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.

With the rapid increase in electric bicycle (e-bikes) use, the rate of associated traffic accidents has also escalated. Prior studies have extensively examined e-bike riders’ injury risks, yet there is a limited understanding of how their behavior contributes to these accidents. This study aims to explore the relationship between e-bike riders’ risk-taking behaviors and the incidence of traffic accidents, and to propose targeted safety measures based on these insights. Utilizing a mixed-methods approach, this research integrates quantitative data from traffic accident reports and qualitative observations from naturalistic studies. The study employs a binary logistic regression model to analyze risk factors and uses observational data to substantiate the model findings. The analysis reveals that assertive driving behaviors among e-bike riders, such as running red lights and speeding, significantly contribute to the high rate of accidents. Moreover, the lack of protective gear and inadequate safety training are identified as critical factors increasing the risk of severe injuries. The study concludes that comprehensive policy interventions, including stricter enforcement of traffic laws and mandatory safety training for e-bike riders, are essential to mitigate the risks associated with e-bike use. The findings advocate for an integrated approach to urban traffic management that enhances the safety of all road users, particularly vulnerable e-bike riders.

Green cosmetics made from organic ingredients are becoming increasingly popular due to their environmentally friendly nature. However, research on consumer behavior towards green cosmetics is rare, especially in developing countries like Pakistan. Previous studies have primarily focused on female consumers, and little is known about the behavior of male consumers. Therefore, this research aims to investigate the behavior of both male and female consumers towards green cosmetic products and analyze the factors that affect their purchase behavior. This study employs a quantitative approach with deductive reasoning and collects data through a questionnaire from major cities in Pakistan. The study finds that eco-awareness, social influence, price-quality instructions, health consciousness, and the need for uniqueness significantly influence consumer purchase behavior when buying green cosmetics. Interestingly, price sensitivity does not significantly affect consumer purchase behavior as consumers are willing to pay for high-quality green cosmetics. Based on the findings, the study recommends promoting eco-awareness and health consciousness among consumers through educational campaigns and workshops launched by the government and the private sector. Future research can explore factors such as age, gender, and specific generations like millennials and Generation Z, as well as packaging, branding, and product design to promote environmentally friendly and health-conscious products. Additionally, comparative studies between countries can identify universal and region-specific factors, and examining the overall impact of green cosmetic products on the environment can highlight areas for improvement in sustainability.

The aim of this study is to examine the contributions of the components of employee engagement on knowledge-sharing behavior alongside possible mediating effect of management support. This study collected data from 395 respondents purposively selected from pharmaceutical organizations in Bangladesh. For input and incorporation of sample data, SPSS version 26 was used, whereas the PLS-SEM (version-4) tool was used to test the hypotheses relationships. The findings reveal significant positive effects of adaptation, devotion, and vitality on both knowledge sharing behavior and management support. Adaptation to new technologies and processes enhances employees' ability and intention to share knowledge, facilitated by robust management support. Similarly, devotion and vitality among employees fosters a supportive environment that is conducive for knowledge exchange. Management support emerges as a critical mediator, amplifying the positive impacts of adaptation, devotion, and vitality on organizational outcomes. These findings address a critical gap in understanding the conditions that enhance knowledge-sharing behaviors in highly regulated industries and provides a valuable framework for organizations to nurture knowledge-sharing cultures that will drive innovation and resilience within emerging markets.