Search Results (56)

This study presents a computational analysis of industry-specific advertising message strategies through the theoretical lens of the FCB (Foote, Cone & Belding) grid framework. Leveraging the AiSAC (AI Analysis System for Ad Creation) system developed by the Korea Broadcast Advertising Corporation (KOBACO), we analyzed 27,000 Korean advertisements across five major industries using advanced machine learning techniques. Through Latent Dirichlet Allocation topic modeling with a coherence score of 0.78, we identified five distinct message strategies: emotional appeal, product features, visual techniques, setting and objects, and entertainment and promotion. Our computational analysis revealed that each industry exhibits a unique “message strategy fingerprint” that significantly discriminates between categories, with discriminant analysis achieving 62.7% classification accuracy. Time-series analysis using recurrent neural networks demonstrated a significant evolution in strategy preferences, with emotional appeal increasing by 44.3% over the study period (2015–2024). By mapping these empirical findings onto the FCB grid, the present study validated that industry positioning within the grid’s quadrants aligns with theoretical expectations: high-involvement/think (IT and Telecom), high-involvement/feel (Public Institutions), low-involvement/think (Food and Household Goods), and low-involvement/feel (Services). This study contributes to media science by demonstrating how computational methods can empirically validate the established theoretical frameworks in advertising, providing a data-driven approach to understanding message strategy patterns across industries. Full article

Churn prediction has become one of the core concepts in customer relationship management within the insurances, telecom, and internet service provider industries, which is essential in customer retention. Therefore, this study attempts to analyze the effectiveness of the advanced machine learning and deep learning models for churn prediction in the evaluation of the models’ performance across different sectors. This would help conclude whether the varied patterns of the churn throughout different sectors to the level that affects the model performance and to what extent. The work includes three datasets: namely, insurance churn, internet service provider customer churn, and Telecom churn datasets. The implementation and comparison conducted in this study of models include XGBoost, Convolutional Neural Networks (CNNs), and Ensemble Deep Learning with the pre-trained hybrid approach. The results show that the ensemble deep learning model outperforms other models in terms of accuracy and F1-score, achieving accuracies of up to 95.96% in the insurance churn dataset and of 98.42% in the telecom churn dataset. Moreover, traditional machine learning models like XGBoost also produced competitive results for selected datasets. The proposed deep learning ensembles reveal the strength and possibility for churn prediction and provide a benchmark for future research relevant to customer retention strategies. Also, the proposed ensemble deep learning model shows stable performance across different sectors, which reflects its ability to capture the varied churn patterns of different sectors. Full article

This paper investigates the spillover effects and transmission networks of systemic risk within China’s national economic sectors under extreme conditions from both time and frequency domain perspectives, building upon the spillover index methodology and calculating the ∆CoVaR index for Chinese industries. The findings indicate the following: (1) Extreme-risk spillovers synchronize across industries but exhibit pronounced time-varying peaks during the 2008 Global Financial Crisis, the 2015 crash, and the COVID-19 pandemic. (2) Long-term spillovers dominate overall connectedness, highlighting the lasting impact of fundamentals and structural linkages. (3) In terms of risk volatility, Energy, Materials, Consumer Discretionary, and Financials are most sensitive to systemic market shocks. (4) On the risk spillover effect, Consumer Discretionary, Industrials, Healthcare, and Information Technology consistently act as net transmitters of extreme risk, while Energy, Materials, Consumer Staples, Financials, Telecom Services, Utilities, and Real Estate primarily serve as net receivers. Based on these findings, the paper suggests deepening the regulatory mechanisms for systemic risk, strengthening the synergistic effect of systemic risk measurement and early warning indicators, and coordinating risk monitoring, early warning, and risk prevention and mitigation. It further emphasizes the importance of avoiding fragmented regulation by establishing a joint risk prevention mechanism across sectors and departments, strengthening the supervision of inter-industry capital flows. Finally, it highlights the need to closely monitor the formation mechanisms and transmission paths of new financial risks under the influence of the pandemic to prevent the accumulation and eruption of risks in the post-pandemic era. Authorities must conduct annual “Industry Transmission Reviews” to map emerging risk nodes and supply-chain vulnerabilities, refine policy tools, and stabilize market expectations so as to forestall the build-up and sudden release of new systemic shocks. Full article

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

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

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

The aspiration of this paper is to examine the impact of corporate social responsibility (CSR) and service quality on customer loyalty and their relationship in the Indian telecommunication industry. A model was proposed and a total of 377 responses were collected using a structured questionnaire. Data were assessed and analyzed using PLS SEM. Multi-group analysis (MGA) was carried out to comprehend the moderating effect of gender, age, education, and income within the model. The results suggest that CSR does not have a direct impact on customer loyalty (CL), but there is an indirect effect when it is mediated through customer satisfaction (CS) and trust (Tr). Service quality (SQ) was found to have a direct impact on CL and while it is also mediated through CS. The results of the MGA revealed that customer satisfaction increases commitment towards customer loyalty and trust among male users. This study highlights that the modern customers are knowledgeable, more aware, and value companies which are focused on CSR activities. Full article

The telecommunication industry relies heavily on a reliable and continuous power supply. Traditional power sources like diesel generators have long been the backbone of telecom infrastructure. However, the growing demand for sustainable and eco-friendly solutions has spurred interest in renewable energy sources. Proton exchange membrane (PEM) fuel cell-based systems, integrated with solar and wind energy, offer a promising alternative. This review explores the potential of these hybrid systems in stationary telecom applications, providing a comprehensive overview of their architecture, energy management, and storage solutions. As the demand for telecommunication services grows, so does the need for a reliable power supply. Diesel generators are linked with high operational costs, noise pollution, and significant greenhouse gas emissions, prompting a search for more sustainable alternatives. This review analyzes the current state of PEM fuel cell systems in telecom applications, examines the architecture of microgrids incorporating renewable energy sources, and discusses optimization methods, challenges, and future directions for energy storage systems. Critical findings and recommendations are presented, highlighting objectives and constraints for future developments. Leveraging these technologies can help the telecom industry reduce fossil fuel reliance, lower operational costs, minimize environmental impact, and increase system reliability. Full article

In response to escalating concerns about climate change, there is a growing imperative to prioritize the decarbonization of the telecom sector and effectively reduce its carbon emissions. This study presents a thorough techno-economic optimization framework for implementing renewable-dominated hybrid standalone systems for the base transceiver station (BTS) encapsulation telecom sector in Pakistan. It is noted that from the results obtained from 42 BTS sites overall, 21 BTS sites had a feasible combination of a photovoltaic battery system, having a diesel generator as a backup source with an average LCOE of 0.1246 USD/kWh to 0.2325 USD/kWh. Thus, seven BTS sites had an optimal combination of biomass, with photovoltaic and battery storage systems and with a varied LCOE of 0.1175 USD/kWh to 0.1318 USD/kWh. Moreover, due to the high flow of hydro water in the north region, five BTS sites presented an ideal configuration of a hydro system coupled with a photovoltaic, wind, and battery storage system, with a varied LCOE of 0.04547 USD/kWh to 0.07419 USD/kWh. Wind energy systems are dominant in the southern region; therefore, five BTS sites presented an ideal combination of a wind energy system coupled with a photovoltaic battery storage system, having DGs as backup sources for sustainability and with a varied LCOE of 0.1096 USD/kWh to 0.1294 USD/kWh. In addition, 02 BTSs had an optimal combination of photovoltaic systems coupled with hydro and wind systems, with diesel generators having a varied LCOE of 0.07618 USD/kWh to 0.04575 USD/kWh. The remaining 02 BTS sites had a feasible combination of wind–hydro-battery and diesel generator–photovoltaic–hydro-battery systems, with an LCOE of 0.7035 USD/kWh and 0.1073 USD/kWh, respectively. Finally, an environmental analysis based on carbon emissions, as well as sensitivity analyses based on different uncertainties, i.e., wind speed, solar irradiance, inflation rate, discount rate, and load demand, was performed to evaluate the behavior of the proposed systems. The optimization of these systems and comparative study findings indicate that the hybrid BTS system is the best option, better than conventional diesel-operated BTS systems in terms of cost-effectiveness, environmental friendliness, and sustainability. Full article

The development of unique optical materials that provide amplification and lasing in new wavelength ranges is a major scientific problem, the solution of which is becoming the basis for the emergence of new optical technologies, which are primarily targeting the expanding of operating wavelengths in silica glass. In fact, one of the notable advances in the field of fiber optics over the past two decades has been the production of a new type of laser-active fibers (namely bismuth-doped fibers), which has made it possible to cover previously inaccessible (for rare-earth-doped fibers) spectral ranges, in particular O-, E-, S-, and U-telecom bands. The advance in this direction has led to further growth of the technological capabilities in the telecom industry for amplification and generation of optical radiation in various wavelength bands, which will result in the near future to overcoming the problem known as “capacity crunch” by means of expanding the data transmission range. Recently, bismuth-doped fibers have been actively studying in order to improve their characteristics, which would allow for efficient implementation of optical devices based on bismuth-doped fibers (BDFs) with deployed telecommunications systems. This is one of the dynamically developing areas, where progress has already manifested in form of emergence of new achievements, in particular commercially available various types of BDFs, as well as a series of novel fiber-optic amplifiers for the O- and E-bands. In this review, a number of scientific studies that have already led to a noticeable progress in the field of optical properties of BDFs and the practical implementation of optical devices (lasers and amplifiers) based on them are presented and discussed, with much attention to the achievements of recent years. Full article

This study investigates the intricate relationships between technology application, smart human resource management (SHRM), and innovation performance within the Jordanian telecom industry. Employing a quantitative research methodology, data were collected from employees of telecommunications firms in Jordan. The results illuminate significant positive associations between technology application, SHRM, and innovation performance, elucidating the pivotal roles of technology and HRM strategies in fostering innovation and bolstering organizational success. Practical implications of the findings advocate for substantial investments in cutting-edge technologies, the integration of intelligent HRM practices, and the prioritization of continuous learning and development initiatives to nurture an innovative workforce. This research contributes to a deeper comprehension of innovation dynamics within the telecommunications sector and furnishes valuable insights for practitioners striving to elevate innovation capabilities within their respective organizations. Full article

Churn is a serious challenge for the telecommunications industry because of the much higher costs of gaining new customers than maintaining existing ones. Therefore, efforts to increase loyalty and decrease customer churn are the focus of telecom’s retention departments. In order to direct antichurn activities, profitable clients who have the highest probability of churning need to be identified. The data used to identify churners are often inaccurate and vague. In this paper, a fuzzy approach to modeling churn intent based on usage data in mobile telecommunications is presented. It appreciates the uncertainty of the data and provides insights into churn modeling. The goal of the study was to evaluate the applicability of the Mamdani and Sugeno models for building a churn model based on a limited but real-world dataset enriched with feature engineering. The additional goal was to find features most usable for churn modeling. Four metrics—accuracy, recall, precision, and F1-score—were used to estimate the performance of the models. The developed fuzzy rule-based systems show that to generalize possible churn identification factors with fuzzy rules, it is advisable to begin with features such as the change in the total amount of the invoice in the last period before the churning compared to the previous one, the total amount of the invoice in the period preceding the churning, the total amount of subscription in two months before the churning, the time of cooperation with the operator, and the number of calls out of the last quarter before leaving. Full article

The latest cellular technology, known as 5G-NR, is intended to significantly speed up and improve the effectiveness of wireless systems. A revolution in the telecom industry has been sparked by the widespread use of and increased reliance on cellular communication technology. Moreover, 5G and B5G technologies are expected to utilize an even higher-frequency range to achieve faster data transmission and lower latency communication. Consequently, while transmitting signals across various types of equipment and infrastructure, the general public is exposed to much higher frequencies of electromagnetic radiation. The increasing need for 5G NR base stations (gNodeB) has heightened public anxiety over potential negative health impacts. This study reviews recent research on the effects of electromagnetic waves on humans, particularly focusing on how these effects influence cognitive functions. Most research to date has not found significant differences in cognitive performance due to ubiquitous mobile communications. However, current research has largely been limited to 4G technologies, and the health effects of exposure to 5G user equipment (UE) and base stations in higher-frequency bands remain unexplored. If subsequent research suggests that exposure to high-frequency wireless networks significantly impacts cognitive functions, the deployment and acceptance of these technologies may face challenges and constraints. Therefore, such investigations are crucial for determining whether next-generation technologies pose no risk to individuals. 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