|
on Information and Communication Technologies |
| By: | Ek-anong Tangrukwaraskul (Faculty of Business Administration, Kasetsart University); Kiriya Kulchanarat (Faculty of Business Administration, Kasetsart University) |
| Abstract: | Thailand's information and communications technology (ICT) sector is experiencing rapid growth according to several factors including increase in technology adoption and government initiatives. This study aims to propose a multi-criteria decision-making (MCDC) model to measure and to compare the financial performance of eighteen ICT firms listed firms in Stock Exchange of Thailand. These firms are examined and assessed using eight financial ratios including liquidity, profitability, leverage, operating and market value ratios collected from DataStream International and Stock Exchange of Thailand for three-year period between 2021 and 2023 to obtain a financial performance score using Technique for Order Preference by Similarity to Ideal Solution Methods (TOPSIS). This study also examines whether the firms can keep their ranking position throughout the three years. This could provide investors with an additional piece of information on making investment decisions. |
| Keywords: | Entropy weight TOPSIS, ICT Sector, Financial Performance, Thailand |
| JEL: | M15 L66 C60 |
| URL: | https://d.repec.org/n?u=RePEc:sek:iefpro:14716382 |
| By: | Dawei Cheng; Yao Zou; Sheng Xiang; Changjun Jiang |
| Abstract: | The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology. This complexity poses greater challenges in detecting and managing financial fraud. This review explores the role of Graph Neural Networks (GNNs) in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection. Specifically, by examining a series of detailed research questions, this review delves into the suitability of GNNs for financial fraud detection, their deployment in real-world scenarios, and the design considerations that enhance their effectiveness. This review reveals that GNNs are exceptionally adept at capturing complex relational patterns and dynamics within financial networks, significantly outperforming traditional fraud detection methods. Unlike previous surveys that often overlook the specific potentials of GNNs or address them only superficially, our review provides a comprehensive, structured analysis, distinctly focusing on the multifaceted applications and deployments of GNNs in financial fraud detection. This review not only highlights the potential of GNNs to improve fraud detection mechanisms but also identifies current gaps and outlines future research directions to enhance their deployment in financial systems. Through a structured review of over 100 studies, this review paper contributes to the understanding of GNN applications in financial fraud detection, offering insights into their adaptability and potential integration strategies. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05815 |
| By: | Beuselinck, Christof; Karavitis, Panagiotis; Kazakis, Pantelis; Mouna, Niswatil |
| Abstract: | This study examines the impact of e-government advancements on corporate tax planning activities. We define e-government as the readiness and capacity of national institutions to use information and communications technologies to deliver public services. Using over 82, 000 worldwide firm-level data from 10, 936 unique firms in 56 countries over the period 2008-2021, we observe a negative association between a country’s e-government advancement and the overall tax avoidance practices of firms. Via path analysis we identify the underlying mechanisms through which e-government affects corporate tax avoidance and document that the total tax enforcement budget but also specific technological features such as AI-machine learning, and robotic process automation explains a sizeable fraction of the negative relationship between e-government advancements and corporate tax avoidance. Additionally, our cross-sectional analysis reveals that the impact of e-government on curbing tax planning is particularly pronounced in environments where firms traditionally accrue tax benefits via investments into organizational capital. Our main findings remain robust after implementing an instrumental variables strategy and conducting various robustness tests. Collectively, our findings indicate that e-government investments can help raise a nation’s tax revenue collection, as such investments are linked to reduced corporate tax avoidance activities. |
| Keywords: | tax avoidance, tax planning, digitalization, e-government, digital governments |
| JEL: | G30 G38 H26 L1 M41 M48 |
| Date: | 2024–11–18 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122742 |
| By: | Kapoor, Amita; Singh, Narotam; Chaudhary, Vaibhav; Singh, Nimisha; Soni, Neha |
| Abstract: | This paper explores the transformative impact of Generative AI (GenAI) on the business landscape, examining its role in reshaping traditional business models, intensifying market competition, and fostering innovation. By applying the principles of Neo-Schumpeterian economics, the research analyses how GenAI is driving a new wave of "creative destruction, " leading to the emergence of novel business paradigms and value propositions. This research incorporates a novel AI-augmented SPAR-4-SLR framework as a key component, offering a systematic and innovative approach to analysing the rapidly evolving GenAI domain. By leveraging co-occurrence network analysis and LLM-based evaluation, this methodology identifies interdisciplinary trends and highlights diverse applications of GenAI. Beyond this, the study extends its scope to explore insights from internet-scraped data, Twitter analytics, and company reports, providing a comprehensive understanding of how GenAI is transforming businesses. This multi-faceted approach underscores GenAI's profound impact across industries such as technology, healthcare, and education, revealing its role in enhancing operational efficiency, driving product and service innovation, and creating new revenue streams. However, the deployment of GenAI also presents significant challenges, including ethical concerns, regulatory demands, and the risk of job displacement. By addressing the multifarious nature of GenAI, this paper provides valuable insights for business leaders, policymakers, and researchers, guiding them towards a balanced and responsible integration of this transformative technology. Ultimately, GenAI is not merely a technological advancement but a driver of profound change, heralding a future where creativity, efficiency, and growth are redefined. |
| Date: | 2024–11–20 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:khptm |
| By: | Challoumis, Constantinos |
| Abstract: | There's a profound transformation occurring in employment and income distribution, primarily driven by the rise of artificial intelligence. In recent years, automation and machine learning technologies have begun to reshape industries, challenging our long-held perceptions of work and economic structures. As AI systems become increasingly sophisticated, they not only enhance productivity but also raise pivotal questions about job displacement and wealth inequality. This blog post examines into the intricate relationship between AI and the economy, exploring the implications for the workforce and our society at large. |
| Keywords: | AI, economy, technology, employment, income distribution |
| JEL: | P0 P3 Z1 Z18 Z19 |
| Date: | 2024–11–19 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122722 |
| By: | Neha Sanjay Ahuja |
| Abstract: | Several organizations are directing Artificial Intelligence (AI) driven expertise to assist and analyze data insights, identify gaps, and transform their decision-making proficiency, especially under high pressure and tight timelines. This research article investigates the impact of AI on decision-making and its consequences for individuals, companies, and society. Decision-making is crucial for achieving organizational goals. Accurate data, reports, and decisions enhance business predictions, transform strategies, enable timely mid-stage implementation reviews, facilitate quick decisions, boost productivity, and lead businesses toward success and future growth. This article also examines how AI transforms internal operations across various departments, from transport to consultation management, predicting demands, adjusting supply and data levels, optimizing results, and reducing operating costs. AI software enables data-driven choices, improves customer targeting, enhances development, and customizes reports for precise strategic decisions. The article provides an overview of AI mechanisms such as automation efficiency and complex management for multilayered issues beyond human capacity, succession planning, and risk monitoring, benefiting departments like HR, Finance, Sales, and Marketing, and industries including Healthcare, Finance, Consulting, Transportation, and Food & Beverage, as well as government authorities in process automation. Various technologies and tools globally facilitate data-driven decision-making. This article highlights the positive impact of AI on management operations and company success while acknowledging that incorrect decisions may disappoint organizations. As AI enhances decision-making, challenges like ethical concerns, algorithmic biases, social implications, and the Human-AI partnership need addressing. Data privacy, transparency, accountability, and explainability are essential for reputation management. Companies must prioritize ethical AI practices and transparency, ensuring unbiased algorithms. This article focuses on governance, regulations, and policies to mitigate biases and ensure AI aligns with organizational goals, with an emphasis on improving AI functionality. |
| Keywords: | Artificial Intelligence, transformation, complex management, quick business decision, timelines, increase productivity, cost reduction, business success, augmentation, accuracy, ethical and social concern, data protection, transparency, bias, Government regulations |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:bfv:sbsrec:001 |