technique for two anisotropic soil slope profiles to assess FS and PF. Then, using one of the supervised methods of ML named LS-SVM, the outcomes (FS and PF) were compared to evaluate the efficiency of the LS-SVM method in predicting the stability of such complex soil slope profiles.

This method increases the computational performance of low-probability analysis significantly. The compared results by FS-PF plots show that the proposed method is valuable for analyzing complex slopes under different probabilistic distributions. Accordingly, to obtain a precise estimate of slope stability, all layers must be included in the probabilistic modeling in the LS-SVM method.

Combining LS-SVM and LEM offers a unique and innovative approach to address the anisotropic behavior of soil slope stability analysis. The initiative part of this paper is to evaluate the stability of an anisotropic soil slope based on one ML method, the Least-Square Support Vector Machine (LS-SVM). The soil slope is defined as complex because there are uncertainties in the slope profile characteristics transformed to LS-SVM. Consequently, several input parameters are effective in finding FS and PF as output parameters.

This study investigates clean energy, commodities, green bonds and environmental, social and governance (ESG) index prices forecasting and assesses the predictive performance of various factors on these asset prices, used for the development of a robust forecasting support decision model using machine learning (ML) techniques. More specifically, we explore the impact of the financial stress on forecasting price.

We utilize feature selection techniques to evaluate the predictive efficacy of various factors on asset prices. Moreover, we have developed a forecasting model for these asset prices by assessing the accuracy of two ML models: specifically, the deep learning long short-term memory (LSTM) neural networks and the extreme gradient boosting (XGBoost) model. To check the robustness of the study results, the authors referred to bootstrap linear regression as an alternative traditional method for forecasting green asset prices.

The results highlight the significance of financial stress in enhancing price forecast accuracy, with the financial stress index (FSI) and panic index (PI) emerging as primary determinants. In terms of the forecasting model's accuracy, our analysis reveals that the LSTM outperformed the XGBoost model, establishing itself as the most efficient algorithm among the two tested.

This research enhances comprehension, which is valuable for both investors and policymakers seeking improved price forecasting through the utilization of a predictive model.

To the authors' best knowledge, this marks the inaugural attempt to construct a multivariate forecasting model. Indeed, the development of a robust forecasting model utilizing ML techniques provides practical value as a decision support tool for shaping investment strategies.

The aim of this study is to investigate how the demographics and appearance cues of potential social engineers influence the likelihood that targets will trust them and accept security risk.

Data were collected through an online survey of 635 participants, including 322 participants from Arab countries and 313 participants from the UK. The survey presented scenarios with 16 personas who offered participants the use of their mobile internet hotspot. These personas were characterized by combinations of age (young vs aged), gender (male vs female), ethnicity (Arab vs UK) and look formality (casual vs formal). The study measured both participants’ offer acceptance and trust in the persona.

Results indicated a higher likelihood of offer acceptance from female and aged personas, as well as a greater trust in these groups. Arab participants showed a preference for personas with Arabian ethnic features. In both samples, trust and acceptance were influenced by the persona’s appearance, which was found to be gender-dependent; with female personas in casual attire and male personas in formal attire being trusted more in comparison to female with formal attire and male with informal, respectively.

Findings highlight the importance of incorporating awareness of appearance-based biases in cybersecurity training, suggesting the need for culturally sensitive training programs to enhance defense against social engineering.

This study distinguishes itself by elucidating the influence of social engineers’ demographic and appearance cues on the likelihood of individuals to take security risks, thus addressing a significant gap in the literature which has traditionally emphasized the profiles of targets.

The purpose of this study is to assess the extent to which the nexus between foreign bank presence (FBP) and inclusive growth is being impacted by the financial development.

The study used a two-stage system generalized method of moment (GMM), using 28 African countries from the period 2000 to 2018.

The study found a positive effect of FBP on inclusive growth. While financial development magnifies the positive effect of FBP, inclusive growth nexus, it has a direct effect on inclusive growth.

For Africa to ascertain the positive effect of FBP on inclusive growth, financial system must be developed to reduce the cream-skim behavior of foreign banks.

This paper assess the extent to which developing economy's developed financial system form synergies with FBP to further enhance the inclusiveness of growth.

This study aims to provide empirical evidence on the return and volatility spillover structures between Bitcoin, Fintech stocks and Asian-Pacific equity markets over time and during different market conditions, and their implications for portfolio management.

We use Time-varying parameter vector autoregressive and quantile frequency connectedness approach models for the connectedness framework, in conjunction with Diebold and Yilmaz’s connectivity approach. Additionally, we use the minimum connectedness portfolio model to highlight implications for portfolio management.

Regarding the uncertainty of the whole system, we show a small contribution from Bitcoin and Fintech, with a higher contribution from the four Asian Tigers (Taiwan, Singapore, Hong Kong and Thailand). The quantile and frequency analyses also demonstrate that the link among assets is symmetric, with short-term spillovers having the largest influence. Finally, Bitcoins and Fintech stocks are excellent diversification and hedging instruments for Asian equity investors.

There is an instantaneous, symmetric and dynamic return and volatility spillover between Asian stock markets, Fintech and Bitcoin. This conclusion should be considered by investors and portfolio managers when creating risk diversification strategies, as well as by policymakers when implementing their financial stability policies.

The study’s major contribution is to analyze the volatility spillover between Bitcoin, Fintech and Asian stock markets, which is dynamic, symmetric and immediate.

The purpose of this paper is to investigate the relationship between board gender equality and environmental, social and governance (ESG) performance in the European banking sector. The study examines whether and how the presence of women on the board of directors (BoD) influences ESG dimensions.

The authors analyzed a sample of 72 European Union banks for the period 2015–2021 and developed an econometric model applying unbalanced panel data regression with firm fixed effects and controls per year. To test the research hypotheses, the authors considered gender equality in terms of female participation on the BoD and measured ESG dimensions by using the ESG score provided by Refinitiv.

The findings suggest a significant positive relationship between the number of women on BoD and the ESG performance of European banks only up to a certain threshold of female directors (at least three women). The study also explores how the proportion of women on BoD influences the individual ESG pillars. The results show that the percentage of female directors has a positive and statistically significant impact on the social dimension of the ESG framework.

The investigation is highly relevant to investors considering ESG issues in their decision-making process. The overall findings support policymakers and regulators on how to improve ESG performance through the design and the application of corporate governance (CG) mechanisms. From a managerial perspective, the study suggests that managers and CEOs should focus their efforts on establishing the right gender combination of directors on bank BoDs.

This paper offers an in-depth examination of the CG practices of banks, and it attempts to bridge the gap in prior literature on the determinants of ESG issues in the European banking industry. To the best of the authors’ knowledge, this study is the first that investigates the relationship between the representation of women on BoDs and the ESG dimensions measured by the Refinitiv Eikon score. The use of critical mass theory adds a fresh perspective to the literature on ESG in Europe since the influence of board gender diversity on ESG performance of the European banks is still unaccounted for. This study addresses this pressing research issue drawing on resource dependence, agency and legitimacy theories.

The purpose is to understand the factors affecting Chinese diet selections and propose strategies for revolutionizing Chinese diets toward healthy ones.

This study implemented an online discrete choice experiment to identify the factors affecting diet selections among urban Chinese consumers. Four different diet patterns were used to label each of the product alternatives in the experiment, which varied in taste and cost. Specifically, implying the healthiness and sustainability of a diet, the diet alternatives included the average diet, the Chinese Food Guide Pagoda diet, the EAT-Lancet diet and the Flexitarian diet. Using consumer data from six provincial capital cities, we used random parameter logit models to estimate their preferences.

Diet type and diet cost were found to be more important in urban Chinese consumers' diet selections than the ability to customize taste. The average diet, although not healthy and sustainable, was preferred most by respondents, signaling the challenges of shifting the consumer diet in China. Increasing the cost of the average diet can significantly promote sustainable healthy diet choices among urban Chinese residents. In other words, improving the affordability of sustainable healthy diets would have the potential to fuel the diet revolution in China.

Instead of choices of a single food item, this paper focused on the individual selection of a diet, where different food products can act as substitutes or as complements for one another. We also proposed a way to assess individual preferences and valuations for several different diets.

The purpose of this paper is to (1) characterize representational and nonrepresentational images; (2) review the literature on representational and nonrepresentational images; (3) introduce the theory of consumption values (TCV) framework vis-à-vis representational and nonrepresentational images; and (4) generate propositions and questions for future research based on that intersection.

Conceptual/literature review with propositions and future research directions.

The authors offer nine propositions and several associated example research questions to explore and document the important ways in which representational and nonrepresentational images can affect the five dimensions of value as outlined in the TCV.

It is the hope that this work serves as a theoretical starting point – surely, there are other theories and frameworks beyond the TCV that may share ties with types of images, which scholars should be encouraged to explore, but if the authors had attempted to document every possible theory, the result would be a limitless document. As such, the authors have honed the efforts on a broad-reaching framework, the TCV, in the attempt to balance theoretical insights with parsimony. Through exploration of these and other avenues, the authors hope that scholars and practitioners alike will benefit from elucidation of theories and effects around representational and nonrepresentational images.

Several practical implications flow from the dimensions and propositions within this work.

Representational and nonrepresentational images have featured prominently throughout visual content and communications for centuries, yet the current body of literature remains scant and underdeveloped in its relationship to marketing. The present work addresses this gap by using the TCV as an overarching framework to generate propositions and future research questions.