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Sustainability is one of the fastest-growing research areas globally. Irrespective of industry and economic activity, it is the need of the day. This study examines the impact of green process innovation and green production on sustainability in Pakistan and India’s cement and plastic manufacturing industries. The study also addresses the moderating role of environmental awareness, which increases the effect of green productivity and green innovation towards sustainability. The research is based on a quantitative approach to addressing the issue in question. Primary data were collected via a closed-ended questionnaire from 657 employees of Pakistan and India’s plastic and cement manufacturing industries, and were analyzed via partial least square structural equation modeling via SmartPLS. The findings show that green productivity and green process innovation have a significant impact on sustainability, while environmental awareness also plays a significant role in sustainable practices in the cement and plastic manufacturing industries of Pakistan and India. The results are helpful for policymakers, industries, and other governmental and non-governmental organizations to ensure sustainability through green process innovation, green productivity, and environmental awareness.

The most dangerous form of cancer is breast cancer. This disease is life-threatening because of its aggressive nature and high death rates. Therefore, early discovery increases the patient’s survival. Mammography has recently been recommended as diagnosis technique. Mammography, is expensive and exposure the person to radioactivity. Thermography is a less invasive and affordable technique that is becoming increasingly popular. Considering this, a recent deep learning-based breast cancer diagnosis approach is executed by thermography images. Initially, thermography images are chosen from online sources. The collected thermography images are being preprocessed by Contrast Limited Adaptive Histogram Equalization (CLAHE) and contrasting enhancement methods to improve the quality and brightness of the images. Then, the optimal binary thresholding is done to segment the preprocessed images, where optimized the thresholding value using developed Rock Hyraxes Dandelion Algorithm Optimization (RHDAO). A newly implemented deep learning structure StackVRDNet is used for further processing breast cancer diagnosing using thermography images. The segmented images are fed to the StackVRDNet framework, where the Visual Geometry Group (VGG16), Resnet, and DenseNet are employed for constructing this model. The relevant features are extracted usingVGG16, Resnet, and DenseNet, and then obtain stacked weighted feature pool from the extracted features, where the weight optimization is done with the help of RHDAO. The final classification is performed using StackVRDNet, and the diagnosis results are obtained at the final layer of VGG16, Resnet, and DenseNet. A higher scoring method is rated for ensuring final diagnosis results. Here, the parameters present within the VGG16, Resnet, and DenseNet are optimized via the RHDAO to improve the diagnosis results. The simulation outcomes of the developed model achieve 97.05% and 86.86% in terms of accuracy and precision, respectively. The effectiveness of the designed methd is being analyzed via the conventional breast cancer diagnosis models in terms of various performance measures.

This study investigates the mediating role of Top Management Commitment (TMC) on the relationship between Energy Management Practices (including Energy Awareness [EAW], Energy Efficiency [EE], and Energy Knowledge [EK]) and Sustainable Tourism Development (STD) in Jerash, Jordan. Amid growing global concerns about environmental sustainability, understanding the dynamics between energy management and sustainable tourism has become critically important. The study utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the collected data. The empirical results reveal that EAW, EE, and EK have a significant positive influence on STD, indicating the direct impact of energy management practices on the sustainability of tourism. Furthermore, the findings suggest that these energy management variables also significantly influence TMC. Intriguingly, TMC emerges as a substantial mediator, enhancing the positive effects of EAW, EE, and EK on STD. This signifies the crucial role of top management's commitment to leveraging energy management practices effectively to foster sustainable tourism development. The study, despite focusing only on the context of Jerash, Jordan, contributes valuable insights to the existing literature and informs managerial practice in the tourism sector. Future research should expand its scope to different geographical regions and consider additional dimensions of energy management practices to further enrich the understanding of sustainable tourism development.

The traditional supply chain system included smart objects to enhance intelligence, automation capabilities, and intelligent decision-making. Internet of Things (IoT) technologies are providing unprecedented opportunities to enhance efficiency and reduce the cost of the existing system of the supply chain. This article aims to study the prevailing supply chain system and explore the benefits obtained after smart objects and embedded networks of IoT are implanted. Short-range communication technologies, radio frequency identification (RFID), middleware, and cloud computing are extensively comprehended to conceptualize the smart supply chain management system. Moreover, manufacturers are achieving maximum benefits in terms of safety, cost, intelligent management of inventory, and decision-making. This study also offers concepts of smart carriage, loading/unloading, transportation, warehousing, and packaging for the secure distribution of products. Furthermore, the tracking of customers to convince them to make more purchases and the modification of shops with the assistance of the Internet of Things are thoroughly idealized.

Real-time sensors for precision irrigation schedulating are used for enhancing water efficiency and optimizing resource usage. Poor resource management can negatively impact traditional farming practices, particularly in regions limited by water shortages. Agriculture is susceptible due to its heavy reliance on water resources. Due to global warming and its potential impacts, there is a growing emphasis on developing strategies to ensure a steady water supply for food production and consumption. As a result, research on reducing water usage in irrigation systems needs to be implemented. While traditional commercial irrigation sensors are often too expensive for smaller farms to adopt, manufacturers are now producing affordable alternatives that can be integrated with network systems to provide cost-effective solutions for efficient irrigation and agricultural monitoring. To minimize a farmer’s efforts, an Internet of Things (IoT)-based drip irrigation system is proposed in this work. Initially, the required data is collected using the IoT sensors. The gathered data is fed into the Adaptive Residual Hybrid network (ARHN) that is developed by using the Spatial Autoencoder and Stacked CapsNet. Here, the Modernized Random Variable-based Frilled Lizard Optimization (MRV-FLO) is utilized to tune the ARHN parameters. Therefore, the required water from the pump for the crops is provided by the ARHN model. In addition, this model makes the work simpler and avoids the wastage of water in the agricultural environment. Finally, the performance of the developed framework is validated over the existing works to prove the efficiency of the recommended method. The main experimental findings of the developed model achieve 99.24% and 97.32% in terms of accuracy and RMSE. Moreover, the statistical findings of the developed model shows 41.9%, 34.9%, 36.0% and 37.1% better performance than LEA-ARHN, FDA-ARHN, AOA-ARHN and FLO-ARHN in terms of best measure. Based on this performance enhancement, the developed model can effectively reduces the farmer’s effort and improves the crop productivity in the agricultural sectors.

Artificial intelligence (AI) is emerging as a disruptive force in many sectors, and using it in accounting isn’t an exception. This conceptual paper explores the role of AI in accounting, for financial reporting, auditing, and financial decision-making and provides accountants an opportunity to improve efficiency, accuracy, and decision support. AI, through data analytics, algorithms, automation, etc. has an important role in the field of accounting with some challenges also. The study also highlights the implications of AI in accounting for achieving several Sustainable Development Goals (SDGs). Firstly, AI-driven automation can restructure financial activities, reducing time and resource consumption, and contributing to SDG 8 (Decent Work and Economic Growth). In addition, by providing real-time data analysis, AI empowers businesses to make sustainable decisions based on real-time data, aligning with SDG 9 (Industry, Innovation, and Infrastructure) and SDG-16 (Peace, Justice, and Strong Institutions) and SDG 17 (Partnerships for the Goals). The paper has implications for policy makers, technology developers, financial institutions and business firms.

The recent integration of humanized robots in education has a transformative potential, but identifying and addressing education and human-centric challenges is necessary for their adoption. This research investigates the relationships of safety issues, cultural issues and character traits concerns the humanized-robots with the adoption in learning environments. The theoretical foundation of the study is based on the Human-Robot Interaction Theory, Social Cognitive Theory and Diffusion of Innovation Theory, which provide insights and understanding regarding the relationships among variables for the adoption of humanized robots in education. The research employs quantitative methodology, where the data was collected through questionnaire surveys from 620 respondents in Pakistan and China and analyzed through Partial Least Squares Structural Equation Modelling. The results reveal that safety issues, character traits and cultural issues have significant relationships with the adoption of humanized-robots in educational settings. The findings offer novel understandings regarding the complex relationships of character, safety and cultural dimensions with humanized-robots adoption unlike the previous studies that focused on usefulness, ease of use, feasibility, outcomes etc. The study integrates the above-mentioned theories in a single framework and extends the understandings regarding technology adoption by incorporating safety, character traits and culture. Furthermore, it also underscores the significance and need of safety, positive character traits and cultural sensitivities for the development and broader acceptance of such technologies. The results provide valuable insights for developers, policymakers and educational institutions for designing culturally adaptive humanized robots, ensuring safety and extensive character traits training for reducing adoption challenges.

Sustainable development has become a crucial goal for policymakers worldwide, with technological progress playing a significant role in achieving this objective. Jordan, a developing country in the Middle East, has been working towards the attainment of Sustainable Development Goals (SDGs) by leveraging technology to promote economic growth while preserving the environment. In this study, we evaluate the impact of technological improvements on SDGs in Jordan by analyzing the relationship between financial development, energy consumption, and economic growth. The analysis covers the period from 1970 to 2021 and employs three econometric techniques, namely the Lee and Strazicich (2013) second-generation econometric approach, the novel Augmented Autoregressive Distributed Lag (AARDL) model, and Frequency Domain Causality (FDC) analysis. The results indicate a positive and significant association between financial development, energy use, and economic growth, with financial development having the strongest impact. Moreover, technological progress plays a crucial role in achieving SDGs by positively affecting financial development, energy consumption, and economic growth. This study highlights the importance of leveraging technology to promote sustainable development and provides valuable insights for policymakers in Jordan and other developing countries.

The research investigated the relationship among Key Performance Indicators (KPIs), risk assessment capabilities and value creation in service sector firms. The study also sought to examine the effect of KPI`s components on risk assessment & value capitalisation, and how they either facilitate or hinder implementation, monitoring and continuous improvement processes. In this context, a quantitative cross-sectional research design was applied using an online survey of shared middle and senior managers in service organizations. After filtering, the final version of segmented sample included a total of 215 respondents engaged in different service businesses. The analysis was determined using Partial Least Squares Structural Equation Modeling. The results showed that all components of KPIs have significant positive relationships with risk assessment and value improvement outcomes First, performance drivers were found to be the most significant predictor of both constructs. As such, the results show that both risk assessment and value improvement had a positive effect on implementation/monitoring processes which in turn enabled continuous improvements. Performance measurement, risk management and value creation in service organizations: A performance at-risk-based conceptual model. The results have numerous managerial, practical and policy implications for the service sector. This drives home the necessity of creating integrated KPI systems that include risk assessment and value improvement factors. In building on existing theory, the study is of substantial interest in that it provides empirical evidence for these organizational mechanisms related to service organizations. Resilient Organizations in the Service Sector picture of Resilience across Performance Management with KPIs, Risk Assessment and Value Creation strategies offering a comprehensive foundation for sustainable organizational success.

The purpose of this research was to analyze the increased performance improvement in the supply chain related to the energy and utilities sector of Jordan through cloud computing, also mediated by knowledge sharing. 150 respondents were analyzed. Research suggests that there was a strong positive relationship between cloud computing and supply chain performance. In addition, cloud computing had a strong and positive correlation with the practice of knowledge sharing. The result indicates that companies with a culture of knowledge sharing among employees were more likely to incorporate the use of cloud computing. The study also indicates that cloud computing adoption had enhanced supply chain performance in the utility and energy sector within Jordan. Therefore, the study also finds that there was a strong and positive relationship between knowledge sharing and well overall performance of supply chain activities. This points to the role that knowledge-sharing practices play in improving the performance of the supply chain within Jordanian utilities and energy sectors. Furthermore, the findings of the mediation study provide strong evidence in support of our hypothesis that knowledge sharing plays a major role as a mediator relating to the relationship between cloud computing adoption and supply chain performance. The observed mediation effect suggests that the positive impact of cloud computing implementation on supply chain performance can be attributed to some extent to its facilitating knowledge exchange practices. However, this research improves the understanding of relationships among cloud computing adoption implementations and information structure flow as well as supply chain performance within Utilities These results emphasize the importance of cloud technologies as information drivers that would lead to enhanced performance in terms of supply chain operations. These are insights that can be used by organizations and businesses aiming at improving their competitiveness and efficiency levels in these sectors.