The Effects of Firm Characteristics on Investor Reaction to IT Investment Announcements

This paper examines the effects of firm characteristics measured by price-to-book (PB) ratio, free cash flow (FCF), and variability of daily stock return (VDR) on investor reaction in the stock market to IT investment announcements. In contrast to previous studies, which focused exclusively on whether or not IT investment announcements led to an abnormal return in the market, this study investigates the extent to which firm characteristics influence the direction and magnitude of cumulative abnormal returns (CARs). Although these firm characteristics critically affect investor reaction to IT investment announcements, existing event studies in the IT literature pay scant attention to them. In spite of the same IT investment (say, developing an ERP system) announcement, the market reaction would vary due to the heterogeneity in financial situations under which the firm operates before the announcement. Contrary to previous studies, the results suggest that IT investment announcements result in significant abnormal returns around the event announcement date when only the announcements made by investing firms were considered. We provide some empirical evidence that investments in IT can have a great impact on firm value. With regard to the firm characteristics in relation to CARs, PB ratio, and variability of daily stock returns significantly affect the investorsí reaction to IT investment announcements. Finally, this study shows IT investment decision makers the implications of drawing greater attention from investors when making IT investment announcements

MatGD: Materials Graph Digitizer

We have developed MatGD (Material Graph Digitizer), which is a tool for digitizing a data line from scientific graphs. The algorithm behind the tool consists of four steps: (1) identifying graphs within subfigures, (2) separating axes and data sections, (3) discerning the data lines by eliminating irrelevant graph objects and matching with the legend, and (4) data extraction and saving. From the 62,534 papers in the areas of batteries, catalysis, and MOFs, 501,045 figures were mined. Remarkably, our tool showcased performance with over 99% accuracy in legend marker and text detection. Moreover, its capability for data line separation stood at 66%, which is much higher compared to other existing figure mining tools. We believe that this tool will be integral to collecting both past and future data from publications, and these data can be used to train various machine learning models that can enhance material predictions and new materials discovery.Comment: 23 pages, 4 figure

Graph-Network-Based Predictive Modeling for Highly Cross-Linked Polymer Systems

In this study, a versatile methodology for initiating polymerization from monomers in highly cross-linked materials is investigated. As polymerization progresses, force-field parameters undergo continuous modification due to the formation of new chemical bonds. This dynamic process not only impacts the atoms directly involved in bonding, but also influences the neighboring atomic environment. Monitoring these complex changes in highly cross-linked structures poses a challenge. To address this issue, we introduce a graph-network-based algorithm that offers both rapid and accurate predictions. The algorithm merges polymer construction protocols with LAMMPS, a large-scale molecular dynamics simulation software. The adaptability of this code has been demonstrated by its successful application to various amorphous polymers, including porous polymer networks (PPNs), and epoxy-resins, while the algorithm has been employed for additional tasks, such as implementing pore-piercing deformations and calculating material properties

The risk analysis for the introduction of collaborative robots in the Republic of Korea

Due to an increasing demand for collaborative robots, called cobots , in industrial settings, this study aims to predict the chance of accidents occurring due to the introduction of cobots in the Korean manufacturing industry determined by a risk model applied Bayesian belief network. This will suggest effective risk mitigation measures. This study focuses on the types of safety monitored stop, as well as distance and speed control which have a higher collision chance compared to the types of power and force limiting which allow for injury-free contact and that of hand guiding which allows the cobot to move itself only by clear user\u27s manipulation. The factors that impact annual accident probability are built on the grounds of the analysis of occupational injuries and fatalities by industrial robots. These factors were then categorized into human, organizational, and technical errors. Each factor\u27s probability was employed from the result of national statistics. If a probability was not available, notional probability was applied based on extensive literature reviews, and author\u27s experiences over 10 years in the occupational safety and health fields due to it is scarce elsewhere. The risk model is constructed with two decision nodes - the employer\u27s and the policymaker\u27s view - and twelve uncertainty nodes. The model showed that the estimated annual accident probability was the same as the average accident rate of the entire manufacturing industry of the Republic of Korea in 2018. This could be interpreted as average-risky . Additionally, the influential factors were analyzed by a sensitivity analysis. By understanding which factors are highly influential, this study suggests three key measures to mitigate the risk by the introduction of cobots in the stages of design and manufacturing, installation, and usage. Researchers and OSH stakeholders may customize the model to assess the risk by the introduction of cobots

Real-time delay-multiply-and-sum beamforming with coherence factor for in vivo clinical photoacoustic imaging of humans

In the clinical photoacoustic (PA) imaging, ultrasound (US) array transducers are typically used to provide B-mode images in real-time. To form a B-mode image, delay-and-sum (DAS) beamforming algorithm is the most commonly used algorithm because of its ease of implementation. However, this algorithm suffers from low image resolution and low contrast drawbacks. To address this issue, delay-multiply-and-sum (DMAS) beamforming algorithm has been developed to provide enhanced image quality with higher contrast, and narrower main lobe compared but has limitations on the imaging speed for clinical applications. In this paper, we present an enhanced real-time DMAS algorithm with modified coherence factor (CF) for clinical PA imaging of humans in vivo. Our algorithm improves the lateral resolution and signal-to-noise ratio (SNR) of original DMAS beam-former by suppressing the background noise and side lobes using the coherence of received signals. We optimized the computations of the proposed DMAS with CF (DMAS-CF) to achieve real-time frame rate imaging on a graphics processing unit (GPU). To evaluate the proposed algorithm, we implemented DAS and DMAS with/without CF on a clinical US/PA imaging system and quantitatively assessed their processing speed and image quality. The processing time to reconstruct one B-mode image using DAS, DAS with CF (DAS-CF), DMAS, and DMAS-CF algorithms was 7.5, 7.6, 11.1, and 11.3 ms, respectively, all achieving the real-time imaging frame rate. In terms of the image quality, the proposed DMAS-CF algorithm improved the lateral resolution and SNR by 55.4% and 93.6 dB, respectively, compared to the DAS algorithm in the phantom imaging experiments. We believe the proposed DMAS-CF algorithm and its real-time implementation contributes significantly to the improvement of imaging quality of clinical US/PA imaging system.11Ysciescopu

An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM

Stimulated by the sophisticated reasoning capabilities of recent Large Language Models (LLMs), a variety of strategies for bridging video modality have been devised. A prominent strategy involves Video Language Models (VideoLMs), which train a learnable interface with video data to connect advanced vision encoders with LLMs. Recently, an alternative strategy has surfaced, employing readily available foundation models, such as VideoLMs and LLMs, across multiple stages for modality bridging. In this study, we introduce a simple yet novel strategy where only a single Vision Language Model (VLM) is utilized. Our starting point is the plain insight that a video comprises a series of images, or frames, interwoven with temporal information. The essence of video comprehension lies in adeptly managing the temporal aspects along with the spatial details of each frame. Initially, we transform a video into a single composite image by arranging multiple frames in a grid layout. The resulting single image is termed as an image grid. This format, while maintaining the appearance of a solitary image, effectively retains temporal information within the grid structure. Therefore, the image grid approach enables direct application of a single high-performance VLM without necessitating any video-data training. Our extensive experimental analysis across ten zero-shot video question answering benchmarks, including five open-ended and five multiple-choice benchmarks, reveals that the proposed Image Grid Vision Language Model (IG-VLM) surpasses the existing methods in nine out of ten benchmarks.Comment: Our code is available at https://github.com/imagegridworth/IG-VL

The Pursuit of Conversion: Effects of Mediating Channels on Product Choices and Purchase Propensities in Social Commerce Platforms

This study elucidates the effectiveness of intermediary channels in driving sales at social commerce sites (SCSs). Using a panel data, we investigate how the external intermediary channels through which consumers arrive at SCSs influence product choice and purchase likelihood. In addition, we scrutinize the extent to which product categories with varying quality moderate the relationship between consumers’ channel-related behaviors and purchase propensities. Furthermore, we examine how external channels “collaborate” with internal channels to increase purchase likelihood. The findings suggest that consumers who enter the SCS through direct apps and portals engage in more proactive purchasing than do consumers landing at the SCS via metasites or e-mail promotions. Consumers who are directed to the SCS through metasites or e-mail promotions are more likely to purchase experience goods than search goods. Contrary to previous findings, consumers’ purchasing propensities decline, rather than increase, across all channels after the implementation of a recommendation system

One-Sided Competition in Two-Sided Social Platform Markets? An Organizational Ecology Perspective

Similar to love, competition can often be unrequited. This study explores the asymmetric pattern of competition driven by membership overlap in two-sided mobile social apps (MSAs) markets. Building on the niche-width dynamics framework, we theorize and validate the relative prevalence and survival capabilities of messaging apps and SNS apps, especially when membership overlap fosters current or potential competition between the two app categories. The analyses—based on panel dataset consisting of information on 8,483 panel members’ exact amount of time used for 21 mobile social apps—show that competition between SNS and messaging apps can be asymmetric in favor of messaging apps. This asymmetric pattern is more pronounced for membership-based competition compared to usage-based competition. In addition, different MSAs developed by same platform providers exhibit synergistic effects, rather than destructive consequences, on each other’s growth. The findings identify the complex nature of competition within-category and between-category competition in MSAs markets