Introduction: Social media advertising is a crucial factor for predicting consumer behavior. Further credibility, authenticity, and sustainability of social postings enhance consumer purchase intentions in general and online shopping in particular. This empirical study investigated the impact of social media advertising on consumer behavior and the role of credibility, perceived authenticity and sustainability. The study also investigated the mediating effect of trust in the relationship between social media advertising and consumer behavior.Methods: To measure the impact of the social media advertising effect on consumer behavior, eight reflective constructs, namely, credibility, sustainability, perceived authenticity, social media advertising effectiveness, satisfaction, purchase intentions and perceived value, were assessed. For the mediation analysis, consumer behavior was modeled as a higher-order construct with three sub-dimensions: satisfaction, perceived value and purchase intentions. Furthermore, the construct social media effectiveness is also modeled as a higher-order construct with four sub-dimensions: credibility, sustainability, authenticity and social media advertisement effectiveness. The data were collected from active social media users who engage in brand advertising and online shopping. A total of 500 valid responses were subjected to exploratory and confirmatory factor analysis, and the hypotheses were tested via structural equation modeling.Results and discussion: The SEM results reveal that constructs credibility and sustainability are strong predictors of consumer behavior in the context of social media advertising effectiveness. The mediating variable of trust partially mediated the relationship between social media effectiveness and consumer behavior. The outcome has several practical theoretical implications for industry. The brand managers should identify that consumer trust and engagement are not only by-products of risk but also actively cultivated through strategic advertising efforts. To maximize the effectiveness of their social media campaigns, brands should focus on building reliability through transparency, enhancing authenticity through reliable storytelling and promoting sustainability with real commitment.

This study investigated the impact of generative AI tools on human resource management practices, organizational commitment, employee engagement, and employee performance. The authors also investigated the mediating role of trust in the relationship between user perception and organizational commitment. A structured questionnaire was used to collect the data from the information technology industry employees to measure the 9 reflective constructs optimism, innovativeness, ease of use, usefulness, trust, organizational commitment, vigor, dedication, and employee performance. Two constructs, ease of use and usefulness, are modeled as a higher-order construct – User Perception, whereas vigor and Dedication are modeled as another higher-order construct, Employee Engagement. The mode-fit indices were assessed for both the higher- and lower-order constructs, and the model-fit indices for both the models, higher- and lower-order constructs reveal an excellent model fit. Positive and statistically significant impacts were observed between the study constructs. The impact of organizational commitment on employee engagement was positive and statistically significant, and in turn, the impact of employee engagement on employee performance was also positive and statistically significant. Trust partially mediated the relationship between user perception and organizational commitment, fostering enhanced employee engagement and performance. The theories of technology readiness, stimulus-organization-response, and technology acceptance model were utilized in the development of the study’s theoretical framework, which provided fresh perspectives on how employee engagement, organizational commitment, and performance are influenced by user experience and trust in the context of generative AI. Theoretically, it opens up new applications such as personalized education and services, digital art, and realistic virtual assistants that were previously unfeasible or impractical for automation. The practical implications are that the field of interdisciplinary research as well as the information technology industry will be greatly impacted by generative AI. To facilitate swift adoption, the principles of generative AI are conceptualized from model-, system-, and application-level perspectives in addition to a social-technical perspective, where they are explicated and defined. In the end, our research has given future scholars a significant research agenda that will enable them to study generative AI from different theoretical perspectives while incorporating the concepts from these theories.

In the present study, the researchers reported the results of an empirical study on remote working and occupational stress and their effects on employees’ job satisfaction, motivation, and performance. Remote working has three subscales: self-proficiency, technology, and teamwork. Intrinsic and extrinsic motivation subscales were included to assess employee motivation. A simple random sampling method was used to select the subjects who are employees of the IT-enabled industries in Hyderabad Metro. A total of 513 responses were obtained on the remote working subscales—the effect on the independent variables, namely, employee self-proficiency, technology, teamwork, and occupational stress, on the dependent variables, namely, job satisfaction, intrinsic motivation, extrinsic motivation, and performance. The measured Cronbach’s alpha was in the range of 0.64–0.77, other reliability statistics split-half (odd-even) correlation was in the range of 0.62–0.84, and theSpearman–Brown prophecy was in the range of 0.70–0.91, demonstrating the reliability and internal consistency of the research instrument. The general linear model results indicated that all the independent variables, namely, self-proficiency, teamwork, and Occupational stress, are statistically significant and influence the outcome variables. The general linear model results also indicated statistically significant age differences in the dependent variables; however, there were no statistically significant gender differences. Of the independent variables, self-proficiency influences job satisfaction, intrinsic motivation, and performance (p < 0.01); teamwork influences employee job satisfaction and extrinsic motivation (p < 0.01 and p < 0.05); and Occupational stress influences performance (p < 0.01), which are statistically significant and thus influence the outcome variables. The model predicted a statistically significant influence of age (p < 0.01) on all the dependent factors, namely, job satisfaction, intrinsic motivation, extrinsic motivation, and performance. The study revealed that remote working is one of the major factors causing anxiety and employee stress. The main reasons are the absence of interaction with peers, the absence of routine fun during breaks, and work–family conflicts. Another observation is that the absence of peer–employee interaction demotivates the employees as there is no competition among the employees during remote working. The authors recommend that organizations develop an integrated human resource policy and performance management system that addresses the issues of employee stress, remote working concerns, peer–employee interactions, and pandemic-type situations. As there are several factors such as occupational stress, job satisfaction, motivation, peer interactions, and remote working concerns, employee stress-coping strategies affect the performance of an employee. The multiple mediation analysis indicates no statistically significant influence of the mediator variables, i.e., occupational stress and job satisfaction, on performance through remote working.

The present research tried to prompt self-regulated learning, autonomy, and self-efficacy of EFL learners through authentic assessment in Iranian EFL classrooms. To do so, 57 Iranian EFL learners were chosen and assigned to two equal groups; the Experimental Group (CG) and the Control Group (CG). Then, three questionnaires were administered to assess the respondents’ self-regulated learning, autonomy, and self-efficacy before doing the treatment. After that, the EG received the treatment via using authentic assessments whereas the CG received the instruction through non-authentic assessments. After teaching 15 English passages to both groups, three post-tests were administered to them to determine the effects of the treatment on their self-regulated learning, autonomy, and self-efficacy. Lastly, the attitude questionnaire was given to the EG students to assess their attitudes toward implementing authentic assessments in EFL classes. The outcomes of ANCOVA revealed that there were significant differences between the post-tests of the EG and the CG. The results displayed that the EG noticeably outdid the CG in self-regulated learning, autonomy, and self-efficacy post-tests. In addition, the results showed that the EG held a positive attitude toward using authentic assessment in EFL classes. It can be concluded that applying authentic assessment in EFL classes can bring about constructive impacts for EFL learners. The findings of this investigation can be extremely valuable for EFL teachers and material designers to consider the impacts of authentic assessments and invest more in using these sorts of assessments.

The pressure on today's factories to boost output is becoming steadily stronger. Automation of mundane tasks that wear people out is necessary to reach the new productivity heights. Self-driving mobile robots have been shown to increase output in a variety of material-handling, transportation, manipulation, and packaging tasks. When many mobile robots are used for these purposes, the question of how to divide up the work becomes more complicated. System efficiency might be enhanced by effectively assigning the robots to the job. The term "Multi-Robot Task Allocation" (MRTA) refers to the challenge of figuring out how to distribute work among several robots. Multiple robots must work together to complete the task under consideration in this study. Optimal utilisation of the robots is crucial to the success of the job completion. When multiple goals must be met at once, optimising the tasks assigned to these robots is essential. This study discovers how to optimise the distribution of work among several robots while satisfying multiple objectives at once.

The scientific and business communities are showing considerable interest in wireless sensor networks (WSN). The availability of low-cost, small-scale components like CPUs, radios, and sensors, which are often combined into a single chip, is crucial. Parallel to the evolution of WSNs, the concepts of the IoT have been evolving in recent years. Wireless communication technologies may play a significant role in the implementation of IoT, despite the fact that IoT does not need or require any particular technology for communication. WSN assisted IoT networks can drive several applications in many industries. The proposed research explores the possibility of enhancing energy efficiency in WSN-assisted IoTN by balancing various challenging sensor network performance metrics. The base station's current placement inside the sensing field is predetermined by the preexisting routing algorithms. Our study examines the impact of base station placement outside and within the prescribed sensing domains on energy consumption and network longevity. In addition, methods for transferring data from the distributed source sensor to the base station while minimizing energy consumption are investigated. In this preliminary study, we focus on developing an algorithm for WSN-Assisted IoTN that can balance network factors such as hop count, communication distance, and residual energy. To further optimize the routing route between local cluster heads and the base station, a novel network architecture is built based on the Ant-optimization model, which uses centroid routing to balance energy consumption among local clusters. An open-source Network Simulator (NS-3) is used to model the behaviour of the proposed routing protocols and compare them to comparable existing network protocols. All of the suggested protocols have the same fundamentals for creating networks, however they vary in terms of routing, optimization, and performance depending on the development effort under consideration.