In this paper, we will provide an extensive analysis of how Generative Artificial Intelligence (GenAI) could be applied when handling Supply Chain Management (SCM). The paper focuses on how GenAI is more relevant in industries, and for instance, SCM where it is employed in tasks such as predicting when machines are due for a check-up, man-robot collaboration, and responsiveness. The study aims to answer two main questions: (1) What prospects can be identified when the tools of GenAI are applied in SCM? Secondly, it aims to examine the following question: (2) what difficulties may be encountered when implementing GenAI in SCM? This paper assesses studies published in academic databases and applies a structured analytical framework to explore GenAI technology in SCM. It looks at how GenAI is deployed within SCM and the challenges that have been encountered, in addition to the ethics. Moreover, this paper also discusses the problems that AI can pose once used in SCM, for instance, the quality of data used, and the ethical concerns that come with, the use of AI in SCM. A grasp of the specifics of how GenAI operates as well as how to implement it successfully in the supply chain is essential in assessing the performance of this relatively new technology as well as prognosticating the future of generation AI in supply chain planning.

Research in the field of online advertising has focused on the effect of in-stream ads on viewers’ attitudes and intentions to purchase. However, little is known regarding the crucial role of viewer’s control in terms of the ‘skip ad option’ towards the attitude to purchase. This research aims to investigate the effect of in-stream ads on viewers’ attitudes to purchasing with the moderating role of viewer control. Primary data was collected from respondents of Vehari district of Pakistan through a questionnaire based on 5 points Likert scale. 370 questionnaires were incorporated after excluding the questionnaires having missing values. Structural equation modelling was used through SmartPLS-3 software in testing the hypotheses. The findings reveal that, in-stream (emotional, informational, and entertaining) ads have positive impact on viewers’ attitudes, and viewers’ control moderates the relationship between in-stream ads and viewers’ attitudes towards the ads. Further, viewers’ attitude toward the ads has a significant positive impact on viewers’ intention to purchase. To the best of our knowledge this is one of the first studies that examines the effect of in-stream ads on viewers’ attitudes to purchasing with the moderating role of viewer control in the context of a developing country, like Pakistan.

Building upon the cognition-affect-behavior model, this research aims to investigate the effect of greenwashing on consumers purchase intentions by testing the mediating effect of perceived betrayal and the moderating effect of brand loyalty. Data was collected through a questionnaire from 339 Chinese consumers involved in the fashion industry. AMOS 24 was employed to test the fitness of the proposed model, and the proposed hypotheses were tested through hierarchical regression analysis using SPSS 23. The results indicate that greenwashing has a negative impact on consumer purchase intentions, with perceived betrayal partially mediating this relationship. Additionally, the findings show that brand loyalty can mitigate the adverse effects of greenwashing on consumer purchase intentions. The findings of this study enhance the existing knowledge on the relationship between greenwashing and consumer purchase intentions, particularly in the fashion industry, and have significant practical implications for companies in this sector aiming to increase consumer purchase intentions.

Over the past decade, the integration of technology, particularly gamification, has initiated a substantial transformation within the field of education. However, educators frequently confront the challenge of identifying suitable competitive game-based learning platforms amidst the growing emphasis on cultivating creativity within the classroom and effectively integrating technology into pedagogical practices. The current study examines students and faculty continuous intention to use gamification in higher education. The data was collected through an online survey with a sample size of 763 Pakistani respondents from various universities around Pakistan. The structural equation modeling was used to analyze the data and to investigate how continuous intention to use gamification is influenced by, extended TAM model with inclusion of variables such as task technology fit, social influence, social recognition and hedonic motivation. The results have shown that task technology has no significant influence on perceived usefulness (PU) where as it has a significant influence on perceived ease of use (PEOU). Social influence (SI) indicates no significant influence on perceived ease of use. Social recognition (SR) indicates positive influence on perceived usefulness, perceived ease of use, and continuous intention. The dimensional analysis indicated that perceived ease of use has insignificant influence on perceived usefulness. Both PEOU and PU exhibit positive influence on attitude. Hedonic motivation (HM) and attitude were observed to have a positive influence on continuous intention (CI). Moreover, gamification is found to efficiently and effectively achieve meaningful goals by tapping intrinsic motivation of the users through engaging them in playful experiences.

The human factor of production is a significant player in increased organizational productivity. Due to the contemporary competitive work environment, the millennial in front-line jobs is faced with demanding work activities, resulting in challenges to their psychological well-being. Therefore, exploring the connectedness between work-life balance, employee engagement and psychological well-being of the millennial becomes imperative. Research was conducted, using an ex-post facto research design, among 320 purposively selected front-line millennial employees, with a mean age of 32 years. The instrument administered in a Google Form survey contained a 44-item self-report questionnaire, comprising work-life balance, employee engagement with components as vigor, dedication and absorption, and employee well-being. Data analyzed revealed that work-life balance significantly predicted employee well-being, accounting for 25% variance. The dimensions of employee engagement (vigor, dedication and absorption) collectively accounted for 7% variance in employee well-being. The study establishes the fact that to enhance the psychological well-being of Millennials in front-line jobs, organizational management should design the work structures to allow for work-life balance, which will as well increase their work engagement. They can encourage employees to find meaning and purpose in their work (dedication), provide opportunities for skill development and autonomy (vigor), and create an environment that allows employees to fully immerse themselves in their tasks (absorption). These could be implemented through organizational development strategies and work design. However, future research should target additional variables, replicate the study in different contexts and among another population of employees, employ longitudinal data collection methods, and increase sample sizes. Furthermore, measures should be taken to minimize the impact of social desirability and enhance the generalizability of the research.

Fog computing (FC) has been presented as a modern distributed technology that will overcome the different issues that Cloud computing faces and provide many services. It brings computation and data storage closer to data resources such as sensors, cameras, and mobile devices. The fog computing paradigm is instrumental in scenarios where low latency, real-time processing, and high bandwidth are critical, such as in smart cities, industrial IoT, and autonomous vehicles. However, the distributed nature of fog computing introduces complexities in managing and predicting the execution time of tasks across heterogeneous devices with varying computational capabilities. Neural network models have demonstrated exceptional capability in prediction tasks because of their capacity to extract insightful patterns from data. Neural networks can capture non-linear interactions and provide precise predictions in various fields by using numerous layers of linked nodes. In addition, choosing the right inputs is essential to forecasting the correct value since neural network models rely on the data fed into the network to make predictions. The scheduler may choose the appropriate resource and schedule for practical resource usage and decreased make-span based on the expected value. In this paper, we suggest a model Neural Network model for fog computing task time execution prediction and an input assessment of the Interpretive Structural Modeling (ISM) technique. The proposed model showed a 23.9% reduction in MRE compared to other methods in the state-of-arts.