This study investigates factors influencing employees' perceptions of the usefulness of Business Process Management Systems (BPMS) in commercial settings. It explores the roles of system dependency... Show More

This study investigates factors influencing employees' perceptions of the usefulness of Business Process Management Systems (BPMS) in commercial settings. It explores the roles of system dependency, system quality, and the quality of information and knowledge in the adoption and use of BPMS. Data were collected using a structured questionnaire from end-users in various firms and analyzed with Partial Least Squares (PLS). The survey evaluated perceptions of service quality, input quality, system attributes, and overall system quality. The findings indicate that service quality, input quality, and specific system attributes significantly influence perceived system quality, while system dependency and information quality are predictors of perceived usefulness. The results highlight the importance of user training, support, and high-quality information in enhancing satisfaction and BPMS. This research offers empirical evidence on the factors impacting user perceptions and acceptance, emphasizing the need for user-centric approaches in BPMS.

Risk prediction has become increasingly crucial in today's complex and dynamic environments. However, existing forecasting methods still face challenges in terms of accuracy and reliability.... Show More

Risk prediction has become increasingly crucial in today's complex and dynamic environments. However, existing forecasting methods still face challenges in terms of accuracy and reliability. Therefore, it is imperative to explore new approaches to better address risks. In response to this need, our study introduces an innovative risk prediction model known as WOA-FPALSTM. What sets this model apart is its seamless integration of deep learning and heuristic algorithms, designed to overcome the limitations of existing approaches. The core component of deep learning, LSTM, excels in sequence modeling by capturing long-term and short-term dependencies in time series data, thereby enhancing the model's ability to model temporal data. Meanwhile, the heuristic algorithm, WOA (Whale Optimization Algorithm), equips our model with global search capabilities, facilitating the discovery of optimal model configurations and significantly improving predictive performance.

This study builds a research model based on sense-making theory and Dervin's sense-making model of 'gap-bridge-uses' to explore the relationship between users' fear of missing out (FoMO) in relation... Show More

This study builds a research model based on sense-making theory and Dervin's sense-making model of 'gap-bridge-uses' to explore the relationship between users' fear of missing out (FoMO) in relation to charity crowdfunding performance, and collected a set of 230 data of users from the famous charitable crowdfunding campaign on TikTok. The research revealed that FoMO could predict approach behaviour towards charitable crowdfunding. And collective psychological ownership (CPO) mediated the effect of FoMO on approach behaviour towards charitable crowdfunding, and psychological resilience (PR) positively moderated the mediating effect of CPO on approach behaviour. This study not only enriched the research about information interaction and sense-making process between users and charitable crowdfunding, but also extended the research boundary of the relationship between FoMO and approach behaviour towards charitable crowdfunding.

This paper proposes a smart learning system built on deep learning and augmented reality (AR) to support employees with practical IoT experimentation, from components and circuit board pin... Show More

This paper proposes a smart learning system built on deep learning and augmented reality (AR) to support employees with practical IoT experimentation, from components and circuit board pin connections to programming and IoT control. For instance, employees can use their mobile phones to capture images of electronic components and access AR-enhanced instructional materials for learning component properties. This AR-assisted learning system offers guidance at each experimental stage to support hands-on practice and troubleshooting. The learning system also incorporates the pair programming teaching method to enhance experimental quality and confidence, enabling employees to collaborate with teammates throughout the process. The system is further equipped with an online whiteboard for Q&A and in-depth theoretical exploration at each stage of the experiment. Additionally, a blockchain platform records and analyzes each employee's learning progress and status, providing a comprehensive view of their development.

This paper proposes an emotion-aware personalized recommendation system (EPR-IoT) based on IoT data and multimodal emotion fusion, aiming to address the limitations of traditional recommendation... Show More

This paper proposes an emotion-aware personalized recommendation system (EPR-IoT) based on IoT data and multimodal emotion fusion, aiming to address the limitations of traditional recommendation systems in capturing users' emotional states of artistic product consumption in real time. With the proliferation of smart devices, physiological signals such as heart rate and skin conductance—which are strongly correlated with emotional states—provide new opportunities for emotion recognition. For example, an increase in heart rate is typically associated with emotions like anxiety, anger, or fear, while a decrease is linked to emotional states like relaxation or joy. Similarly, skin conductance rises with emotional arousal, particularly during stress or fear. These physiological signals, combined with text, speech, and video data of art products, are fused to construct an art emotion-driven recommendation model capable of dynamically adjusting the recommended content.

In domains such as e-commerce and media recommendations, personalized recommendation systems effectively alleviate the issue of information overload. However, existing systems still face challenges... Show More

In domains such as e-commerce and media recommendations, personalized recommendation systems effectively alleviate the issue of information overload. However, existing systems still face challenges in multimodal data processing, data sparsity, and dynamic changes in user preferences. This paper proposes a Hierarchical Generative Reinforcement Learning Recommendation Optimization framework (HG-RLRO) that addresses these issues by integrating multimodal data, Generative Adversarial Networks (GAN), Inverse Reinforcement Learning (IRL), and Hierarchical Temporal Difference Learning (HTD). HG-RLRO employs a multi-agent architecture to handle textual and image data and utilizes GAN to generate simulated user behavior data to mitigate data sparsity. IRL dynamically infers user preferences across multiple time scales.

In response to the challenges posed by globalization and rapid technological advancements, traditional static pricing models are no longer sufficient to capture the dynamic nature of consumer... Show More

In response to the challenges posed by globalization and rapid technological advancements, traditional static pricing models are no longer sufficient to capture the dynamic nature of consumer behavior and market fluctuations. This study proposes a “Multi-dimensional Dynamic Pricing Optimization and Consumer Behavior Prediction Model Driven by Big Data,” which integrates multi-source data and reinforcement learning to improve dynamic pricing strategies. Through a hybrid model architecture using Random Forest and LSTM, it captures both static and time-series features. Experimental results show that the proposed model significantly outperforms baseline models, achieving a 43% reduction in Mean Squared Error (MSE), a 28% decrease in Mean Absolute Percentage Error (MAPE), a 6.5% increase in Accuracy, and a 14.7% increase in Cumulative Revenue. These findings confirm the model's ability to enhance prediction accuracy, optimize pricing strategies, and maximize revenue, demonstrating its potential for real-world applications in industries like e-commerce, finance, and advertising.

In the era of the digital economy in which disruptive information technologies such as artificial intelligence, blockchain, big data, and cloud computing prevail, digital marketing is increasingly... Show More

In the era of the digital economy in which disruptive information technologies such as artificial intelligence, blockchain, big data, and cloud computing prevail, digital marketing is increasingly becoming the focus of common attention in practice and academia. In this paper, companies listed on the GEM from 2019 to 2023 are selected as research samples to empirically test the impact and intrinsic role of digital marketing on enterprise performance. The results show that digital marketing significantly stimulates enterprise performance, and its incentive effect is mainly reflected in strategic innovation rather than substantive innovation. It is still valid after a series of endogeneity and robustness tests such as instrumental variable method and propensity score matching method. Based on the new perspective of information dynamic capability, this study opens the “black box” of digital marketing enabling enterprise performance and providing certain enlightenment for enterprises to effectively realizing the “win-win” of digital transformation and innovation ability improvement.

Predictive maintenance is gaining increasing attention in the field of industrial equipment management as an effective strategy to enhance equipment reliability and reduce maintenance costs. Deep... Show More

Predictive maintenance is gaining increasing attention in the field of industrial equipment management as an effective strategy to enhance equipment reliability and reduce maintenance costs. Deep learning has become a focal point due to its exceptional ability to process time series data and recognize complex patterns. To address challenges related to accuracy and robustness in predicting equipment failures, this study proposes a novel model that combines deep reinforcement learning (DDPG) with gated recurrent units (GRU), alongside Bayesian Optimization for hyperparameter tuning. The DDPG component learns the dynamic interactions between actions and states, adapting to the specific characteristics of different devices. The GRU module is designed to capture temporal dependencies in sensor data.

As concerns over environmental pollution and the reduction of greenhouse gas emissions intensify, sustainable strategies in supply chain transportation are critical. This paper proposes a novel... Show More

As concerns over environmental pollution and the reduction of greenhouse gas emissions intensify, sustainable strategies in supply chain transportation are critical. This paper proposes a novel approach to optimizing transportation routes and reducing carbon emissions in a green supply chain using deep reinforcement learning. The research targets a three-tier green supply chain consisting of manufacturers, third-party logistics providers (3PL), and retailers. First, a carbon reduction model for transportation is established, accounting for both product greenness and carbon emissions that influence demand. The study then introduces a Proximal Policy Optimization (PPO)-based contract model, combining cost-sharing and profit-sharing mechanisms between retailers and logistics providers to incentivize eco-friendly practices.

Against the backdrop of increasingly severe global environmental changes, accurately predicting and meeting renewable energy demands has become a key challenge for sustainable business development.... Show More

Against the backdrop of increasingly severe global environmental changes, accurately predicting and meeting renewable energy demands has become a key challenge for sustainable business development. Traditional energy demand forecasting methods often struggle with complex data processing and low prediction accuracy. To address these issues, this paper introduces a novel approach that combines deep learning techniques with environmental decision support systems. The model integrates advanced deep learning techniques, including LSTM and Transformer, and PSO algorithm for parameter optimization, significantly enhancing predictive performance and practical applicability. Results show that our model achieves substantial improvements across various metrics, including a 30% reduction in MAE, a 20% decrease in MAPE, a 25% drop in RMSE, and a 35% decline in MSE. These results validate the model's effectiveness and reliability in renewable energy demand forecasting. This research provides valuable insights for applying deep learning in environmental decision support systems.