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Overcrowding continues to afflict emergency departments (EDs), and its attendant consequences are becoming increasingly severe. The burden of the COVID-19 pandemic is further escalating the situation worldwide. One of the most critical questions is how to adequately quantify what constitutes overcrowding and determine implications for operations management in improving service efficiency. This paper aims to discuss the aforementioned.

The authors propose the time and class complexity measures for ED service systems, taking into account important patient-level and system characteristics. Using an extensive data set from a Canadian ED, the authors investigate the performance of complexity-based measures in predicting service delays.

The authors find that the complexity measure is potentially more important than some well-known crowding metrics. In particular, EDs can improve service efficiency by managing the level of complexity within a desirable interval. Furthermore, complexity exposes how the interplay between demand-side behavioral changes and supply-side responses affects operational performance. Moreover, the results suggest that arrival patterns—the number of patients of each class arriving per time and times between events (arrivals and service completions)—increase the risk of service delays more than the demand volume.

This paper is the first to provide an extensive investigation into the application of the complexity-based measure for ED crowding. The study demonstrates potential values to be gained in ED service systems if complexity measure is incorporated into their operations management decisions.

This study aims to focus on analysing the impact of green human resource management (GHRM) on the green performance (GP) of Spanish wineries, as well as the mediating effect of employee well-being (EW) on this linkage. In addition, age, size and membership in a protected designation of origin are introduced as control variables to increase the precision of the cause-effect relationships examined.

The study proposes a conceptual model based on previous studies, which is tested using structural equations partial least squares structural equation modelling with data collected from 196 Spanish wineries between September 2022 and January 2023.

The findings of the research reveal the existence of a positive and significant relationship between the GHRM development and the GP of Spanish wineries, as well as the partial mediation of EW in this association.

The uniqueness and significance of this study can be attributed to several crucial factors. First, it enhances the understanding and knowledge regarding the advantages associated with GHRM development. Second, no prior research has conducted a comprehensive study on GHRM as a catalyst for GP within the context of Spanish wineries. Third, to the best of the authors’ knowledge, no previous study has analyzed the mediating role of EW as a mediator in the relationship between GHRM and GP of wineries.

This current study draws a comparison between the performance indicators of public sector banks (PSBs) and private sector banks (or non-PSBs) in India. The study controls for the impact of COVID-19.

The study uses strongly balanced panel data for seven years of 12 PSBs and 10 non-PSBs from the Nifty PSU Bank Index and Nifty Private Bank Index. The study applies panel data methodology to arrive at the results.

The study demonstrates that the behavior of indicators of performance and returns volatility for PSBs and non-PSBs differs substantially. While factors like capital adequacy ratio (CAR), cost management (COST), liquidity (LIQ), inflation and economic growth exhibit a similar impact on both categories of Indian banks, the effect of credit risk (RISK), market power (POWER) and COVID-19 on performance and returns stability is different for PSBs and non-PSBs.

There is a limited sample size of banks in India.

PSBs and non-PSBs need distinct treatments when calibrating performance indicators.

The performance and stability of banks are essential for society at large, the depositors and the investors.

The study provides vibrant implications for insight for banks to calibrate the variables that determine performance and stability, regulators and policymakers for effective governance of the banking ecosystem and effective utilization of public funds and capital. The findings are relevant for policymaking today, when the government is considering the privatization of a few PSBs.

This study draws on Organisation Justice Theory and Social Exchange Theory to examine the effects of the performance appraisal process (PAP) on employee commitment (ECO) via a serial mediation of performance appraisal outcome (PAO) and employee reward (ERE).

Survey data were collected from a sample of 363 academics across UK Higher Education Institutions (HEIs), including post-1992 and pre-1992 universities. We tested our hypotheses using partial least squares structural equation modeling (PLS-SEM) with a bias-corrected bootstrapping method.

The findings show that the PAP positively influences ECO and ERE. PAO and ERE mediate the relationship between the PAP and ECO. However, no significant relationship was found between PAO and ECO.

This study has significant implications for HEIs as it underscores the need for managers to ensure the clarity and accuracy of the PAP and to structure rewards to reflect employees’ efforts, considering they affect ECO.

This study contributes to the current debate on performance appraisal by highlighting the extent to which employees’ commitment to an organisation depends on the PAP, PAO and reward.

The appraisal of corporate reputation based on third-party corporate social responsibility (CSR) indices appears to have been institutionalized. The endorsement of such an approach by sustainability custodians and influencers undermines the uptake of the morality and legitimacy of CSR. This study takes a social realist perspective, which suggests that social phenomena such as CSR and corporate reputation are shaped by social structures and power relations. This study aims to contribute to a deeper understanding of the complex relationship between CSR and corporate reputation and understand ways in which the constructs are influenced by cognitive factors.

This study surveyed 411 respondents across five shopping malls and analyzed the data using path analysis of the structural equation modeling (SEM) technique. The mall-intercept survey sought to critically assess expectations of CSR vis-à-vis evaluation of corporate reputation. Based on a case study of three Johannesburg Stock Exchange listed companies, CSR expectations were measured along the philanthropic, economic, ethical and legal dimensions, while evaluation of corporate reputation was based on product quality, financial performance and social responsibility. SEM path analysis was used to extrapolate the predictive outcomes of CSR on corporate reputation.

Reputation for product quality and social responsibility is underpinned by the fulfillment of ethical CSR expectations, while philanthropic gestures enhance the evaluation of financial performance. Legal CSR significantly influences the reputation for social responsibility and product quality. Fulfillment of economic CSR expectations influences the reputation for product quality. However, no relationship was established between economic performance and social responsibility. Involvement in economic, philanthropic and particularly, legal CSR, are not indicative of the reputation for financial performance. Conversely, companies’ involvement in economic CSR does not suggest a higher propensity for social responsibility.

The predictive outcomes of CSR expectations on corporate reputation can reveal situated understanding of actual perceptions of corporate behavior.

Ethical business conduct is synonymously associated with social responsibility while espoused corporate philanthropy signals strong financial performance. The awareness of consumers’ cognitive evaluation of corporate reputation can offer a pathway to corporate communication professionals, policy makers and agencies to rethink and reposition CSR efforts.

Insensitivity to taken-for-granted cultural prescriptions and reliance on market-based reputational rankings undermine mutually beneficial stakeholder relationships and the social license to operate.

This study brings to the fore, cognitively dominated indicators of consumers’ perceptions of the reputation for CSR, to foster nuanced and halo-removed approaches to social responsibility. The authors show for the first time how companies’ skewed focus on corporate philanthropic giving paradoxically signals a capitalistic notion of social responsibility and unethical business conduct. This study offers a halo-removed orientation to the appraisal of CSR and corporate reputation.

This book aimed to conceptualise a construction workforce management model suitable for effectively managing workers in construction organisations. To this end, this chapter presents the conceptualised model, which consists of seven workforce management practices with their respective measurement variables. Drawing from existing theories, models, and practices, the chapter concludes that a construction organisation that will attain its strategic objectives in the current fourth industrial revolution era must be willing to promote effective recruitment and selection, compensation and benefits, performance management and appraisal, employee involvement and empowerment, training and development, as well as improving workers emotional intelligence and handling external environment pressure. These practices can promote proactiveness, participation, and improved skills and can lead to effective commitment, better quality, and flexibility within the organisation.

ESG (Environment, Social, Governance) has taken on increased importance in recent years for all stakeholders, with the S dimension now taking on a stronger focus in the real estate space. This paper proposes a new metric to be used in the S space to assess improvements in aspects such as gender equality and cultural diversity in real estate. It adds to the S metrics currently available to see the more effective delivery of the S dimension into real estate investment decision-making.

A new S metric in ESG is proposed and validated. Using this metric, examples regarding gender equality and cultural diversity are assessed among leading real estate players in Australia. This S metric is assessed over a number of time periods to demonstrate the improvements in gender equality and cultural diversity in these major real estate players.

This new S metric is seen to be highly effective and robust in capturing the changes in various aspects of the S dimension in ESG in the real estate space today; particularly concerning gender equality and cultural diversity. It is clearly able to demonstrate the significant changes in increased participation of women at the more senior leadership levels by leading players in the real estate space.

With ESG becoming a critical issue in the real estate sector, issues involved in the S space will take on increased significance going forward. This is critical, as the elements of the S dimension such as gender equality and cultural diversity are important aspects for an effectively functioning real estate industry. The S metric developed in this paper can be used for benchmarking purposes over time, as well as between real estate players, between sub-sections within a real estate organisation, and comparing against other industry sectors. It is also relevant in all organisations, and is not just limited to the real estate sector. Additional metrics in the S space are an important development to further empirically assess the effective delivery of the S dimension of ESG in the real estate sector and more broadly.

This paper specifically proposes this new S metric in ESG in the real estate industry. This is a key issue for the real estate industry going forward at all levels, as it will facilitate a more diverse real estate industry and more effective real estate investment decision-making. This S metric is applicable in all organisational sectors where the S dimension of ESG is important.

Unstructured data such as images have defied usage in property valuation for a long time. Instead, structured data in tabular format are commonly employed to estimate property prices. This study attempts to quantify the shape of land lots and uses the resultant output as an input variable for subsequent land valuation models.

Imagery data containing land lot shapes are fed into a convolutional neural network, and the shape of land lots is classified into two categories, regular and irregular-shaped. Then, the intermediate output (regularity score) is utilized in four downstream models to estimate land prices: random forest, gradient boosting, support vector machine and regression models.

Quantification of the land lot shapes and their exploitation in valuation led to an improvement in the predictive accuracy for all subsequent models.

The study findings are expected to promote the adoption of elusive price determinants such as the shape of a land lot, appearance of a house and the landscape of a neighborhood in property appraisal practices.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

This study is the maiden attempt to understand the effect of specific human resource practices (HRPs) on employee retention (ER) with the mediation of job satisfaction (JS) and moderation of work experience (WE) and job hopping (JH) in the context of the textile industry.

This study adopted a quantitative methodology and applied quota sampling to gather data from employees (n = 365) of leading textile companies in India. The conceptual model and hypotheses were tested with the help of Partial Least Squares-Structural Equation Modelling (PLS-SEM).

The findings of a path analysis revealed that compensation and performance appraisal (CPA) have the highest impact on JS followed by employee work participation (EWP). On the other hand, EWP had the highest impact on ER followed by grievance handling (GRH). The study revealed that JS significantly mediates between HRPs like CPA and ER. During Multi-group analysis (MGA) it was found that the importance of EWP and health and safety (HAS) was more in employee groups with higher WE, but it was the opposite in the case of CPA. In the case of JH behavior, the study observed that EWP leads to JS in loyal employees. Similarly, JS led to ER, and the effect was more pronounced for loyal employees.

In the context of the Indian textile industry, this work is the first attempt to comprehend how HRPs affect ER. Secondly, it confirmed that JS is not a guaranteed mediator between HRPs and ER, it could act as an insignificant, partial or full mediator. Additionally, this study establishes the moderating effects of WE and JH in the model through multigroup analysis.