Search Results (46)

Due to increased competition in the marketplace, companies in all industries are facing the problem of customer attrition. In order to expand their market share and increase profits, companies have shifted from the concept of ‘acquiring new customers’ to ‘retaining old customers’. In this study, we design a deep learning model based on multi-network feature extraction and an attention mechanism, convolutional neural network–bidirectional long and short-term memory network–fully connected layer–coordinate attention (CNN-BiLSTM-FC-CoAttention), and apply it to customer churn risk assessment. In the data preprocessing stage, the imbalanced dataset was processed using the SMOTE-ENN hybrid sampling method. In the feature extraction stage, a sequence-based CNN and time-based BiLSTM are combined to extract the local and time series features of the customer data. In the feature transformation stage, high-level features are extracted using a fully connected layer of 64 Relu neurons and the sequence features are reshaped into matrix features. In the attention enhancement stage, the extracted feature information is refined using a coordinate attention learning module to fully learn the channel and spatial location information of the feature map. To evaluate the performance of the proposed model, we include public datasets from telecom, bank and insurance industries for ten-fold cross-validation experiments, and the results show that the CNN-BiLSTM-FC-CoAttention model outperforms the comparison models in all metrics. Our proposed model improves the accuracy and generalisation of the model prediction by combining multiple algorithms, enabling it to be widely used in multiple industries. As a result, the model gives enterprises a better and more general decision-making reference for the timely identification of potential churn customers. Full article

Nowadays, in the complex business network system, the interaction of firms across borders is facing several challenges. Many studies in the literature also suggest numerous approaches to overcome these challenges. However, a few of the obstacles for internationalizing firms were studied and the challenges are increasing against firms’ growth opportunities cross-border. Taking this into account, the present research emphasized the roles of enterprises’ e-business adoptions of countries on cross-border firms’ internationalization by drawing from network theory and technology–organization–environment frames. By employing a fixed effect model to 365 enterprises, leaders’ attitudes of preferring technology-intensive firms, network infrastructure, risk-averting attitudes, country’s market size, multilingual services, e-government status, threats from competitors, reliable utility sources, human capital quality, costs of adoptions and telecom services enrichments, and costs of adopting different online services need to be taken into account before internationalization of born global companies. Full article

App-based marketing has been widely used in the telecommunications industry to both serve and draw in new customers. Typically, telecom providers must invest an amount of company resources to develop and maintain the operations mechanism of information technology platforms (e.g., mobile apps); therefore, it is important to take the issue of marketing effectiveness into account. For example, the mismatch between what telecom providers offer in their mobile apps and customers’ marketing requirements plays a significant role in determining unmet knowledge and presentation gaps that are related to the marketing domain. This research intends to propose an integrated Fuzzy MCDM model based on 4Ps (Product, Price, Place, Promotion) and 4Cs (Customer Needs, Cost, Convenience, Communication) models for evaluating mobile telecom applications (MTAs). Therefore, the 4Ps and 4Cs models are extended to develop a hierarchy model for evaluating MTAs. Next, fuzzy theory is applied to handle the subjectiveness of qualitative evaluation criteria while the Analytic Hierarchy Process (AHP) is applied to synthesize the weight and score of the evaluation criteria. The proposed model is applied to evaluate, rank, and analyze the MTA of three telecom providers in the Kingdom of Saudi Arabia (KSA) (e.g., STC, Zain, and Mobily). The conducted case study ensures the usability and applicability of the proposed model. The evaluation results offer several managerial actions for achieving ideal app-based marketing. Full article

The article presents study results showing the increasing role of unregulated activity as a boost for innovative processes in energy companies and their investment appeal. A summary of academic literature, reports by leading consulting companies, and international energy agencies make it possible to outline the landscape of the most economically viable areas of business activity in the energy industry, the most promising of which are energy efficiency, design and deployment of EV charging networks, smart grids, and telecom services. Analysis of financial performance statements of over 30 energy companies from different countries demonstrates the contribution of unregulated activities to their financial stability, which shows in growing profits, capitalization, and stock prices. It is revealed that despite the active promotion of unregulated activities by the state that primarily seeks to achieve the goals of the low-carbon transition, there is a stronger government presence in the capital structure of energy companies, which in the future will slow down investment activity in the industry. In this regard, the discussion considers the barriers to organizing unregulated business in the electricity sector and methods to eliminate them. In particular, a set of necessary conditions is defined, under which entrepreneurship in the industry contributes to improving the efficiency of the main business processes—production and transportation of energy. Full article

The performance of composite-based triboelectric nanogenerators (C–TENGs) was significantly enhanced through laser surface patterning and graphite coating. The laser etching process produced accurate and consistent patterns, increasing surface area and improving charge accumulation. SEM imagery confirmed the structural differences and enhanced surface properties of the laser-etched C–TENGs. Graphite fibers further augmented the contact surface area, enhancing charge accumulation and diffusion. Experimental results demonstrated that the optimized C–TENGs, especially those with line patterns and graphite coating, achieved a maximal 98.87 V open-circuit voltage (V_OC) and a 0.10 µA/cm² short-circuit current density (J_SC) under a 20 N external force. Environmental tests revealed a slight decrease in performance with increased humidity, while long-term stability tests indicated consistent performance over three weeks. Practical application tests showed the potential of C–TENGs integrated into wearable devices, generating sufficient energy for low-power applications, thereby highlighting the promise of these devices for sustainable energy solutions. Full article

In the rapidly evolving telecommunications industry, organizations in Bangladesh are facing the challenge of improving their performance to stay competitive. However, there is limited research on how big data analytics (BDA) impacts organizational performance (OP) in this context. Therefore, this study examines the impact of BDA on OP in Bangladesh’s telecommunications industry, with green innovation (GI) and knowledge management (KM) as mediating variables, and big data analytics technical capabilities (BDATCs) as a moderating variable. We collected data from 384 management-level employees across five major telecom companies in Bangladesh using a structured survey questionnaire. Our analysis employed partial least squares structural equation modeling (PLS-SEM) with Smart-PLS 4.0 software. The findings indicate that BDA positively influences OP, and both GI and KM significantly mediate this relationship. However, while BDATCs enhance the BDA–OP relationship, they do not significantly moderate the BDA–GI link. These results underscore the importance of integrating BDA with KM and GI to boost organizational performance. Telecom companies should invest in advanced data analytics, foster a culture of sustainability, and enhance knowledge management practices to achieve superior performance. This study contributes to the Resource-Based View (RBV) theory by demonstrating the strategic role of BDA, GI, and KM in a developing economy context. Future research should expand this investigation across different sectors and consider longitudinal approaches to capture the dynamic nature of BDA’s impact on organizational performance. Full article

Telecommunications firms face intense competition driven by rapid innovation and shifting consumer expectations. To remain competitive, companies are adopting Total Quality Management (TQM) to enhance customer satisfaction, corporate stability, and sustainability. This study examines TQM’s effects on customer satisfaction within Gulf Cooperation Council (GCC) countries’ telecommunications sector using secondary data from three firms’ quarterly reports (2019–2023). Descriptive, correlation, and regression analyses with STATA software reveal a significant increase in net promoter scores, indicating firms’ commitment to meeting evolving customer needs. Employee engagement and process management positively affect customer satisfaction, while continuous improvement practices and customer focus do not show a statistically significant influence. The research underscores TQM’s importance in fostering sustainable customer satisfaction by enabling telecom companies to adopt customer-centric strategies for achieving sustainable growth and long-term success. Aligning business processes with customer needs, especially in complaint handling, is crucial. The study advocates for implementing advanced customer relationship management (CRM) systems to better understand customer preferences. These strategic initiatives are vital for telecom firms to maintain competitiveness, enhance customer satisfaction, and contribute to the region’s overall economy. Full article

Biomedical information retrieval for diagnosis, treatment and prognosis has been studied for a long time. In particular, image recognition using deep learning has been shown to be very effective for cancers and diseases. In these fields, scaphoid fracture recognition is a hot topic because the appearance of scaphoid fractures is not easy to detect. Although there have been a number of recent studies on this topic, no studies focused their attention on surgical treatment recommendations and nonsurgical prognosis status classification. Indeed, a successful treatment recommendation will assist the doctor in selecting an effective treatment, and the prognosis status classification will help a radiologist recognize the image more efficiently. For these purposes, in this paper, we propose potential solutions through a comprehensive empirical study assessing the effectiveness of recent deep learning techniques on surgical treatment recommendation and nonsurgical prognosis status classification. In the proposed system, the scaphoid is firstly segmented from an unknown X-ray image. Next, for surgical treatment recommendation, the fractures are further filtered and recognized. According to the recognition result, the surgical treatment recommendation is generated. Finally, even without sufficient fracture information, the doctor can still make an effective decision to opt for surgery or not. Moreover, for nonsurgical patients, the current prognosis status of avascular necrosis, non-union and union can be classified. The related experimental results made using a real dataset reveal that the surgical treatment recommendation reached 80% and 86% in accuracy and AUC (Area Under the Curve), respectively, while the nonsurgical prognosis status classification reached 91% and 96%, respectively. Further, the methods using transfer learning and data augmentation can bring out obvious improvements, which, on average, reached 21.9%, 28.9% and 5.6%, 7.8% for surgical treatment recommendations and nonsurgical prognosis image classification, respectively. Based on the experimental results, the recommended methods in this paper are DenseNet169 and ResNet50 for surgical treatment recommendation and nonsurgical prognosis status classification, respectively. We believe that this paper can provide an important reference for future research on surgical treatment recommendation and nonsurgical prognosis classification for scaphoid fractures. Full article

Comprehensive understandings about how to realize service capability greenness in the telecom sector are still rare. In this paper, a non-serial telecom supply chain consisting of an infrastructure supplier, a content provider and a telecom operator is formulated under environmental regulation. The telecom operator aims to find the optimal green procurement ratio between traditional and green equipment. Some common real-life situations are assumed, and the service capacity greenness problems are solved by game theory regarding coordination and interaction among supply chain partners. The results show that the prevailing concern of managers’ “energy saving is not money saving” is the direct reason for a mixed purchase strategy. Further, when diseconomy of purchasing energy-saving equipment reaches a certain threshold, tightening environmental regulation may cause telecom companies to reduce the proportion of energy-saving equipment purchased. Finally, the telecom sector is characterized by its booming service capacity per equipment, which benefits green purchase ratio greatly. When the other six influencing factors are relatively stable, the driving force of telecommunication technology update will push the telecom sector to a greener future. Full article

In smart cities, bicycle-sharing systems have become an essential component of the transportation services available in major urban centers around the globe. Due to environmental sustainability, research on the power-assisted control of electric bikes has attracted much attention. Recently, fuzzy logic controllers (FLCs) have been successfully applied to such systems. However, most existing FLC approaches have a fixed fuzzy rule base and cannot adapt to environmental changes, such as different riders and roads. In this paper, a modified FLC, named self-tuning FLC (STFLC), is proposed for power-assisted bicycles. In addition to a typical FLC, the presented scheme adds a rule-tuning module to dynamically adjust the rule base during fuzzy inference processes. Simulation and experimental results indicate that the presented self-tuning module leads to comfortable and safe riding as compared with other approaches. The technique established in this paper is thought to have the potential for broader application in public bicycle-sharing systems utilized by a diverse range of riders. Full article

Customer churn prediction (CCP) implies the deployment of data analytics and machine learning (ML) tools to forecast the churning customers, i.e., probable customers who may remove their subscriptions, thus allowing the companies to apply targeted customer retention approaches and reduce the customer attrition rate. This predictive methodology improves active customer management and provides enriched satisfaction to the customers and also continuous business profits. By recognizing and prioritizing the relevant features, such as usage patterns and customer collaborations, and also by leveraging the capability of deep learning (DL) algorithms, the telecom companies can develop highly robust predictive models that can efficiently anticipate and mitigate customer churn by boosting retention approaches. In this background, the current study presents the Archimedes optimization algorithm-based feature selection with a hybrid deep-learning-based churn prediction (AOAFS-HDLCP) technique for telecom companies. In order to mitigate high-dimensionality problems, the AOAFS-HDLCP technique involves the AOAFS approach to optimally choose a set of features. In addition to this, the convolutional neural network with autoencoder (CNN-AE) model is also involved for the churn prediction process. Finally, the thermal equilibrium optimization (TEO) technique is employed for hyperparameter selection of the CNN-AE algorithm, which, in turn, helps in achieving improved classification performance. A widespread experimental analysis was conducted to illustrate the enhanced performance of the AOAFS-HDLCP algorithm. The experimental outcomes portray the high efficiency of the AOAFS-HDLCP approach over other techniques, with a maximum accuracy of 94.65%. Full article

Telecommunication stations, particularly operating in rural areas, are usually powered by diesel generators due to the lack of access to the utility grid. However, the growing cost of energy due to the constantly increasing fuel prices and the related greenhouse gas emissions contributing to global warming have driven telecom companies to seek better energy management solutions. In this paper, we study the economic feasibility of an environmentally friendly power supply system for rural telecommunication station in the city of Skikda, northeast Algeria. The proposed system is a standalone hybrid PV–wind system with pre-existing diesel generators and battery storage. Different system configurations are considered in the study: (a) diesel generators only, (b) PV–diesel–battery, (c) wind–diesel–battery, (d) PV–wind–diesel–battery, and lastly (e) PV–wind–battery; this helps to select the optimal solution based on the lowest net present cost (NPC) and the cost of energy (COE) of each configuration. The optimization is performed using HOMER PRO software 3.14.2 version. The results showed that a hybrid system of 5 kW DG, 3.81 kW of PV capacity, three wind turbines, and a 14-battery bank is the best design for the proposed power system with an NPC of USD 85673 and a COE of USD 0.214. The greenhouse gas emissions were considerably reduced by more than half making the proposed system a technically, economically, and environmentally viable solution. Full article

The role of business intelligence in driving strategic planning in organizations have received considerable attention from many scholars. Nonetheless, there remains a promising area for further research, especially when considering moderating variables on effects such as knowledge management, which has contributed to businesses’ appreciation of the importance of business intelligence. To this end, in this study, the researchers constructed a conceptual model based on existing literature by incorporating relevant research variables. A questionnaire survey was conducted among a random sample of 307 employees selected from three telecom companies in Jordan. The researchers then utilized structural equation modeling with AMOS 21.0 to validate and test the model. The findings of the study revealed that business intelligence has a significant positive influence on strategic foresight. Furthermore, the analysis indicated that knowledge management mediates the relationship between business intelligence and strategic foresight. The implications and recommendations of academic research are also discussed. Full article

In an era marked by increasing concerns about environmental sustainability, the telecommunications industry faces a pressing need to examine its commitment to sustainable development practices. Therefore, this study investigated the drivers and constraints influencing the adoption of such practices within the industry, with particular emphasis on the roles and interactions of ecosystem players. The research employed structural equation modeling (SEM) in AMOS to test the hypotheses and multilayer perceptron (MLP), which is an artificial neural network model, to assess the importance of each variable in the context of sustainable development adoption (SDA). This study analyzed data obtained from a diverse sample of telecommunications professionals, including telecom operators, device manufacturers, technology providers, and content and service providers. The findings reveal that stakeholder expectations held the highest normalized importance, suggesting their paramount influence in driving sustainable practices within the industry. Competitive advantage emerged as the second most significant factor, contributing to the adoption of sustainable strategies by companies. Conversely, cost and ROI concerns presented a constraint that potentially hindered SDA. This research contributes to the comprehensive understanding of sustainable development in the high-tech sector, aiding industry practitioners and policymakers in fostering a more sustainable future for the telecommunications industry. The implications derived from the sensitivity analysis provide valuable insights into prioritizing efforts and resources to enhance sustainable development adoption in the telecommunications sector. Full article

Knowledge plays a pivotal role as a strategic asset for organizations that aim to improve and sustain competitive advantage. Despite the implementation of knowledge management systems to promote knowledge sharing, many employees exhibit knowledge-hiding behavior, deliberately withholding crucial information in the workplace. In this context, the current study aims to investigate the impact of knowledge-hiding behavior on entrepreneurial orientation (EO) within organizations. Specifically, we seek to explore how knowledge hiding influences employees’ inclination towards entrepreneurial behaviors such as innovation, risk-taking, and proactiveness. By examining the potential negative effects of knowledge hiding on entrepreneurial behaviors, we aim to identify barriers to innovation and risk taking in organizations. Furthermore, we examine the mediating role of factual autonomy in the relationship between knowledge hiding and entrepreneurial orientation. Understanding the mediating role of factual autonomy can provide valuable insights into the mechanisms through which knowledge hiding impacts entrepreneurial behavior. Additionally, we aimed to investigate the impact of knowledge hiding on organizational-level outcomes, specifically entrepreneurial orientation, and job autonomy. To investigate this phenomenon, we conducted a cross-sectional multilevel study involving 214 employees from 16 different companies in the Romanian business sector, including telecom, banking, retail, services, and IT&C. Our findings reveal that knowledge hiding has a significant impact on job autonomy and entrepreneurial orientation. The proposed model accounted for 45.9% of the variance in entrepreneurial orientation and 37.7% of the variance in job autonomy. These results have important implications for both theory and practice, highlighting the need for further exploration into how knowledge hiding impacts different aspects of organizational work design. The present examination serves as a valuable research platform for understanding the multidimensional irregularities within organizations and highlights the importance of addressing knowledge hiding behavior to foster a culture of innovation and risk-taking in organizations. Full article