Studies in Economics and Finance

Industry-specific determinants of shareholder value creation

John Henry Hall
Article information:
To cite this document:
John Henry Hall , (2016),"Industry-specific determinants of shareholder value creation", Studies in
Economics and Finance, Vol. 33 Iss 2 pp. 190 - 208
Permanent link to this document:
http://dx.doi.org/10.1108/SEF-08-2014-0155
Downloaded on: 22 June 2016, At: 02:12 (PT)
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

References: this document contains references to 42 other documents.

To copy this document: permissions@emeraldinsight.com
The fulltext of this document has been downloaded 60 times since 2016*
Users who downloaded this article also downloaded:
(2016),"The impact of capital shocks on M & A transactions", Studies in Economics and Finance,
Vol. 33 Iss 1 pp. 126-146 http://dx.doi.org/10.1108/SEF-11-2014-0224
(2016),"Mergers and acquisitions: a review. Part 1", Studies in Economics and Finance, Vol. 33 Iss 1
pp. 147-188 http://dx.doi.org/10.1108/SEF-03-2015-0078
(2016),"Assessing foreign funds geographical focus timing skill", Studies in Economics and Finance,
Vol. 33 Iss 2 pp. 209-221 http://dx.doi.org/10.1108/SEF-11-2013-0168

Access to this document was granted through an Emerald subscription provided by emerald-
srm:232579 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald
for Authors service information about how to choose which publication to write for and submission
guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as
well as providing an extensive range of online products and additional customer resources and
services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the
Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for
digital archive preservation.
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

*Related content and download information correct at time of download.

 The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1086-7376.htm

SEF
33,2
Industry-specific determinants of
shareholder value creation
John Henry Hall
Department of Financial Management, University of Pretoria,
190 Pretoria, South Africa
Received 8 August 2014
Revised 4 March 2015
26 March 2015 Abstract
23 April 2015
Accepted 24 April 2015
Purpose – Prior studies on determinants of shareholder value creation have reported conflicting and
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

sometimes confusing results. In this study, to obtain more refined and industry-specific results
regarding variables determining shareholder value creation, an analysis was performed focusing on
different categories of firms or industries.
Design/methodology/approach – Two dependent and 11 independent variables were applied to
five different industries to obtain the best set of significant value drivers of shareholder value creation
for a particular industry.
Findings – Market value added (MVA) is a better indicator of shareholder value created compared to
a market adjusted return. Accounting-based variables (EPS, ROA and NOPAT) are superior to
economic-based variables (EVA and ROCE) in explaining shareholder value creation, but results differ,
depending on the dependent variable chosen as shareholder value creation measure. For each industry,
there is a unique set of variables that determine shareholder value creation; the industrial goods
industry has seven significant value drivers, namely, EPS, NOPAT, ROCE, the Spread, EVA, EBEI and
REVA, whilst for the food and beverages industry, there were only two significant value drivers (EPS
and ROA).
Originality/value – These findings imply that management, analysts and shareholders should,
depending on the specific industry in which their firm operates, take into account a more specific set of
variables when making their financial decisions, including compensation or reward structuring.
Keywords Economic value added, Earnings per share, Industry analysis, Market adjusted return,
Market value added, Shareholder value creation
Paper type Research paper

Introduction
It is one of the business orthodoxies of our age that the main goal of every enterprise is
to maximize shareholder value. Even without embarking on the philosophical waters of
whether this is indeed the most prudent or ultimate goal of a firm, a question that
continually arises is how shareholder value creation can be explained and accurately
measured. Finding an answer to this question is increasingly difficult, as the corporate
world is constantly witnessing the birth of new shareholder value creation measures
and is, therefore, faced with an ever-increasing array of research findings on ways to
express shareholder value creation. The main traditional (accounting-based) measures
to quantify shareholder value creation are earnings per share (EPS), return on equity
(ROE), return on assets (ROA) and dividend per share (DPS). These traditional measures
Studies in Economics and Finance have now been challenged and supplemented by economic-based measures of
Vol. 33 No. 2, 2016
pp. 190-208
shareholder value creation, such as economic value added (EVA), market value added
© Emerald Group Publishing Limited
1086-7376
(MVA), cash flow return on investment (CFROI), cash value added (CVA) and refined
DOI 10.1108/SEF-08-2014-0155 economic value added (REVA).
 Numerous studies have been undertaken in the past few decades to decide which Industry-
measure best expresses shareholder value creation. Sharma and Kumar (2010) specific
summarize the results of 112 studies on EVA, and Hall (2013) discusses the results of 18
studies on such measures conducted during the period from 1991 to 2011. Possible
determinants
reasons for differences between the results of these and other studies on shareholder
value creation measures seem to be the shareholder value creation measure(s) used, the
compilation of the sample, the country on whose data the analysis was conducted and 191
the statistical technique(s) used. Moreover, most of these studies try to explain
shareholder value creation, share price or excess market returns in respect of a
homogenous sample of companies. Inevitably, these varying and sometimes conflicting
findings are not able to provide a firm’s management with a clear blueprint or path
towards efficient shareholder value creation for that particular firm.
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

Only a limited number of studies (Biddle and Seow, 1990; Lee and Kim, 2009) have been
conducted on a heterogeneous sample of companies, analyzing different industries. The
present study expands this research and contributes to the existing literature by analyzing
five industries in an analysis of South African data and by incorporating more dependent
and independent variables than are usually investigated in previous studies of this nature.
The particular focus of this study is to determine whether, for different industries, there are
different external shareholder value creation measures that express shareholder value
creation best for those industries. In addition, the study attempts to establish whether
accounting-based or economic-based internal value drivers are superior in explaining
shareholder value creation and whether this is still the case if the external shareholder value
creation measure as the dependent variable is changed. The main objective of this research
is to establish a set of variables or value drivers that are unique and significant in
determining shareholder value creation for a particular industry.
Five different industries are analyzed, using two different shareholder value creation
measures as dependent variables, namely, MVA and a market adjusted stock return
(MAR), in combination with 11 different independent shareholder value variables. To
achieve the main objective of this study, some independent variables were eliminated
after the initial analysis by means of a step-wise multiple regression analysis to derive
a set of significant independent variables that explain shareholder value creation best
for a particular industry. The industries concerned are construction and materials, food
and beverages, industrial goods (manufacturing) and retail and technology.
The findings of this study make several contributions to the existing body of
knowledge on shareholder value creation measures. First, it was determined that in all
five industries under review, MVA is a better external shareholder value creation
measure than the market adjusted return. The reason for this originates from the
different characteristics of the specific shareholder value creation measure applied; one
measure is simply better than another. Second, an unique or specific set of variables that
create shareholder value is identified for each of the five industries included in the study;
for example, for the industrial goods industry, there is seven different variables that
explain shareholder value, namely, EPS, net operating profit after tax (NOPAT), return
on capital employed (ROCE), the Spread, which is the difference between the ROCE and
the weighted average cost of capital (WACC), EVA, earnings before extraordinary
income and tax (EBEI) and REVA, whilst for the food and beverages industry, there is
only two significant value drivers (EPS and ROA). The reasons for the differences in
value drivers for the different industries are based on the different industry
 SEF characteristics; some industries are more capital intensive, some more labour intensive,
33,2 whilst some industries are more subject to competition and substitute products. From a
practical viewpoint, the results of this study will assist management to know exactly,
depending on the industry classification of the firm, where to direct its efforts to
maximize shareholder value. Furthermore, shareholders will recognize that each
industry has its own specific set of value drivers. Finally, stock analysts and portfolio
192 managers will be made aware of the fact that there are specific differences in variables
that create shareholder value for the different types of industries in which they plan to
invest and for which they might want to perform share valuations, apply valuation
methods or make investment recommendations to their clients.
In the remainder of this paper, a brief overview of the relevant literature is given,
followed by a discussion of the research method. An analysis and discussion of the
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

empirical results follow. In the conclusion to this study, recommendations are made,
based on the findings.

Literature review
Shareholder value creation and its measurement are arguably amongst the most
frequently researched topics in corporate finance. The principle of “accounting profit”
was long been known to and used by management, shareholders and analysts, but a
movement beyond accounting profit and towards “economic profit” was initiated
decades ago by authors such as Fruhan (1979), Rappaport (1986) and Stewart (1991),
who, with his associate Stern, developed and popularized EVA. With the development of
economic profit measures such as EVA, MVA, CFOI, CVA and REVA, the battle lines
were drawn against the more traditional accounting-based measures, such as EPS, ROE,
ROA and DPS. The critical question is then which measure (accounting or economic) is
best at explaining shareholder value creation.
The sheer number of research studies conducted on shareholder value creation
measures is overwhelming, and the results are bewildering, as it seems that every
study’s results were affected by factors such as the compilation of the sample, the period
under review, the source of the data and even the statistical techniques adopted. An
analysis of 18 such studies revealed that 6 different dependent variables and 22 different
independent variables had been used in research spanning seven different countries
over a period of 20 years (1991 to 2011), revealing no fewer than 11 different independent
variables that explained shareholder value creation best in the studies concerned (Hall,
2013). Studies which found economic-based measures to be more useful rather than
accounting-based variables included studies by Stewart (1991, 1994), Stern (1993),
Milunovich and Tsuei (1996), O’Byrne (1996), Bacidore et al. (1997), Chen and Dodd
(1997, 2001), Hall (1999), Worthington and West (2004), Chmelikova (2008) and Lee and
Kim (2009). By contrast, studies by Biddle et al. (1997), Salvaiy (1997), Bao and Bao
(1998), De Villiers and Auret (1998), De Wet (2005), Ismail (2006), Maditinos et al. (2006,
2009), Kyriazis and Anastassis (2007), Erasmus (2008), Kumar and Sharma (2011),
Arabsalehi and Mahmoodi (2012), Abdoli et al. (2012), Mollah et al. (2012), Hall (2013)
and Alloy Niresh and Alfred (2014) found that accounting-based variables performed
better in explaining shareholder value creation than economic-based measures. It falls
beyond the scope of this study to discuss the results of the abovementioned studies in
more detail, but it is important to point out that the inconsistencies of their results may
lead managers, investors, shareholders and researchers to ask the following questions:
 Why are there so many inconsistencies in the results of these studies? Based on Industry-
these inconsistent results, which performance measure is actually the best to explain the specific
value drivers of shareholder value creation? More importantly, what can be done to
determine with greater certainty a more reliable shareholder value creation measure and
determinants
value driver for a specific firm or type of firm? Surely, one shareholder value creation
measurement cannot be universally applied?
This study aims to address these issues by refining and adding to the data being 193
analyzed. This is achieved, first, by increasing the number of dependent and
independent variables to obtain a more concise and specific answer as to which
shareholder value creation measure is best for a specific industry. Furthermore, by
classifying a sample of companies in different industries (as opposed to keeping them in
one homogenous group), it was envisaged that not only would a clearer indication be
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

obtained of which shareholder value creation measure better explains value creation in
a specific industry but it would be possible to deduce the value drivers that feature as
significant variables explaining shareholder value creation in a specific industry.
In analyzing the results of the abovementioned studies, as well as the results of a
study by Hall (2013), a number of dependent and independent variables were identified
for use in the present study. All the dependent and independent variables were used in
one way or another in the prior studies. The dependent variables for the present study
are MVA (market value minus capital used) and the market adjusted stock return (MAR)
(a firm’s 12-month compounded stock return adjusted for the financial year-end of the
specific firm). The independent variables which were regressed against the dependent
variables in the present study were EVA (return on economic capital minus the WACC,
multiplied by capital); EVA growth (GEVA) (the growth in EVA over two consecutive
years); REVA (EVA based on the market value of economic capital instead of the book
value of capital); EBEI; NOPAT; NI (the net income attributable to shareholders); ROA;
EPS; ROE; ROCE; and the Spread. The independent variables, thus, consist of five
economic-based variables, namely, EVA, EVAgrowth, REVA, ROCE and the Spread.
The remaining six independent variables are accounting-based measures.
To achieve the objectives of this study, the following hypotheses were developed and
tested:
H1. Whether market value added or market adjusted stock return is the best
shareholder value creation measure differs between industries.
H2. Irrespective of the industry concerned, the impact of economic-based value
indicators of shareholder value creation is higher than that of accounting-based
measures.
H3. The internal value indicators (drivers) with the highest impact on shareholder value
creation differ between different industries.
H4. The internal value indicators (drivers) with the highest impact on shareholder
value creation differ depending on whether market value added or market
adjusted stock return is used as the external shareholder value creation
measure.
H5. Each industry has a unique set of variables determining shareholder value.
These hypotheses were tested using the methodology described in the next section.
 SEF Research method
33,2 The research method followed is set out below. The various industries that were selected
for analysis, the dependent and independent variables, as well as the statistical
techniques that were applied, are discussed. The data used for this study were obtained
from the iNETBFA database, a South African supplier of high-quality financial data.

194 Industries
Firms listed on the Johannesburg Stock Exchange (JSE) for the period 2001 to 2011 were
selected. The data set consisted of 192 companies. The industry classification used by
the JSE was also applied to identify the different industries. The first selection criterion
for an industry to be included in the analysis of this study was based on the
characteristics of the industry in comparison to other industries; the goal was to have a
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

number of non-related or different types of industries to analyze, as it was hypothesised

that different industries should have different variables that explain shareholder value
creation. The second criterion was the number of firms in the industry, as the sample
size had to be large enough to provide significant statistical results. Based on these
criteria, five industries were selected for analysis in this study, namely, construction and
materials (28 firms), food and beverages (19 firms), industrial goods (manufacturing) (61
firms), retail (23 firms) and technology (22 firms).

Dependent variables and independent variables

The two dependent variables that were identified in the literature study were MVA and
MAR. The 11 independent variables, further identified by an “(a)” for accounting-based
and an “(e)” for economic-based measures, used in this study were EVA(e),
EVAgrowth(e), REVA(e), EBEI(a), NOPAT(a), NI(a), ROA(a), EPS(a), ROE(a), ROCE(e)
and the Spread(e). These variables were calculated for all firms over the 11-year period
under review. Outliers that fell outside three standard deviations from the mean were
discarded.

Statistical model specification

Most prior studies cited above used ordinary least squares (OLS) analysis for a set of
cross-sectional time series data. By applying panel data analysis, observations can be
conducted on multiple phenomena over various periods for the same firm (Baltagi,
2008). This results in more reliable regression techniques for the cross-sectional time
series data and greatly enhances the validity of regression results. For the current study,
the data set is an unbalanced panel. The multiple regression model used to test the
information content of the independent variables on the dependent variables, based on
panel data regression analysis, was the same as that used by Hall (2013):

mvaits ⫽ ␤0 ⫹ ␤1evaits ⫹ ␤2evagrowthits ⫹ ␤3revaits ⫹ ␤4ebeiits ⫹ ␤5nopatits

(1)
⫹ ␤6niits⫹ ␤7roaits ⫹ ␤8epsits ⫹ ␤9roeits ⫹ ␤10roceits ⫹ ␤11spreadits ⫹ ␧its.

In equation (1) above, mvaits is the MVA for Firm i in Period t for Industry s, evaits is the
amount of economic value added for Firm i in Period t for Industry s and so on. ␧its is a
stochastic error term for Firm i at Time t for Industry s; i ⫽ 1 to 192; t ⫽ 1 (2001) to 11
(2011) and s ⫽ 1 to 5 for the five different industries:
 marits ⫽ ␤0 ⫹ ␤1evaits ⫹ ␤2evagrowthits ⫹ ␤3revaits ⫹ ␤4ebeiits ⫹ ␤5nopatits Industry-
(2)
⫹ ␤6niits ⫹ ␤7roaits ⫹ ␤8epsits ⫹ ␤9roeits ⫹ ␤10roceits ⫹ ␤11spreadits ⫹ ␧its. specific
determinants
In equation (2) above, marits is the MAR for Firm i in Period t for Industry s, evaits is the
amount of EVA for Firm i in Period t for Industry s and so on. In the final analysis on the
data of this study, a backward step-wise multiple regression analysis was performed to
determine the significant independent variables for each industry. The model used was 195
specified as follows:

mvait1 ⫽ ␤0 ⫹ ␤1evait1 ⫹ ␤2evagrowthit1 ⫹ ␤3revait1 ⫹ ␤4ebeiit1 ⫹ ␤5nopatit1 (3a)

⫹ ␤6niit1 ⫹ ␤7roait1 ⫹ ␤8epsit1 ⫹ b9roeit1 ⫹ ␤10roceit1 ⫹ ␤11spreadit1 ⫹ ␧it1
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

mvait1 ⫽ ␤0 ⫹ ␤1evait1 ⫹ ␤5nopatit1 ⫹ ␤7roait1 ⫹ ␤8epsit1 ⫹ ␧it1 (3b)

A number of tests were performed to assess the validity of the data. Each sample
(industry) was tested and corrected where applicable for serial correlation, stationarity
and heteroskedasticity. In the next section, the results from the empirical analysis are
presented and discussed.

Empirical results
The empirical results of this study are discussed in three stages, namely, the model
validity, multiple regression results and the step-wise regression results.

Model validity
To assess the validity of cross-sectional effects, the pooled models (models with a
common intercept) are assessed against models with individual cross-sectional terms
(fixed effects models).
Table I above shows the re-estimated models. The results indicate that for all
industries MVA has higher explanatory power both in terms of the adjusted R2 and the
F-value, and it is therefore the preferred dependent variable. In all the industries, the
combination of the independent variables explains more than 60 per cent of the variation

Dependent Durbin-Watson
Industry variable Adj. R2 F-value p-value LDW DW UDW SC

Construction and
materials MAR 0.399 5.754 0.000 1.8072 1.949 2.0971 No SC
Construction and
materials MVA 0.853 42.625 0.000 1.8072 2.030 2.0971 No SC
Food and beverages MAR 0.017 1.110 0.331 1.8072 2.222 2.0971 Negative SC
Food and beverages MVA 0.770 22.050 0.000 1.8072 2.292 2.0971 Negative SC
Industrial goods MVA 0.802 35.352 0.000 1.8734 1.774 2.0791 Positive SC
Retail MAR 0.191 2.595 0.000 1.8072 2.131 2.0971 Negative SC Table I.
Retail MVA 0.835 35.085 0.000 1.8072 1.849 2.0971 No SC Second round fixed
Technology MAR 0.148 2.15 0.001 1.8072 2.195 2.0971 Negative SC effects model
Technology MVA 0.609 11.330 0.000 1.8072 1.782 2.0971 Positive SC estimation
 SEF of MVA. By contrast, the ability of the independent variables to explain the variability
33,2 in MAR was much lower in each industry, with the explanatory power of the models
failing to reach the 20 per cent mark in most cases.

Multiple regression analysis

196 In Table I, from the p-values for each of the models, it can be seen that for the food and
beverages industry, the MAR-based model had to be rejected at both a 5 and 10 per cent
level of significance; therefore, in the food and beverages industry, the chosen
independent variables do not explain firm performance, based on the shareholder value
creation measure MAR. From this finding, it is inferred that for manufacturing-based
firms, with a large part of the food and beverages industry also falling broadly into the
manufacturing-type category, the chosen independent variables cannot sufficiently
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

explain the variation in performance, based on MAR. In this regard, the MVA-based
models perform better, with a 77 per cent explanatory power for the food and beverages
industry and an 80 per cent explanatory power for the industrial goods industry. It is
also noted that for the construction and materials industry, the MVA-based model
provides an explanatory value of 85 per cent, more than twice the explanatory power of
40 per cent of the MAR-based model.
Based on the results of this analysis, it appears that overall, MVA performs better
than MAR as an expression for shareholder value created in all five industries, which
leads to the conclusion that the first hypothesis has to be rejected, namely, that the
shareholder value creation measure that best expresses shareholder investment for
different industries differs. The finding of this study, that MVA is consistently a better
method to express shareholder value creation in all industries than MAR, contradicts
Hall’s (2013) finding that MAR was superior to MVA in a totally non-homogenous
sample and for capital-intensive firms. Table II contains the results of the regression
statistics of the present study.
The regression coefficients show that whilst a number of the models are significant
overall, for a number of independent variables, this may not be the case. Again, for
models based on MAR, it can be seen that only a relatively small number of independent
variables are significant, whilst a larger number of variables in the MVA models are
significant at a 5 per cent or a 10 per cent level. In total, there are 12 significant variables
for the MAR models and 28 significant variables for the MVA models. This further
supports the conclusion that purely based on the number of significant variables, MVA
should be the primary choice for analyzing firms when categorised by industry, thereby
confirming the rejection of the first hypothesis.
Analysing the independent variables that were significant, it was found that
REVA(e) featured six times, ROCE(e) five times, EPS(a) five times and NOPAT(a) five
times. This result indicates that there is no clear winning group of independent variables
(accounting-based vs economic-based) explaining shareholder value creation. The table
also shows that for the shareholder creation measurement MVA, EPS, which is an
accounting-based variable, is significant in all industries, making it the only
independent variable to display significance in all five industries (although it was not
the variable with the highest impact in all industries). This implies that the second
hypothesis, which states that economic-based variables have a higher impact in
explaining shareholder value creation than accounting-based indicators, has to be
rejected. The results of this analysis, thus, indicate that the accounting-based variables
 Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

Construction and Construction and Food and Food and Industrial

Independent materials materials beverages beverages goods Retail Retail Technology Technology
variable MAR MVA MAR MVA MVA MAR MVA MAR MVA

Coefficient
EBEI (a) 0.000020 0.000000 ⫺0.000010 0.000000 0.00000* 0.00001* 0.00000** 0.000000 0.000000
EPS (a) ⫺0.002160 ⫺0.00112* ⫺0.021650 0.0007* 0.0001* 0.029770 0.00298* 0.131470 0.00391**
EVA (e) ⫺0.000020 0.00000* 0.000010 0.00000* 0.00000* ⫺0.00008* 0.000000 0.000080 0.000000
GEVA (e) 0.002310 0.000990 0.05811* ⫺0.000100 0.00139** 1.39995** ⫺0.033450 ⫺5.750020 0.062590
NI (a) 0.000000 0.000000 0.000000 0.000000 0.000000 0.00007* ⫺0.00001** 0.000050 0.000000
NOPAT (a) ⫺0.000040 0.00000* 0.000000 0.00000** 0.00000* ⫺0.000010 0.00001* ⫺0.00042** 0.000000
REVA (e) 0.00005* 0.000000 0.00000* 0.000000 0.00000* 0.00002* 0.000000 0.00001** 0.00000*
ROA (a) ⫺1.191000 0.094550 ⫺1.164140 ⫺0.01445* 0.003950 0.074070 ⫺0.51863* 0.826160 ⫺0.001360
ROCE (e) 0.133310 ⫺0.00559** 0.400820 ⫺0.000210 0.00265** ⫺0.045650 0.13586* ⫺0.17584** 0.00152**
ROE (a) 0.039680 ⫺0.002310 ⫺0.206050 ⫺0.0005** 0.000050 0.011600 0.001250 0.07117* 0.000300
Spread (e) 2.038210 ⫺0.002420 2.142180 0.004900 ⫺0.01448** 0.049640 0.06768* ⫺0.068720 ⫺0.00935*

Regression statistics
Adj. R2 0.399 0.853 0.017 0.77 0.802 0.191 0.835 0.148 0.609
p-value 0.000 0.000 0.331 0.000 0.000 0.000 0.000 0.001 0.000
DW 1.949 2.030 2.222 2.292 1.774 2.131 1.849 2.195 1.782

Notes: * Significant at a% level; ** significant at a 10% level; “(a)” refers to accounting-based variables; “(e)” refers to economic-based variables

Table II.
197

for final models.

determinants
specific
Industry-

Regression statistics
 SEF have a higher impact on shareholder value than the economic-based variables. This
33,2 finding corresponds with the findings of studies by Biddle et al. (1997), Chen and Dodd
(1997), Bao and Bao (1998), De Villiers and Auret (1998), Ismail (2006), Kyriazis and
Anastassis (2007), Erasmus (2008) and Kumar and Sharma (2011). A possible reason for
the fact that accounting-based variables is superior compared to economic-based
variables in explaining shareholder value creation may be that the market (both
198 investors and analysts) tend to rely on and react to accounting earnings as an indicator
of the financial well-being of a company. Management reports accounting or financial
results, not only in the financial press but also at analysts’ meetings and in
presentations.
A scrutiny of the various industries focusing on the MVA model reveals that in the
industrial goods industry, eight significant independent variables (three accounting and
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

five economic measures) are important, with ROCE(e) coming out the highest. The retail
industry is driven by seven significant variables (five accounting and two economic
measures), with ROCE(e) coming out the highest. The food and beverages industry is
driven by five significant variables (four accounting measures and one economic
measure), with EPS(a) coming out the highest. From these results, one can conclude that
for different industries, different variables explain shareholder value and, more
importantly, that the internal value drivers with the highest explanation of or impact on
shareholder value differ between the various industries. This leads H3 to be accepted.
A number of explanations can be offered for why the various industries have
different internal value drivers. First, industries that are more capital intensive (the
industrial sector), where the operating leverage has a relative big influence on
profitability, would have different value drivers than a labour-intensive industry (such
as the retail or technology sectors). Hall (2013) found a difference between the value
drivers of capital- and labour-intensive firms. Second, firms or industries that have a
higher debt ratio relative to other firms or industries tend to experience a bigger
financial leverage effect on profitability, returns and shareholder value creation;
therefore, different variables have a significant impact on shareholder value creation.
Third, during an economic downswing (such as that experienced since 2008), industries
such as construction are more affected than industries with a more inelastic demand for
their products, such as food and beverages or the retail industry. Biddle and Seow (1990)
also found that factors such as industry entry barriers and product type may account for
differences in variables that explain returns in different industries.
Although the results of this study show that the MVA model performs significantly
better than the MAR model in explaining shareholder value creation, it would be useful
to compare the two models on the basis of the specific significant independent
variable(s) (value driver(s)) that have the highest impact (positive or negative) on the
dependent variable in each of the industries. In the case of the MAR model, REVA(e),
GEVA(e) (twice) and ROCE(e) have the highest impact on the shareholder value creation
measurement, MAR. It is noteworthy that all these variables are economic-based
variables. In the case of MVA, the variables with the highest impact are ROCE(e) (twice),
EPS(a) (twice) and ROA(a). This finding leads to the acceptance of H4, namely, that the
internal value indicator with the highest impact on shareholder value differs depending
on what measure of shareholder value is being used. The finding that different
shareholder value creation measures have different value drivers that have the highest
impact on them is consistent with the results reported by Hall (2013), who found that the
 ROA had a positive value for MAR but a negative value for MVA and that the Spread Industry-
had a higher value for MAR than for MVA. Overall, in the analyses of the various specific
industries, the variables that have the highest impact on shareholder value creation are
the following: ROCE(e) appeared three times, GEVA(e) appeared twice, EPS(a) twice and
determinants
both REVA(e) and ROA(a) once.
To conclude this part of the analysis, the overall performance of the models would
suggest that the choice of shareholder value creation measure is affected by the choice of 199
industry that is being analyzed, but it may be useful to consider alternative
specifications for these models, particularly focusing on a smaller number of
independent variables. To achieve the main objective of this study (to determine a set of
significant value drivers for a specific industry), a multiple step-wise regression
analysis was performed. Doing so enabled the independent variables with the smallest
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

impact on shareholder value creation to be eliminated in a scientific way to identify a set

of significant independent value drivers for each industry.

Step-wise regression analysis

Based on the results obtained in the analysis above, it was decided to analyze the models
with a reduced number of independent variables. In this study, a backward elimination
step-wise regression was used to find the final combination of independent variables
deemed most appropriate for each industry. In addition, an F-test for redundancy was
also performed. These results, as well as the model estimation results, are reported in the
table below. Based on the results of this analysis, the first four hypotheses were once
again tested, as was H5 (Table III).
The adjusted R2 values show that for all but the technology industry, models based
on the MVA have an explanatory power of 80 per cent or more. In the case of the
technology industry, the explanatory power of the MVA-based model is just above 60
per cent. In the case of the MAR, however, models based on this dependent variable have
low to very low explanatory power, with all models accounting for less than 20 per cent
of the variation in the dependent variable. It is therefore concluded that for all industries,
MVA is a more appropriate shareholder value creation measure. This finding again
leads to the rejection of H1. It was found in this analysis that for all industries, MVA was
the preferred method of expressing shareholder value created. Table IV sets out the
coefficients and their significance for each of the estimated models and indicates which
independent variables were excluded from each estimation.
In Table IV, it is clear that on average, four independent variables were retained in
each model. In most cases, the coefficients are found to be significant at least at a 10 per
cent level of significance. In the MVA models, the variable that appears most is EPS(a),
which appears four times. The variables NOPAT(a), ROA(a), ROCE(e) and the Spread(e)
all appear three times, whereas EVA(e) appears twice. Value drivers that do not appear
at all are the GEVA(e), NI(a) and ROE(a). In total, 21 significant appearances are
recorded, of which 12 are accounting-based variables and 9 are economic-based
variables. Based on this analysis, H2, again, has to be rejected. The results of this
analysis prove that after the elimination of redundant or non-significant variables, the
accounting-based variables still explain most of the external shareholder value
measures chosen. The reason for the superior performance of accounting-based
variables probably lies in the fact that accounting earnings are reported by firms’
managements, feeding the market with accounting information. One should also not
 Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

33,2
SEF

200

Table III.

model estimation
Final fixed effects
Model significance Redundancy Durbin-Watson
Industry Dependent variable Adj. R2 F-value p-value F-value p-value LDW DW UDW SC

Construction and materials MAR 0.040 7.088 0.000 0.779 0.621 1.8072 2.019 2.0971 No SC
Construction and materials MVA 0.851 50.715 0.000 1.613 2.103 1.8072 0.132 2.0971 Positive SC
Food and beverages MVA 0.776 32.211 0.000 0.471 0.893 1.8072 2.295 2.0971 Negative SC
Industrial goods MVA 0.803 37.577 0.000 0.391 0.815 1.8734 1.803 2.0791 Positive SC
Retail MAR 0.193 3.031 0.000 0.937 0.479 1.8072 2.115 2.0971 Negative SC
Retail MVA 0.837 42.886 0.000 0.668 0.676 1.8072 1.799 2.0971 Positive SC
Technology MAR 0.162 2.6282 0.000 0.537 0.806 1.8072 2.194 2.0971 Negative SC
Technology MVA 0.608 14.592 0.000 1.047 0.402 1.8072 1.795 2.0971 Positive SC
 Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

Construction and Construction and Food and Industrial

materials materials beverages goods Retail Retail Technology Technology
Independent variable MAR MVA MVA MVA MAR MVA MAR MVA

Coefficient
EBEI (a) ⫻ ⫻ ⫻ ⫺0.0001* 0.0114** ⫻ ⫻ ⫺0.0002*
EPS (a) ⫻ ⫺0.001* 0.0008* 0.0001* ⫻ 0.0026* ⫻ ⫻
EVA (e) ⫻ 0.0021* ⫻ 0.0004* ⫺0.0737* ⫻ ⫻ ⫻
GEVA (e) ⫻ ⫻ ⫻ ⫻ ⫻ ⫻ ⫻ ⫻
NI (a) ⫻ ⫻ ⫻ ⫻ 0.0692* ⫻ ⫻ ⫻
NOPAT (a) ⫺0.0432* ⫺0.0021* ⫻ ⫺0.0003* ⫻ 0.0043* ⫺0.326** ⫻
REVA (e) 0.0333* ⫻ ⫻ ⫺0.0001* 0.0249* ⫻ ⫻ ⫻
ROA (a) ⫻ 0.0499** ⫺0.0097* ⫻ ⫻ ⫺0.5263* 0.7395* ⫻
ROCE (e) ⫻ ⫻ ⫻ 0.0026 ⫻ 0.1379* ⫺0.1839** 0.0014*
ROE (a) ⫻ ⫻ ⫻ ⫻ ⫻ ⫻ 0.0653* ⫻
Spread (e) 1.9152* ⫻ ⫻ ⫺0.0125* ⫻ 0.0640 ⫻ ⫺0.0091*
Regression statistics
Adj. R2 0.040 0.851 0.776 0.803 0.193 0.837 0.162 0.608
p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
DW 2.019 0.132 2.295 1.803 2.115 1.799 2.194 1.795

Notes: * Significant at a 5% level; ** significant at a 10% level; “(a)” refers to accounting-based variables; “(e)” refers to economic-based variables
201

variables.
Table IV.

independent
reduction of
determinants
specific
Industry-

following stepwise
Regression statistics
 SEF forget that the external shareholder value creation measurement MVA is calculated on
33,2 the basis of the accounting values contained in financial statements.
A scrutiny of the various industries, focusing on the MVA model, reveals that for the
industrial goods industry, there are seven significant independent variables (three
accounting and four economic measures), with ROCE(e) coming out the highest. For the
retail industry, there are five significant variables (three accounting and two economic
202 measures), with ROA(a) coming out the highest. For the construction and materials
industry, there are four significant variables (three accounting measures and one
economic measure), with ROA(a) coming out the highest. For the technology sector,
there are three significant variables (one accounting measure and two economic
measures), with the Spread(e) coming out the highest. From these results, it is possible to
conclude that different sectors have different variables explaining shareholder value.
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

Even more importantly, the internal value drivers with the highest explanation of or
impact on shareholder value differ for each of the industries. This means that H3 has to
be accepted. A number of industry-specific factors, such as barriers to entry, product
type, maturity of the industry and the capital versus labour intensity of the industry,
influence the internal value drivers that explain shareholder creation best. The results of
this study indicate that in the retail industry, ROA has the highest impact. This was to
be expected, as the retail industry is labour intensive, with a relatively low investment in
fixed assets, compared to the industrial industry.
The results of this study show that of the two dependent variables used for
shareholder value expression in this study, MAR and MVA, MVA is the preferred
shareholder measurement. The Spread(e), EVA(e) and ROA(a) have the highest impact
on the shareholder value creation measurement, MAR. In the case of MVA, the variables
with the highest impact are ROA(a) (three times), the Spread(e) once and ROCE(e) also
once. This finding regarding the variables selected by means of the step-wise regression
as the most significant value drivers explaining shareholder value, leads H4 (that the
internal value indicator with the highest impact on shareholder value differs depending
on what measure of shareholder value is being used) to be accepted. Overall, in the eight
industries, the variables that have the highest impact on shareholder value creation are
ROA(a) four times, the Spread(e) two times and EVA(e) and ROCE(e) once each. To
address H5 (that every different industry or sector should have a different set of value
drivers), Table V was compiled.

Construction and Industrial goods

materials Food and beverages (manufacturing) Retail Technology

EPS (a) EPS (a) EPS (a) EPS (a)

ROA (a) ROA (a) ROA (a)
NOPAT (a) NOPAT (a) NOPAT (a)
ROCE (e) ROCE (e) ROCE (e)
Table V. Spread (e) Spread (e) Spread (e)
Significant variables EVA (e) EVA (e)
per industry (using EBEI (a) EBEI (a)
MVA as shareholder REVA (e)
value creation
measure) Notes: “(a)” Refers to accounting-based variables; “(e)” refers to economic-based variables
 Table V shows that the EPS is a significant variable in four industries, whilst a number Industry-
of variables (NOPAT, ROA, ROCE and the Spread) are significant in three industries. specific
Although their study is not entirely comparable to the current study, Lee and Kim (2009) determinants
have shown in an analysis of three different sectors in the hospitality industry that there
may also be a difference between sectors in terms of measures that explain shareholder
returns. In this study, the fact that EPS appears to be relevant for all but one of the
industries underlines once again the importance of earnings as a driver of shareholder 203
value creation. Asset management as represented by the ROA is significant for the
construction industry (which is both capital- and asset-intensive), the food and
beverages (which is also capital intensive and entails manufacturing activities) and the
retail industry, where working capital (inventory and debtors) plays an important role in
shareholder value creation. However, the technology sector, which is labour intensive,
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

does not have ROA as a significant value driver: human capital (which is not quantified
in the financial statements) is probably a significant determinant of shareholder value.
The fact that both the NOPAT and the Spread (essentially return ratios) appear as
significant variables in the same three industries (industrial goods, retail and
technology) can be explained by the fact that competition amongst firms in those
industries appears to be particularly fierce. The merger of South African retailer
Massmart with the US retailer Walmart in 2011, which gave the group greater
bargaining power in purchases and greater retail rental space negotiation powers,
illustrates this point. The ferocity of the competition amongst South African retailers is
also evident from a recent report that a South African retailer, Shoprite, instituted legal
proceedings against Massmart (Walmart) relating to the products that these two
retailers sell in one shopping complex (Watson, 2013).
It is further noteworthy that the industrial goods sector has, by a fair margin, the
most significant value drivers (seven) explaining shareholder value creation. This might
be because industrial manufacturing firms’ financial statements might contain the most
complete information to calculate shareholder value creation. A similar argument can be
made for the retail industry, with five significant variables. By contrast, for the
technology sector, with only three significant variables, probably the most significant
value drivers are human capital, patents, trade-marks and goodwill, all variables that
are either not quantifiable or are usually excluded from the shareholder value
calculation.
It should be noted that whilst there is some overlap in respect of the independent
variables for each industry, the most appropriate combinations for each industry are
unique. One can therefore conclude that the unique characteristics of each industry
necessitate the use of different independent variables to best explain the variations in
the shareholder value measurement. These findings lead to the acceptance of H5.
This notion is echoed by Merchant (2014), who states that value creation is not only
multi-dimensional but depends on firm-specific factors. Steenkamp (2014) found that the
processes and ways in which brand value contributes to firm value differ for different
firms. In addition, Fabrizi (2014) found that the chief marketing officer of a company, if
correctly incentivised, could contribute more to the value of a company than the chief
executive officer, whilst Basuroy et al. (2014) found that executive compensation plays
an important role in explaining firm value. A superior supply chain management
system could also give a company a comparative advantage in value creation (Ellinger
 SEF et al., 2012). An increase in share prices can be expected with an increased level of
33,2 corporate social responsibility disclosure (De Klerk, de Villiers and van Staden, 2015).
In summary, the results of the present study show that shareholder value creation
measures do indeed differ between industries and that accounting-based variables are
superior to economic-based variables in explaining shareholder value. However, the
main objective of this study was to investigate whether for different industries, there are
204 different value drivers which determine shareholder value creation. In this regard, it was
found that for different industries, there are indeed unique sets of value drivers which
have been shown to have an impact on shareholder value. The significance of this
finding is highlighted in the conclusion to this study.

Conclusion
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

Shareholder value creation and its measurement are arguably some of the most
frequently researched topics in corporate finance. One reason for it remaining such a
relevant and hotly contested research topic is that researchers and the corporate world
continue to generate new shareholder value creation measurements. This study set out
to refine the search for the best shareholder value creation measure by not only
analyzing a bigger range of shareholder value creation measures than most previous
studies do but also conducting an analysis on specific samples as represented by
different industries or sectors. The main objective of this study was to find, for each of
the specific industries, a set of variables that best explain shareholder value creation for
that industry. A literature overview of shareholder value creation measures revealed not
only an increase in the number of shareholder value creation measures available but also
a bewildering array of studies that attempted to analyze, prove, disprove or provide
results and findings to shareholders, industry, academia and the creators of these
shareholder value creation measures. The results of the current study are significant, as
they fill a gap in literature – prior studies mainly used homogenous samples, in contrast
to the current study, which analyzes five different industries with two different
shareholder value creation measures, namely, MVA and MAR.
The findings indicate that MVA is preferred in five different industries as a
shareholder value creation measure, compared to MAR. The study shows that
accounting-based variables (EPS, ROA and NOPAT) are dominant compared to
economic-based variables (EVA, the Spread and ROCE) in explaining shareholder value
creation. The impact of these variables differs between industries. In addition, the
variables that explain shareholder value creation differ when different shareholder
value creation measures are used. Finally, the results of this study indicate that for each
of the five industries analyzed, there is a unique set of variables that determine
shareholder value creation. EPS features as a significant value driver in four of the five
industries analyzed in this study. The five different industries analyzed all have a
different number of value drivers; for example, for the industrial goods industry, there
are seven different variables that explain shareholder value (EPS, NOPAT, ROCE, the
Spread, EVA, EBEI and REVA), whilst for the food and beverages industry, there are
only two significant value drivers (EPS and ROA).
The research methodology of this study provides a refined method of analyzing
shareholder value creation measures. It has been proven that results do indeed vary
when different value creation measures are being used and that one will not find the
same set of results if different industries are being analyzed. Therefore, a one-size-fits-all
 approach is inadequate, as different industries have different sets of variables that Industry-
explain shareholder value creation. specific
Based on the results of the present study, a number of recommendations can be made. determinants
First, portfolio managers need to concentrate on MVA, as opposed to a MAR as one of
their portfolio selection criteria. In addition, portfolio managers need to take into account
the different value drivers of industries in their analyses and recommendations. It has
also been established that accounting-based variables, especially EPS, have a high 205
impact on shareholder value creation; it is therefore recommended that earnings reports
and announcements should be actively used in considering investment decisions, as the
results of the current study show that shareholder value follows earnings.
Second, the results of this study can be used by boards of directors to find ways to
compensate their employees in a more fair and equitable manner. For example, based on
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

the results set out in Table V, if the board of directors of a firm in the construction
industry wants to reward employees, performance indicators such as the EPS, ROA,
NOPAT and EVA can be used, whilst the employees of a firm in the retail industry
should be rewarded based on improvements in the EPS, ROA, NOPAT, ROCE and the
Spread. At the same time, the value drivers for the different industries as presented in
Table V are those that management should concentrate on when making decisions on
the operational effectiveness of their organizations.
Although this paper concentrates only on financial performance measures, managers
should design performance measurement systems that are designed to capture
information on all aspects of their businesses. In this regard, Bryant et al. (2004) cite
research that shows that both employees’ actions and intangible assets, neither of which
is captured by traditional performance measures, should be included in the assessment
of a company’s success. Iazzolino et al. (2014) propose that a firm’s performance must be
measured not only by financial performance measures such as EVA but also through
multi-criteria methodologies such as the Balanced Scorecard.
In conclusion, the results of this study suggest that the unique characteristics of each
industry determine the optimal choice of shareholder value creation measure for that
industry and that each industry requires the use of a unique set of variables to determine
shareholder value creation. Each shareholder value creation measure brings with it
inherently different features which may be more suited to a particular industry than
those of other measures. In this regard, further analysis using more dependent variables
(such as Tobin’s Q ratio or the market to book ratio) is recommended; as such, research
may enhance the chances of finding and refining the search for an optimal set of
variables that management can concentrate on to enhance shareholder value creation.
Indeed, conducting an analysis of the value drivers of a specific firm might be the
ultimate goal in answering the burning question of what determines shareholder value.

References
Abdoli, M., Shurvarzi, M. and Farokhad, A.D. (2012), “Economic value added vs accounting
residual income: which one is a better criterion for measurement of created shareholders
value?”, World Applied Sciences Journal, Vol. 17 No. 7, pp. 874-881.
Alloy Niresh, J. and Alfred, M. (2014), “The association between economic value added, market
value added and leverage”, International Journal of Business and Management, Vol. 9
No. 10, pp. 126-133.
 SEF Arabsalehi, M. and Mahmoodi, I. (2012), “The quest for superior financial performance measures”,
International Journal of Economics and Finance, Vol. 4 No. 2, pp. 116-126.
33,2
Bacidore, J.M., Boquist, J.A., Millbourn, T.T. and Thakor, A.V. (1997), “The search for the best
financial performance measure”, Financial Analysts Journal, Vol. 53 No. 3 (May/June),
pp. 11-20.
Baltagi, B.H. (2008), Econometric Analysis of Panel Data, Wiley, New York, NY.
206 Bao, B. and Bao, D. (1998), “Usefulness of value added and abnormal economic earnings: an
empirical examination”, Journal of Business Finance & Accounting, Vol. 25 Nos 1/2,
pp. 251-264.
Basuroy, S., Gleason, K.C. and Kannan, Y.H. (2014), “CEO compensation, customer satisfaction,
and firm value”, Review of Accounting and Finance, Vol. 13 No. 4, pp. 326-352.
Biddle, G.C. and Seow, G.S. (1990), “The estimation and determinants of associations between
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

returns and earnings: evidence from cross-industry comparisons”, Journal of Accounting,

Auditing & Finance, Vol. 6 No. 2, pp. 183-232.
Biddle, G.C., Bowen, R.M. and Wallace, J.S. (1997), “Does EVA® beat earnings? Evidence on
associations with stock returns and firm values”, Journal of Accounting and Economics,
Vol. 24 No. 3, pp. 301-336.
Bryant, L., Jones, D.A. and Widener, S.K. (2004), “Managing value creation within the firm: an
examination of multiple performance measures”, Journal of Management Accounting
Research, Vol. 16 No. 1, pp. 107-131.
Chen, S. and Dodd, J.L. (1997), “Economic value added (EVATM): an empirical examination of a
new corporate performance measure”, Journal of Managerial Issues, Vol. 9 No. 3,
pp. 318-333.
Chen, S. and Dodd, J.L. (2001), “Operating income, residual income and EVATM: which metric is
more value relevant?”, Journal of Managerial Issues, Vol. 13 No. 1, pp. 65-86.
Chmelikova, G. (2008), “Economic value added versus traditional performance metrics in the
Czech food-processing sector”, International Food and Agribusiness Management Review,
Vol. 11 No. 4, pp. 49-65.
De Klerk, M., de Villiers, C. and van Staden, C. (2015), “The influence of corporate social disclosure
on share prices”, Pacific Accounting Review, Vol. 27 No. 2, pp. 208-228.
De Villiers, J.U. and Auret, C.J. (1998), “A comparison of EPS and EVA as explanatory variables
for share price”, Journal for Studies in Economics and Econometrics, Vol. 22 No. 2, pp. 47-63.
De Wet, J.H.V.H. (2005), “EVA versus traditional accounting measures of performance as drivers
of shareholder value – a comparative analysis”, Meditari Accountancy Research, Vol. 13
No. 2, pp. 1-16.
Ellinger, A., Shin, H., Northington, W.M., Adams, F.G., Hofman, D. and O’Marah, K. (2012), “The
influence of supply chain management competency on customer satisfaction and
shareholder value”, Supply Chain Management: An International Journal, Vol. 17 No. 3,
pp. 249-262.
Erasmus, P. (2008), “The relative and incremental information content of the value based financial
performance measure Cash Valued Added (CVA)”, Management Dynamics, Vol. 17 No. 1,
pp. 2-15.
Fabrizi, M. (2014), “Chief marketing officer’s equity incentives: economic determinants and effects
on shareholder value”, European Journal of Marketing, Vol. 48 Nos 9/10, pp. 1757-1781.
Fruhan, W.E. (1979), Financial Strategy: Studies in the Creation, Transfer, and Destruction of
Shareholder Value, Irwin, Homewood, IL.
 Hall, J.H. (1999), “Correlating internal and external performance yardsticks in the evaluation of Industry-
corporate wealth creation”, Meditari Accountancy Research, Vol. 7 No. 1, pp. 123-143.
specific
Hall, J.H. (2013), “Toward improved use of value creation measures in financial decision-making”,
Journal of Applied Business Research, Vol. 29 No. 4, pp. 1175-1188.
determinants
Iazzolino, G., Laise, D. and Migliano, G. (2014), “Measuring value creation: VAIC and EVA”,
Measuring Business Excellence, Vol. 18 No. 1, pp. 8-21.
Ismail, A. (2006), “Is economic value added more associated with stock return than accounting 207
earnings? The UK evidence”, International Journal of Managerial Finance, Vol. 2 No. 4,
pp. 343-353.
Kumar, S. and Sharma, A.K. (2011), “Further evidence on relative and incremental information
content of EVA and traditional performance measures from select Indian companies”,
Journal of Financial Reporting & Accounting, Vol. 9 No. 2, pp. 104-118.
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

Kyriazis, D. and Anastassis, C. (2007), “The validity of the economic value added approach: an
empirical application”, European Financial Management, Vol. 13 No. 1, pp. 71-100.
Lee, L. and Kim, W.G. (2009), “EVA, refined EVA, MVA, or traditional performance measures for
the hospitality industry?”, International Journal for Hospitality Management, Vol. 28 No. 1,
pp. 439-445.
Maditinos, I.D., Sevic, Z. and Theriou, N.G. (2006), “The introduction of economic value added in
the Greek corporate sector”, The Southeuropean Review of Business & Accounting, Vol. 4
No. 2, pp. 1-11.
Maditinos, I.D., Sevic, Z. and Theriou, N.G. (2009), “Modeling traditional accounting and modern
value-based performance measures to explain stock market returns in the Athens stock
exchange (ATE)”, Journal of Modelling in Management, Vol. 4 No. 3, pp. 182-201.
Merchant, H. (2014), “Shareholder value creating strategies for emerging markets”, in Pattnaik, C.
and Kumar, V. (Eds), Emerging Market Firms in the Global Economy (International Finance
Review), Emerald Group Publishing Limited, Bingley, Vol. 15, pp. 147-179.
Milunovich, S. and Tsuei, A. (1996), “EVA in the computer industry”, Journal of Applied Corporate
Finance, Vol. 9 No. 1, pp. 104-115.
Mollah, S., al Farooque, O. and Karim, W. (2012), “Ownership structure, corporate governance and
firm performance”, Studies in Economics and Finance, Vol. 29 No. 4, pp. 301-319.
O’Byrne, S.F. (1996), “EVA and market value”, Journal of Applied Corporate Finance, Vol. 9 No. 1,
pp. 116-125.
Rappaport, A. (1986), Creating Shareholder Value: The New Standard for Business Performance,
Free Press, New York, NY.
Salvaiy, S.C.W. (1997), “The accounting variable and stock price determination”, Studies in
Economics and Finance, Vol. 18 No. 1, pp. 26-61.
Sharma, A.K. and Kumar, S. (2010), “Economic value added (EVA) – literature review and relevant
issues”, International Journal of Economics and Finance, Vol. 2 No. 2, pp. 200-220.
Steenkamp, J. (2014), “How global brands create firm value: the 4V model”, International
Marketing Review, Vol. 31 No. 1, pp. 5-29.
Stern, J. (1993), “Value and people management”, Corporate Finance, Vol. 1 No. 1, pp. 35-37.
Stewart, G.B. (1991), The Quest for Value: The EVATM Management Guide, Harper Business,
New York, NY.
Stewart, G.B. (1994), “EVA: fact or fantasy”, Journal of Applied Corporate Finance, Vol. 7 No. 2,
pp. 71-84.
 SEF Watson, L. (16 October, 2013), “Winkels in Kaapse sentrum veg oor kos en drank (Shops in Cape
centre fight over food and alcohol)”, available at: http://beeld.com/sake/2013-10-16-winkels-
33,2 in-kaapse-sentrum-veg-oor-kos-endrank (accessed 17 October 2013).
Worthington, A.C. and West, T. (2004), “Australian evidence concerning the information content
of Economic Value Added”, Australian Journal of Management, Vol. 29 No. 2, pp. 201-223.

208 About the author

Professor John Henry Hall is a Member of the Department of Financial Management at the
University of Pretoria in the Republic of South Africa. He has published numerous articles in
scholarly journals (some of which have received best paper awards) and has presented research
papers at a number of conferences both locally and internationally. He has supervised a number
of doctoral and Master’s students. John Henry Hall can be contacted at: john.hall@up.ac.za
Downloaded by United Arab Emirates University At 02:12 22 June 2016 (PT)

For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com