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This study investigates the characteristics of business and management consulting firms (firm size, international affiliation and scope of operation) affecting the adoption rate (i.e. recency of adopting big data analytics (BDA) as a new idea) and usage level of BDA. Ten critical areas of BDA application to business and management consulting were investigated, (1) Human Resource Management; (2) Risk Management; (3) Financial Advisory Services; (4) Innovation and Strategy; (5) Brand Building and Product Positioning; (6) Market Research/Diagnostic Studies; (7) Scenario-Based Planning/Business Simulation; (8) Information Technology; (9) Internal Control/Internal Audit; and (10) Taxation and Tax Management.

Survey data was obtained through a structured questionnaire from one hundred and eighteen (118) consultants in Nigeria from diverse consulting firm settings in terms of size, international affiliation and scope of operation (Big 4/non-Big 4 firms). Data was analyzed using descriptive statistics, cluster analysis, multivariate analysis of variance (MANOVA), multivariate discriminant analysis and multivariable logistic regression.

Whereas organizational characteristics such as firm size, international affiliation and scope of operation significantly determine the adoption rate of BDA, two attributes (international affiliation and scope of operation) significantly explain BDA usage level. Internationally affiliated consulting firms are more likely to record higher usage level of BDA than local firms. Also, the usage level of BDA by the Big 4 accounting/consulting firms is expected to be higher in comparison to non-Big 4 firms.

Contrary to common knowledge that firm size is positively associated with the adoption of an innovation, the study found no evidence to support this claim in respect of the diffusion of BDA. Overall, it appears that the scope of operation is the strongest organizational factor affecting the diffusion of BDA among consulting firms.

The study contributes to knowledge by exposing the factors promoting the uptake of BDA in a developing country. The originality of the current study stems from the consideration that it is the first, to the researchers' knowledge, to investigate the application of BDA by consulting firms in the Nigerian context. The study adds to literature on management accounting in the digital economy.

The purpose of this research is to develop a multivariate statistical model to classify the commercial banks in Lebanon in cohesive categories on the basis of their financial characteristics revealed by the financial ratios. This article uses factor analysis as a data reduction technique to reduce 52 financial ratios into seven financial ratios that adequately explain differences in performance among 50 banks in Lebanon. Implication for mergers between commercial banks is based on cluster analysis that is used to divide Lebanese banks into four performance clusters. Multidiscriminant analysis is used to examine the relative importance of the financial ratios discriminating between these clusters.

The purposes of this paper are 1) to develop model of long term financial health for logistics companies in Thailand 2) to identify factors that determine long term financial stability. Many researchers currently provide factors affecting financial health. Most factors refer to financial ratios, not many non-financial ratios such as age and size have been mentioned. This paper considers both financial and non-financial ratios that affect financial performance of Logistics companies in Thailand. The study has covered some interesting non-financial ratios such as Nationality of Shareholders, type of network in Logistics Company, growth rate (consisted of sales growth rate/profit growth rate/asset growth rate / Liability growth rate) and variable of growth rates. The target group is 110 logistics companies in Thailand enlisted from Department of International Trade Promotion Ministry of Commerce, Royal Thai Government. The group is divided into three categories according to financial health of company; Healthy financial, Unhealthy (Distress) and normal situation. The Multidiscriminant Analysis (MDA) is applied to analyze the differentiations among the three categories. Significant variables from MDA will be used as the independent variables for Multimonial Logistic Regression Analysis (MLRA) to identify factors that determine long terms financial stability. This paper find CF/D, RE/TA, BE/TL, Size, Age, Type of network, Nationality of Shareholders and Number of Shareholders are significant factors determine long term financial stability of Logistics company in Thailand.

Fast‐food and franchising should present a formidable marriage when coping with declining economic performance. Compares the performance of 54 (18 franchisors; 18 fast‐food franchisees; 18 fast‐food companies) between 1987 and 1990 using Taffler′s Z‐score analysis. Finds that franchisors′ performance during the period declined but remained better than that of fast‐food franchisees and fast‐food companies. Fast‐food franchisees have remained buoyant but there are incidences of potential failures. Outlines differing solutions to return these companies to health.

This study aims to explore the relative efficiency of the working capital management (WCM) for Emirati firms before and during the coronavirus crisis. Next, this study explores the potential impact of WCM on the likelihood of financial distress.

A data envelopment analysis (DEA) was applied to assess the relative efficiency of the WCM. This study uses the emerging market Z-score model to predict the likelihood of financial distress. The logistic regression was applied to investigate the impact of the efficiency of WCM on firms’ financial distress.

The results of this study model showed a negative and significant influence of the efficiency of WCM on firms’ financial distress likelihood.

The findings have important implications for many stakeholders, including decision makers, WC managers, financiers, investors, financial consultants, researchers and others, in increasing their awareness of firms’ WCM performance before and during the crisis. Further, the results could have implications for trading strategies as investors seek attractive economic gains from their investment in firms that care about WCM.

The implications of WCM performance on social interests would cause firms’ decision makers to operate efficiently and achieve the best practices to minimise the probability of firms' financial distress.

This study advances a novel contribution to the literature by introducing a novel model to assess WCM based on DEA technology.