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This paper aims to provide evidence of an unintended observable consequence of International Financial Reporting Standards (IFRS) adoption by examining opportunistic use of earnings management through revenue as well as expense items classification shifting in the year of transition.

To document classification shifting, the authors take advantage of the Korean mandatory IFRS adoption in 2011, when broad discretion was given to publicly traded companies’ managers to present operating profits.

It is found that companies strategically use both revenues and expenses to manage core earnings at the time of transition by shifting other income as a common tactic to improve their operating performance and special expenses just to meet or beat earnings targets.

Given the concerns of the Securities and Exchange Commission (SEC) about classification shifting behavior and the debate over whether the SEC should mandate the use of IFRS for US companies, the findings of this study are timely and contribute to authors’ understanding of the unintended consequences of mandatory IFRS adoption.

This project attempts to present a space component inventory classification system for space inventory replenishment and management. The authors propose to adopt a classification system that can incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment.

A fuzzy-based approach with ABC classification is proposed to incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment of the soil preparation system (SOPSYS) which is used in grinding and sifting Phobos rocks to sub-millimeter size in the Phobos-Grunt space mission. An information system was developed using the existing platform and was used to support the key aspects in performing inventory classification and purchasing optimization.

The proposed classification system was found to be able to classify the inventory and optimize the purchasing decision efficiency. Based on the information provided from the system, implementation plans for the SOPSYS project and related space projects can be proposed.

The paper addresses one of the main difficulties in handling qualitative or quantitative classification criteria. The model can be implemented using mathematical calculation tools and integrated into the existing inventory management system. The proposed model has important implications in optimizing the purchasing decisions to shorten the research and development of other space instruments in space missions.

Inventory management in the manufacture of space instruments is one of the major problems due to the complexity of the manufacturing process and the large variety of items. The classification system can optimize purchasing decision-making in the inventory management process. It is also designed to be flexible and can be implemented for the manufacture of other space mission instruments.

Item classification based on ABC‐XYZ analysis is of high importance for strategic supply and inventory control. It is common to perform the analysis with past consumption data. In this context, the purpose of this study is to test the hypothesis that an integration of demand forecasts can improve the performance of item classification, in particular the performance of ABC‐XYZ analysis.

For the study, real data of an industrial enterprise in the mechanical engineering sector (focal company) were analyzed and evaluated.

The study shows that a comprehensive data analysis of the focal company can recommend a specific implementation of the ABC‐XYZ classification. In contrast to the classic method of making the ABC‐XYZ analysis based on consumption data only, the approach developed in this paper offers considerable advantages. These are quantifiable in respect to an assumed optimal reference classification.

The evaluation of the results is very promising and applicable to other branches besides mechanical engineering.

Managers generally have discretion in determining how components of earnings are presented in financial statements in distinguishing between “normal” earnings and items classified as unusual, special, significant, exceptional or abnormal. Prior research has found that such intra‐period classificatory choice is used as a form of earnings management. Prior to 2001, Australian accounting standards mandated that unusually large items of revenue and expense be classified as “abnormal items” for financial reporting, but this classification was removed from accounting standards from 2001. This move by the regulators was partly in response to concerns that the abnormal classification was being used opportunistically to manage reported pre‐abnormal earnings. The purpose of this paper is to extend the earnings management literature by examining the reporting of abnormal items for evidence of intra‐period classificatory earnings management in the unique Australian setting.

This study investigates associations between reporting of abnormal items and incentives in the form of analyst following and the earnings benchmarks of analysts' forecasts, earnings levels, and earnings changes, for a sample of Australian, top‐500 firms, for the seven‐year period from 1994 to 2000.

The findings suggest there are systematic differences between firms reporting abnormal items and those with no abnormal items. Results show evidence that, on average, firms shifted expense items from pre‐abnormal earnings to bottom line net income through reclassification as abnormal losses.

The paper's findings suggest that the standard setters were justified in removing the “abnormal” classification from the accounting standard. However, it cannot be assumed that all firms acted opportunistically in the classification of items as abnormal. With the removal of the standardised classification of items outside normal operations as “abnormal”, firms lost the opportunity to use such disclosures as a signalling device, with the consequential effect of limiting the scope of effectively communicating information about the nature of items presented in financial reports.

This paper aims to examine whether firms in the decline stage of lifecycle manipulate core or operating income through misclassification of operating expenses as income-decreasing special items.

The sample comprises of firms from an emerging market, India with data from 1996 to 2011. The paper uses the methodology given in McVay’s (2006) work and multiple regressions.

Managers of Indian firms also engage in classification shifting, primary incentive being the desire to avoid reporting of operating losses. Furthermore, the use of classification shifting is dependent upon the stage of lifecycle in which firm is in. Specifically, firms in the decline stage of lifecycle are more likely to use classification shifting to avoid reporting of operating losses.

The paper sheds light on a critical phase of the firm lifecycle, decline, which increases the possibility of the use of classification shifting, an earnings management technique which is tough to detect. Firms in decline, thus, may be trying to fool the investors who are infusing capital to save the company from going bankrupt; regulators, who are likely to focus less on troubled firms; and auditors, who may not be expecting core income manipulation in such firms.

The paper extends the literature on classification shifting and presents first evidence that such shifting is more likely to take place during the decline phase of firm lifecycle.

This study aims to investigate the effectiveness of a teaching method (TLS (Teaching/Learning Sequence)) based on a social constructivist paradigm on students' conceptualisation of classification of food.

The study compared the TLS model developed by the researcher based on the social constructivist paradigm with the Regular Teaching Method (RTM) in use in the school. Students exposed to the TLS model constituted the experimental group, while the students exposed to the RTM constituted the control group. The design was a pre‐test/post‐test control design with a retention‐phase.

Results before intervention revealed gross misconceptions of pupils' classification of food, while after intervention there was significant improvement of the TLS over RTM.

The context of the research is limited to students' conceptualisation of classification of food items into the five classes of food, i.e. carbohydrate, protein, fat, vitamin, and mineral. Although food group allocation was validated from previous literature, it is still a potential limitation as a single food item may contain more than one nutrient, which makes its classification in a single food class difficult.

Understanding whether students' conceptualisation of classes of food is changed by the information they received by established methods would be valuable when devising methods of delivering nutrition education.

Food provision in schools must support the messages that pupils receive through the formal curriculum.

Education can be immensely helpful in the pursuit of better understanding by children regarding healthy eating.

Group technology has become an important and viable approach to manufacturing systems, and provides significant benefits in inventory management, production process flow, throughput efficiency, tooling, and purchasing. It focuses on classification systems as the main approach, as opposed to production flow analysis. Many firms try to use a code such as a GT code as a means of both classifying and identifying components. This article points to some of the pitfalls that accompany this practice, and advocates the use of independent systems—one for identification and others for classification; thus, separating these two functions.

Presents a multicriteria approach to the ABC classification problem in inventory control. The proposed method, based on Saaty′s Analytic Hierarchy Process, rates items on both qualitative and quantitative criteria. Demonstrates the model through an example, using real data from the maintenance department stock room of a pharmaceutical company. A series of simulation experiments show how the resulting classification can benefit inventory control in this company.

The purpose of this paper is to present a new hybrid approach based on criticality analysis and optimisation to deal with spare parts inventory management in the initial provisioning phase in the mining industry. Spare parts represent a significant part of mining companies' expenditures, so it is important to develop new approaches to reduce the total inventory value of these items.

This hybrid approach combines qualitative and quantitative methods based on VED (vital, essential and desirable) analysis, analytical hierarchical process (AHP), and e-constraint optimisation method to obtain the spare parts to be stocked. The study was applied to a large mining company. The mineral sector was chosen due to the great importance to the emerging Brazilian economy and the lack of researches in this sector. In addition, the spare parts have a relevant weight on the total inventory cost.

Present a novel approach combining multi-objective optimisation and multi-criteria evaluation approaches to tackle the inventory decision in spare parts management. This work also defines and classifies relevant criteria for spare parts management in the mineral sector validated by specialists. The proposed approach achieves an average increase of 20.2% in the criticality and 16.6% in the number of items to be stocked compared to the historical data of the surveyed company.

This paper applies the proposed approach to a mining company in Brazil. Future research in other companies or regions should analyse the adequacy of the criticality criteria, hierarchy and weights adopted in this paper.

The proposed approach is useful for mining industries that deal with a large variety of resource constraints as it helps in formulating appropriate spare part strategies to rationalise financial resources at both tactical and strategic levels.

The paper presents a new hybrid method combining the AHP a multi-criteria decision making (MCDM) approach coupled with e-constraint optimisation to deal with spare parts inventory management allowing for a better spare parts inventory analysis in the initial provisioning phase and providing managers with a systematic tool to analyse the trade-off between spare parts criticality and total inventory value.

Large collections of patent documents disclosing novel, non-obvious technologies are publicly available and beneficial to academia and industries. To maximally exploit its potential, searching these patent documents has increasingly become an important topic. Although much research has processed a large size of collections, a few studies have attempted to integrate both patent classifications and specifications for analyzing user queries. Consequently, the queries are often insufficiently analyzed for improving the accuracy of search results. This paper aims to address such limitation by exploiting semantic relationships between patent contents and their classification.

The contributions are fourfold. First, the authors enhance similarity measurement between two short sentences and make it 20 per cent more accurate. Second, the Graph-embedded Tree ontology is enriched by integrating both patent documents and classification scheme. Third, the ontology does not rely on rule-based method or text matching; instead, an heuristic meaning comparison to extract semantic relationships between concepts is applied. Finally, the patent search approach uses the ontology effectively with the results sorted based on their most common order.

The experiment on searching for 600 patent documents in the field of Logistics brings better 15 per cent in terms of F-Measure when compared with traditional approaches.

The research, however, still requires improvement in which the terms and phrases extracted by Noun and Noun phrases making less sense in some aspect and thus might not result in high accuracy. The large collection of extracted relationships could be further optimized for its conciseness. In addition, parallel processing such as Map-Reduce could be further used to improve the search processing performance.

The experimental results could be used for scientists and technologists to search for novel, non-obvious technologies in the patents.

High quality of patent search results will reduce the patent infringement.

The proposed ontology is semantically enriched by integrating both patent documents and their classification. This ontology facilitates the analysis of the user queries for enhancing the accuracy of the patent search results.