The growing interest in social robotics makes it relevant to examine the potential of robots as persuasive agents and, more specifically, to examine how robot characteristics influence the way people experience such interactions and comply with the persuasive attempts by robots. The purpose of this research is to identify how the (ostensible) gender and the facial characteristics of a robot influence the extent to which people trust it and the psychological reactance they experience from its persuasive attempts. This paper reports a laboratory study where SociBot™, a robot capable of displaying different faces and dynamic social cues, delivered persuasive messages to participants while playing a game. In-game choice behavior was logged, and trust and reactance toward the advisor were measured using questionnaires. Results show that a robotic advisor with upturned eyebrows and lips (features that people tend to trust more in humans) is more persuasive, evokes more trust, and less psychological reactance compared to one displaying eyebrows pointing down and lips curled downwards at the edges (facial characteristics typically not trusted in humans). Gender of the robot did not affect trust, but participants experienced higher psychological reactance when interacting with a robot of the opposite gender. Remarkably, mediation analysis showed that liking of the robot fully mediates the influence of facial characteristics on trusting beliefs and psychological reactance. Also, psychological reactance was a strong and reliable predictor of trusting beliefs but not of trusting behavior. These results suggest robots that are intended to influence human behavior should be designed to have facial characteristics we trust in humans and could be personalized to have the same gender as the user. Furthermore, personalization and adaptation techniques designed to make people like the robot more may help ensure they will also trust the robot.
Due to the complexity of autonomous mobile robot's requirement and drastic technological changes, the safe and efficient path tracking development is becoming complex and requires intensive knowledge and information, thus the demand for advanced algorithm has rapidly increased. Analyzing unstructured gain data has been a growing interest among researchers, resulting in valuable information in many fields such as path planning and motion control. Among those, motion control is a vital part of a fast, secure operation. Yet, current approaches face problems in managing unstructured gain data and producing accurate local planning due to the lack of formulation in the knowledge on the gain optimization. Therefore, this research aims to design a new gain optimization approach to assist researcher in identifying the value of the gain's product with a qualitative comparative study of the up-to-date controllers. Gains optimization in this context is to classify the near perfect value of the gain's product and processes. For this, a domain controller will be developed based on the attributes of the Fuzzy-PID parameters. The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). It is expected that the domain controller will give a positive impact to the path planning position and angular PID controller algorithm that meet the autonomous demand.
Introduction: In the current landscape marked by swift digital transformations and global disruptions, comprehending the intersection of digitalization and sustainable business practices is imperative. This study focuses on the food industries of China and Pakistan, aiming to explore the influence of digitalization on cleaner production. Methods: Employing a cross-sectional design, data were gathered through online surveys involving a diverse group of employees. Special attention was given to the emergent phenomenon of technostress and its subsequent implications for individuals in the workplace. Results: The findings of the study demonstrate a significant impact of digitalization on both resource mobilization and interaction quality within the surveyed food industries. Notably, technostress emerged as a mediating factor, shedding light on the psychological challenges associated with digital transitions. The study further reveals the moderating role of the COVID-19 pandemic, altering the dynamics among the variables under investigation. Discussion: From a theoretical perspective, this research contributes to the cleaner production literature by bridging it with the human-centric nuances of technological adaptation. On a practical level, the study emphasizes the importance of aligning digital strategies with resource mobilization to achieve sustainable outcomes. For the food industry and potentially beyond, the research offers a roadmap for integrating digital tools into operations, ensuring efficiency, and promoting cleaner production.