The substantial focus on achieving corporate sustainability has necessitated the implementation of green human resource management (GHRM) practices. The purpose of this paper is to reveal the industries’ perspective of the impact of GHRM practices (i.e., green recruitment and selection, green pay and rewards, and green employee involvement and green training) on corporate sustainability practices. Data were collected from 200 human resource professionals in major industrial sectors of a developing country. Partial least squares structural equation modelling was used to test the study hypotheses and multigroup analysis (MGA) between industrial sectors. The findings show a positive impact of three GHRM practices, i.e., green recruitment and selection, green pay and rewards, and green employee involvement on corporate sustainability. However, green training has no significant association with corporate sustainability, which is interesting. Furthermore, the multigroup analysis (MGA) revealed partial and significant differences among different sectors. The results provide more contextualized social, environmental, and economic implications to academics and practitioners interested in green initiatives. To date, limited research has been conducted to investigate whether GHRM practices can be an effective strategy in increasing corporate sustainability in a developing country context. Particularly, the industry’s perspective on the subject matter was rather absent in the existing literature. The present study fills this gap and contributes to the existing literature by providing the industry’s perspective on GHRM and corporate sustainability.

The global need to preserve ecology has propelled the green movement across the globe. An emerging managerial challenge for all organizations is to protect natural resources by reducing their negative impact on the environment and increase sustainable performance. Greening is the need of the age to conserve natural resources. This study investigates the impact of green human resource management practice—i.e., green hiring—on the sustainable performance of public and private healthcare organizations. A quantitative research approach was used for data collection. Scale survey of 160 responses was gathered from public and private healthcare organizations. Partial least square–structural equation modeling was used for data analysis. The study results suggest that green recruitment has a positive and significant impact on environmental performance, economic performance, and social performance. Path coefficients test also revealed that green performance management and compensation significantly mediate the relationship between green hiring and sustainable performance of public and private healthcare organizations. This study is helpful for organizations in adapting GHRM practices that will benefit the organizations in all ways. This study also provides a better understanding to policymakers on how to promote GHRM practices and increase sustainability in organizations.

This study aims to establish the link of key Islamic banking sustainability indicators with the United Nations’ Sustainable Development Goals (UN SDGs) as a policy recommendation for sustainable development and to mitigate the distressing impacts of the COVID-19 pandemic on the triple bottom line (people, planet, and profit). To identify the key Islamic banking sustainability indicators, the authors selected the most cited sustainability measurement indexes in Islamic banking. Initially, the indexes were divided into 10 broader themes, and then the key Islamic banking sustainability indicators were shortlisted from each theme based on their high-frequency distribution. The shortlisted sustainability indicators were then ratified to be in line with Islamic philosophy based on “Maqasid al-Shariah” (objectives of Shariah) and were subsequently grouped into the three dimensions of economic, environmental, and social sustainability based on the axial coding method. Finally, the categorized sustainability indicators were aligned with the relevant UN SDGs through the axial coding method for policy formulation, and respectively 12 propositions were developed for policy formulation. This study labeled the methodological process of this study as the ECA method (exploration, categorization, alignment). The new ECA method offers a reverse extension in the “SDG compass” developed by the Global Reporting Initiative (GRI) for aligning business policies with the UN SDGs. The process of aligning Islamic banking sustainability indicators with the UN SDGs will provide a roadmap to recovery from the COVID-19 pandemic in terms of economic, environmental, and social issues. Due to the diversity of the UN SDG framework, it covers multiples aspects for sustainable development. Therefore, considering the UN SDGs in terms of various banking instruments will mitigate the multiple distressing impacts of COVID-19 on the triple bottom line (people, planet, and profit), it will also promote a sustainable development agenda.

The diversity of data collected on both social networks and digital interfaces is extremely increased, raising the problem of heterogeneous variables that are not often favourable to classification algorithms. Despite the significant improvement in machine learning (ML) and predictive analysis efficiency for classification in customer relationship management systems (CRM), their performance remains very limited by heterogeneous data processing, class imbalance, and feature scales. This impact turned out to be more important for simple ML methods which in addition often suffer from over-fitting. This paper proposes a succinct and detailed ML model building process including cross-validation of the combination of SMOTE to balance data and ensemble methods for modelling. From the conducted experiments, the random forest (RF) model yielded the best performance of 0.86 in terms of accuracy and f1-scoreusing balanced data. It confirms the literature summary about this topic which shows that RF was among the most effective algorithms for customer predictive classification issues. The constructed and optimized models were interpreted by Shapley values and feature importance analysis which shows that the “age” feature was the most significant while “HasCrCard” was the less one. This process has proven effective in bridging previously reported research gaps and the resulting model should be used for supporting bank customer loyalty decision-making.

Environmental issues have gradually gained attention in the last decade because of increased global warming and high waste production. Therefore, this article aims to add value to the environment management research by analyzing green product innovation through market orientation. Moreover, this study includes green self-efficacy as a mediator, being less focused in the past literature to examine employees’ confidence in innovating green products according to customers’ needs. In addition, resource bricolage is also introduced as a moderator because fewer studies display the empirical results about organizations producing or tend to produce innovated green products with a limited number of resources. Data were collected from 477 employees of small and medium-sized enterprises using a self-administered questionnaire in Pakistan. Empirical results revealed by SmartPLS software delineate that market orientation has a positive and significant impact on green self-efficacy and green product innovation. Moreover, green self-efficacy shows a significant mediation impact between market orientation and green product innovation. Additionally, resource bricolage also moderates the relationship between market orientation and green product innovation. Overall, the study contributes to theoretical and practical knowledge about green product innovation in tackling the world’s environmental issues.

The use of machine learning (ML) methods has been widely discussed for over a decade. The search for the optimal model is still a challenge that researchers seek to address. Despite advances in current work that surpass the limitations of previous ones, research still faces new challenges in every field. For the automatic targeting of customers in a banking telemarketing campaign, the use of ML-based approaches in previous work has not been able to show transparency in the processing of heterogeneous data, achieve optimal performance or use minimal resources. In this paper, we introduce a class membership-based (CMB) classifier which is a transparent approach well adapted to heterogeneous data that exploits nominal variables in the decision function. These dummy variables are often either suppressed or coded in an arbitrary way in most works without really evaluating their impact on the final performance of the models. In many cases, their coding either favours or disfavours the learning model performance without necessarily reflecting reality, which leads to over-fitting or decreased performance. In this work, we applied the CMB approach to data from a bank telemarketing campaign to build an optimal model for predicting potential customers before launching a campaign. The results obtained suggest that the CMB approach can predict the success of future prospecting more accurately than previous work. Furthermore, in addition to its better performance in terms of accuracy (97.3%), the model also gives a very close score for the AUC (95.9%), showing its stability, which would be very unfavourable to over-fitting.