Hasil untuk "Accounting. Bookkeeping"

Jie Zhou, Xin Chen, Jie Zhang et al.

Large language models are transforming learning, cognition, and research across many fields. Effectively integrating them into professional domains, such as accounting, is a key challenge for enterprise digital transformation. To address this, we define vertical domain accounting reasoning and propose evaluation criteria derived from an analysis of the training data characteristics of representative GLM models. These criteria support systematic study of accounting reasoning and provide benchmarks for performance improvement. Using this framework, we evaluate GLM-6B, GLM-130B, GLM-4, and OpenAI GPT-4 on accounting reasoning tasks. Results show that prompt design significantly affects performance, with GPT-4 demonstrating the strongest capability. Despite these gains, current models remain insufficient for real-world enterprise accounting, indicating the need for further optimization to unlock their full practical value.

I. Grigg

Classical double entry accounting has provided the foundation for accounting within the firm for many centuries. The digitally signed receipt, an innovation from financial cryptography, gives rise to exactly duplicated entries for each of 3 parties or roles, the outcome of which we call triple entry accounting. This presents a challenge to double entry bookkeeping by expanding the use of accounting from inside firms to activity between the firms. When applied to digital cash and digital assets, the approach of negotiating a single signed receipt between parties lowers costs by delivering reliable data to support stronger accounting, and makes much stronger governance possible in a way that positively impacts on the future needs of corporate and public accounting. By turning the opinions of firm owners into facts agreed between firms, triple entry bookkeeping creates the bulletproof accounting layer to support aggressive uses and adversarial users such as are found in the Bitcoin system of transactions.

P. W., Wickramasekara, G.D.A., Imalie, A.D.K. et al.

Bookkeeping is an important factor in the field of management that plays a major role in the sustainability of any business institution. Poor accounting often results in poor management of finance flow, slow business growth, and challenges in acquiring business credits from formal financial institutes. In Matara District in Sri Lanka, micro-enterprises play a remarkable role in the retail market and the incomes of many households. However, evidence shows that many micro-enterprises experience a lot of barriers in bookkeeping. Therefore, this study seeks to find out the bookkeeping issues of micro-enterprises in the retail sector of Matara District in Sri Lanka. This research explores the main challenges that limit standard bookkeeping practices and provides recommendations on how to overcome those discovered challenges. This research utilizes a qualitative research method to identify the bookkeeping issues experienced by micro-enterprises operating in the retail sector of the Matara District, Sri Lanka. The sampling method utilized in the research was the purposive sampling method. Twenty participants were selected for the interviews. The primary data was obtained through semi-structured interviews. The data collected were analyzed by the Thematic Analysis method. The analysis of the study highlights four primary themes: Limited Financial Literacy, Resource Constraints, Technological Gaps, and Regulatory Challenges. This study contributed to the existing literature on micro-enterprises by presenting a deep insight into the bookkeeping issues existing among micro-enterprises in a developing economy. The research provides recommendations for policymakers, development institutions, and micro-enterprise owners to develop bookkeeping practices and improve the basic accounting literacy of the micro-enterprises. The research stresses the requirement of educational programs for the development of bookkeeping literacy and the application of user-friendly technologies in the field of micro-enterprises. The requirement of governmental support measures is proposed for the improvement of bookkeeping. Finally, the study helps micro-enterprises to be sustainable, transparent, and competitive by improving performance, productivity, and efficiency, which is critically important for the development of Districts in developing economies.

Theophanis C. Stratopoulos, Victor Xiaoqi Wang

Recent advances in artificial intelligence, particularly generative AI (GenAI) and large language models (LLMs), are fundamentally transforming accounting research, creating both opportunities and competitive threats for scholars. This paper proposes a framework that classifies AI-accounting research along two dimensions: research focus (accounting-centric versus AI-centric) and methodological approach (AI-based versus traditional methods). We apply this framework to papers from the IJAIS special issue and recent AI-accounting research published in leading accounting journals to map existing studies and identify research opportunities. Using this same framework, we analyze how accounting researchers can leverage their expertise through strategic positioning and collaboration, revealing where accounting scholars' strengths create the most value. We further examine how GenAI and LLMs transform the research process itself, comparing the capabilities of human researchers and AI agents across the entire research workflow. This analysis reveals that while GenAI democratizes certain research capabilities, it simultaneously intensifies competition by raising expectations for higher-order contributions where human judgment, creativity, and theoretical depth remain valuable. These shifts call for reforming doctoral education to cultivate comparative advantages while building AI fluency.

Claire Li, David Freeborn

This study explores how AI-powered digital innovations are reshaping organisational accountability in a transnational governance context. As AI systems increasingly mediate decision-making in domains such as auditing and financial reporting, traditional mechanisms of accountability, based on control, transparency, and auditability, are being destabilised. We integrate the Technology Acceptance Model (TAM), Actor-Network Theory (ANT), and institutional theory to examine how organisations adopt AI technologies in response to regulatory, ethical, and cultural pressures that transcend national boundaries. We argue that accountability is co-constructed within global socio-technical networks, shaped not only by user perceptions but also by governance logics and normative expectations. Extending TAM, we incorporate compliance and legitimacy as key factors in perceived usefulness and usability. Drawing on ANT, we reconceptualise accountability as a relational and emergent property of networked assemblages. We propose two organisational strategies including internal governance reconfiguration and external actor-network engagement to foster responsible, legitimate, and globally accepted AI adoption in the accounting domain.

Renée Menéndez, Viktor Winschel

We present a category theoretical formulation of the Monetary Macroeconomic Accounting Theory (MoMaT) of Menéndez and Winschel [2025]. We take macroeconomic (national) accounting systems to be composed from microeconomic double-entry systems with real and monetary units of accounts. Category theory is the compositional grammar and module system of mathematics which we use to lift micro accounting consistency to the macro level. The main function of money in MoMaT is for the repayment of loans and not for the exchange of goods, bridging the desynchronisation of input and output payments of producers. Accordingly, temporal accounting consistency is at the macroeconomic level. We show that the accounting for macroeconomies organised by a division of labor can be consistent and stable as a prerequisite for risk and GDP sharing of societies. We exemplify the theory by five sectoral agents of Labor and Resource owners, a Company as the productive sector, a Capitalist for profits, and a Bank as the financial sector providing loans to synchronise the micro and the macro levels of an economy. The dynamics is described by eight sectoral macroeconomic bookings in each period demonstrating stable convergence of the MoMaT in numerical simulations. The categorical program implements a consistent evolution of hierarchical loan repayment contracts by an endofunctor. The universal constructions of a limit verify all constraints as the sectoral investment and learning function at the macroeconomic level. The dual colimit computes the aggregated informations at the macro level as usual in the mathematics of transitions from local to global structures. We use visual diagrams to make complex economic relationships intuitive. This paper is meant to map economic to categorical concepts to enable interdisciplinary collaboration for digital twins of monetary accounting systems.

Abdul Galib, Nurwahyuni Syahrir, Hasnidar Hasnidar

Main Purpose -  This study aims to reveal the role of awareness of sustainable practices, culture, and perceived behavioral control in improving tax compliance as an effort to maintain sustainable business practices for MSMEs in West Sulawesi by internalizing the Pappasang Kalindaqdaq Mandar. Method -  The research method used in this study is a mixed method with a concurrent model to analyze quantitatively and qualitatively simultaneously. Main Findings -  The results confirmed the theory of planned behavior, whereby awareness of sustainable practices and culture has a significant influence on increasing MSMEs' intention to behave in a compliant manner towards taxation, but perceived behavioral control did not have a significant influence. These findings indicate that aspects of awareness of sustainable practices and internalization of Pappasang Kalindaqdaq Mandar culture have a strong dominance in explaining MSME tax compliance in West Sulawesi. Theory and Practical Implications - The strong dominance of tax awareness and culture, but not accompanied by a significant influence on perceived behavioral control, requires further investigation. Further in-depth interviews are needed to obtain more in-depth information from tax authorities and MSMEs to uncover the actual role of control.  Novelty -  This research explores non-economic aspects from various perspectives such as awareness of sustainable practices (internal), culture (external), and perceived behavioral control (control belief) in improving tax compliance (external).

Mabonga Eric, Nankya Christine Kiige, Omache Henry et al.

This study delves into the relationship between bookkeeping practices and the financial performance of agribusiness enterprises in Mbale City. A mixed-method approach was employed, combining quantitative data collected via questionnaires and qualitative insights gathered from interviews with management personnel. The findings reveal a significant correlation between robust bookkeeping practices and enhanced financial performance. Statistical analyses, including regression models and ANOVA, affirm that effective bookkeeping accounts for a substantial portion of the variance in financial performance among these enterprises. Notably, the study showcases that bookkeeping practices positively impact financial performance by nearly 30%. These results underscore the critical role of meticulous record-keeping in shaping the fiscal health and future strategies of agribusiness enterprises. The study aligns with previous research highlighting the pivotal link between accounting records, business performance, and informed decision-making. Interviews with participants further confirm the emphasis placed on comprehensive record-keeping in driving operational efficiency and strategic planning within these enterprises.

Atasiah Kathrin Belen, Angel Joy Atilano, Cecilia Fajarda et al.

Small and Midsize enterprises (SMEs) play a crucial role in many nations by contributing to employment, income generation, poverty reduction, and economic development. This study aimed to assess the challenges encountered by Small and Midsize Enterprises in bookkeeping and tax compliance. It used quantitative descriptive research design to investigate the accounting practices of 268 small and medium-sized enterprises (SMEs) in Batangas City using survey questionnaire. The data was encoded, tallied, and interpreted using various statistical tools such as frequency and percentage distribution. All data was treated using a statistical software called SPSS version 28 to further interpret the results of the study using an alpha level of 0.05. Majority of the respondents were operating for 1-3 years, merchandising, sole proprietor and with in-house accounting department. Resource constraints, compliance practices, and knowledgeable factors often affect the bookkeeping and tax compliance of SMEs in Batangas City. There are highly significant differences in responses when grouped according to profile variables related to years in operation, form of business ownership, and accounting department. While significant in resource constraints and knowledge, there is no significant relationship for compliance practices related to the nature of business. A proposed strategy was developed to address the challenges encountered.

Cristine Mae Casas, Eunice Cariaga, Khate Marie Adlawan et al.

Carriaga Traders Mart (CTM), an established retail business, has transitioned from a small grocery store to a thriving enterprise, offering a diverse array of affordable products. Their sustained success is attributed to meticulous bookkeeping practices. This study explores how a family-owned business can effectively implement bookkeeping practices to address unique bookkeeping challenges, offering valuable insights from CTM's experiences that benefit entrepreneurs, investors, and policymakers. Positive Accounting Theory (PAT) underpinned this investigation, shedding light on how economic, political, market factors, and contracting considerations influence bookkeeping practices, particularly in the context of CTM. The research employed a qualitative case study approach. Through extensive interviews with key financial management personnel, it gained comprehensive insights into the company's bookkeeping and financial practices. Participants included individuals directly involved in CTM's bookkeeping and financial management, meticulously selected based on their expertise in the field. The study's findings underscored CTM's meticulous bookkeeping practices, encompassing technology-driven sales recording, rigorous expense management, and a commitment to financial transparency. The study recommends that entrepreneurs adopt advanced accounting software and technology to enhance efficiency, implement risk mitigation strategies, and prioritize cross-functional personnel training to strengthen their financial practices. For financial managers, the study suggests investing in ongoing training, diversifying financial tools and methods, and emphasizing skill development to navigate the ever-evolving business landscape effectively. In conclusion, this research contributes to the discourse on bookkeeping practices, bridging theoretical insights from PAT with practical knowledge from CTM. It underscores the importance of a versatile and adaptive approach to bookkeeping practices in real-world business settings.

Jiayuan Luo, Songhua Yang, Xiaoling Qiu et al.

Large Language Models (LLMs) like ChatGPT and GPT-4 have demonstrated impressive proficiency in comprehending and generating natural language. However, they encounter difficulties when tasked with adapting to specialized domains such as accounting. To address this challenge, we introduce Kuaiji, a tailored Accounting Large Language Model. Kuaiji is meticulously fine-tuned using the Baichuan framework, which encompasses continuous pre-training and supervised fine-tuning processes. Supported by CAtAcctQA, a dataset containing large genuine accountant-client dialogues, Kuaiji exhibits exceptional accuracy and response speed. Our contributions encompass the creation of the first Chinese accounting dataset, the establishment of Kuaiji as a leading open-source Chinese accounting LLM, and the validation of its efficacy through real-world accounting scenarios.

Rahul Kumar, Amar Raja Dibbu, Shrutendra Harsola et al.

Several large-scale datasets (e.g., WikiSQL, Spider) for developing natural language interfaces to databases have recently been proposed. These datasets cover a wide breadth of domains but fall short on some essential domains, such as finance and accounting. Given that accounting databases are used worldwide, particularly by non-technical people, there is an imminent need to develop models that could help extract information from accounting databases via natural language queries. In this resource paper, we aim to fill this gap by proposing a new large-scale Text-to-SQL dataset for the accounting and financial domain: BookSQL. The dataset consists of 100k natural language queries-SQL pairs, and accounting databases of 1 million records. We experiment with and analyze existing state-of-the-art models (including GPT-4) for the Text-to-SQL task on BookSQL. We find significant performance gaps, thus pointing towards developing more focused models for this domain.

Yuxiang Sun, Jingyi Li, Mengdie Lu et al.

Big data revolutionizes accounting and auditing, offering deep insights but also introducing challenges like data privacy and security. With data from IoT, social media, and transactions, traditional practices are evolving. Professionals must adapt to these changes, utilizing AI and machine learning for efficient data analysis and anomaly detection. Key to overcoming these challenges are enhanced analytics tools, continuous learning, and industry collaboration. By addressing these areas, the accounting and auditing fields can harness big data's potential while ensuring accuracy, transparency, and integrity in financial reporting. Keywords: Big Data, Accounting, Audit, Data Privacy, AI, Machine Learning, Transparency.

Vijaya Kanaparthi

Continuous innovations profoundly impact the financial and commercial domains, reshaping conventional business practices. Among the disruptive forces, Artificial Intelligence (AI), Machine Learning (ML), and blockchain technology stand out prominently. This study aims to evaluate the integration of blockchain, AI, and ML within financial accounting practices. It suggests a potential revolutionary impact on financial accounting through the adoption of blockchain technology and ML, promising reduced accounting expenses, heightened precision, real-time financial reporting capabilities, and expeditious auditing processes. AI's role in automating repetitive financial accounting tasks assists organizations in circumventing the need for additional staff, thereby minimizing associated costs. Consequently, to bolster efficiency, businesses are increasingly embracing blockchain technology and AI applications in their financial accounting operations.

Mengming Michael Dong, Theophanis C. Stratopoulos, Victor Xiaoqi Wang

This paper provides a review of recent publications and working papers on ChatGPT and related Large Language Models (LLMs) in accounting and finance. The aim is to understand the current state of research in these two areas and identify potential research opportunities for future inquiry. We identify three common themes from these earlier studies. The first theme focuses on applications of ChatGPT and LLMs in various fields of accounting and finance. The second theme utilizes ChatGPT and LLMs as a new research tool by leveraging their capabilities such as classification, summarization, and text generation. The third theme investigates implications of LLM adoption for accounting and finance professionals, as well as for various organizations and sectors. While these earlier studies provide valuable insights, they leave many important questions unanswered or partially addressed. We propose venues for further exploration and provide technical guidance for researchers seeking to employ ChatGPT and related LLMs as a tool for their research.

Palloma Rossany Maciel Rodrigues Oliveira, Gilberto José Miranda, Janser Moura Pereira

Esta pesquisa teve como objetivo investigar o efeito da legibilidade do Relato Integrado (RI) na captação de recursos e na geração de valor em empresas do setor de energia elétrica no Brasil. Por meio de modelos de regressão linear e modelos Probit com estrutura de dados em painel, a pesquisa examinou a relação entre a legibilidade do RI, medida pelo índice de Flesch e variáveis como grau de alavancagem financeira (GAF), ratings de crédito, oferta pública de ações (IPO e Follow-on) e o valor das empresas (com o Q de Tobin como proxy). A amostra consistiu em 23 empresas listadas na B3, abrangendo um período de 10 anos (2013 a 2022). Os resultados indicam que, para a amostra investigada, a legibilidade não está relacionada com a captação de recursos e geração de valor. Conjecturamos que esses resultados podem estar relacionados a fatores específicos do cenário analisado, como país, setor e período. Este estudo contribui para a compreensão da avaliação do conteúdo do RI no mercado brasileiro e contribui para o debate junto a estudos internacionais, destacando a legibilidade como um aspecto que diverge de estudos anteriores, sendo desconsiderado por investidores e credores no setor de energia elétrica no Brasil.

Timur Babakol, Yu David Liu

With the rapid growth of Artificial Intelligence (AI) applications supported by deep learning (DL), the energy efficiency of these applications has an increasingly large impact on sustainability. We introduce Smaragdine, a new energy accounting system for tensor-based DL programs implemented with TensorFlow. At the heart of Smaragdine is a novel white-box methodology of energy accounting: Smaragdine is aware of the internal structure of the DL program, which we call tensor-aware energy accounting. With Smaragdine, the energy consumption of a DL program can be broken down into units aligned with its logical hierarchical decomposition structure. We apply Smaragdine for understanding the energy behavior of BERT, one of the most widely used language models. Layer-by-layer and tensor-by-tensor, Smaragdine is capable of identifying the highest energy/power-consuming components of BERT. Furthermore, we conduct two case studies on how Smaragdine supports downstream toolchain building, one on the comparative energy impact of hyperparameter tuning of BERT, the other on the energy behavior evolution when BERT evolves to its next generation, ALBERT.

Jiachen T. Wang, Saeed Mahloujifar, Tong Wu et al.

Bounding privacy leakage over compositions, i.e., privacy accounting, is a key challenge in differential privacy (DP). The privacy parameter ($\eps$ or $δ$) is often easy to estimate but hard to bound. In this paper, we propose a new differential privacy paradigm called estimate-verify-release (EVR), which addresses the challenges of providing a strict upper bound for privacy parameter in DP compositions by converting an estimate of privacy parameter into a formal guarantee. The EVR paradigm first estimates the privacy parameter of a mechanism, then verifies whether it meets this guarantee, and finally releases the query output based on the verification result. The core component of the EVR is privacy verification. We develop a randomized privacy verifier using Monte Carlo (MC) technique. Furthermore, we propose an MC-based DP accountant that outperforms existing DP accounting techniques in terms of accuracy and efficiency. Our empirical evaluation shows the newly proposed EVR paradigm improves the utility-privacy tradeoff for privacy-preserving machine learning.

Azzouz Elhamma

Research Question: Does Covid-19 crisis moderate significantly the relationship between mandatory International Financial Reporting Standards (IFRS) adoption and economic growth in developing countries, especially in the MENA (Middle East and North Africa) region and SSA (Sub-Saharan Africa) countries? Motivation: Two sources of motivation are behind this study. First, research works on the impact of mandatory IFRS adoption on macroeconomic indicators such as economic growth are still scarce. Second, studying the impact of mandatory IFRS adoption on economic growth before and during the Covid-19 crisis allows to better understand this relationship in times of crisis. Idea: This article aims to investigate the moderating role of Covid-19 crisis in the relationship between mandatory IFRS adoption and economic growth in developing countries. Tools: The study was conducted based on panel data from 30 developing countries (15 MENA countries and 15 SSA countries) during the period 2017–2020. Collected data were analysed by using the Generalized Least Squares (EGLS/weighted cross-section) with fixed effect estimation technique. Findings: The main results of the study show that mandatory IFRS adoption has a positive impact on economic growth of the full sample, and that this positive impact is reduced during Covid-19 crisis. Contribution: The study results are very useful to policymakers and regulators in developing countries, especially in crisis periods.

Kwaku Peprah Adjei, Robert Bob O'Hara, Anders G. Finstad et al.

1. Species identification errors may have severe implications for the inference of species distributions. Accounting for misclassification in species distributions is an important topic of biodiversity research. With an increasing amount of biodiversity that comes from Citizen Science projects, where identification is not verified by preserved specimens, this issue is becoming more important. This has often been dealt with by accounting for false positives in species distribution models. However, the problem should account for misclassifications in general. 2. Here we present a flexible framework that accounts for misclassification in the distribution models and provides estimates of uncertainty around these estimates. The model was applied to data on viceroy, queen and monarch butterflies in the United States. The data were obtained from the iNaturalist database in the period 2019 to 2020. 3. Simulations and analysis of butterfly data showed that the proposed model was able to correct the reported abundance distribution for misclassification and also predict the true state for misclassified state.