Search

How do we notice what is arriving? As authors, we collaboratively play with the continuation and momentum of emergence presented as a polyvocal text. As part of our research, we video-taped a collaborative mark-making activity led by two graduate students in a PhD cohort. What emerges from our interaction of situated dialogic inquiry is a heteroglossic performative text on emergence. The first 40 seconds of the video is woven into a paper that in itself becomes an ontological practice in emergence. The paper, in message and mode, delves into a series of moments and becomes a series of moments that ask the question: What is the play of interrelatedness in a collective inquiry of emergence?

Despite firms' continued interest in using influencers to reach their target consumers, academic and practical insights are limited on what levers an influencer can use to enhance audience engagement using their posts. We demonstrate that posting stories with or about people whom they share close ties with—such as family, friends, and romantic partners—can be one effective lever. Content that incorporates close social ties can be effective for several reasons: it may increase perceptions of authenticity, enhance perceived similarity, increase the perception that the influencer possesses more warmth, and could satisfy viewers' interpersonal curiosity. We analyze texts and photographs of 55,631 posts of 763 influencers on Instagram, and after controlling for several variables, we find robust support that consumers "like" posts that reference close social ties. Furthermore, this effect enhances when first-person pronouns are used to describe special moments with these close ties. We supplement the Instagram data with an experimental approach and confirm the relationship between close ties and consumer engagement. Managerially, this is a useful insight as we also show that sponsored posts tend to be perceived negatively compared to non-sponsored posts; yet, embedding social ties on the sponsored posts can mitigate consumers' negative responses. [ABSTRACT FROM AUTHOR]

Accurate flood prediction models and effective flood preparedness rely on thoroughly understanding rainfall–runoff dynamics. Similarly, effective rainfall–runoff models account for multiple interrelated parameters for robust runoff prediction. Process-based physical models offer valuable insights into hydrological processes, but their effectiveness can be hindered by data limitations or difficulties in acquiring specific data. Motivated by the frequent flooding events and limited data availability in the East Branch DuPage watershed, Illinois, this study addresses a critical gap in research by investigating effective discharge prediction methods. In this study, two significant machine learning (ML) models, artificial neural network (ANN) and support vector machine (SVM), were employed for discharge prediction. Historical data spanning from 2006 to 2021 were utilized to assess the performance of the models. Hyperparameter tuning was performed on the models to optimize their performance, and root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R2), and the normalized root mean squared error (NRMSE) were used as evaluation metrics. Although both machine learning models demonstrated strong performance, the analysis revealed that the ANN model emerged as the more reliable option for predicting discharge in the watershed. Crucially, the ANN model surpassed the SVM model's performance, achieving superior accuracy in predicting peak discharge events within the study area. Our findings have the potential to assist decision-makers and communities in implementing more dependable flood mitigation strategies, particularly in regions where hydrology data are limited. [ABSTRACT FROM AUTHOR]

Recently, conversation on diversity and inclusion has been at the forefront in the media as well as the workplace. Though research has examined how diversity impacts organizational culture and decision-making, little attention has been given to how corporate diversity impacts consumers' responses to the firm. This article establishes a link between diversity and the perceived morality of market actors. A series of studies demonstrate that greater diversity (racial, gender, or national) in a corporate team leads to perceptions of greater morality of the firm and its representatives and, as a consequence, results in more favorable consumer attitudes and behavior toward the firm. This positive effect arises because consumers perceive diverse teams as possessing higher perspective-taking abilities. Since marketplace morality is concerned with the greater good, we argue that higher perceptions of perspective-taking signal that the team will safeguard the broad interests of the community rather than serve narrow interest groups. The findings have broad implications since consumers are increasingly concerned with moral consumption. Our research suggests that diversity in the workforce is not only important for team performance and social equity but can shape consumers' sentiments and behavior toward the firm. [ABSTRACT FROM AUTHOR]

Floods are amongst the most destructive and costly natural disasters impacting communities around the globe. The severity and reoccurrence of flooding events have been more common in recent years as a result of the changing climate and urbanization. Best Management Practices (BMPs) are commonly used flood management techniques that aim to alleviate flooding and its impacts by capturing surface runoff and promoting infiltration. Recent studies have examined the effectiveness of BMPs in countering the effects of flooding; however, the performance of such strategies still needs to be analyzed for possible future climate change. In this context, this research employs climate model-driven datasets from the North American Regional Climate Change Assessment Program to evaluate the effects of climate change on urban hydrology within a study region by calculating historical and projected 6 h 100-year storm depths. Finally, the climate-induced design storms are simulated in the PCSWMM model, and the three BMP options (i.e., porous pavement, infiltration trench, and green roof) are evaluated to alleviate the impact of flooding events. This study quantifies the impact of changing climate on flood severity based on future climate models. The results indicate that peak discharge and peak volume are projected to increase by a range of 5% to 43% and 8% to 94%, respectively. In addition, the results demonstrated that green roofs, Permeable Pavement, and infiltration trenches help to reduce peak discharge by up to 7%, 14%, and 15% and reduce flood volume by up to 19%, 24%, and 29%, respectively, thereby presenting a promising solution to address the challenges posed by climate change-induced flooding events. [ABSTRACT FROM AUTHOR]

Using a sample of individuals who hold target retirement funds (TRFs), we examine how people use arithmetic to estimate their retirement age. We find a robust "zero" bias where investors have a strong preference for TRFs that end with zero compared to TRFs that end with five. The evidence is consistent that the bias is an outcome of people using imprecise arithmetic, specifically rounding up and down in the computational estimation required to estimate their retirement year. The zero bias manifests itself in people born in years ending between eight and two. Those born in zero- through two-ending years select TRFs that imply they intend to retire at 70, whereas those born in eight- and nine-ending years choose TRFs that imply retiring at 60. The choices can significantly lower or increase wealth by altering the contribution amounts and exposing investors to risk incompatible with their age profile. The bias is particularly costly for those who are risk averse and select later TRFs but is also most beneficial to risk-averse consumers who choose early TRFs. We experimentally confirm that the contribution rates are related to the TRF choices and that the use of imprecise mathematical rounding is implicated in the bias. [ABSTRACT FROM AUTHOR]