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The global market for cloud gaming is growing rapidly. How gamers evaluate the service quality of this emerging form of cloud service has become a critical issue for both researchers and practitioners. Building on the literature on service quality and software as a service, this study develops and validates a gamer-centric measurement instrument for cloud gaming service quality.

A three-step measurement instrument development process, including item generation, scale development and instrument testing, was adopted to conceptualize and operationalize cloud gaming service quality.

Cloud gaming service quality consists of two second-order constructs of support service quality and technical service quality with seven first-order dimensions, namely rapport, responsiveness, reliability, compatibility, ubiquity, smoothness and comprehensiveness. The instrument exhibits desirable psychometric properties.

Practitioners can use this new measurement instrument to evaluate gamers' perceptions toward their service and to identify areas for improvement.

This study contributes to the service quality literature by utilizing qualitative and quantitative approaches to develop and validate a new measurement instrument of service quality in the context of cloud gaming and by identifying new dimensions (compatibility, ubiquity, smoothness and comprehensiveness) specific to it.

In smart cities striving for innovation, development, and prosperity, hydrogen offers a promising path for decarbonization. However, its effective integration into the evolving energy landscape requires understanding regional intricacies and identifying areas for improvement. This chapter examines hydrogen transport from production to utilization, evaluating technologies’ pros, cons, and process equations and using Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) tool to assess these technologies based on multiple criteria. It also explores barriers and opportunities in hydrogen transport within the 21st-century energy transition, providing insights for overcoming challenges. Evaluation criteria for hydrogen transport technologies were ranked by relative importance, with energy efficiency topping the list, followed by energy density, infrastructure requirements, cost, range, and flexibility. Safety, technological maturity, scalability, and compatibility with existing infrastructure received lower weights. Hydrogen transport technologies were categorized into three performance levels: low, medium, and high. Hydrogen tube trailers ranked lowest, while chemical hydrides, hydrail, liquid organic hydrogen carriers, hydrogen pipelines, and hydrogen blending exhibited moderate performance. Compressed hydrogen gas, liquid hydrogen, ammonia carriers, and hydrogen fueling stations demonstrated the highest performance. The proposed framework is crucial for next-gen smart cities, cutting emissions, boosting growth, and speeding up development with a strong hydrogen infrastructure. This makes the region a sustainable tech leader, improving air quality and well-being. Aligned with Gulf Region goals, it is key for smart cities. Policymakers, industries, and researchers can use these insights to overcome barriers and seize hydrogen transport tech opportunities.

The deep integration of emerging technologies such as artificial intelligence (AI) has triggered a transformation in career patterns, requiring employees to promptly adapt to changes in the vocational environment through career crafting. While human resource (HR) practices play a role in guiding employees' career self-management, further exploration is needed to determine the specific HR practices that organizations should adopt to facilitate employees' career crafting. This research takes developmental HR practices as a new premise for career crafting and examines the underlying mechanisms influencing the relationship between the two.

Using a three-stage time lag method with a two-month lag in each stage, we collected sample data from 386 employees in the intelligent manufacturing industry and employed multiple regression analysis to test the research hypotheses.

The results of this paper show that developmental HR practices positively influence employees' career crafting behaviors through their future work self and that AI awareness moderates this relationship.

Drawing upon career construction theory, we propose that developmental HR practices cultivate employees' future work self, thereby eliciting career crafting behaviors. Moreover, the mediating role of future work self is more pronounced when employees have a high level of AI awareness, as this awareness stimulates self-cognition-driven behaviors. These findings enrich the understanding of career crafting by shedding light on the career construction process in the relationship between developmental HR practices and career crafting and by identifying the boundary conditions for the relationship.

Purpose: The primary purpose is to discuss the productivity and digitalisation interaction at the theoretical level, analyse the productivity and digitalisation differences between the European Union (EU)-14 and EU-13 countries, and evaluate the digitalisation impact on the manufacturing sector labour productivity of the EU countries.

Need for study: The average added value created per capita in new EU countries (EU-13) is one-third lower than in old EU countries (EU-14). To increase productivity, manufacturing companies must adapt to modern trends and take advantage of industrial digitisation opportunities. Digitisation can improve production efficiency, reduce costs, and improve product quality, allowing continuous monitoring and analysis of production data, enabling informed decisions and faster problem-solving.

Methodology: Analysis of scientific literature, comparing viewpoints, insights, and conclusions. The empirical study includes calculating rates of change of indicators, differences between EU-14 and EU-13, and structural analysis. The impact of digitisation on the productivity of EU countries is studied by creating a correlation matrix and using regression analysis: ordinary least square models.

Findings: EU-13 countries are behind EU-14 in labour productivity and manufacturing digitalisation. Digitalisation positively impacts productivity per employee. A faster increase in digitisation, industrial robot use, and e-commerce sales could significantly increase productivity in EU-13, reducing productivity differences between countries.

Practical implications: This study highlights the need for policy promoting digitisation innovation, particularly in EU-13 countries, to be implemented by both national and EU-based economic development and regional and cohesion institutions.

The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant difference in length between the content and its title. In addition, relying solely on textual discrepancies between the title and content to distinguish between real and fake news has proven ineffective. The purpose of this paper is to develop a new approach called semantic enhancement network with content–title discrepancy (SEN–CTD), which enhances the accuracy of fake news detection.

The SEN–CTD framework is composed of two primary modules: the SEN and the content–title comparison network (CTCN). The SEN is designed to enrich the representation of news titles by integrating external information and position information to capture the context. Meanwhile, the CTCN focuses on assessing the consistency between the content of news articles and their corresponding titles examining both emotional tones and semantic attributes.

The SEN–CTD model performs well on the GossipCop, PolitiFact and RealNews data sets, achieving accuracies of 80.28%, 86.88% and 84.96%, respectively. These results highlight its effectiveness in accurately detecting fake news across different types of content.

The SEN is specifically designed to improve the representation of extremely short texts, enhancing the depth and accuracy of analyses for brief content. The CTCN is tailored to examine the consistency between news titles and their corresponding content, ensuring a thorough comparative evaluation of both emotional and semantic discrepancies.

The objectives of the proposed model are: aiding nursing staff in documentation tasks, which can be onerous and stressful; and helping management by offering an estimate of the nursing workload, which can be considered for administrative purposes, such as staff scheduling.

An exploratory-descriptive study was conducted in order to identify, investigate, and describe the problem of documenting nursing activities and workload estimation in an intensive care unit. Technological solutions were explored, and models were proposed to address these issues.

Cross-dataset experiments were performed, and the model was able to offer an adequate estimate of the nursing workload. The results suggest that continuous retraining is essential for maintaining high accuracy. While the proposed model was considered in the context of an adult ICU, it can be adapted to other contexts, such as elderly care.

While the proposed solution seems promising, further research is required, such as deploying this system in an ICU and facing challenges in the areas of computer security, medical ethics, and patient data privacy. More patients’ variables could also be collected to improve the workload estimates.

Nursing workload assessment is critical to improve the cost-benefit ratio in health care, offer high-quality patient care, and reduce unnecessary expenses, and this process is usually manual. An automated device can automatically document the amount of time spent in patient care activities in a more transparent, efficient, and accurate manner, freeing staff for more urgent activities and keeping management better informed about day-to-day nursing operations.

Current developments in the FinTech payment industry have shown a rapid revolution in Industry 4.0, and understanding the factors affecting individual acceptance of facial recognition payment (FRP) is crucial. Hence, this study aims to evaluate the benefits and risks of FRP system adoption in Malaysia.

The perceived risks and benefits framework is adopted as the foundation in this study to examine the various risks and benefits that users perceive, along with the trust factor, to study the relationships between these variables. Data were collected via an online questionnaire, and the hypotheses were tested using Partial Least Squares analysis on 277 responses.

The results revealed that perceived risk is a significant predictor of users' intention to use the FRP system. Privacy risk and financial risk significantly influence perceived risks, while security risk does not. Although convenience, perceived ease of use and perceived trust positively influence perceived benefits, perceived benefits do not significantly influence adoption intention. Moreover, perceived trust negatively affects perceived risks while positively affecting both perceived benefits and adoption intention. Additionally, personal innovativeness moderates the relationship between perceived risks and the intention to use the FRP system.

This study helps policymakers and service providers understand individuals’ concerns and expectations regarding FRP systems. It aids practitioners in developing strategies to build trust, address innovativeness differences and mitigate risks, serving as a roadmap for integrating these systems into Malaysia's financial landscape.

This study distinguishes itself from prior research by evaluating FRP system adoption in Malaysia through the lens of perceived risks and benefits framework. It also explores personal innovativeness as a moderator, examining its impact on the relationship between usage intention and perceived risks and benefits. Additionally, it highlights perceived trust as a crucial factor influencing individuals' intention to adopt FRPs.

Assisted training using upper limb rehabilitation robots is beneficial for flaccid paralysis patients in recovering their functional abilities. In the assisted training mode, the patient’s motor ability is limited by factors such as limb muscle tension, and it is prone for the rehabilitation robot to deviate from the prescribed training trajectory. A sliding mode control method based on a fixed time observer is proposed to address the problem of delayed trajectory tracking response of upper limb rehabilitation robots caused by external disturbances such as patient limbs.

First, aiming at the problem of estimating and compensating for external disturbances in the upper limb rehabilitation robot system, a fixed time observer was designed based on the robot’s dynamic model. Second, the composite sliding mode reaching law combining the smooth function and the power-exponential function is proposed to shorten the convergence time of system states in the startup phase, thereby reducing chattering in the control process and realizing the real-time tracking of the training trajectory by the control system.

The proposed method provides a solution for the trajectory tracking speed of upper limb rehabilitation robot controllers. In the circular trajectory tracking control, compared to the sliding-mode control method combined with the variable-exponential composite reaching law based on the fixed-time observer, the method in this paper reduces the time for the system state to reach the sliding surface by 0.89 s and improves the response speed by 0.66%.

The composite sliding mode approach law based on smooth function and power exponent function can reduce the time it takes for the system state to reach and remain on the sliding surface and improve the trajectory tracking speed of upper limb rehabilitation robots. This controller improves the accuracy of trajectory control and ensures the robustness of auxiliary rehabilitation training.

A more accurate comprehension of data elements and the exploration of new laws governing contemporary data in both theoretical and practical domains constitute a significant research topic.

Based on the perspective of evolutionary economics, this paper re-examines economic history and existing literature to study the following: changes in the “connotation of production factors” in economics caused by the evolution of production factors; the economic paradoxes formed by data in the context of social production processes and business models, which traditional theoretical frameworks fail to solve; the disruptive innovation of classical theory of value by multiple theories of value determination and the conflicts between the data market monopoly as well as the resulting distribution of value and the real economic society. The research indicates that contemporary advancements in data have catalyzed transformative innovation within the field of economics.

The research indicates that contemporary advancements in data have catalyzed disruptive innovation in the field of economics.

This paper, grounded in academic research, identifies four novel issues arising from contemporary data that cannot be adequately addressed within the confines of the classical economic theoretical framework.

This study aims to analyse the relationship between a patent’s network position in a knowledge search network and the likelihood and speed of patent transactions. Additionally, it explores whether patent scope moderates these relationships.

In this empirical study, the authors collected a sample of patents in the artificial intelligence industry over the period of 1985–2018. Then, the authors examined the direct roles of degree centrality, betweenness centrality and closeness centrality on the likelihood and speed of patent transactions and the moderating role of patent scope in the knowledge search network using the logit and accelerated failure time models.

The findings reveal that degree centrality positively affects both the likelihood and speed of patent transactions, while betweenness centrality enhances the likelihood, and closeness centrality significantly boosts both. However, regarding the speed of patent transactions, closeness centrality is the most impactful, followed by degree centrality, with no significant influence of betweenness centrality. Additionally, the patent scope moderates how betweenness centrality affects the likelihood of transactions.

This study has limitations owing to its exclusive use of data from the Chinese Intellectual Property Office, lack of visibility of the confidential terms of most patent transactions, omission of transaction directionality and focus on a single industry, potentially restricting the breadth and applicability of the findings. In the future, expanding the data set and industries and combining qualitative research methods may be considered to further explore the content of this study.

This study has practical implications for developing a better understanding of how network structure in the knowledge search network affects the likelihood and speed of patent transactions as well as the identification of high-value patents. These findings suggest future directions for patent holders and policymakers to manage and optimise patent portfolios.

This study expands the application boundaries of social network theory and the knowledge-based view by conducting an in-depth analysis of how the position characteristics of patents within the knowledge search network influence their potential and speed of transactions in the technology market. Moreover, it provides a theoretical reference for evaluating patent value and identifying high-quality patents by quantifying network positions. Furthermore, the authors construct three centrality measures and explore the development of patent transactions, particularly within the context of the developing country.