The purpose of this study is to investigate the role of AI capability (AIC) on organizational creativity (OC), green innovation (GI), and sustainable performance (SP). It also aims to investigate the mediating roles of OC and GI, as well as the moderating role of knowledge sharing culture (KNC). This study used quantitative methodology and utilized a survey to collect data from 421 employees in different organizations in Bangladesh. We used the structural equation modeling (SEM) technique to analyze the data. This study finds that AI capability significantly influences OC, GI, and SP. OC and GI work as mediators, and KNC serves as a moderator among the suggested relationships. This study is notable for its novelty in examining multiple unexplored aspects in the current body of research. This research also provides valuable insights for policymakers and practitioners regarding the effective integration of AI to enhance organizational competitiveness.

The increasing prevalence of malware presents a critical challenge to cybersecurity, emphasizing the need for robust detection methods. This study uses a binary tabular classification dataset to evaluate the impact of feature selection, feature scaling, and machine learning (ML) models on malware detection. The methodology involves experimenting with three feature scaling techniques (no scaling, normalization, and min-max scaling), three feature selection methods (no selection, Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA)), and twelve ML models, including traditional algorithms and ensemble methods. A publicly available dataset with 11,598 samples and 139 features is utilized, and model performance is assessed using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Results reveal that the Light Gradient Boosting Machine (LGBM) achieves the highest accuracy of 97.16% when PCA and either min-max scaling or normalization are applied. Additionally, ensemble models consistently outperform traditional ML models, demonstrating their effectiveness in enhancing malware detection. These findings offer valuable insights into optimizing preprocessing and model selection strategies for developing reliable and efficient malware detection systems.

The purpose of this study is to understand Green Human Resource Management (GHRM) practices, considerations that facilitate Green Organizational Culture (GOC) and Organizational Agility (OA), and the connection between these factors and the social performance (SP) and environmental performance (EP) of businesses. The target population of the current study comprises a Bangladeshi organization (manufacturing, university and service). In this quantitative study, data were collected using structured questionnaires. The final sample consisted of 445 respondents, and the data were analyzed using the Structural Equation Modeling (SEM) technique. Both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were conducted to ensure the validity and reliability of the measurement model. The results show that social GHRM has a beneficial effect on social and environmental performance factors. This research also discovered employee’s ideas about GRRM, GOC OA, and improving an organization’s social and environmental performance for long-term growth. Our results show that hiring, teaching, evaluating, and rewarding employees in ways that avoid harming the natural world are all examples of HR management practices that help create a green workplace culture. This work adds to the resource-based view (RBV) and transitive leadership (TL) theory by showing the things that contribute to an organization’s green culture and agility help the link between social and environmental performance and green HRM practices. Organizations in Bangladesh can benefit greatly from the GHRM model that this study suggests, as OC and OA have a good effect on both society and the environment.

The rise of e-commerce necessitates sustainable practices for a greener future. While the e-commerce boom offers immense economic opportunities, minimizing its environmental impact and upholding ethical business conduct are essential. Current research on e-commerce heavily focuses on information technology (IT) for understanding and improving consumer acceptance. However, a critical gap exists in exploring IT's potential contributions to sustainability, crime prevention, and environmental safety. This study bridges this gap by exploring the role of IT integration in managerial practices to enhance environmental protection, crime prevention, and foster sustainability within Bangladesh's booming e-commerce sector. Focusing on e-commerce managers' perspectives, the research examines effective leadership strategies for IT implementation. Additionally, it utilizes the Technology Acceptance Model (TAM) to analyze the relationships between user perception and its impact on e-commerce performance. Using a structured questionnaire, we collected the data from 418 e-commerce managers. The research design incorporates a robust framework, hypothesis formulation, and methodological rigor grounded in TAM principles. The findings reveal a significant positive contribution of IT to environmental protection, crime prevention, and overall sustainability within the e-commerce sector. Managers' perceptions highlight that effective IT utilization ensures environmental safety, safeguards data against criminal activity, and promotes organizational sustainability. Furthermore, this research provides a roadmap for e-commerce businesses to accelerate their sustainability efforts. It also equips academics with valuable insights to advance knowledge on building a secure and sustainable future in the digital age.

The increasing prevalence of diabetes necessitates the development of effective early detection methods to mitigate its health impacts. This paper investigates the impact of feature transformation and machine learning (ML) models on the early detection of diabetes using a binary tabular classification dataset. We explore three feature transformation techniques, no transformation, normalization, and min-max scaling, to assess their influence on the performance of various ML models. To comprehensively evaluate the effectiveness of these preprocessing techniques, we experimented with twelve different ML models, including both traditional algorithms and ensemble methods. A publicly available dataset has been used for this research, containing 768 samples and 8 features. To ensure their effectiveness, the models are assessed using several evaluation metrics, including accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Among the ML models, Light Gradient Boosting Machine (LGBM) achieved the highest accuracy of 82.91% when min-max scaling was applied to the data. Our results demonstrate the varying effectiveness of different combinations of feature transformation techniques and ML models in enhancing diabetes detection performance. Furthermore, it has been observed that the ensemble models generally achieved better performance than traditional ML models. These findings provide valuable insights for optimizing preprocessing and model selection strategies in the development of robust early diabetes detection systems.