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To describe and analyse the adoption of economic value added (EVA) income as a benchmark for setting pricing and other policies of a monopolistic state‐owned enterprise in the absence of normal benchmarking mechanisms.

By earning zero economic value added profits the enterprise earns its cost of capital and escapes claims of monopolistic pricing and possible regulation. To test the success of this policy the financial series of the enterprise are developed from the date of incorporation in 1989 along with the economic value added series. The normal accounting profits are compared with the value added results. The value added results are used as a proxy for the pricing and other operational decisions of the firm that are not directly observable. The validity of the economic value added approach to provide a suitable benchmark is examined.

Provides evidence that the enterprise was successful in avoiding charges of monopolistic pricing and subsequent regulation by linking pricing and other policies to its economic results. This was in a period when similar enterprises were regulate or threatened with regulation. The economic environment in the later years of the study have changed the goals of the enterprise.

This is a case study, so the success of this New Zealand based enterprise in benchmarking its policies to economic value added cannot be generalised to other companies and environments.

Provides a useful way to benchmark profits where a monopoly position may attract regulation. It also provides a system of benchmarking if other industry information is not available.

This paper identifies a unique position where the objective was to minimise economic income, rather than the usual goal of income maximisation.

The current search for operational criteria and tests of firm performance is largely focused on the Economic Value Added (EVA) framework. While reasserting the essential soundness of this approach the paper seeks to improve its application by proposing a version of EVA which anchors the opportunity cost of equity capital on market rather than book values. The case for this is argued on general grounds and the resulting model is convenient for examining the possible effects of the gearing factor. The practicability of the model is illustrated by applying the proposed ‘EVA’ formula to a mixed set of accounting and stock market data from a sample of UK companies.

With increasing pressure on firms to deliver shareholder value, there has been a renewed emphasis on devising measures of corporate financial performance and incentive compensation plans that encourage managers to increase shareholder wealth. One professedly recent innovation in the field of internal and external performance measurement is a trade‐marked variant of residual income known as economic value‐added (EVA). This paper attempts to provide a synoptic survey of EVA's conceptual underpinnings and the comparatively few empirical analyses of value‐added performance measures. Special attention is given to the GAAP‐related accounting adjustments involved in EVA‐type calculations.

This study proposes a comparison between Value Added Intellectual Coefficient (VAIC) and one of the most important performance evaluation methods, the Economic Value Added (EVA), starting from a re-interpretation of the VAIC.

The empirical data were gathered from AMADEUS Bureau van Dijk and consist of 2,596 companies operating in Northern Italy, from six different economic sectors, observed for the year 2011. A correlation analysis was carried out in order to highlight whether there is a relationship between the two concepts of VAIC and EVA.

Results show that EVA and VAIC have no significant relationships; as a matter of fact, EVA is based on financial theory, whereas VAIC is focalised on the assessment of Intellectual Capital Efficiency (ICE).

Managers could be misled due to the fact that they often make decisions by taking into account only financial indicators such as EBIT, EVA, etc. Although methods like EVA have improved modern accounting systems, they do not take into account information linked to ICE. Therefore, these two perspectives can be useful in a context in which firms' performances are measured through multi-criteria methodologies (i.e. Balanced scorecard).

The proposal describes the differences between VAIC and EVA considering these two concepts as not contrasting. In fact, in order to better measure firms' performances, it could be useful to consider VAIC and EVA as an integrated vision in order to develop multi-criteria evaluation systems, rather than consider them separately.

This paper attempts to propose the uses of a capital budgeting tool, the Economic Value Added (EVA)for a university. Although there are reports of widespread use of the EVA in many for‐profit organisations, there is no evidence in literature that it has been adopted as a capital budgeting tool for a university. In this paper the application of the EVA for a university is proposed. It shows how the EVA can increase the awareness of the importance of asset utilisation in universities and guide universities to better resource management. EVA is proposed for use in a university setting in two different segments: for‐profit and non‐profit. The EVA has been adjusted with a new measure, Academic Value Added Ratio (AVAR) to reflect the university’s objective. The perception of academic staff in the case study university in Thailand with regards to the concept of applying the EVA to a university is further investigated. The results indicate that most members of management staff do not oppose this concept if it is implemented in a proper way.

Studies that examine the relationship of economic value added (EVA) to market value did not isolate the EVA effect in conjunction with controlling for the economic effect of the market. Since the EVA metric is viewed as value‐added apart from the market, operational managers will benefit from a procedure that separates the market driven versus firm driven (EVA) effects. Our paper examines the effects of the economy and EVA on MVA. The results indicate that EVA and GDP significantly affect MVA. Furthermore, the MVA‐EVA relationship shows a systematic bias between the largest MVA firms and the smallest MVA firms. Overall, our study provides implications for corporate executives utilizing EVA to evaluate managerial performance linked to MVA.

The paper aims to find out the information content of performance measures from accounting and value-based measures that best explain the total shareholder return.

To achieve this aim, static and dynamic panel data regression analysis is applied to the sample of 56 Indian companies taken from the Nifty Midcap 100 Index, between 2012 and 2019.

It is found that accounting-based measures have more relative information content in predicting total shareholder return as compared to value-based measures. Economic value added (EVA) and cash value added (CVA) do not add to the information content provided by accounting-based measures. A combination of accounting-based measures and value-added intellectual coefficient (VAIC) adds marginally to the information content provided by accounting-based measures in explaining the total shareholder return. Dynamic panel regression analysis shows that return on assets (ROA), return on capital employed (ROCE), return on equity (ROE) and EVA have a significant impact on total shareholder return.

In this study, along with EVA, other measures from value-based measures, i.e. CVA are empirically tested to explain the total shareholder return. Intellectual capital efficiency computed by VAIC is also empirically tested along with accounting-based measures, EVA, CVA and market value added (MVA). To bring robustness to findings, data are tested by using dynamic panel regression analysis.

The main objective of this study is to examine the claim of economic value added (EVA) proponents about its superiority as a financial performance measure compared to five traditional performance measures, i.e. net operating profit after tax (NOPAT), cash flow from operations (OCF), earnings per share (EPS), return on capital employed (ROCE) and return on equity (ROE) in Indian manufacturing sector, and simultaneously provide its empirical evidences. To achieve this, relative and incremental information content of various performance measures and their relationship with market value added (MVA) is tested and examined.

Principal component analysis (PCA) is one of the important multivariate methods utilized in business research for data reduction, latent variable modeling, multicollinearity resolution, etc. The present sample consists of 608 firm‐year observations from the Indian manufacturing sector for the period 2000‐2007. Firstly, principal component analysis (PCA) is employed to determine the important variables that explain market value. Secondly, alongside PCA, multiple regression models (OLS) are used to examine the relative and incremental information content of EVA and traditional performance measures.

These results about PCA reveal that variables like NOPAT, OCF, ROE, ROCE and EVA have maximum influence on the market value (MVA) of the sample companies, whereas EPS has a negative loading, so, EPS is discarded for further analysis. Further, the PCA loading matrix reveals that NOPAT, OCF, ROE and ROCE outscore EVA. The regression results regarding the relative information content test reveal that NOAPT and OCF outperform EVA in explaining the market value of Indian companies. The incremental information content test shows that EVA makes a marginal contribution to information content beyond NOPAT, OCF, ROCE and ROE. Overall, these empirical results about Indian companies do not support the Stern Stewart hypothesis that EVA is superior to traditional accounting‐based measures in association with market value of the firm.

The study concludes that along with financial variables, other non‐financial variables such as employees, product quality, etc., should be considered in order to capture the unexplained variation in the market value of Indian companies.

This study implements inflation adjustments, as proposed by International Accounting Standard 15 (IAS15), to determine an inflation‐adjusted version of Economic Value Added (EVA). The relationships between the nominal (EVAnom) and inflation‐adjusted (EVAreal) versions of EVA, and market‐adjusted share returns are investigated, and compared with those of residual income, earnings and operating cash flow. Relative information content tests suggest that earnings have the strongest relationship with share returns, while the results of the incremental tests indicate that the EVAnom and EVAreal components do not provide statistically significant information content beyond that provided by residual income.

The main aim of this paper is to examine the claim that economic value added (EVA) advocates its superiority over the traditional accounting-based financial performance measures, i.e. profit after tax (PAT), earnings per share (EPS), return on assets (ROA), return on equity (ROE) and return on investment (ROI) in the Indian manufacturing sector and at the same time, give empirical facts. It also tests and examines the information content of various performance measures and their relationship with stock returns.

The paper uses the sample of 534 Indian manufacturing companies from the Bombay Stock Exchange (BSE) during the period 2000–2018. Multiple regression models are applied to examine the information content of EVA and traditional performance measures in explaining shareholders’ returns.

Relative information content tests revealed that traditional accounting-based measures such as EPS, ROE and ROA performed better than EVA in explaining the returns of Indian manufacturing companies. Incremental information content of EVA adds little contribution to information content above traditional performance measures. The claim of superiority of EVA over accounting-based measures in association with shareholder returns is proved invalid in Indian manufacturing companies.

This study concludes that EVA has no superiority over traditional accounting-based financial performance measures in explaining stock returns of Indian manufacturing companies. To achieve heftiness in outcomes, panel data are tested by using Breusch–Pagan–Godfrey (BPG) test for heteroskedasticity, Hausman’s test for fixed and random effect, variance inflation factor (VIF) test for multicollinearity and Durbin–Watson test for autocorrelation.