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Business competitiveness is critical for a thriving economy that requires companies to be more efficient and innovative to outperform their rivals. This paper investigates the main determinants of business competitiveness from the resource-based view (RBV) perspective.

Data were collected from 140 discrete and large manufacturing firms in Malaysia through a cross-sectional quantitative-based survey with a convenience sampling procedure. The findings from the PLS-SEM analysis showed that implementing LAG manufacturing significantly amplifies business competitiveness.

It was found that cost leadership strategy drives lean and agile manufacturing implementation, while differentiation positively amplifies the implementation of lean, agile and green manufacturing. This study contributes to the body of knowledge and provides insight to practitioners in tailoring strategies to steer manufacturing firms toward being more competitive.

This study identifies the effect of LAGP implementation on business competitiveness. This paper will benefit practitioners and managers by providing insights into tailoring strategies to steer manufacturing firms towards being more competitive. This paper follows a structure that includes: an introduction to the study, a review of relevant literature on business competitiveness, lean, agile and green manufacturing implementation, the development of hypotheses, the presentation of research methodology and findings, and finally, a conclusion with a discussion, implications, limitations and suggestions for future research.

Agile new product development (ANPD) attracts researchers and practitioners by its ability to rapidly reconfigure products and related processes to meet the needs of emerging markets. To increase ANPD adoption, this study aims to identify ANPD enablers (ANPDEs) and create a structural framework that practitioners can use as a quick reference.

Initially, a comprehensive literature review is conducted to identify ANPDEs, and a structural framework is developed in consultation with an expert panel using a hybrid robust best–worst method interpretive structural modeling (ISM). During the ISM process, the interactions between the ANPDEs are investigated. The ISM result is used as input for fuzzy Matrice d’Impacts croises-multiplication appliqúean classment means cross-impact matrix multiplication applied to classification (MICMAC) analysis to investigate enablers that are both strong drivers and highly dependent.

The study’s findings show that four ANPDEs are in the low-intensity cluster and thus are excluded during the structural frame development. ISM output shows that “Strong commitment to NPD/top management support,” “Availability of resources,” “Supplier commitment/capability” and “Systematic project planning” are the important ANPDEs. Based on their driving and dependence power, the clusters formed during the fuzzy MICMAC approach show that 16 ANPDEs appear in the dependent zone, one ANPDE in the linkage zone and 14 ANPDEs in the driving zone.

This research has intense functional consequences for researchers and practitioners within the industry. Industry professionals require a conservative focus on the established ANPDEs during ANPD adoption. Management has to carefully prepare a course of action to avoid any flop during ANPD adoption.

The framework established is a one-of-a-kind study that provides an integrated impression of important ANPDEs. The authors hope that the suggested structural framework will serve as a blueprint for scholars working in the ANPD domain and will aid in its adoption.

One of the major challenges faced by the world at present is management and treatment of waste. Especially, waste such as polyethylene (plastics) is non-degradable and is causing great damage to our environment. Aquatic environment is one among them that is getting affected by these plastic wastes. Water pollution is a great issue faced in many countries and steps to reduce it are being taken on a wide scale. Unwanted aquatic plants grown in ponds and lakes create problems like totally covering up the surface of the lake that blocks the sunlight for aquatic species and also reducing their total storage. Identifying such unwanted plants and plastics is a very essential part in treating and management of waste. Detection and classification help us to achieve this. With the help of satellites, drone-shot images of many oceans are captured, and the amount of plastic content present is detected using artificial intelligence. In artificial intelligence, we have many algorithms and platforms that help us to achieve object detection. Tensorflow is one such framework that helps us to perform object detection with the help of pre-trained models present in it, and thus, it is used in this study. Object detection uses computer vision to detect objects from images. Convolutional neural networks are a subset of machine learning that is helpful in image processing – in other words, processing of pixel data. In this study, we used the ResNet-50 model involving transfer learning for classifying unwanted plants and plastics. Lakes and ponds are the major places among the other aquatic environments where these kinds of wastes are found, and therefore, this study concentrates on waste present in these aquatic bodies. The lakes and ponds present near residential areas act as a place for storing excess rainwater, which prevents flooding. Many cities, especially residential areas, face a lot of water stagnation problems during the rainy season. Ponds and lakes near these areas contain unwanted plants and plastics present, which makes it a problem to store the rainwater that comes during monsoon. Another problem is that they don’t provide sunlight to enter deep into water, making the aquatic species difficult to survive. Preserving and maintaining such lakes from getting filled with non-degradable plastics and unwanted plant growth becomes very important. Therefore, the lakes and ponds present in such residential areas would be useful to detect the unwanted waste.

In this study, the focus is on detection and classification of the plastics and unwanted plants. The dataset is very important for this study, which is an image dataset. There was not any readily available image data of unwanted plastics available online, and therefore, the images were captured from the lakes and ponds in Kanchipuram district. Images of duckweed, plastics, bulrush and leaves of sky lotus were taken. This dataset consisted a total of 200 images, with 50 images belonging to each category. Having this as the dataset, detection and classification were carried out.

The object detection took place for the plastic, duckweed, bulrush and leaves of sky lotus and the performance metrics such as precision and recall was evaluated to test the accuracy of the detections. Precision is used to calculate the number of correctly identified positive identifications. This is done by dividing the sum of true positives and false positives from the number of true positives. True positives are nothing but the number of correct predictions of positive identifications, and false positives are the number of false predictions of positive identifications. Similarly, recall is used to calculate the number of actual positives identified. We can calculate recall by dividing the sum of true positives and false negatives from the total number of true positives. Here false negatives are the number of false predictions of false identification. This performance metrics was evaluated for the trained model, and we obtained an average precision of 0.81 and an average recall of 0.86. The high precision and recall values of our model show that the model produces accurate results. Therefore, the model is producing good performance in detecting the unwanted plants and plastics from lakes and ponds. The evaluation results were visualized with the help of TensorBoard and are available in fig-4 and fig-5. The loss rate is visualized and is available in fig-6. We can see that the loss rate has reduced over the steps as we pass from 1,000 to 4000th step.

The work was originally carried out in the Kanchipuram district of Tamil Nadu.

Green funds represent a hybrid approach that integrates both environmental and financial considerations. Firms also strive to balance social benefits with economic performance. This study aims to analyze how green fund shareholdings impact firms’ dual performance and explores the underlying mechanisms and boundary conditions.

This study uses a sample of A-share companies listed on China’s exchanges from 2008 to 2022. A fixed effects model is used to assess the dual value of green funds in enhancing both environmental and financial performance while also exploring viable pathways to achieve a “win-win” outcome.

Green fund shareholdings significantly enhance both financial and environmental performance, with corporate reputation and corporate transparency acting as mediators. Media oversight and executive compensation positively moderate the relationship between green fund shareholdings and dual performance. In competitive industries, the influence of green fund shareholdings on environmental performance is more pronounced than their effect on financial performance. In the context of politically connected firms, green fund shareholdings have a reduced impact on financial performance, with no significant difference in environmental performance. In addition, the impact of green funds on ownership structure is heterogeneous, promoting dual performance in private firms but not in state-owned enterprises.

This study enhances the understanding of green funds’ dual investment logic, provides deeper insights into their role in fostering sustainable corporate development and extends the application of institutional logic in enterprise management.

Intelligent prediction of node localization in wireless sensor networks (WSNs) is a major concern for researchers. The huge amount of data generated by modern sensor array systems required computationally efficient calibration techniques. This paper aims to improve localization accuracy by identifying obstacles in the optimization process and network scenarios.

The proposed method is used to incorporate distance estimation between nodes and packet transmission hop counts. This estimation is used in the proposed support vector machine (SVM) to find the network path using a time difference of arrival (TDoA)-based SVM. However, if the data set is noisy, SVM is prone to poor optimization, which leads to overlapping of target classes and the pathways through TDoA. The enhanced gray wolf optimization (EGWO) technique is introduced to eliminate overlapping target classes in the SVM.

The performance and efficacy of the model using existing TDoA methodologies are analyzed. The simulation results show that the proposed TDoA-EGWO achieves a higher rate of detection efficiency of 98% and control overhead of 97.8% and a better packet delivery ratio than other traditional methods.

The proposed method is successful in detecting the unknown position of the sensor node with a detection rate greater than that of other methods.