Search Results (3,598)

In the big data era, understanding and influencing consumer behavior in digital marketing increasingly relies on large-scale data and AI-driven analytics. This narrative, concept-driven review examines how big data technologies and machine learning reshape consumer behavior analysis across key decision-making areas. After outlining the theoretical foundations of consumer behavior in digital settings and the main data and AI capabilities available to marketers, this paper discusses five application domains: personalized marketing and recommender systems, dynamic pricing, customer relationship management, data-driven product development and fraud detection. For each domain, it highlights how algorithmic models affect targeting, prediction, consumer experience and perceived fairness. This review then turns to synthetic data as a privacy-oriented way to support model development, experimentation and scenario analysis, and to dark data as a largely underused source of behavioral insight in the form of logs, service interactions and other unstructured records. A discussion section integrates these strands, outlines implications for digital marketing practice and identifies research needs related to validation, governance and consumer trust. Finally, this paper sketches future directions, including deeper integration of AI in real-time decision systems, increased use of edge computing, stronger consumer participation in data use, clearer ethical frameworks and exploratory work on quantum methods. Full article

As a vital component of urban tourism, urban theme parks increasingly face experience homogenization and intensifying competition. Accordingly, the implementation of refined digital marketing and operational strategies based on visitor digital behavior has become increasingly essential. In this context, tourists’ social media sharing has become a crucial link between destination marketing and visitors’ experience construction. Within the SOBC (Stimulus–Organism–Behavior–Consequence) framework, this study examines how theme park servicescapes (S) shape sharing motivations (O), which, in turn, influence multimodal sharing intentions (B—text, image + text, video) and subsequently contribute to memorable theme park experience (C). A two-stage, mixed-method design was employed, and the study considered visitors to Beijing Universal Studios and Shanghai Disney Resort. Semi-structured interviews and grounded analysis identified five motivations: altruism, self-presentation, affective expression, hedonic motivation, and community identification. Testing was performed using a survey (N = 604), along with structural equation modeling. The findings indicate that the staff-related social environment exerts significant positive effects on all five motivations, whereas the effects of the physical environment are more selective. Motivations differentially predict modal intentions: text aligns with altruism and affective expression; image + text aligns with altruism, community identification, and self-presentation; and video aligns with self-presentation, hedonism, community identification, and affective expression. All three intentions positively affect memorable theme park experience. These results clarify how motivations map onto content forms and validate a support SOBC framework from servicescapes to memorable experience, offering actionable implications for experience design and digital marketing. Full article

Extreme movements in financial time series pose challenges for risk management and forecasting, particularly when their timing is irregular and difficult to anticipate. This study aims to develop a probabilistic framework for detecting and predicting such events using daily Bitcoin returns as a case study. We first identify extreme positive and negative return events using the Isolation Forest algorithm and estimate their empirical recurrence patterns using a dynamic frequency table to derive baseline parametric probabilities. A 7-day Hawkes excitation kernel is then applied to capture short-run self-exciting dynamics, and both components are integrated using logistic regression to produce real-time probability forecasts. The results show that positive events occur more frequently than negative ones and that prediction accuracy improves over time: Brier scores, which measure the accuracy of probabilistic predictions, decrease as additional event data accumulate, and log loss values exhibit a consistent downward trend. Overall, by combining anomaly detection, empirical inter-arrival estimation, and excitation dynamics into a unified structure, the proposed framework offers a transparent and adaptable tool for forecasting extreme events in the financial market. Full article

A wide range of factors affect the dynamic and complex environment that is the commodity market. The most significant of these are external drivers, such as political decisions and weather conditions, which cannot be directly controlled. Nevertheless, specific characteristics and price behaviors are exhibited by individual commodities, which manifest through seasonal patterns and characteristic fluctuations. This study aimed to analyze the day-ahead electricity market and identify the key factors shaping electricity price formation. Particular focus was given to the role of meteorological variables and the interrelationships between the prices of other commodities, such as natural gas, coal, and oil. The analysis combined empirical techniques, such as Fourier transform and correlation analysis, with a predictive LSTM model using a Seq2Seq architecture to forecast short-term electricity prices. A basic forecast of electricity prices in the day-ahead market was provided by a simple predictive model that was developed based on the findings. The results highlight the interconnectedness of energy markets and confirm that external factors play a crucial role in shaping electricity prices. Full article

The food processing industry is undergoing a profound transformation with the integration of Artificial Intelligence (AI), evolving from traditional automation to intelligent, adaptive systems aligned with Industry 5.0 principles. This review examines AI’s role across the food value chain, including supply chain management, quality control, process optimization in key unit operations, and emerging areas. Recent advancements in machine learning (ML), computer vision, and predictive analytics have significantly improved detection in food processing, achieving accuracy exceeding 98%. These technologies have also contributed to energy savings of 15–20% and reduced waste through real-time process optimization and predictive maintenance. The integration of blockchain and Internet of Things (IoT) technologies further strengthens traceability and sustainability across the supply chain, while generative AI accelerates the development of novel food products. Despite these benefits, several challenges persist, including substantial implementation costs, heterogeneous data sources, ethical considerations related to workforce displacement, and the opaque, “black box” nature of many AI models. Moreover, the effectiveness of AI solutions remains context-dependent; some studies report only marginal improvements in dynamic or data-poor environments. Looking ahead, the sector is expected to embrace autonomous manufacturing, edge computing, and bio-computing, with projections indicating that the AI market in food processing could approach $90 billion by 2030. Full article

In the face of escalating energy demand, this research proposes a demand-side management (DSM) strategy that focuses on appliance-level load shifting in residential environments. The proposed approach utilizes detailed energy consumption forecasts that are generated by ensemble machine learning models, which predict usage at both whole-household and individual appliance levels. This granular forecasting enables the development of customized load-shifting schedules for controllable devices. These schedules are optimized using a metaheuristic genetic algorithm (GA) with the objectives of minimizing consumer energy costs and reducing peak demand. The iterative nature of GA allows for continuous fine-tuning, thereby adapting to dynamic energy market conditions. The implemented DSM technique yields significant results, successfully reducing the daily energy consumption cost for shiftable appliances. Overall, the proposed system decreases the per-day consumer electricity cost from 237 cents (without DSM) to 208 cents (with DSM), achieving a 12.23% cost saving. Furthermore, it effectively mitigates peak demand, reducing it from 3.4 kW to 1.2 kW, which represents a substantial 64.7% reduction. These promising outcomes demonstrate the potential for substantial consumer savings while concurrently enhancing the overall efficiency and reliability of the power grid. Full article

The SENDER (Sustainable Consumer Engagement and Demand Response) project aims to develop an innovative interface that engages energy consumers in Demand Response (DR) programs by developing new technologies to predict energy consumption, enhance market flexibility, and manage the exploitation of Renewable Energy Sources (RES). The current paper presents a replication study for consumer Digital Twins (DTs) that simulate energy consumption patterns and occupancy behaviors in various households across three pilot sites (Austria, Spain, Finland) based on six-month historical and real-time data related to loads, sensors, and relevant details for every household. Due to data limitations and inhomogeneity, we conducted a replication analysis focusing only on Austria and Spain, where available data regarding power and motion alarm sensors were sufficient, leading to a replication scenario by gradually increasing the number of households. In addition to limited data and short time of measurements, other challenges faced included inconsistencies in sensor installations and limited information on occupancy. In order to ensure reliable results, data was filtered, and households with common characteristics were grouped together to improve accuracy and consistency in DT modeling. Finally, it was concluded that a successful replication procedure requires sufficient continuous, frequent, and homogeneous data, along with its validation. Full article

To address the high volatility of spot market prices and the feature extraction limitations of single models, a short-term electricity price forecasting method based on a reinforcement learning dynamic soft ensemble strategy is proposed. First, a complementary dual-branch architecture is constructed: the CNN-LSTM-Attention branch mines local temporal features, while the Transformer branch captures long-range global dependencies. Second, the Q-learning algorithm is introduced to model weight optimization as a Markov Decision Process. An intelligent agent perceives fluctuation states to adaptively allocate weights, overcoming the rigidity of traditional ensembles. Case studies on PJM market data demonstrate that the proposed model outperforms advanced benchmarks in MAE and RMSE metrics. Notably, prediction accuracy is significantly improved during price spikes and negative price periods. The results verify that the strategy effectively copes with market concept drift, supporting reliable bidding and risk mitigation. Full article

The increasing penetration of renewable energy introduces significant challenges to grid stability and economic performance due to the intermittent and non-dispatchable nature of solar and wind generation. These fluctuations contribute to grid congestion, frequency control issues, and price volatility, reducing revenue predictability for renewable producers. It is then clear that the challenge of energy transition can be addressed by making the introduction of renewable sources into the electricity grid sustainable. Battery Energy Storage Systems (BESSs) have emerged as a flexibility resource providing time-shifting, frequency and voltage support, congestion management, and energy arbitrage. In response, several Transmission System Operators (TSOs), such as Terna in Italy in cooperation with photovoltaic (PV) and wind power producers, have initiated flexibility projects. However, these projects are limited and should be accompanied by liberalization measures that allow BESSs to be economically sustainable only under market conditions. This study evaluates the techno-economic feasibility of utility-scale BESSs either integrated into large PV/wind farms or stand-alone for providing grid flexibility services and profit increase for the producers. Both market conditions and TSO incentives will be considered. A two-step mixed integer linear (MILP) optimization approach is employed: first, an optimization schedules BESS charge and discharge operations based on historical generation and market data; second, the Net Present Value (NPV) is maximized to determine optimal system sizing and profit. The model is validated through real case studies and sensitivity analyses including BESS degradation, market volatility, and regulatory factors. The developed model is ultimately applied to compare the study cases, and the analysis shows that, under specific conditions, the arbitrage of a stand-alone BESS can be as profitable as the incentives offered by TSOs. Full article

This study investigates the use of DFLSS to mitigate customer churn in a prominent telecommunications provider facing challenges from competitive pricing, regulatory changes, and evolving customer expectations. Employing the DMADV methodology, the research developed a proactive retention strategy using techniques such as propensity modeling, customer segmentation, and predictive analytics to identify churn drivers. Targeted interventions, which include future-dated loyalty discounts, outbound retention campaigns, and process optimization through DOEs were implemented and pilot-tested. The pilot involved approximately 5000 high-risk customers per month, resulting in a 6% increase in customers under contract, a 2% improvement in rates, and a 6% reduction in repeat call rates, equating to 2880 fewer calls annually. Financially, the strategy preserved an estimated 10% in revenue over 12 months, while operational enhancements delivered a 2% cost reduction annually through reduced repeat calls. These findings highlight the importance of proactive outreach and continuous improvement in managing churn. Limitations of this study include the narrow market scope and the need for broader validation. The research contributes to the limited literature on LSS in Western telecom markets and provides a replicable model for practitioners. Future work may explore integrating artificial intelligence to enhance churn prediction and retention strategies. Full article

This study examines how key organizational resources shape work–life balance (WLB), behavioural work–life conflict (BWLC), and work engagement (WE) among employees in the private sector. Drawing on the Job Demands–Resources (JD-R) model and the Conservation of Resources (COR) theory, we test an integrated framework in which leader support, co-worker support, and family-friendly policies predict WLB and BWLC, which in turn influence work engagement. Data collected from employees in Slovenian private-sector organizations were analyzed using structural equation modelling. The results show that leader support, co-worker support, and family-friendly policies significantly enhance WLB, with leader support demonstrating the strongest effect. BWLC is negatively associated with WLB, confirming that behavioural spillover between domains diminishes employees’ perceived balance. Leader support is the only organizational resource that significantly reduces BWLC, while co-worker support and family-friendly policies show no direct effect. Furthermore, WLB is a strong positive predictor of work engagement, whereas BWLC does not directly predict WE. These findings highlight the importance of work–life balance for understanding the relationship between organizational resources and work engagement, and they underscore the crucial role of leader behaviour in shaping boundary management. The findings should be interpreted within the context of Slovenian private-sector organizations and comparable regulated labour-market settings. Full article

This paper examines bank failures during the subprime mortgage crisis, emphasizing sibling dynamics within multi-bank holding companies (MBHCs). While traditional risk indicators effectively predict failures for one bank holding companies (OBHCs), they exhibit limited explanatory power for MBHCs, where internal capital markets and interdependencies across affiliates shape risk outcomes. We extend the standard failure framework by incorporating group-level characteristics that capture sibling network structure and the distribution of risk across affiliates. Using pre-crisis data from 2006 to 2007, we show that group structure significantly influences failure risk. Larger sibling networks reduce individual bank failure risk through diversification, while greater size dispersion across affiliates increases vulnerability by constraining internal resource allocation. Beyond these aggregate effects, we introduce a weakest link approach that identifies the most distressed affiliate based on extreme tail risk in capitalization, asset quality, liquidity, earnings, and income volatility, capturing organizational fragility that aggregate measures miss. Concentrated vulnerabilities at a single affiliate significantly amplify failure risk throughout the holding company, even after controlling for traditional bank-level fundamentals and parent-level characteristics. These findings, derived from the 2007–2010 crisis, a severe stress test of holding company structures, identify organizational dynamics: resource competition among siblings and concentrated vulnerabilities at the weakest affiliate. Supervisory frameworks should explicitly account for within-group interdependencies rather than relying solely on individual bank metrics or aggregate indicators when monitoring bank holding company structures. Full article

In recent years, the green financial market has been exhibiting heightened volatility daily, largely due to policy changes and economic shifts. To explore the broader potential of predictive modeling in the context of short-term volatility time series, this study analyzes how fine-tuning hyperparameters in predictive models is essential for improving short-term forecasts of market volatility, particularly within the rapidly evolving domain of green financial markets. While traditional econometric models have long been employed to model market volatility, their application to green markets remains limited, especially when contrasted with the emerging potential of machine-learning and deep-learning approaches for capturing complex dynamics in this context. This study evaluates the performance of several data-driven forecasting models starting with machine-learning models: regression tree (RT) and support vector regression (SVR), and with deep-learning ones: long short-term memory (LSTM), convolutional neural network (CNN), and gated recurrent unit (GRU) applied to over a decade of daily estimated volatility data coming from three distinct green markets. Predictive accuracy is compared both with and without hyperparameter optimization methods. In addition, this study introduces the quantile loss metric to better capture the skewness and heavy tails inherent in these financial series, alongside two widely used evaluation metrics. This comparative analysis yields significant numerical and graphical insights, enhancing the understanding of short-term volatility predictability in green markets and advancing a relatively underexplored research domain. The study demonstrates that deep-learning predictors outperform machine-learning ones, and that including a hyperparameter tuning algorithm shows consistent improvements across all deep-learning models and for all volatility time series. Full article

Understanding high-dimensional dependencies in modern financial systems requires time series models that capture both contemporaneous and dynamic linkages. This study develops a sparse spatio-temporal vector autoregressive framework to analyse the network structure of the Korean won exchange rate against 36 major trading-partner currencies. The model combines the generalised Yule–Walker equations with structured penalisation to jointly estimate instantaneous and lagged interactions in a data-driven manner. This approach allows for the recovery of economically meaningful spillover networks while maintaining tractability in high dimensions. Using daily data from 2019 to 2023, the results reveal pronounced contemporaneous spillovers among currencies closely tied to Korea’s trade and financial networks, notably the U.S. dollar, Chinese yuan, Japanese yen, and key ASEAN currencies. Monte Carlo simulations confirm the estimator’s consistency and convergence properties, while empirical forecasting exercises demonstrate systematic improvements in both mean-squared and robust error metrics relative to benchmark VAR and spatial autoregressive models. The evidence highlights that modelling sparse, high-dimensional time series structures enhances predictive accuracy and interpretability, particularly under nonstationary and heterogeneous conditions. The proposed framework provides a flexible tool for exploring interconnected time series in economics and finance, offering new insights into exchange-rate linkages and risk transmission in globally integrated markets. Full article

The development and application of therapeutic oligonucleotides, such as siRNA, miRNA, ASOs and aptamers, is a rapidly growing field in biomedicine. These molecules are undergoing extensive preclinical and clinical testing, and the market for synthetic RNA drugs is expanding. However, several challenges remain, including targeted delivery and high costs associated with development, screening and production. One significant advance has been the creation of GalNAc-conjugates, which selectively target ASGPR and deliver oligonucleotides to hepatocytes. Although these conjugates have shown promising results, their widespread use is limited by the lack of effective synthesis methods. Thus, the development of new methods for the synthesis of ligand-oligonucleotide conjugates is an important task to which this study is devoted. In this study, we created a library of siRNA conjugates with the GalNAc L-96 ligand to suppress the expression of the PCSK9 gene associated with elevated LDL and an increased risk of developing cardiovascular diseases. The selection of the most effective siRNA molecules was carried out using an algorithm previously developed by our research group, which considers thermodynamic stability, predicted specificity and effectiveness. To experimentally confirm the effectiveness of conjugates, an in vitro model based on the cultivation of hepatocyte cells was developed. Optimization of the conjugate synthesis process has significantly reduced the cost of manufacturing technology, which creates the potential for efficient scaling of synthesis for transfer and application in the pharmaceutical industry. The results of the study showed that the development of the siRNA sequence optimized in silico resulted in a significant increase in the inhibitory effect of the GalNAc-siRNA conjugate compared to a compound similar to a commercial drug. Full article