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This study aims to explore the largely under-researched area of user perceptions of artificial intelligence (AI)-driven recommendations and how the visibility of AI-driven labels influences impulsive buying behavior on social media platforms. Addressing a gap in existing literature, it further examines the moderating effects of generational differences and the mediating role of consumer knowledge, providing new insights into how these factors shape consumer responses to AI-driven content in social media.

This study used a quantitative approach, using consumer surveys to explore the direct and indirect impacts of AI-driven recommendations and the visibility of AI-driven labels on impulsive buying behavior through social media. A conditional process analysis was conducted to examine how generational differences act as a moderator and how consumer knowledge of AI serves as a mediator in these relationships. This analysis integrated these factors into a single framework to provide a detailed understanding of how consumers respond to AI-driven personalization in social media.

This study confirms that AI-driven personalization effectively nudges impulsive buying on social media, with personalized recommendations impacting behavior more subtly than AI labels. Consumer knowledge does not mediate this effect, while generational differences emerge as a significant moderator; Millennials are found to be more responsive to both recommendations and labels in comparison to Gen Z, possibly due to less familiarity with AI technologies present in social media.

This study identifies limitations such as its reliance on consumer perceptions from a questionnaire rather than direct interactions with AI-driven features. While insightful, future research should incorporate actual user data, like clickstreams, and include a wider range of generational cohorts to deepen the understanding of AI’s impact on impulsive buying behavior.

This study bridges gaps in the literature by examining the combined effects of AI-driven recommendations and visibility of AI-driven labels on impulsive buying behavior, offering new insights into the role of generational differences and consumer knowledge in AI-based social media marketing.

Artificial intelligence (AI)-driven marketing has transformed the landscape of consumer interactions, but it also raises ethical concerns regarding perceived manipulation and subsequent unethical young consumer behavior. This study aims to investigate the direct and indirect effects of AI-driven marketing on unethical young consumer behavior, with digital literacy as a moderating variable. The authors introduce and conceptualize a digital literacy construct that influences how young consumers perceive and react to manipulative AI-driven marketing tactics.

Using the Elaboration Likelihood Model (ELM) and the Persuasion Knowledge Model (PKM), this research explores how digital literacy influences the reception of AI-driven marketing and moderates the effects of perceived manipulative tactics. This study adopts a three-wave, time-lagged survey method among young consumers in urban Pakistan, integrating measures of AI-driven marketing techniques, perceived manipulation, digital literacy and unethical consumer behavior.

The results reveal that perceived manipulation mediates the relationship between AI-driven marketing and unethical young consumer behavior. Digital literacy significantly moderates this effect, indicating that higher digital literacy levels can mitigate the negative impacts of perceived manipulation. Conversely, lower digital literacy amplifies the negative impacts of perceived manipulation.

The findings underscore the need for marketers to foster transparency and ethical practices in AI-driven strategies. Enhancing consumer digital literacy can serve as a protective factor against unethical marketing practices.

This study contributes to the understanding of digital literacy’s protective role against unethical AI-driven marketing practices. It extends existing models of persuasion and consumer response by demonstrating how digital literacy reshapes traditional consumer response frameworks in the context of AI-driven environments.

The present paper will discuss newly developed fully digital sensorless induction motor and permanent magnet motor synchronous motor drives, which employ natural field orientation (NFO). So far only vector‐type of NFO induction motor drives have been discussed in the literature, and very limited experimental results have been shown. In addition, the paper will also discuss new sensorless DTC‐type of NFO induction motor drives (NFO‐DTC drives). Using fully digital implementations of the new NFO‐type induction motor and permanent magnet drives, experimental results will be shown for various operating conditions, including slow and fast reversals at very low speed. Robustness to parameter deviations will also be demonstrated. The developed new types of NFO drives can also work at zero stator frequency and sustained zero frequency operation will also be demonstrated. The drives have been tested in basically two environments: where the load is a dc motor; and where a crane drive is implemented. In contrast to other sensorless crane drives, which develop stability problems, it was found that the new NFO drives can operate in a stable manner under all operating conditions including zero frequency. This allows for many new applications.

The purpose of this paper is to study the electric vehicle (EV) drive efficiency of a traction motor considering regenerative braking according to various motor cores.

A software program was developed to predict the driving performance of an EV. It determines the driving mileage, the required power of the traction motor, and the operation points on a torque-speed map when drive cycles are given. The driving performance is calculated from the battery capacity, vehicle specification, and efficiency map of the traction motor computed using the finite element analysis.

As a result, the motor core is a significant design variable for raising the driving mileage of an EV. It is noted that the change of electrical steels used for the motor core is the lowest priced method of increasing the driving range by 2 km.

The comparative analysis of motor core by replacing 35PN250 to 25PNX1250F results in improvement effects traveling 4.62 and 5.16 km farther in the Simplified Federal Urban Driving Schedule (SFUDS) and Highway Fuel Economy Driving Schedule (HWFET), respectively. It was also verified that regenerative braking system is able to enhance drive efficiency by 29-31.3 km in the SFUDS and 6.5-7.3 km in the HWFET. From comparison of price rise for increasing driving mileage by 2 km, it is noted that the change of electrical steels used for the motor core is the lowest priced method.

EVERY day we get calls from customers who want to know “What drive should I buy?” There is no single right answer for everyone, but chances are there's a right choice based on your circumstances [including the applications(s) you plan to use with it]. Here are some qualities you should look for in a drive.

There are two main types of speed/position sensorless closed‐loop variable‐speed electrical drives: sensorless and quasi‐sensorless drives. In sensorless drives, the classical speed and position sensors (transducers) are absent and are replaced by mathematical‐model‐based and/or artificial‐intelligence‐based estimators. In quasi‐sensorless drives, instead of conventional speed/position sensors, smart sensor bearings are used (e.g. SKF smart sensor bearings). The present paper discusses the latest developments in the field of sensorless and quasi‐sensorless variable‐speed high‐performance drives. Thus, a new family of sensorless and quasi‐sensorless induction motor and permanent magnet synchronous motor drives are also discussed (e.g. new sensorless vector and DTC drives, etc.). Methods which enable the operation of these drives at very low speed and also at zero stator frequency are discussed.

Analysis of characteristic driving operations can help develop supports for drivers with different driving skills. However, the existing knowledge on analysis of driving skills only focuses on single driving operation and cannot reflect the differences on proficiency of coordination of driving operations. Thus, the purpose of this paper is to analyze driving skills from driving coordinating operations. There are two main contributions: the first involves a method for feature extraction based on AdaBoost, which selects features critical for coordinating operations of experienced drivers and inexperienced drivers, and the second involves a generating method for candidate features, called the combined features method, through which two or more different driving operations at the same location are combined into a candidate combined feature. A series of experiments based on driving simulator and specific course with several different curves were carried out, and the result indicated the feasibility of analyzing driving behavior through AdaBoost and the combined features method.

AdaBoost was used to extract features and the combined features method was used to combine two or more different driving operations at the same location.

A series of experiments based on driving simulator and specific course with several different curves were carried out, and the result indicated the feasibility of analyzing driving behavior through AdaBoost and the combined features method.

There are two main contributions: the first involves a method for feature extraction based on AdaBoost, which selects features critical for coordinating operations of experienced drivers and inexperienced drivers, and the second involves a generating method for candidate features, called the combined features method, through which two or more different driving operations at the same location are combined into a candidate combined feature.

The purpose of this research is to achieve a novel magnetic nutation drive for an industry robotic wrist reducer.

A novel magnetic nutation drive is proposed, and the structure and principle of the designed magnetic nutation drive are described in this study. Three-dimensional finite element analysis is used to compute the magnetic and torque of the magnetic nutation drive. Furthermore, a prototype of this novel magnetic nutation drive device is developed with 3D printing technology and tested to verify the feasibility of the proposed structure and principle.

The simulation and experimental results indicated that the proposed magnetic nutation drive device could meet the desired specifications, and that this novel magnetic nutation drive device successfully realized the non-contact transmission ratio of 105:1 required for a robotic wrist reducer.

This novel magnetic nutation drive is low-cost and easy to make and use, and which provides the non-contact transmission ratio of 105:1 required for a robotic wrist reducer.

For the first time, this research applies the permanent magnet drive technology to nutation drive and puts forward a new non-contact nutation drive mode. The novel drive mode can solve some problems of the traditional mechanical contact nutation drive, such as vibration, friction loss, mechanical fatigue and necessity of lubrication. The proposed non-contact nutation drive device can achieve a high reduction ratio with compact structure and can be suitable for industry application.

As CDROMs become a mass medium for entertainment and reference, the prices of CDROM discs have been dropping while the speed of CDROM drives has been increasing. For example, Essex Entertainment is now marketing 50 CDROM titles through music, book and budget retail stores at $9.95 per disc. Two years ago, the street price of single‐speed single‐disc CDROM drives was in the range of $200–400. Today, a similar drive that runs four times as fast (quad‐speed) can be purchased for about the same price. Meanwhile, sextuple‐speed drives are already available and there is no indication that the limits of CDROM drive speed have as yet been reached. This article discusses both quad‐speed and sextuple‐speed CDROM disc drives, and it also takes note of the many types of multiple disc drives that are being offered — some able to deploy hundreds of CDROM discs for almost instant use by equally large numbers of users.

This study illustrates the conceptual framework that expands the knowledge of the fundamental components that describe how AI-driven servant leadership (SEL) influences the job resources (JR), work engagement (WE) and job performance (JP) of tourism and hospitality employees.

The empirical study was conducted on a sample of 953 international tourism and hospitality employees who were selected via a purposive and snowball sampling approach in a cross-sectional survey. The analysis was performed using a partial least square-structural equation modeling.

The results of this study confirmed the positive impact of AI-driven SEL on employee JR with the boundary conditions of AI-driven SEL.

This study finding assists tourism and hospitality practitioners in understanding that in the near future, AI will have a major effect on the nature of work, including the impact on leadership styles. Hence, AI-driven SEL holds both positive (through direct impact on JR) and negative (via boundary conditions) impacts on employees’ JP and ultimately organizational success. Accordingly, managers should employ AI-driven SEL to increase employees’ JR, and once employees achieve high WE, they should constrict AI-driven SEL boundary conditions and their influence between JR and WE and WE and JP.

This study offers a novel and original conceptual model that advances AI-driven social theory, SEL theory and job demands-resources (JD-R) theory by synthesizing, applying and generalizing gained knowledge in a methodical way.