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.

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.

Social media influencer marketing has emerged as an essential marketing strategy in the online interactive environment. This study investigates the impact of influencer-consumer fit (ICF) on behavioral intentions; intention to co-create brand value (ICC) and purchase intention (PI), with the serial mediation of influencer authenticity (IA) and attitude toward brand (ATB). A self-administered questionnaire was distributed to followers of social media influencers in Pakistan. The data were collected from 421 female followers of social media influencers through survey and partial least squares—structural equation modeling was used for data analysis. The findings reveal that ICF impacts IA, while the latter impacts ATB. ATB in turn impacts behavioral intentions. The direct effects suggest that ICF impacts consumers’ PI but not the ICC. However, with the serial mediation of IA and ATB, the relationship becomes significant. The findings of this study may assist managers in building brand strategies to achieve excellence in a highly dynamic and competitive market by leveraging the power of influencer marketing.

Accurate prediction of US Treasury bond yields is crucial for investment strategies and economic policymaking. This paper explores the application of advanced machine learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models, in forecasting these yields. By integrating key economic indicators and policy changes, our approach seeks to enhance the precision of yield predictions. Our study demonstrates the superiority of LSTM models over traditional RNNs in capturing the temporal dependencies and complexities inherent in financial data. The inclusion of macroeconomic and policy variables significantly improves the models’ predictive accuracy. This research underscores a pioneering movement for the legacy banking industry to adopt artificial intelligence (AI) in financial market prediction. In addition to considering the conventional economic indicator that drives the fluctuation of the bond market, this paper also optimizes the LSTM to handle situations when rate hike expectations have already been priced-in by market sentiment.

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.

This study aimed to explore the influence of entrepreneurial skills development on entrepreneurial confidence in university students. Using an empirical approach, a structured questionnaire was administered to 322 students at a university in Lima, Peru, to assess participants’ perceptions of self-awareness and self-assessment, problem solving, communication and presentation of ideas, as well as their entrepreneurial confidence. The data collected were analysed using structural equation modelling (SEM), which allowed for the identification of significant relationships between the variables. The results revealed that self-awareness, problem solving and effective communication have a positive and determinant influence on the development of entrepreneurial skills, which in turn significantly strengthen students’ entrepreneurial confidence. These findings highlight the importance of incorporating the promotion of entrepreneurial skills in university education, as this can increase students’ readiness and willingness to successfully start and manage their own entrepreneurial projects.

This research quantitatively examines how technology-mediated formative assessment techniques affect student learning outcomes in middle school education. The research investigates the correlation between instructors’ technology use, attitudes, and student performance in several academic disciplines using surveys and evaluations conducted with teachers and students. Results show strong positive connections between how often technology is used, the specific digital tools used, how effective technology-mediated formative assessment is judged to be, and the results of student learning. On the other hand, obstacles to implementation were shown to have a negative relationship with student accomplishment. The research emphasizes that technology-mediated formative assessment is more successful in some subjects, emphasizing the necessity to customize teaching methods for each subject’s requirements. The study revealed a positive correlation between student learning outcomes and the frequency of technology use, the types of digital tools used, and the perceived effectiveness of technology-mediated formative assessment. These results suggest ways to improve the use of technology and formative assessment in middle school instruction.

The introduction of artificial intelligence (AI) marks the beginning of a revolutionary period for the global economic environments, particularly in the developing economies of Africa. This concept paper explores the various ways in which AI can stimulate economic growth and innovation in developing markets, despite the challenges they face. By examining examples like VetAfrica, we investigate how AI-powered applications are transforming conventional business models and improving access to financial resources. This highlights the potential of AI in overcoming obstacles such as inefficient procedures and restricted availability of capital. Although AI shows potential, its implementation in these areas faces obstacles such as insufficient digital infrastructure, limited data availability, and a lack of necessary skills. There is a strong focus on the need for a balanced integration of AI, which involves aligning technological progress with ethical considerations and economic inclusivity. This paper focuses on clarifying the capabilities of AI in addressing economic disparities, improving productivity, and promoting sustainable development. It also aims to address the challenges associated with digital infrastructure, regulatory frameworks, and workforce transformation. The methodology involves a comprehensive review of relevant theories, literature, and policy documents, complemented by comparative analysis across South Africa, Nigeria, and Mauritius to illustrate transformative strategies in AI adoption. We propose strategic recommendations to effectively and ethically utilize the potential of AI, by advocating for substantial investments in digital infrastructure, education, and legal frameworks. This will enable Africa to fully benefit from the transformative impact of AI on its economic landscape. This discourse seeks to offer valuable insights for policymakers, entrepreneurs, and investors, emphasizing innovative AI applications for business growth and financing, thereby promoting economic empowerment in developing economies.