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Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).

The study aims at examining whether or not poor macro-economic conditions can lead auditors to change their risk management policies when performing an audit.

The present study is based on a questionnaire distributed to auditors working at the branches of the big four audit firms in Egypt over two rounds under different economic conditions. The responses in each of the two rounds were analyzed to identify any similarities or differences in auditors' behavior when performing analytical procedures under different economic conditions.

Auditors appear to alter their risk management strategies during challenging economic times. The present study results suggest that auditors increase their dependence on non-financial data and information as supporting evidence when assessing audit risk during times of economic difficulties. The findings also show that when the macro-economic trends are declining, audit firms tend to assign the performance of analytical procedures to more experienced audit personnel (i.e. senior auditors, audit managers and partners) with less of this work being done by the audit staff.

The present study is based on a sample of 40 respondents. It is recommended for future research to use a larger sample size as results may differ for a greater sample. The present research did not consider the effect of auditors' specialization in a certain industry on the audit judgment during an audit engagement. Future research would examine the impact of auditors' industry specialization on audit judgments during periods of unfavorable economic conditions. The present study is based on a survey that aims at capturing auditors' perception. Further research would use other research techniques (e.g. laboratory experiment) to examine the effect of the general economic conditions on auditors' assessment of audit risk.

Auditors need to give sufficient attention to the analyses of non-financial information of their audit clients during the performance of the analytical procedures under unstable economic conditions rather than depending solely on financial information. Moreover, audit firms could use a much richer labor mix for audit teams through increasing their reliance on experienced senior auditors, audit managers and partners during periods of deteriorating macro-economic conditions to mitigate risk and improve audit judgment.

This study adds to the scarce literature in developing countries investigating the influence of external economic factors on the audit process. The present research provides information to practitioners and educators about risk management policies that could be considered in case of performing analytical procedures during an audit conducted under poor economic conditions.

This study attempts to identify fundamental determinants of bond ratings for non-financial and financial firms. Further the study aims to develop a parsimonious bond rating model and compare its efficacy across statistical and range of machine learning methods in the Indian context. The study is motivated by the insufficiency of prior work in the Indian context.

The authors identify the critical determinants of non-financial and financial firms using multinomial logistic regression. Various machine learning and statistical methods are employed to identify the optimal bond rating prediction model. The data cover 8,346 bond issues from 2009 to 2019.

The authors find that industry concentration, sales, operating leverage, operating efficiency, profitability, solvency, strategic ownership, age, firm size and firm value play an important role in rating non-financial firms. Operating efficiency, profitability, strategic ownership and size are also relevant for financial firms besides additional determinants related to the capital adequacy, asset quality, management efficiency, earnings quality and liquidity (CAMEL) approach. The authors find that random forest outperforms logit and other machine learning methods with an accuracy rate of 92 and 91% for non-financial and financial firms.

The study identifies important determinants of bond ratings for both non-financial and financial firms. The study interalia finds that the random forest technique is the most appropriate method for bond ratings predictions in India.

Better bond ratings may mitigate corporate defaults.

Unlike prior literature, the study identifies determinants of bond ratings for both non-financial and financial firms. The study also experiments with modern machine learning techniques besides the traditional statistical approach for model building in case of relatively under researched market.

The purpose of this paper is to explore the links between strategy implementation, performance measurement and strategic alignment within a highly diversified group of firms.

A mix of qualitative and quantitative approaches was used, and data were gathered in two different periods. In the first phase, preliminary interviews were followed by a survey across all the firms of the group and by semi‐structured interviews in four companies. Semi‐structured interviews were conducted four years later to explore changes in both strategy and performance measurement systems (PMSs).

This research contributes to the debate on the appropriateness of introducing PMSs as formal management control mechanisms. The analysis of data led to three main findings. First, the introduction of IT systems and specific governance mechanisms alone enabled the implementation of strategy across the group only to a limited extent. Second, the lack of a comprehensive PMS appeared to have negative effects on both the formulation and implementation of strategy. Third, following a phase of substantial expansion, both strategy and measurement systems had to be changed to provide a greater sense of direction and to gather data on non‐financial aspects of the business.

This research considers the case of a group of firms, which aimed to achieve strategy implementation and alignment without introducing a comprehensive PMS. This paper provides empirical evidence of the potential limitations of such an approach, and illustrates the changes to strategy and performance measurement made by the company considered.

This study examines the effect of non-financial variables related to governance on the accuracy of financial distress prediction among Egyptian listed small and medium-sized enterprises (SMEs), by using the logistic regression technique.

This study used a sample of 24 Egyptian-listed SMEs in each year, totaling 120 firm observations, of which 25 were classified distressed and 95 of them non-distressed between 2014 and 2018. The variables for the study included five financial variables and thirteen non-financial variables related to governance. The models were developed using financial variables alone as well as combining financial and non-financial variables related to governance.

The results showed that the model with financial variables had a prediction accuracy of 91.7% , whereas models with a combination of financial and non-financial variables related to governance predict with comparatively better accuracy of 92.7 and 93.6% .

Although the results seem to be conclusive, it could be noted that the non-distressed sample was not paired with the distressed sample. Other studies showed that paired samples increase the financial distress prediction rate. Furthermore, due to the small sample size, this study was unable to create a hold-out sub-sample for the accuracy test.

The proposed distress prediction model for SMEs is effective for stakeholders, including banks and other financial institutions, in the assessment of the credit risk of SMEs. Using such a model, they could better identify SMEs with a higher risk of failure in their lending decisions. Moreover, SME managers' could be interested in using such models as a tool for planning corrective action, in addition to planning and controlling current operations to avoid financial failure in the future.

This study contributes to financial distress prediction literature in different ways. First, few studies were conducted in the area of financial distress among SMEs. Second, neither of these studies was conducted within the Egyptian context, nor any of them had used non-financial variables related to governance in the prediction of financial distress among SMEs.

Top management routinely accuses the investment community of being too “short‐term, bottom‐line‐oriented” in its assessments of share value. The difficulty of making strategic investments in such an environment is widely bemoaned. A new study by the Ernst & Young Center for Business Innovation, however, yields a surprising finding: major investors' decisions are in fact significantly influenced by non‐financial performance information. It turns out that over a third of the typical investor's allocation decision is attributable not to the Financials but to other information on performance areas perceived to be leading indicators of future profitability. These include perceptions of a company's strategic vision and the company's ability to execute against it, the credibility of management, the prospects of innovations in the pipeline, the ability to attract talented people, and so on.

The purpose of this paper is to present the results of a survey study on quality performance measurement practices in the Turkish top 500 manufacturing companies. The study evaluates both financial and non‐financial aspects of quality performance measures in Turkish manufacturing companies.

The methodology of the study was a postal questionnaire survey. The survey was conducted with the top 500 industrial enterprises in Turkey specified by the Istanbul Chamber of Industry (ICI) for the year 2005. These firms are selected and ranked by ICI according to production‐based sales.

Two major findings of the study are: Turkish manufacturing companies utilize non‐financial measures more frequently than financial measures; and Turkish managers perceive non‐financial measures to be more effective than financial measures.

The sample is restricted to the top 500 industrial enterprises in Turkey. As the data in this study were collected from the manufacturing companies, the findings should not be generalized to other sectors.

The study is unique in reflecting the general practices and perceptions of manufacturing companies on quality performance measures across Turkey.

Auditors’ skills and knowledge of data analytics and big data can influence their judgment at the audit planning stage. At this stage, the auditor will determine the level of audit risk and estimate how long the audit will take. This study aims to test whether big data and data analytics affect auditors’ judgment by adopting the cognitive fit theory.

This was an experimental study involving 109 accounting students as participants. The 2 × 2 factorial design between subjects in a laboratory setting was applied to test the hypothesis.

First, this study supports the proposed hypothesis that participants who are provided with visual analytics information will rate audit risk lower than text analytics. Second, participants who receive information on unstructured data types will assess audit risk (audit hours) higher (longer) than those receiving structured data types. In addition, those who receive information from visual analytics results have a higher level of reliance than those receiving text analytics.

This research has implications for external and internal auditors to improve their skills and knowledge of data analytics and big data to make better judgments, especially when the auditor is planning the audit.

Previous studies have examined the effect of data analytics (predictive vs anomaly) and big data (financial vs non-financial) on auditor judgment, whereas this study examined data analytics (visual vs text analytics) and big data (structured and unstructured), which were not tested in previous studies.

This paper aims to understand how the tasks of management accountants (MA) are affected by data analytics (DA).

A qualitative methodology was deemed most appropriate given the exploratory nature of the research questions (RQ). In total, 10 open-ended interview questions were used to gather the evidence. The case study design was inductive, yielding rich data from 29 respondents representing 20 different organizations.

Answers were provided to three interrelated RQs about the use of DA by MA, namely, what are their responsibilities? How does this work support inference, prediction and assurance? And how can they ensure insights from DA can be turned into decisions that add value? The findings also indicate that MA have not taken charge of the data analytic opportunities and at present, their activities remain largely focused on descriptive and financial data analysis rather than more complex activities using external data, operational data and modeling.

The limitation of this research is that it is based on a relatively small, geographically restricted sample (20 organizations in south-central Canada) as well by interviews that were only 60 min in duration.

Provides a base for the existing practice of management accounting with DA.

Explains the social relationship between DA and management accounting.

Documented and explained the extent of actual DA use by MA.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.