This study explores the intricate relationship between emotional cues present in food delivery app reviews, normative ratings, and reader engagement. Utilizing lexicon-based unsupervised machine learning, our aim is to identify eight distinct emotional states within user reviews sourced from the Google Play Store. Our primary goal is to understand how reviewer star ratings impact reader engagement, particularly through thumbs-up reactions. By analyzing the influence of emotional expressions in user-generated content on review scores and subsequent reader engagement, we seek to provide insights into their complex interplay. Our methodology employs advanced machine learning techniques to uncover subtle emotional nuances within user-generated content, offering novel insights into their relationship. The findings reveal an inverse correlation between review length and positive sentiment, emphasizing the importance of concise feedback. Additionally, the study highlights the differential impact of emotional tones on review scores and reader engagement metrics. Surprisingly, user-assigned ratings negatively affect reader engagement, suggesting potential disparities between perceived quality and reader preferences. In summary, this study pioneers the use of advanced machine learning techniques to unravel the complex relationship between emotional cues in customer evaluations, normative ratings, and subsequent reader engagement within the food delivery app context.

This study aims to advance understanding of the factors affecting Generation Z employee commitment in the workplace of the information and technology (IT) companies in Vietnam. A survey of 450 Generation Z employees in IT companies shows that company remuneration, reward and welfare, work environment, colleagues, direct manager, promotion, job characteristics, green initiatives are positively related to Generation Z organizational commitment. More specifically, work environment and direct manager have the highest effect on Generation Z employee commitment to organization while promotion and colleagues have the lowest effect on Generation Z employee commitment to organization. Research results also revealed that green initiatives of the organization have significant effect on Generation Z employee commitment in companies. This finding suggests that including green initiatives in corporate strategy is a valuable approach for improving Generation Z employee commitment to organization. We discuss the implications for theory, practice, limitations, and directions for future research.

This study investigates the influence of perceived value and perceived risk on consumer intentions to purchase counterfeit luxury goods, drawing upon an integrated theoretical framework encompassing perceived value theory, risk perception theory, and consumer behavior models. Through a quantitative research design involving a structured survey and Structural Equation Modeling (SEM), the study examines the relationships among perceived value dimensions (functional, emotional, social, economic), perceived risk factors (financial, social, performance), consumer attitudes, and purchase intentions. The findings reveal that perceived value positively influences purchase intentions, with consumer attitudes acting as a critical mediating mechanism. Conversely, perceived risk negatively impacts purchase intentions, with this relationship also mediated by consumer attitudes. Furthermore, Bayesian Network analysis uncovers the indirect pathways through which perceived risk shapes purchase intentions via its influence on consumer attitudes. By integrating these theoretical frameworks and employing advanced analytical techniques, this study contributes to a comprehensive understanding of the complex decision-making processes underlying counterfeit luxury goods consumption. The findings provide valuable insights for policymakers, luxury brand managers, and consumer protection agencies in devising targeted strategies to address consumer perceptions of value and risk, ultimately mitigating the proliferation of counterfeit luxury goods.

The St. Peter Sandstone of the American Midwest is presented today in textbooks as a simple and unproblematic example of “layer-cake geology.” The thesis of this paper is that the very simplicity of St. Peter Sandstone has made it challenging to characterize. In widely separated states, the sandstone appeared under different names. Several theories about how it formed began to circulate. The story of the St. Peter is not only the story of the assemblage of a stratigraphic unit over a vast area during three centuries, but also the role the study of the provenance of this unit played in the development of sedimentology in the early twentieth century, research that was made all the more challenging by its “simple” mineralogy. Indeed, the St. Peter has been controversial since it was first described.

We report a method for effectively and homogeneously incorporating carbon nanotubes (CNTs) in the form of double-wall (DWCNTs) and multi-wall (MWCNTs) structures into commercial paints without the use of additives, surfactants, or chemical processes. The process involves the physical mixing of the nanotubes and polymers using the cavitation energy of an ultrasonic bath. It is a simple, fast method that allows for uniform distribution of carbon nanotube bundles within the polymer for direct application. Due to the hydrophobic properties of the carbon nanotubes as grown, we used paint samples containing 0.3% by mass of both types of CNTs and observed an improvement in waterproofing through wettability and water absorption through immersion tests on the samples. Different solvents such as water, formaldehyde, and glycerin were used, and the results showed an increase in paint impermeability of 30% and 25% with the introduction of DWCNTs and MWCNTs, respectively. This indicates a promising, economically viable, and revolutionary method for applying nanotechnology in the polymer industry.