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The purpose of this paper is to show that multivariate t-distribution assumption provides a better description of stock return data than multivariate normality assumption.

The EM algorithm is applied to solve the statistical estimation problem almost analytically, and the asymptotic theory is provided for inference.

The authors find that the multivariate normality assumption is almost always rejected by real stock return data, while the multivariate t-distribution assumption can often be adequate. Conclusions under normality vs under t can be drastically different for estimating expected returns and Jensen’s αs, and for testing asset pricing models.

The results provide improved estimates of cost of capital and asset moment parameters that are useful for corporate project evaluation and portfolio management.

The authors proposed new procedures that makes it easy to use a multivariate t-distribution, which models well the data, as a simple and viable alternative in practice to examine the robustness of many existing results.

We provide a new characterization of the equality of two positive-definite matrices A and B, and we use this to propose several new computationally convenient statistical tests for the equality of two unknown positive-definite matrices. Our primary focus is on testing the information matrix equality (e.g. White, 1982, 1994). We characterize the asymptotic behavior of our new trace-determinant information matrix test statistics under the null and the alternative and investigate their finite-sample performance for a variety of models: linear regression, exponential duration, probit, and Tobit. The parametric bootstrap suggested by Horowitz (1994) delivers critical values that provide admirable level behavior, even in samples as small as n = 50. Our new tests often have better power than the parametric-bootstrap version of the traditional IMT; when they do not, they nevertheless perform respectably.

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 paper is to identify significant factors of environmental variables and pollutants that have an effect on PM_2.5 through wavelet and regression analysis.

In order to provide stable data set for regression analysis, multiresolution analysis using wavelets is conducted. For the sampled data, multicollinearity among the independent variables is removed by using principal component analysis and multiple linear regression analysis is conducted using PM_2.5 as a dependent variable.

It is found that few pollutants such as NO₂, NO_x, SO₂, benzene and environmental factors such as ambient temperature, solar radiation and wind direction affect PM_2.5. The regression model developed has high R² value of 91.9 percent, and the residues are stationary and not correlated indicating a sound model.

The research provides a framework for extracting stationary data and other important features such as change points in mean and variance, using the sample data for regression analysis. The work needs to be extended across all areas in India and for various other stationary data sets there can be different factors affecting PM_2.5.

Control measures such as control charts can be implemented for significant factors.

Rules and regulations can be made more stringent on the factors.

The originality of this paper lies in the integration of wavelets with regression analysis for air pollution data.

The purpose of this study is to investigate secondary school students’ pedagogical use of information communication and technologies (ICT), their attitudes toward integration of ICT and the differences in their attitudes based on gender, school type and location.

The data was collected and analyzed using descriptive statistics, multivariate analysis of variance and multiple regression analysis.

The study revealed that students’ use of ICT to support their learning was low. Students mostly used ICT to communicate with peers. Students’ methods of assimilating knowledge were through teacher-centred teaching, even though they somewhat used ICT for collaborative and inquiry learning. Students in public schools perceived the use of ICT more valuable than students in private schools. Also, the study provided evidence that students in urban and rural schools differed in their attitudes in terms of perceived value and cost of ICT use, but no differences in attitude in terms of expectancy of success were found to exist among students in all locations.

The study provided further evidence that the value of ICT positively related to students’ pedagogical use of ICT, but the effect was very limited. This implies that although students have positive attitudes toward the benefits of ICT in learning; many do not integrate the technology into their learning. Lastly, the study was able to provide additional evidence that perceived cost negatively related to students’ pedagogical use of technology, but the result was very small. This indicates that since many students do not utilize ICT in their learning they rarely encounter barriers or challenges when it comes to the integration of the technology into their studies.

The purpose of this article is to explore the concept of availability, both empirically and theoretically, in the context of three Swedish organisations, and identifies the structural influences on availability patterns for work and family.

The article is based on quantitative case studies using employer records and an employee questionnaire in three organisations. Multivariate descriptive statistics and multivariate logistic regression are used to illustrate and analyse patterns of availability for work and family.

The descriptive data demonstrate the influence of the organisational context and type of production process, as well as gender, on availability patterns. Patterns of work availability appeared to differ across the organisations to a greater extent than patterns of family availability, which were highly gendered. The logistic regression results indicated that: occupation was a significant influence on both temporal and spatial availability patterns across the organisations; gender was the most significant influence on time spent on household work and part‐time working for parents with young children; age of employees and age of employees' children were the most significant factors influencing the use of time off work for family.

Analysis limited to case studies. More extensive quantitative research would be needed to make empirical generalisations. Qualitative research would be needed to establish whether and how employees are able to make use of different availability patterns to improve their work‐life balance.

The concept of availability is a new way of trying to capture and analyse tensions in people's everyday lives as they try to manage multiple demands.

This study aims to identify factors that influence corporate environmental disclosure (CED) quality.

Using content analysis, an index and scoring scheme were applied to annual reports, stand-alone reports and corporate homepages of a sample of 116 oil and gas companies in 19 developing countries (DCs).

The results of this study reveal that out of 12 hypothesized variables, only 5 variables (company size, foreign ownership, profitability, leverage and membership of industry’s associations) are positively related to the CED quality.

The study has implications in enhancing the understanding of CED practices by oil and gas companies in DCs and the factors that influence the quality of such disclosure. Thus, the results of the study serve as input toward the development of improved regulations concerning CED for the oil and gas industry and provide guidelines to the regulators to make relevant decisions on social and environmental information items to be incorporated in the regulatory standards.

The current study attempts to fill the gaps in the literature by examining CED quality (rather than its quantity), concentrating on environmental disclosure made on the three main mediums of reporting. The study also extends previous research of CED by investigating some factors that have the potential to influence the content-quality of environmental disclosure, such as type of company (independent or constrain company) and industry’s association membership which have never been examined in the related literature.

This paper aims to identify and explain the composition, determinants, relevance and effects of underlying profit and emphasis placed on underlying profit in annual reports.

The paper uses multivariate analysis of data from New Zealand listed companies from 2006 to 2010 disclosing both generally accepted accounting principles (GAAP) profit and underlying profit. Value relevance is measured in relation to annual stock returns of companies.

Tax, financial cost and depreciation and amortization are the three main items excluded from GAAP profit to derive underlying profit. Firms that have lower audit quality and industries prone to higher price fluctuation of assets and higher depreciation and amortization expenses use underlying profit. Also, underlying profit is used by firms with higher differences between statutory and target profits, higher analyst following and higher proportion of independent board of directors. Underlying profit has a weak negative association with annual market returns and significant positive association with volume of shares traded. Finally, the relevance of underlying profit is lower for firms that emphasize underlying profit in their annual reports.

Underlying profit is negatively related to the economic performance of the company in the market, whereas GAAP profit is positively related.

New Zealand has experienced a sharp increase in the use of underlying profits in annual reports. This research adds to our understanding of the use of underlying profit by New Zealand listed companies.

Nowadays, structural equation modeling is a buzz word in the arena of research in management, social sciences, and other equivalent fields. Although the theoretical base bears its significance in building the measurement and structural models, assessing different goodness-of-fit indices (GOFI) equally retains its importance for model validity and conformity. There are various alternative GOFI available for the researchers and the threshold values of each differ. The present paper discussed all the well-accepted and reported GOFI and their threshold value, which will be a great help to researchers and practitioners who use structural equation modeling in research. The author has also presented the different GOF values and validity results of her current research carried out in an Indian power transmission organization in Odisha, India.