How Do Social Media Shape the Information Environment in the Financial Market?

Internet users create social media that enable information to be transferred more efficiently. In this work we focus on a typical social media platform Wikipedia and examine how management’s voluntary disclosure reacts to information arrivals on Wikipedia. In doing so, we seek to answer the question of whether social media indeed improve the information environment for investors in the financial market. Our analysis is based on a unique dataset collected from the modification history of firm entries on Wikipedia, and thus we are able to identify information arrivals on Wikipedia. We find that information arrivals on Wikipedia affect the timing of management disclosure of bad news, and the effect is in sharp contrast to the way in which traditional media affect management disclosure. Further, we find consistent evidence that information arrivals on Wikipedia preempt the negative reaction of the market to bad news. In contrast, more news coverage in traditional media exacerbates the problem of optimistic analyst forecasts. Together these findings emphasize that social media have an identifiable effect on both the management side and the investor side in the financial market

Between Attention and Portfolio Adjustment: Insights from Machine Learning-based Risk Preference Assessment

Financial firms recommend products to customers, intending to gain their attention and change their portfolios. Based on behavioral decision-making theory, we argue attention’s effect on portfolio adjustment is through the risk deviation between portfolio risk and their risk preference. Thus, to fully understand the adjustment process, it is necessary to assess customers’ risk preferences. In this study, we use machine learning methods to measure customers’ risk preferences. Then, we build a dynamic adjustment model and find that attention’s impact on portfolio adjustment speed is stronger when customers’ risk preference is higher than portfolio risk (which needs an upward adjustment) and when customers’ risk preference is within historical portfolio risk experience. We conducted a field experiment and found that directing customers’ attention to products addressing the risk deviation would lead to more portfolio adjustment activities. Our study illustrates the role of machine learning in enhancing our understanding of financial decision-making

Multimodal Data Augmentation for Image Captioning using Diffusion Models

Image captioning, an important vision-language task, often requires a tremendous number of finely labeled image-caption pairs for learning the underlying alignment between images and texts. In this paper, we proposed a multimodal data augmentation method, leveraging a recent text-to-image model called Stable Diffusion, to expand the training set via high-quality generation of image-caption pairs. Extensive experiments on the MS COCO dataset demonstrate the advantages of our approach over several benchmark methods, and particularly a significant boost when having fewer training instances. In addition, models trained on our augmented datasets also outperform prior unpaired image captioning methods by a large margin. Finally, further improvement regarding the training efficiency and effectiveness can be obtained after intentionally filtering the generated data based on quality assessment

Extracting Business Value from IT: A Sensemaking Perspective of Post-Adoptive Use

How can firms extract value from already-implemented information technologies (IT) that support the work processes of employees? One approach is to stimulate employees to engage in post-adoptive extended use, i.e.,to learn and apply more of the available functions of the implemented technologies to support their work. Such learning behavior of extending functions in use is ingrained in a process by which users make sense of the technologies in the context of their work system.This study draws on sensemaking theory to develop a model to understand the antecedents, contingencies, and consequences of customer service employees’ extended use of customer relationship management (CRM)technologies. The model is tested using multi-source longitudinal data collected through a field study of one of the world’s largest telecommunications service providers. Our results suggest that employees engage in post-adoptive sensemaking at two levels: technology and work system. We found that sensemaking at both of these levels impacts the extended use of CRM technologies. Employees’ sensemaking at the technology level is influenced by employees’ assessment of technology quality,while employees’ sensemaking at the work system level is influenced by customers’ assessment of servicequality. Moreover, in the case of low technology quality and low service quality, specific mechanisms for employee feedback should be conceptualized and aligned at two levels: through employee participation at the technology level and through work system coordination at the work system level. Such alignment can mitigate the undesirable effect of low technology quality and low service quality,thereby facilitating extended use. Importantly, we found that extended use amplifies employees’ service capacity, leading to better objective performance. Put together, our findings highlight the critical role of employees’ sensemaking about the implemented technologies in promoting their extended use of IT and improving their work performance

Impacts of Reminding Social Nudges on User Engagement Behaviors: Evidence from a Field Experiment

Social nudges are well recognized for their effectiveness in promoting desired behaviors. However, online information overload makes social nudges less appealing. Reminding people about social nudges may boost their efficacy. We investigate the treatment and persistent effect of a reminding social nudge on user engagement with a new function and the spillover effect on user engagement with an existing function through a large-scale randomized field experiment. Our results indicate that compared with a social nudge, the reminding social nudge reduces user engagement with the new function over the treatment period. Interestingly, after removing the nudges, users who received the reminding social nudge are more engaged with the new function than users received the social nudge. The reminding social nudges designed for the new function also have a negative spillover impact on user engagement with the existing function. Theoretical and practical implications about using nudges to introduce new functions are discussed

The Impact of Information Explicitness and Timing on Facilitating Online Learning: A Field Experiment

Online learning systems aim to support learners’ learning process by providing various kinds of information. However, scarce research has focused on examining whether such information support can indeed foster an active learning process and ultimately achieve enhanced learning outcome. This study draws upon active learning theory, which posits that effective information support should facilitate learners’ “generation” and “reflection” process. We examined two characteristics of information support to facilitate such an active learning process, information explicitness and presentation timing (during or after a learning task). A field experiment was conducted on an online learning platform. Our findings revealed that when provided during a task, less explicit information would improve learning outcomes by encouraging generation activities. Furthermore, for learners with a stronger knowledge base, more explicit information support provided after a task assisted in the reflection process, leading to improved learning outcomes. The mechanisms were revealed by using cursor tracking technology

IoT and Wearable Devices-Enhanced Information Provision of AR Glasses: A Multi-Modal Analysis in Aviation Industry

While Augmented Reality (AR) glasses are now instrumental in industries for delivering work-related information, the current one-size-fits-all information provision of AR glasses fails to cater to diverse workers’ needs and environmental conditions. We propose a framework for harnessing Internet of thing (IoT) and wearable technology to improve the adaptability and customization of information provision by AR. As a preliminary exploration, this short paper develops a multi-modal data processing system for work performance classification in the aviation industry. Using machine learning algorithms for multi-modal feature extraction and classifier construction, this framework provides a more objective and consistent evaluation of work performance compared to single-modal approaches. The proposed analytics architecture can provide valuable insights for other industries struggling to implement IoT and mixed reality

Isolated Diatomic Ni-Fe Metal-Nitrogen Sites for Synergistic Electroreduction of CO2

Polynary single‐atom structures can combine the advantages of homogeneous and heterogeneous catalysts while providing synergistic functions based on different molecules and their interfaces. However, the fabrication and identification of such an active‐site prototype remain elusive. Here we report isolated diatomic Ni‐Fe sites anchored on nitrogenated carbon as an efficient electrocatalyst for CO2 reduction. The catalyst exhibits high selectivity with CO Faradaic efficiency above 90 % over a wide potential range from −0.5 to −0.9 V (98 % at −0.7 V), and robust durability, retaining 99 % of its initial selectivity after 30 hours of electrolysis. Density functional theory studies reveal that the neighboring Ni‐Fe centers not only function in synergy to decrease the reaction barrier for the formation of COOH* and desorption of CO, but also undergo distinct structural evolution into a CO‐adsorbed moiety upon CO2 uptake.This research was undertaken with the assistance of resources provided by the National Computing Infrastructure (NCI) facility at the Australian National University allocated through both the National Computational Merit Allocation Scheme supported by the Australian Government and the Australian Research Council grant LE160100051 (Maintaining and enhancing merit-based access to the NCI National Facility, 2016–2018). This work was supported by the Australian Research Council (DP160103107, FT170100224)

10KP: A phylodiverse genome sequencing plan.

Understanding plant evolution and diversity in a phylogenomic context is an enormous challenge due, in part, to limited availability of genome-scale data across phylodiverse species. The 10KP (10,000 Plants) Genome Sequencing Project will sequence and characterize representative genomes from every major clade of embryophytes, green algae, and protists (excluding fungi) within the next 5 years. By implementing and continuously improving leading-edge sequencing technologies and bioinformatics tools, 10KP will catalogue the genome content of plant and protist diversity and make these data freely available as an enduring foundation for future scientific discoveries and applications. 10KP is structured as an international consortium, open to the global community, including botanical gardens, plant research institutes, universities, and private industry. Our immediate goal is to establish a policy framework for this endeavor, the principles of which are outlined here