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.

Artificial Intelligence (AI) has become a pivotal force in transforming the retail industry, particularly in the online shopping environment. This study investigates the impact of various AI applications—such as personalized recommendations, chatbots, predictive analytics, and social media engagement—on consumer buying behaviors. Employing a quantitative research design, data was collected from 760 respondents through a structured online survey. The snowball sampling technique facilitated the recruitment of participants, focusing on diverse demographics and their interactions with AI technologies in online retail. The findings reveal that AI-driven personalization significantly enhances consumer purchase intentions and satisfaction. Multiple regression analysis shows that AI personalization (β = 0.35, p < 0.001) has the most substantial impact on purchase intention, followed by chatbot effectiveness (β = 0.25, p < 0.001), predictive analytics (β = 0.20, p < 0.001), and social media engagement (β = 0.15, p < 0.01). Similarly, AI personalization (β = 0.30, p < 0.001), predictive analytics (β = 0.25, p < 0.001), and chatbot effectiveness (β = 0.20, p < 0.001) significantly influence consumer satisfaction. The hierarchical regression analysis underscores the importance of ethical considerations, showing that ethical and transparent use of AI increases consumer trust and engagement. Model 1 explains 45% of the variance in consumer behavior (R² = 0.45, F = 154.75, p < 0.001), while Model 2, incorporating ethical concerns, explains an additional 10% (R² = 0.55, F = 98.25, p < 0.001). This study highlights the necessity for retailers to leverage AI technologies ethically and effectively to gain a competitive edge, improve customer satisfaction, and drive long-term success. Future research should explore the long-term impacts of AI on consumer behavior and the integration of emerging technologies such as augmented reality and the Internet of Things (IoT) in retail.

This research looks into the differences in technological practices across Gen-X, Gen-Y, and Gen-Z employees in the workplace, with an emphasis on motivation, communication, collaboration, and productivity gaps. The study uses a systematic literature review to identify factors that contribute to these variations, taking into account each generation’s distinct experiences, communication methods, working attitudes, and cultural backgrounds. Bridging generational gaps, providing ongoing training, and incorporating cross-generational and technology-enhanced practices are all required in today’s workplace. This study compares the dominating workplace generations, Gen-X and Gen-Y, with the emerging Gen-Z. A review of the literature from 2010 to 2023, which was narrowed down from 1307 to 20 significant studies, emphasizes the importance of organizational management adapting to generational changes in order to increase productivity and maintain a healthy workplace. The study emphasizes the need of creating effective solutions for handling generational variations in workplace.

Metal organic framework is a class of hybrid network of supramolecular solid materials comprised of a large number of inorganic and organic linkers all bounded to metal ions in a well-organized fashion. This type of compounds possess a greater surface area with an advantage of changing pore sizes, diversified and beautiful structure which withdrew an intense interest in this field. In the present review articles, the structural aspects, classification, methods of synthesis, various factors affecting the synthesis and stability, properties and applications have been discussed. Recent advances in the field and new directions to explore the future scope and applications of MOFs have been incorporated in this article to provide current status of the field.

This study investigates the willingness of Indonesian consumers, particularly in West Java, to pay for green products by applying and expanding the Theory of Planned Behavior (TPB). It examines how perceived green product value and willingness to pay premiums influence consumer intentions and behavior toward green purchases. The research highlights the gap between consumers’ willingness to pay for environmentally friendly products and the actual sales of such products. By incorporating perceived value and willingness to pay into the TPB framework, the study aims to find what factors that can address the gap particularly in a developing country context to contribute to shaping a pro-environmental socio-cultural community in Indonesia and mitigates country’s significant environmental challenges. In the context of 251 young consumers in Indonesia, this study finds that subjective norms do not significantly influence purchase intentions. However, attitudes and behavioral controls do effectively encourage green behavior, suggesting that societal norms for green behavior may not be fully established. In addition, while willingness to pay a premium and perceived value of green purchases can influence green behavior, consumers are generally reluctant to pay higher prices for environmentally friendly products.

This systematic literature review (SLR) delves into the realm of Artificial Intelligence (AI)-powered virtual influencers (VIs) in social media, examining trust factors, engagement strategies, VI efficacy compared to human influencers, ethical considerations, and future trends. Analyzing 60 academic articles from 2012 to 2024, drawn from reputable databases, the study applies specific inclusion and exclusion criteria. Both automated and manual searches ensure a comprehensive review. Findings reveal a surge in VI research post-2012, primarily in journals, with quantitative methods prevailing. Geographically, research focuses on Europe, Asia Pacific, and North America, indicating gaps in representation from other regions. Key themes highlight trust and engagement’s critical role in VI marketing, navigating the balance between consistency and authenticity. Challenges persist regarding artificiality and accountability, managed through brand alignment and transparent communication. VIs offers advantages, including control and cost efficiencies, yet grapple with authenticity issues, addressed through human-like features. Ethically, VI emergence demands stringent guidelines and industry cooperation to safeguard consumer well-being. Looking ahead, VIs promises transformative storytelling, necessitating vigilance in ethical considerations. This study advocates for continued scholarly inquiry and industry reflection to navigate VI marketing evolution responsibly, shaping the future influencer marketing landscape.