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This study aims to investigate the influence of two perceived organizational ethics systems (perceived ethics training quality and integrity-based climate) on the ethical decision-making (EDM) of tax accountants in Ghana. The study also examines the moderating role of the decision-makers’ financial situation on the quality ethics training–EDM relationship.

Survey data from 356 tax accountants were analyzed using the partial least squares structural equation modeling technique.

The results show that the two ethics systems influence EDM, but their extent of influence varies across the stages of EDM. Specifically, quality ethics training is a better predictor of EDM at the ethical issue recognition stage, whereas integrity-based climate is a better predictor of EDM at the ethical intention stage. The study also found that decision-makers’ financial situation predicts the ethical recognition stage of EDM but does not moderate the quality ethics training–EDM relationship.

This study recommends the concurrent deployment of quality ethics training and an integrity-based work climate to improve ethical behavior. Policymakers should also emphasize a work climate that promotes honesty, conscientiousness and ethical principles (integrity-based climate) to improve ethical intentions.

This study applied the interactionist theory by capturing the relative effects of two organizational ethics systems and an individual-level situational factor in a single model. To the best of the authors’ knowledge, this is the first study that tests the moderation effect of decision-makers’ financial situation on the ethics training–EDM relationship in a developing country context.

The study aims to investigate the impact of indirectly evoked incentives, in the form of supervisor’s preferences, on the decision about accounting policy regarding depreciation method selection and to examine subsequent post-decision distortion by evaluating the depreciation method.

The authors conducted two experiments with control and treatment groups, manipulating the supervisor’s indirectly evoked preferences. In Study 2, the authors also measured the evaluation of both depreciation methods to investigate post-decisional distortion regarding the assessment of the depreciation method chosen in a decision task. Study 1 was conducted among 85 accounting students, while Study 2 consisted of 200 accountants.

Both studies revealed the significant impact of supervisor’s indirectly evoked preferences on accounting policy decisions. Participants who were aware of supervisors’ preferences were more likely to choose the depreciation method that was consistent with those preferences. The authors also found that those participants attached a higher value to the depreciation method, providing evidence that adherence to the supervisor’s preferences results in a distorted assessment of the depreciation methods.

First, this study shows that indirectly evoked supervisors’ preferences may lead to a departure from substantive criteria resulting in low-quality accounting outcomes. Second, the assessment of the depreciation method is inseparable from the situational context, as the evaluation of the depreciation method is interdependent upon the preferences of the choice of a depreciation method and the fulfillment of those preferences.

The goal of this study is to investigate the predictive performance of the machine and deep learning methods in predicting the CAC 40 index and its 40 constituent prices of the French stock market during the COVID-19 pandemic. The study objective in forecasting the CAC 40 index is to analyze if the index and the individual prices will preserve the continuous increase they acquired at the beginning of the administration of vaccination and containment measures or if the negative effect of the pandemic will be reflected in the future.

The authors apply two machine and deep learning methods (KNN and LSTM) and compare their performances to ARIMA time series model. Two scenarios have been considered: optimistic (high values) and pessimistic (low values) and four periods are examined: the period before COVID-19 pandemic, the period during the COVID-19, and the period of vaccination and containment. The last period is divided into two sub-periods: the test period and the prediction period.

The authors found that the KNN method performed better than LSTM and ARIMA in forecasting the CAC 40 index for both scenarios. The authors also identified that the positive effect of vaccination and containment outweighs the negative effect of the pandemic, and the recovery pattern is not even among major companies in the stock market.

The study empirical results have valuable practical implications for companies in the stock market to respond to unexpected events such as COVID-19, improve operational efficiency and enhance long-term competitiveness. Companies in the transportation sector should consider additional investment in R&D on communication and information technology, accelerate their digital capabilities, at least in some parts of their businesses, develop plans for lights out factories and supply chains to keep pace with changing times, and even include big data resources. Additionally, they should also use a mix of financing sources and securities in order to diversify their capital structure, and not rely only on equity financing as their share prices are volatile and below the pre-pandemic level. Considering portfolio allocation, the transportation sector was severely affected by the pandemic. This displays that transportation equities fail to be a candidate as a good diversifier during the health crisis. However, the diversification would be worth it while including assets related to the banking and industrial sectors. On another strand, the instability of this period induced an informational asymmetry among investors. This pessimistic mood affected the assets' value and created a state of disequilibrium opening up more opportunities to benefit from potential arbitrage profits.

The impact of COVID-19 on stock markets is significant and affects investor behavior, who suffered amplified losses in a very short period of time. In this regard, correct and well-informed decision-making by investors and other market participants requires careful analysis and accurate prediction of the stock markets during the pandemic. However, few studies have been conducted in this area, and those studies have either concentrated on some specific stock markets or did not apply the powerful machine learning and deep learning techniques such as LSTM and KNN. To the best of our knowledge, no research has been conducted that used these techniques to assess and forecast the CAC 40 French stock market during the pandemic. This study tries to close this gap in the literature.

A common finding of many surveys of corporate financial management practice is that the actual usage of normative financial models is infrequent. This is mainly due to the complexity of the decision environment, the use of information processing heuristics by decision makers, and the presence of psychological factors that influence the decision maker's behaviour. This paper reviews certain psychological concepts and their effects on the validity of traditional fundamental analysis in finance. Second, it discusses how cognitive limitations of human information processing may interfere with the implementation of certain decision rules in financial management, especially when decision makers experience information overload. Third, it provides a review of the advances that have been suggested to overcome the cognitive limitations of human information processing and to evaluate whether such advances can circumvent the psychological biases inherent in human decision makers.

Regarding the immense real estate divestitures that have taken place over the last couple of years, some stakeholders have begun to wonder if these short‐term activities may affect the long‐term competitive advantage of a company. While it appears reasonable that property divestitures enhance the financial situation of a company from a so‐called owner perspective, there is no equivalent quantitative evaluation for the loss in space utilisation and flexibility from a user perspective. Consequently, real estate decision making is based upon an insufficient information basis and is dominated by the investment perspective. In order to better align corporate real estate and real estate investment functions better, this paper introduces a formal decision model which describes the situation of corporate real estate decision makers. They have to trade off entrepreneurial flexibility gained by real estate holdings against the financial opportunity cost of freeing up capital. Making use of a prototype decision situation, the paper demonstrates how the decision maker can improve the underlying information basis for property divestment decisions, using a real option approach. Hence, real estate decisions gain in two respects: they are more transparent and, more importantly, their design is more suitable if the company wants to employ real estate holdings to increase the overall value of the company.

The article aims to analyse the perception of the internal users regarding the usefulness of the municipal financial reporting in the context of decision making in the Portuguese local administration.

This research is quantitative and positive, based on a cross‐section analysis through the online application of a questionnaire to the decision makers (politicians and technicians) of the 308 Portuguese municipalities. The approach is based on the paradigm of information usefulness.

The results indicate a high usefulness for the decision making of the municipal financial reporting, in its current form and content. However, this usefulness would increase if information, other than what is mandatory, were introduced. In general, the two different groups of decision makers, politicians and technicians, behave somewhat differently, regarding the usefulness that the financial reporting holds for them. The technical decision makers consider it of greater value. There is no statistical evidence that shows that there is a link between the training area and professional experience of the internal decision makers and the usefulness of the municipal financial reporting. Both types of users show a preference for the information set within the budgetary accounts, although accrual‐based information also proves to be of excellent value.

The main limitations of this study are related to the way information was collected (questionnaire) to obtain empirical evidence. The questionnaire, sent by email, despite reaching a wide‐ranging and dispersed population, does not assess the truthfulness and integrity of the responses. Furthermore, it does not make it possible to really identify the usefulness of the municipal financial reporting. The use of perceptive measures can also represent a threat to the study's internal validity.

The results of this study have important repercussions on the internal decision makers concerning the usefulness of the municipal financial reporting. Particularly, since the general approach towards the usefulness of the reporting could become a solid work basis for the regulatory bodies to enhance the current reporting model in the light of its suitability within the internal decision making.

Research on this study is original as it provides, as far as the authors are aware, the first empirical evidence of the perceptions of internal users on the usefulness of the municipal financial reporting in Portugal, in regard to decision making.

This paper aims to introduce and illustrate how discrete choice experiments (DCEs) can be used by accounting researchers and present an agenda of accounting-related research topics that might usefully benefit from the adoption of DCEs.

Each major phase involved in conducting a DCE is illustrated using a capital budgeting case study. The research agenda is based on a review of experimental research in financial accounting, management accounting and auditing.

DCEs can overcome some of the problems associated with asking decision-makers to rank or rate alternatives. Instead, they ask decision-makers to choose an alternative from a set. DCEs arguably better reflect the realities of real-world decision-making because decision-makers need to make trade-offs between all of the alternatives relevant to a decision. An important advantage that DCEs offer is their ability to calculate willingness-to-pay estimates, which can enable the valuation of non-market goods. Several streams of experimental accounting research would appear well-suited to investigation with DCEs.

While every effort has been made to ensure that this illustration is as generic to as the many potential studies as possible, it may be that researchers seeking to utilise a DCE need to refer to additional literary sources. This study, however, should serve as a useful starting point.

Accounting researchers are expected to benefit from reading this article by being: made aware of the DCE method and its advantages; shown how to conduct a DCE; and provided with an agenda of accounting-related research topics that might usefully benefit from application of the DCE methodology.

It is the authors’ understanding that this is the first article directed to accounting academics regarding the conduct of DCEs for accounting research. It is hoped that this study can provide a useful platform for accounting academics to launch further research adopting DCEs.

In this study the factors behind the decision‐makers’ erroneous judgements regarding failure prediction (classification of firms as bankrupt and non‐bankrupt) are analysed. The purpose is to find out the factors causing incorrect responses, i.e. the cases in which the decision‐maker is for some reason incapable of using the given information to arrive at the correct classification. The following five possible sources of disturbance in this decision‐making were hypothesized: firm‐specific factors, data, decision‐maker‐specific factors, external factors, and failure process. In further analysis these factors were empirically operationalized and their significance was tested applying logistic (logit) analysis separately for the Type I and Type II classification errors identified in an HIP study. The results indicated that the effect of all of the five hypothesized factors on misclassifications is statistically significant. The inconsistency of the cues (firm‐specific factors) may be the main factor causing errors in evaluation. Moreover, the failure process is another important factor (Type I error). Thus, human bankruptcy prediction can be improved mainly by checking the consistency of financial statements (that they give a true view of the firm’s economic status) and by paying special attention to timely identification of the possible failure process. Future HIP studies on bankruptcy prediction and also other economic events should pay attention to control the kinds of sources of disturbance identified in this study, to maintain validity.

The focus is the interplay of cognitive capabilities (mathematical understanding and heuristic problem solving) and learning from feedback. Furthermore, the authors analyze the role of individual factors in designing appropriate feedback systems for complex decision-making situations. Based on a learning model the purpose of this paper is to present an experimental study analyzing the feedback effectiveness in a repeated complex production planning task. Referring to individual characteristics in terms of educational background and problem solving capabilities of the decision maker the authors compare different forms of feedback systems.

The authors performed four experiments bi-weekly based on a realistic production planning situation. Participants received – depending on the treatment – different types of feedback concerning the final outcomes of the production plans. For testing the hypotheses, the authors conducted ANCOVAs and additional post hoc tests for each subgroup to explore the effects of different types of feedback on the subgroups’ decision-making performance.

The authors show that feedback information is not always helpful, but due to acquired knowledge and problem solving capabilities can even be harmful. The authors also show that, depending on the decision maker’s individual characteristics and her past performance, the type of feedback is crucial for the learning process.

The study provides important information about feedback design taking individual characteristics of decision makers (educational background, work experience) into account. Applying the results of the study can increase decision-making performance and enhance learning of production planning tasks.

The findings extend previous literature reporting that the performance in complex decision-making tasks depends on educational background and on the ability to cope with the phenomena of cognitive load, working memory limitations and the capability to utilize relevant heuristics to prevent information overload. Some of our results, e.g., the negative impact of non-financial feedback of high-performing economists, contradict the general findings in the literature.