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This study aims to provide empirical evidence to verify the dimensional structure of artificial intelligence (AI) Chatbot quality and examine the impact of these dimensions on consumer satisfaction and brand advocacy among Gen Z in the fast food industry in Egypt.

The empirical data was obtained with an electronic self-administered survey instrument from 397 young consumers who had prior experience using AI Chatbots across multiple fast food brands in Egypt. Structural equation modeling was used to analyze the formulated hypotheses.

The results showed that AI Chatbot quality dimensions, specifically information authenticity and system compliance, significantly enhance young consumers’ satisfaction. In addition, information authenticity of AI Chatbot quality was observed to wield a significant influence on young consumers’ advocacy. In contrast, an insignificant relationship was noticed between satisfaction and advocacy. Moreover, the mediating role of consumer satisfaction was not established.

Given that Gen Z is more technology savvy and computer literate, marketers and practitioners of fast food brands should invest in AI tools to respond to young consumers’ expectations and improve their perception of their services.

This study uses stimulus-organism-response theory to understand the mediating effect of young consumers’ satisfaction in the relationship between AI Chatbot quality and consumer brand advocacy within the fast food industry. Also, it introduced two novel main constructs of AI Chatbot quality, namely, information authenticity and system compliance.

Providing high-quality service for airline companies cannot be overstated, as it directly impacts their survival and competitiveness. Hence, there is an increasing need to measure loyalty in the air transport industry. Loyal customers are highly valued as they are less price-sensitive and require minimal communication efforts. Despite this, there is limited knowledge about the factors that influence a passenger’s level of loyalty towards an airline company. Therefore, understanding the precursors of loyalty is essential. This research investigates the serial mediation effect of consumer brand identification (CBI) and perceived value on the relationship between service quality and loyalty.

In this research, quality is related to loyalty through value and CBI. A conclusive research design was adopted to determine whether value and CBI have a serial mediating effect on the quality-loyalty link. The research framework includes a three-path mediation model.

Based on the analysis of 406 questionnaires, the study concludes that a serial mediation effect of CBI and perceived value on the quality-loyalty relationship exists. The research results indicate that CBI has a greater influence on improving customer loyalty than perceived value.

This study contributes to limited research on the serial mediating effect of value and CBI in the quality-loyalty relationship. It also provides empirical evidence on the determinants of loyalty as part of the Turkish aviation industry. This study highlights the significance of CBI as a critical factor for airlines to maintain their competitiveness in the market.

The purpose of this paper is to investigate the accurate monitoring and assessment of steel bar corrosion in concrete based on deep learning multi-sensor information fusion method. The paper addresses the issue of traditional corrosion assessment models relying on sufficient data volume and low evaluation accuracy under small sample conditions.

A multi-sensor integrated corrosion monitoring equipment for reinforced concrete is designed to detect corrosion parameters such as corrosion potential, current, impedance, electromagnetic signal and steel bar stress, as well as environmental parameters such as internal temperature, humidity and chloride ion concentration of concrete. To overcome the small amount of monitoring data and improve the accuracy of evaluation, an improved Siamese neural network based on the attention mechanism and multi-loss fusion function is proposed to establish a corrosion evaluation model suitable for small sample data.

The corrosion assessment model has an accuracy of 98.41%, which is 20% more accurate than traditional models.

Timely maintenance of buildings according to corrosion evaluation results can improve maintenance efficiency and reduce maintenance costs, which is of great significance to ensure structural safety.

The corrosion monitoring equipment for reinforced concrete designed in this paper can realize the whole process of monitoring inside the concrete. The proposed corrosion evaluation model for reinforced concrete based on Siamese neural network has high accuracy and can provide a more accurate assessment model for structural health testing.

This paper aims to enhance the machining accuracy of hybrid robots by treating the moving platform as the first joint of a serial robot for direct position measurement and integrating external grating sensors with motor encoders for real-time error compensation.

Initially, a spherical coordinate system is established using one linear and two circular grating sensors. This system enables direct acquisition of the moving platform’s position in the hybrid robot. Subsequently, during the coarse interpolation stage, the motor command for the next interpolation point is dynamically updated using error data from external grating sensors and motor encoders. Finally, fuzzy proportional integral derivative (PID) control is applied to maintain robot stability post-compensation.

Experiments were conducted on the TriMule-600 hybrid robot. The results indicate that the following errors of the five grating sensors are reduced by 94%, 93%, 80%, 75% and 88% respectively, after compensation. Using the fourth drive joint as an example, it was verified that fuzzy adaptive PID control performs better than traditional PID control.

The proposed online error compensation strategy significantly enhances the positional accuracy of the robot end, thereby improving the actual processing quality of the workpiece.

This method presents a technique for achieving online error compensation in hybrid robots, which promotes the advancement of the manufacturing industry.

This paper proposes a cost-effective and practical method for online error compensation in hybrid robots using grating sensors, which contributes to the advancement of hybrid robot technology.

We aim to examine two issues. First, we intend to identify the best performing expected return proxies. Second, we investigate whether the expected return proxies for individual stocks can track the corresponding realized returns during extremely good or extremely bad times of the economic environment related to business conditions, stock market valuation and broad market performance.

We construct four sets of expected return proxies, including: (1) characteristic-based proxies; (2) standard risk-factor-based proxies; (3) risk-factor-based proxies that allow betas to vary with firm characteristics and (4) macroeconomic-variable-based proxies. First, we estimate expected returns for individual stocks using newly developed methods and evaluate the performance of these expected return proxies based on the minimum variance criterion of Lee et al. (2020). Second, we regress expected return proxies and realized returns on indicator variables that capture the extreme phases of the economic environment. Then we compare the estimated coefficients from these two sets of regressions and see if they are similar in magnitude via formal hypothesis testing.

We find that characteristic-based proxies and risk-factor-based proxies that allow betas to vary with firm characteristics are the two best performing proxies. Therefore, it is important to allow betas to vary with firm characteristics in constructing expected return proxies. We also find that model-based expected return proxies do a reasonably good job capturing actual returns during extremely bad and extremely good phases of business cycles measured by leading economic indicators, consumer confidence and business confidence. However, there is a large gap between the adjustment of model-based expected returns and realized returns during extreme episodes of stock market valuation or broad market performance.

We examine four types of expected return proxies and use the newly developed methodology as in Lee et al. (2020) to see which one is the best. In addition, we document whether model-based expected returns from individual stocks adjust partially or fully to keep pace with actual returns in response to changing economic conditions. No prior studies have examined these two issues.

The purpose of this study is to reveal the significant contribution of MXene on enhancing tribological properties and to obtain the influence mechanism of various factors on friction characteristics of rolling bearing under extreme conditions.

Under extreme working conditions, the friction characteristics of rolling bearings directly determine the safety and reliability of the transmission system. In this study, MXene is added to the origin lubricating grease (OLG) of rolling bearing to enhance their friction characteristics. Then, the effects of inner ring speed, radial load, grease filling volume and other factors on the friction coefficient of rolling bearing are analyzed using the Taguchi method.

The results indicate that the ranking of various factors affecting the friction coefficient is: radial load, inner ring speed, MXene additive content in grease and grease filling volume. Especially, the radial load and inner ring speed exhibit extremely significant effects, while the MXene additive content in grease (P < 0.05) has a significant influence on the friction coefficient of rolling bearing. The optimal condition for rolling bearing lubricated with MXene additives lubricating grease (MALG) achieves the lowest friction coefficient of 0.0049 under 1,000 rpm, 9 kN and 35% grease filling volume.

This study could offer reference solution for utilizing MXene nano-lubrication to fufill the demands of precision, heavy-load, or long-lifespan bearings. Furthermore, the lubrication approach has the potential to be expanded into aerospace, defense, and various industrial fields, thereby significantly promoting its practial engineering applications.

This study aims to investigate the information-seeking behaviours of home buyers – primarily owner-occupants – using digital real estate platforms, a key element in the industry’s shift towards digital services. It focuses on first-time buyers and repurchasers to examine how these platforms assist in the home-buying process and influence buyer behaviour in Taiwan.

A mixed methods approach was adopted, combining quantitative surveys and qualitative interviews to gather comprehensive data on user experiences and preferences.

The research identifies brand perception, search functionality and search results as critical factors influencing platform usage. Furthermore, it reveals an increasing demand for innovative artificial intelligence-driven search features to enhance user experience and platform convenience, reflecting evolving user expectations.

By addressing the specific context of Taiwan’s real estate market, this study provides novel insights into the interplay between digital platform features and user behaviour. The findings offer practical recommendations for improving platform design to better align with user needs.

This study proposed an extended theory of planned behaviour (TPB), that is, considering that behavioural beliefs, normative beliefs and control beliefs (beliefs) will influence perceived service quality (PSQ) on food delivery services. PSQ (behavioural intention) will influence electronic word-of-mouth (EWOM) (behaviour). In addition, exogenous variables including information from online ratings and consumer groups will affect the strength of the relationship between received service quality and EWOM on food delivery service.

This study aimed to investigate the mediation (PSQ) and moderation (Online ratings and consumer groups) effects on the extended TPB for Taiwanese consumers (n = 823).

This study first found a positive relationship between different beliefs and PSQ (behavioural intention). In addition, there is a positive relationship between PSQ and EWOM. Online rating has a moderating effect between PSQ and EWOM. Consumer group has a moderating relationship between PSQ and EWOM.

This study first found that the three stages of beliefs-intention-behaviour for consumers on food delivery service are reciprocal with two paths, starting with offline-to-online in terms of generating the positive relationship between individual belies and PSQ. Next, it can generate positive power to return online with a behaviour of EWOM. In addition, online ratings can enhance and strengthen the positive effect between PSQ and EWOM.

Apartments have become a representative urban housing type for mass housing construction; therefore, interest in low-rise residential areas has decreased. Low-rise residential areas have proved to be a poor residential environment for all aspects of life because of the density of the old buildings. Using the Moa Towns project as a case study, this research explores the steps for revitalizing low-rise residential environments, emphasizing communities.

The study first investigated the historical changes in urban housing types in Korea. Through a review of the related literature, a systematic analysis framework was developed. The residential environments of the case study and the questionnaire survey were analyzed through the framework, with a focus on the quality of housing.

To improve the quality of housing for residents, a housing plan is needed to ensure safety and comfort. To establish an accessible residential environment infrastructure, improve the quality of housing, and encourage social relationships between neighbors, community facilities within a 15-min walk should be installed. To increase interaction between neighbors, a circular pedestrian system can be created by establishing a community street.

This study is significant in that it focuses on people in revitalizing the community; identifies factors for physical improvement, exchange activities, and facility support; and presents a conceptual framework for building a residential environment in low-rise residential areas. The results are critical to the quality of housing and community facilities for low-rise residential development.

The purpose of this study is to investigate the influence of geometric parameters of removable panels on the sealing characteristics of ceramic wafer seal structure subjected to high-temperature gas flow.

Based on the laminar flow Reynolds equation, the theoretical and numerical calculation models were constructed to investigate the influence of external convex deformation of removable panel on leakage rate. The theoretical formula for leakage rate after deformation of the removable panel was derived, and the flow field and leakage characteristics of ceramic wafer seal under different operating parameters were studied.

The leakage rate exhibits consistent trends between theoretical calculation, numerical simulation and experimental value, with a maximum discrepancy of 8.9%. This validates the accuracy of both the theoretical model and numerical simulation. As the deformation angle of the removable panel increases, the sealing gap gradually widens, resulting in a compromised sealing effect. Moreover, the leakage rate in the central region of the sealing area is lower compared to that at both ends.

The leakage of the ceramic wafer seal structure under the removable panel with different deformation angles can be monitored based on Reynolds equation. The pseudo-transient numerical calculation method can be used to determine the leakage value of the micro-state ceramic wafer seal structure. These research findings provide a theoretical foundation and numerical investigation approach for studying ceramic wafer seal structures.