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Reactive and direct dyes are the most frequently used dyes for cotton fabrics. Cellulosic fibers pose a great affinity toward them. However, both dyes consume large quantities of salts for exhaustion; these electrolytes (salt) are discharged as effluents posing environmental problems and disorders in aquatic life. Therefore, this study aims to explore alternative approaches to minimize salt consumption.

For this study, the combination of chitosan and keratin, being sustainable biopolymers, are used as mordants instead of salts for the cationization of 100% cotton and chief value cotton (CVC) during dyeing with direct and reactive dyes. Color strength, exhaustion rates and color fastness to washing, rubbing and perspiration have been evaluated in this paper. In this research, keratin solution is applied on cotton and CVC (with the cotton to polyester ratio of 80%:20%) fabrics by pad-dry-cure, while chitosan solution has been applied with the exhaust method. The pretreated fabrics are dyed with both direct and reactive dyes with 2% and 4% depth of shade (DOS). The performance of the cationized fabrics with salt-free dyeing method was compared with the conventional dyeing.

The results of this study showed positive impact on exhaustion rates ranged from 19% to 69% of the values obtained without salt in 2% DOS and a range of 22%–47% in 4% DOS of reactive and direct dyes with treated samples. Color fastness to crocking results indicated the improvement of results by a factor of 0.5–1 after treatment and good wash fastnesses rating (4.5/5) were achieved for the chitosan-keratin treated fabrics equivalent to untreated samples. In addition, antimicrobial testing was performed on both treated and untreated fabrics of cotton and CVC. The results indicated that chitosan treated samples showed greater inhibition toward microbial activity as compared to keratin treated samples. Fourier transform infrared (FTIR) spectroscopy was used to identify the functional groups of chitosan and keratin treated fabric samples to confirm their presence. In the FTIR spectra, the chitosan was characterized by its hydroxyl, carbonyl and amide III peaks indicating the presence of chitosan and keratin was detected by the presence of primary amines. Scanning electron microscopy analysis was conducted in which it was visibly seen that the fibers have been coated with chitosan and keratin laid after being dyed.

For the first time, direct and reactive dyes are applied on chitosan and keratin-treated cotton and CVC to obtain multifunctional and eco-friendly fabrics.

Exposure to indoor air pollutants poses a significant health risk, contributing to various ailments such as respiratory and cardiovascular diseases. These unhealthy consequences are specifically alarming for athletes during exercise due to their higher respiratory rate. Therefore, studying, predicting and curtailing exposure to indoor air contaminants during athletic activities is essential for fitness facilities. The objective of this study is to develop a neural network model designed for predicting optimal (in terms of health) occupancy intervals using monitored indoor air quality (IAQ) data.

This research study presents an innovative approach employing a long short-term memory (LSTM) recurrent neural network (RNN) to determine optimal occupancy intervals for ensuring the safety and well-being of occupants. The dataset was collected over a 3-month monitoring campaign, encompassing 15 meteorological and indoor environmental parameters monitored. All the parameters were monitored in 5-min intervals, resulting in a total of 77,520 data points. The dataset collection parameters included the building’s ventilation methods as well as the level of occupancy. Initial preprocessing involved computing the correlation matrix and identifying highly correlated variables to serve as inputs for the LSTM network model.

The findings underscore the efficacy of the proposed artificial intelligence model in forecasting indoor conditions, yielding highly specific predicted time slots. Using the training dataset and established threshold values, the model effectively identifies benign periods for occupancy. Validation of the predicted time slots is conducted utilizing features chosen from the correlation matrix and their corresponding standard ranges. Essentially, this process determines the ratio of recommended to non-recommended timing intervals.

Humans do not have the capacity to process this data and make such a relevant decision, though the complexity of the parameters of IAQ imposes significant barriers to human decision-making, artificial intelligence and machine learning systems, which are different. Present research utilizing multilayer perceptron (MLP) and LSTM algorithms for evaluating indoor air pollution levels lacks the capability to predict specific time slots. This study aims to fill this gap in evaluation methodologies. Therefore, the utilized LSTM-RNN model can provide a day-ahead prediction of indoor air pollutants, making its competency far beyond the human being’s and regular sensors' capacities.

This study aims to address the critical need for innovative financing solutions in the global housing sector, focusing specifically on Malaysia’s distinct housing finance system encompassing both conventional and Islamic loans. The primary objective is to develop a transformative housing finance model that addresses affordability challenges and reshapes the Malaysian housing landscape.

The study presents an alternate housing finance model for Malaysia, integrating lower monthly payments and reduced household debt. Key variables include house price appreciation rates, interest rates, initial guarantee fees and loan-to-value ratios. Inspired by the Help to Buy (HTB) scheme, the model aligns with proven global initiatives for enhanced affordability, balancing payment amounts, loan interest rates and acceptable price thresholds.

The study’s findings promise to address affordability disparities and reshape Malaysia’s housing finance landscape. The emphasis is on introducing a structured repayment plan that offers a sustainable path to homeownership, particularly for low-income families. Incorporating the future value adaptation concept, inspired by reverse mortgages and Islamic finance, enhances adaptability, ensuring long-term sustainability despite economic shifts.

The proposed model promotes widespread access to homeownership, offering practical solutions for policymakers to improve affordability, prompting adaptable risk management strategies for financial institutions and empowering potential homebuyers with increased flexibility.

The study introduces a transformative housing finance model for Malaysia, merging elements from reverse mortgages, Islamic finance and the HTB scheme, offering potential applicability to similar systems globally.

Environmental, social and governance (ESG) greenwashing is a form of social responsibility response that appears compliant but is substantively oppositional. As an abnormal social behavior, existing research has rarely focused on the deep-seated strategic logic behind ESG greenwashing. Business strategy emerges as the linchpin for companies undertaking a series of decision-making actions. Consequently, this research seeks to provide new insights into the strategic drivers behind corporate greenwashing and the role of institutional investors in mitigating these practices.

The research utilizes empirical analysis based on data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2010 to 2022. ESG performance data is sourced from the Bloomberg ESG Disclosure Ratings and Thomson Reuters’ Asset4 database. Business strategy is assessed using six key indicators. The study employs institutional theory as the analytical framework, examining the impact of business strategy on ESG greenwashing and investigating the internal mechanisms driving these behaviors.

The study finds that compared with defender strategies, prospector strategies are more likely to lead to ESG greenwashing behavior. Specifically, aggressive business strategies tend to facilitate corporate ESG greenwashing. Mechanism analysis indicates that, compared to defenders, prospectors induce ESG greenwashing by increasing information asymmetry (reputation effect) and being constrained by financing limitations (profit-seeking effect). From an external governance perspective, this study finds that institutional investor ownership can mitigate the impact of business strategy on ESG greenwashing. Furthermore, additional research confirms that in heavily polluting industries, the positive effect of business strategy on ESG greenwashing is more pronounced, whereas implementing the Environmental Protection Tax Law curtails the impact of business strategy on ESG greenwashing.

This study analyzes the role of business strategy in ESG greenwashing, particularly in the context of emerging economies such as China, contributing uniquely to the literature on corporate decision-making and green management. The research extends the application of institutional theory to the field of corporate environmental strategy and introduces the concepts of reputation and profit-seeking effects, offering fresh perspectives on understanding ESG greenwashing behavior. It also provides empirical evidence of the governance role of institutional investors in addressing managerial opportunism related to ESG greenwashing, enriching the existing theoretical framework. Finally, the study highlights the need to establish stronger institutional and managerial mechanisms to effectively tackle corporate greenwashing, offering valuable insights for future research and practice.

This study aims to examine the relationship between climate change disclosure performance (CCDP) and audit pricing. The moderating effect of corporate governance characteristics on that relationship is also investigated.

Using a sample of top 300 Australian Securities Exchange listed non-financial firms over the period 2008–2019, this study investigates the association between CCDP and audit fees. The findings are robust to a difference-in-difference test thereby alleviating potential endogeneity concerns.

CCDP is found to be significantly positively related to external auditor fees.

The findings show some important implications for firm management, regulators, investors and auditors. This study presents empirical evidence that climate change, as a factor of external risk, influences audit fees.

Firms with governance structures characterized by larger more independent boards, larger audit committees and audit committees with a higher level of independence significantly moderate the relationship between CCDP and audit fees.

Investors’ demand for firm transparency and disclosure of information regarding the risks of climate change, effects and opportunities has increased significantly over the past decade, as these factors could have a significant effect on valuation and investment decisions.

Importantly, stakeholders need to be aware of the costs of climate change, the quantification of climate change impacts and how firms address climate change in their business risk management processes. This study quantifies the impact of CCDP on auditor risk assessments via audit fees.

Although existing literature has highlighted the benefits of sustainability practices for business, few studies explore their potential downside. To address this gap, this study aims to examine the relationship between public sustainability mindset and financial misconduct in local firms.

Based on survey results from International City/County Management Association, the author aggregates data on public sustainability mindset at the metropolitan statistical area (MSA) level in the USA. The author uses linear regression analysis to investigate the hypotheses. Robustness tests are also performed using approaches such as propensity score matching, two-stage least squares, falsification test and alternative measure of sustainability mindset.

This study finds that in MSAs with a stronger public sustainability mindset, local firms are more likely to engage in financial misconduct. Moreover, this association is mitigated by the availability of employment opportunities in the area, indicating that job security concerns have a moderating effect. Additional test suggests that firms with more integrity culture are less likely to engage in financial misconduct, even in areas where residents have a strong sustainability mindset.

This paper could be of interest to both policymakers and managers as it illustrates an unexpected impact of public sustainability awareness on financial compliance issues. It also provides cautions when prompting sustainability mindset among the public and suggests potential solutions to address the problem.

The study of mindfulness is still shown to be of interest in different aspects of organisations and/or businesses. Therefore, this study aims to present an integrative, multi-level model of mindfulness based on a holistic approach that can contribute to better governance practices and lead to competitive advantages.

To fulfil this aim, an extensive integrative review of the literature, from the main articles about this topic, was made.

This study shows that the concept of mindfulness, a conscious presence or full attention and its relation with organisations or firms’ personal, behavioural and social characteristics, in the current context of great adversity, uncertainty and unpredictability, is of interest at the individual, organisational and social level.

This conceptual study has important implications for both practice and theory. It demonstrates that mindfulness significantly impacts the manager/business person’s ecosystem at the individual, organisational and social levels, particularly in relation to Sustainable Development Goals.

This study introduces a comprehensive theoretical model that explains this relationship and organises information from a multi-level perspective. This approach can contribute to the advancement of theory by clarifying and discussing the role of mindfulness at the individual, organisational and societal levels. It also identifies opportunities and outlines future research directions, aiming to promote more sustainable development.

Analyst team forecasts are the most frequent form of earnings expectations available to investors, with teams issuing more than 70% of research reports in 2016. Prior research provides differing evidence on whether analyst teams issue higher or lower quality forecasts than individual analysts.

I use a sample of more than 17,000 hand-collected analyst reports representing 7,586 forecasts from 89 companies in three industries from 1994–2005.

I document that analyst teams benefit from an assembly bonus, and issue more accurate forecasts than individual analysts only in time periods when teams would be expected to benefit from an assembly bonus.

I outline multiple factors within the control of brokerage houses that impact teams’ relative forecast quality, such as the number of members in the team, how long the team has worked as a unit and the costliness of integrating information when forming a forecast.

Given the preponderance of analyst teams and the strength of market reaction to their forecasts, it is valuable to document factors both in the past and present likely to affect analyst teams’ relative forecast accuracy.

This study aims to explore the relationship between social capital and real earnings management (REM).

Using the social capital index from 1990 to 2014, this study investigates whether managers are less likely to carry out real earnings management when firms headquartered in a county with greater social capital and whether this impact will differ according to firm characteristics and the external environment.

Social capital is negatively linked to a firm’s REM, as a manager’s mindset toward misconduct might be more constrained by a better social environment and a lower tendency to undertake real earnings manipulation. Furthermore, we find that the effect of social capital on real earnings management is stronger for firms with geographically concentrated structures, weaker external monitoring, Sarbanes-Oxley Act adoption and greater pressure to meet earnings targets.

This study sheds light on the relation between social capital and accounting decisions by exploring whether social capital can influence real earnings management and provides evidence that social capital has a beneficial impact on reducing certain misbehaviors in financial reporting and that the effect is stronger when a firm has a geographically concentrated structure, weaker external monitoring, SOX adoption and less pressure to meet earnings targets.

Corporate ESG performance has attracted widespread attention from various sectors of society. This paper aims to investigate whether analysts’ ESG attention can convey additional information to the market and consequently influence stock pricing efficiency.

Using A-share listed companies from 2014 to 2021 as the research subjects, this paper employs a deep learning algorithm, word2vec, to construct an ESG dictionary. Text analysis is then applied to create an analysts’ ESG attention index, delving into its impact on stock pricing efficiency.

Empirical research reveals that: (1) Analysts' ESG attention effectively enhances stock pricing efficiency, with a more significant impact from analysts’ attention to environmental (E) and social (S) factors compared to governance (G); (2) Further analysis indicates that this effect becomes more pronounced when there is higher disparity in corporate ESG ratings, greater marketization in the province where the company is located, and a higher institutional ownership percentage and (3) The mechanism by which analysts' ESG attention influences stock pricing efficiency is through an elevation in investor attention and stock liquidity. Additionally, it is observed that analysts prioritize ESG information to enhance their reputation and business capabilities.

From the perspective of ESG rating divergence, this paper innovatively uses analyst reports to construct ESG attention indicators and analyzes their impact on the efficiency of stock pricing.