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Article
Publication date: 27 June 2008

H. Kostakis, C. Sarigiannidis, B. Boutsinas, K. Varvakis and V. Tampakas

This paper aims to present a methodology for activity‐based costing, which combines simulation modeling and association rule mining, one of the core data‐mining techniques. The…

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Abstract

Purpose

This paper aims to present a methodology for activity‐based costing, which combines simulation modeling and association rule mining, one of the core data‐mining techniques. The objective of the proposed methodology is to deal with the problem of defining cost drivers.

Design/methodology/approach

Activity‐based costing uses the output produced by the simulation of cost drivers as inputs. As opposed to the integration of the ABC technique with simulation modeling, the possibility of estimating an empirical distribution of the simulated cost drivers does not exist in the proposed methodology. This is achieved with the use of data‐mining techniques and is based on the proposition that, if an association is found between a cost driver, whose estimation or calculation is time‐consuming, and another cost driver, which can easily be estimated or calculated, then the latter can lead to the estimation or calculation of the former.

Findings

The extracted association rules correspond to existing dependencies between the cost drivers.

Originality/value

The paper presents a combined methodology to deal with the problem of defining cost drivers in activity‐based costing. An example of the proposed methodology in healthcare is also presented.

Details

International Journal of Accounting & Information Management, vol. 16 no. 1
Type: Research Article
ISSN: 1834-7649

Keywords

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Publication date: 8 March 2018

David Y. Chan and Miklos A. Vasarhelyi

The traditional audit paradigm is outdated in the real time economy. Innovation of the traditional audit process is necessary to support real time assurance. Practitioners and…

Abstract

The traditional audit paradigm is outdated in the real time economy. Innovation of the traditional audit process is necessary to support real time assurance. Practitioners and academics are exploring continuous auditing as a potential successor to the traditional audit paradigm. Using technology and automation, continuous auditing methodology enhances the efficiency and effectiveness of the audit process to support real time assurance. This paper defines how continuous auditing methodology introduces innovation to practice in seven dimensions and proposes a four-stage paradigm to advance future research. In addition, we formulate a set of methodological propositions concerning the future of assurance for practitioners and academic researchers.

Available. Open Access. Open Access
Article
Publication date: 28 August 2019

Mark Lokanan, Vincent Tran and Nam Hoai Vuong

The purpose of this paper is to evaluate the possibility of rating the credit worthiness of a firm’s quarterly financial report using a dynamic anomaly detection method.

18947

Abstract

Purpose

The purpose of this paper is to evaluate the possibility of rating the credit worthiness of a firm’s quarterly financial report using a dynamic anomaly detection method.

Design/methodology/approach

The study uses a data set containing financial statements from Quarter 1 – 2001 to Quarter 4 – 2016 of 937 Vietnamese listed firms. In sum, 24 fundamental financial indices are chosen as control variables. The study employs the Mahalanobis distance to measure the proximity of each data point from the centroid of the distribution to point out the extent of the anomaly.

Findings

The finding shows that the model is capable of ranking quarterly financial reports in terms of credit worthiness. The execution of the model on all observations also revealed that most financial statements of Vietnamese listed firms are trustworthy, while almost a quarter of them are highly anomalous and questionable.

Research limitations/implications

The study faces several limitations, including the availability of genuine accounting data from stock exchanges, the strong assumptions of a simple statistical distribution, the restricted timeframe of financial data and the sensitivity of the thresholds for anomaly levels.

Practical implications

The study opens an avenue for ordinary users of financial information to process the data and question the validity of the numbers presented by listed firms. Furthermore, if fraud information is available, similar research can be conducted to examine the tendency for companies with anomalous financial reports to commit fraud.

Originality/value

This is the first paper of its kind that attempts to build an anomaly detection model for Vietnamese listed companies.

Details

Asian Journal of Accounting Research, vol. 4 no. 2
Type: Research Article
ISSN: 2443-4175

Keywords

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Article
Publication date: 17 February 2022

Umama Rahman and Miraj Uddin Mahbub

The data created from regular maintenance activities of equipment are stored as text in industrial plants. The size of these data is increasing rapidly nowadays. Text mining…

402

Abstract

Purpose

The data created from regular maintenance activities of equipment are stored as text in industrial plants. The size of these data is increasing rapidly nowadays. Text mining provides a chance to handle this huge amount of text data and extract meaningful information to improve various processes of an industrial environment. This paper represents the application of classification models on maintenance text records to classify failure for improving maintenance programs in the industry.

Design/methodology/approach

This paper is presented as an implementation study, where text mining approaches are used for binary classification of text data. Naive Bayes and Support Vector Machine (SVM), two classification algorithms are applied for training and testing of the models as per the labeled data. The reason behind this is, these algorithms perform better on text data for classifying failure and they are easy to handle. A methodology is proposed for the development of maintenance programs, including classification of potential failure in advance by analyzing the regular maintenance data as well as comparing the performance of both models on the data.

Findings

The accuracy of both models falls within the acceptable limit, and performance evaluation of the models concludes the validation of the results. Other performance measures exhibit excellent values for both of the models.

Practical implications

The proposed approach provides the maintenance team an opportunity to know about the upcoming breakdown in advance so that necessary measures can be taken to prevent failure in an industrial environment. As predictive maintenance incurs a high expense, it could be a better replacement for small and medium industrial plants.

Originality/value

Nowadays, maintenance is preventive-based rather than a corrective approach. The proposed technique is facilitating the concept of a proactive approach by minimizing the cost of additional maintenance steps. As predictive maintenance is efficient but incurs high expenses, this proposed method can minimize unnecessary maintenance operations and keep control over the budget. This is a significant way of developing maintenance programs and will make maintenance personnel ready for the machine breakdown.

Details

Journal of Quality in Maintenance Engineering, vol. 29 no. 1
Type: Research Article
ISSN: 1355-2511

Keywords

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Article
Publication date: 16 February 2022

Fevzeddin Ülker and Ahmet Küçüker

The individual machine learning methods used for fault detection and classification have accuracy performance at a certain level. A combined learning model composed of different…

308

Abstract

Purpose

The individual machine learning methods used for fault detection and classification have accuracy performance at a certain level. A combined learning model composed of different base classifiers rather than an individual machine learning model is introduced to ensure diversity. In this way, this study aims to improve the generalization capability of fault detection and classification scheme.

Design/methodology/approach

This study presents a probabilistic weighted voting model (PWVM) with multiple learning models for fault detection and classification. The working principle of this study’s proposed model relies on weight selection and per-class possibilities corresponding to predictions of base classifiers. Moreover, it can improve the power of the prediction model and cope with imbalanced class distribution through validation metrics and F-score.

Findings

The performance of the proposed PWVM was better than the performance of the individual machine learning methods. Besides, the proposed voting model’s performance was compared with different voting mechanisms involving weighted and unweighted voting models. It can be seen from the results that the presented model is superior to voting mechanisms. The performance results revealed PWVM has a powerful predictive model even in noisy conditions. This study determines the optimal model from among voting models with the prioritization method on data sets partitioned different ratios. The obtained results with statistical analysis verified the validity of the proposed model. Besides, the comparative results from different benchmark data sets verified the effectiveness and robustness of this study’s proposed model.

Originality/value

The contribution of this study is that PWVM is an ensemble model with outstanding generalization capability. To the best of the authors’ knowledge, no study has been performed using a PWVM composed of multiple classifiers to detect no-faulted/faulted cases and classify faulted phases.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 41 no. 5
Type: Research Article
ISSN: 0332-1649

Keywords

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Article
Publication date: 29 January 2020

Di Wu, Yong Choi and Ji Li

This paper aims to focus on applications of stochastic linear programming (SLP) to managerial accounting issues by providing a theoretical foundation and practical examples. SLP…

733

Abstract

Purpose

This paper aims to focus on applications of stochastic linear programming (SLP) to managerial accounting issues by providing a theoretical foundation and practical examples. SLP models may have more implications – and broader ones – in industry practice than deterministic linear programming (DLP) models do.

Design/methodology/approach

This paper introduces both DLP and SLP methods. In addition, continuous and discrete SLP models are explained. Applications are demonstrated using practical examples and simulations.

Findings

This research work extends the current knowledge of SLP, especially concerning managerial accounting issues. Through numerical examples, SLP demonstrates its great ability of hedging against all scenarios.

Originality/value

This study serves as an addition to building a cumulative tradition of research on SLP in managerial accounting. Only a few SLP studies in managerial accounting have focused on the development of such an instrument. Thus, the measurement scales in this research can be used as the starting point for further refining the instrument of optimization in managerial accounting.

Details

International Journal of Accounting & Information Management, vol. 28 no. 1
Type: Research Article
ISSN: 1834-7649

Keywords

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Article
Publication date: 15 March 2011

Mohd Daud Norzaidi, Siong Choy Chong, Mohamed Intan Salwani and Binshan Lin

The purpose of this paper is to investigate whether intranet functionalities predict perceived usefulness, which in turn influences intranet usage and whether such usage affects…

1935

Abstract

Purpose

The purpose of this paper is to investigate whether intranet functionalities predict perceived usefulness, which in turn influences intranet usage and whether such usage affects job performance of managers.

Design/methodology/approach

About 150 of 357 managers engaged by numerous organizations in the port industry in Malaysia, namely port authority, terminal operator, marine department, immigration department, and royal customs and excise department which utilized intranet were sampled using a set of self‐reporting questionnaires.

Findings

The results of structural equation modeling indicate that intranet functionalities influence perceived usefulness, usage, and indirectly predict port managers' performance.

Research limitations/implications

The paper focuses only on the perspective of intranet usage among middle managers working in the port industry in Malaysia.

Practical implications

Suggestions are provided on how the maritime industry in particular and other industries in general can improve their intranet adoption to achieve organizational goals.

Originality/value

This paper draws attention to the imperative of having proper intranet functionalities in place in light of its indirect impact on job performance improvements.

Details

Kybernetes, vol. 40 no. 1/2
Type: Research Article
ISSN: 0368-492X

Keywords

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Article
Publication date: 2 May 2017

Normah Omar, Zulaikha ‘Amirah Johari and Malcolm Smith

This paper aims to explore the effectiveness of an artificial neural network (ANN) in predicting fraudulent financial reporting in small market capitalization companies in…

3837

Abstract

Purpose

This paper aims to explore the effectiveness of an artificial neural network (ANN) in predicting fraudulent financial reporting in small market capitalization companies in Malaysia.

Design/methodology/approach

Based on the concepts of ANN, a mathematical model was developed to compare non-fraud and fraud companies selected from among small market capitalization companies in Malaysia; the fraud companies had already been charged by the Securities Commission for falsification of financial statements. Ten financial ratios are used as fraud risk indicators to predict fraudulent financial reporting using ANN.

Findings

The findings indicate that the proposed ANN methodology outperforms other statistical techniques widely used for predicting fraudulent financial reporting.

Originality/value

The study is one of few to adopt the ANN approach for the prediction of financial reporting fraud.

Details

Journal of Financial Crime, vol. 24 no. 2
Type: Research Article
ISSN: 1359-0790

Keywords

Available. Open Access. Open Access
Article
Publication date: 10 May 2023

Marko Kureljusic and Erik Karger

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current…

85596

Abstract

Purpose

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

Design/methodology/approach

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

Findings

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Research limitations/implications

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Practical implications

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

Originality/value

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

Details

Journal of Applied Accounting Research, vol. 25 no. 1
Type: Research Article
ISSN: 0967-5426

Keywords

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Article
Publication date: 1 November 2011

Hara Kostakis, George Boskou and George Palisidis

This paper seeks to demonstrate an application of a methodology, which is based on the integration of three techniques, with the purpose of modelling activity‐based costing (ABC…

4832

Abstract

Purpose

This paper seeks to demonstrate an application of a methodology, which is based on the integration of three techniques, with the purpose of modelling activity‐based costing (ABC) in restaurants. The proposed methodology serves as a tool for effectively computing values of cost drivers in the restaurant industry, as well as making accurate cost estimations.

Design/methodology/approach

The methodology is based on the integration of three techniques: simulation modelling, association rule mining (ARM) and ABC. Simulation modelling is used to model process variability and produce a range of cost values, instead of a point estimate of the cost, by generating a range of values for the simulated cost drivers. The advantage of the proposed methodology lies on the effective utilization of ARM in the ABC model; it extracts dependencies between a cost driver, whose estimation is time‐consuming, with another cost driver, which can easily be calculated. These associations can assist the estimation of the empirical distributions of those cost drivers, which were difficult to calculate.

Findings

The extracted associations verify the hypothetical relations between the cost drivers. The output produced is more precise values of the cost drivers that are included in an ABC model and were difficult to estimate. More accurate cost estimate means better pricing decisions for the restaurant managers.

Originality/value

The proposed methodology is an innovative technique that provides more accurate accounting information in the restaurant industry.

Details

Journal of Modelling in Management, vol. 6 no. 3
Type: Research Article
ISSN: 1746-5664

Keywords

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