CAPITAL INVESTMENT DECISION MAKING

ABSTRACT
This study examines the capital investment decision making under
uncertainty since the application of investment appraisal practices trends
towards increasing greater superiority with the performing of multiple
tools and procedures in the current investment markets which are evolving
within an increasingly volatile and intertwined with global network,
investments are strongly exposed to uncertainties. Therefore, this study
focused on investment decision making under uncertainty of emerging
market economy of 186 Sri Lankan companies. A comprehensive primary
survey was conducted to collect data and exploratory factor analysis had
been performed to identify the uncertainty factors. The hierarchical multiple
regression analysis was performed to investigate the impact of uncertainty on
the application of capital budgeting practices in investment decision making.
The results of the study revealed that an increase in financial uncertainty was
associated with the application of net present value (NPV) based advanced
capital budgeting and sophisticated capital budgeting practices and the
size of the company was also related to the application of NPV based and
sophisticated capital budgeting practices.
Keywords: capital budgeting practices, financial uncertainty, firm size,
emerging economy.
CAPITAL INVESTMENT DECISION MAKING
UNDER UNCERTAINTY: PERSPECTIVES
OF AN EMERGING ECONOMY
Lingesiya Kengatharan
Department of Financial Management,
University of Jaffna, Sri Lanka
ARTICLE INFo
Article History:
Received: 29 May 2017
Accepted: 26 September 2017
Published: 30 December 2017
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Asia-Pacific Management Accounting Journal, Volume 12 Issue 2
INTRODUCTION
Nowadays, complex methods are used for making capital investment
decision purely depends on theories of capital budgeting because of risk
factors, uncertainty, contingency factors and hazards (Kaczmarek, 2015;
Singh, Jain & Yadav, 2012; Zhang, Huang & Tang, 2011; Kersyte, 2011;
Bock & Truck, 2011; Byrne & Davis, 2005; Cooper et al., 2002; Arnold &
Hatzopoulos, 2000; Mao, 1970 and Dickerson, 1963). After the advent of
full-fledged globalization and in the era of cutthroat competition (Verma,
Gupta & Batra, 2009), advanced developments in technologies, other
macro environmental factors and demographic factors are intruding into the
budgeting practices (Verbeeten, 2006). In a world of geo-political, social as
well as economic uncertainty, strategic financial management is under the
process of change, in turn requiring a re-examination of the fundamental
assumption as in efficient market hypothesis (Fama, 1970) that cuts across
traditional boundaries of the financial management. Increased volatility in
unpredictable changes would create more cut-throat competition than ever
before (Smith, Smithson & Wilford, 1989). Therefore, effective handling
of uncertainty is an important and often complex task in analysis of capital
investment decision (Macmillan, 2000).
This study focuses on examining the extent to which uncertainty
factors impact on application of capital budgeting practices in investment
decision making for an emerging country. A consideration of the impact
of uncertainty, information asymmetry and other complications on the
budgeting exercise gives one the view that there is no unique correct
technique and that there is a need for multiple methods in practices (e.g;
Pike, 1988; Arnold & Hatzopoulos, 2000; Verbeeten, 2006, Kaczmarek,
2015). Uncertainty factors and its influence on the use of capital budgeting
practices in investment decision making vary across countries because of
the nature of country, culture, politics, investment policy, monetary policy,
taxation system besides the regulatory and legal framework. To the best
of my knowledge, there is no studies on this focus in Sri Lanka. Therefore
studying capital investment decision making under uncertainty for an
emerging economy would provide invaluable knowledge into existing
literature. Therefore the research question of the study would be: to what
extent uncertainty factors impact on the use of capital budgeting practices
in investment decision making in the Sri Lankan emerging economy?
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Capital Investment Decision Making Under Uncertainty
LITERATURE REVIEW
Uncertainty
‘Uncertainty is the gap between the information currently available and
the information required to make the decision’ (Verbeeten, 2006). Recently,
‘uncertainty is defined as the range of an outcome, and risk is the probability
of gain or loss associated with a particular outcome’ (Al-Harthy, 2010).
Classification of Uncertainties
There are many categorizations for the uncertainty concept related
to investments presented in the literatures. Different authors viewed
uncertainties in different way. Therefore, classifications of uncertainties
are vary over the years. It was classified by Townsend (1969) as business
uncertainties and project uncertainties. Later it was viewed in 1980s like
market uncertainties and company uncertainties (Seidler & Carmichael,
1981), static uncertainties and dynamic uncertainties (Fanning, 1983).
However, uncertainties were focused in 1990s that strategic uncertainties,
operational uncertainties and financial uncertainties (Vojta, 1992), general
uncertainties, industry uncertainties and firm uncertainties (Miller, 1992),
direct and indirect uncertainties (Pringle & Cannoly, 1993) business and
financial uncertainties (Baril, Benke & Buetow, 1996) and endogenous and
exogenous uncertainties (Folta, 1998). Further, uncertainties were classified
in 2000s as market, industry and firm specific uncertainties (Bulan, 2005)
and input uncertainties, financial uncertainties, social uncertainties and
market uncertainties (Verbeeten, 2006).
Capital Budgeting Practices
Verbeeten (2006) defined capital budgeting as ‘capital budgeting
practices are the methods and techniques used to evaluate and select an
investment project’ (i.e., the decision making role of the accounting system).
Different perspectives on capital budgeting practices have been listed below

organizations implement procedures and guidelines that require
a systematic identification and uncertainty analysis to ensure that
the uncertainty is taken into account in capital budgeting decisions
that uncertainty is increasing day by day the world of globalization.
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Asia-Pacific Management Accounting Journal, Volume 12 Issue 2
Thus, capital budgeting practices are considered as an uncertainty
management tool (Verbeeten, 2006) by identifying and price
uncertainty, organizations strive to balance (costs) uncertainty and
profit.
Uncertainty affects capital budgeting practices as composite of
organizational structures and decision making processes. Thus, the
capital budgeting practices are part of the system of governance of
the organization (Verbeeten, 2006).
In order to make the suitable investment decision, capital budgeting
is the ‘process of evaluating and selecting long term investment
consistent with the firm owners’ goal of wealth maximization’ (Gitman,
1988). Therefore, capital budgeting is an integral part of the corporate
plan of an organization (Ekeha, 2011).
Capital budgeting is a fundamental element and used everywhere as
a tool for planning, control and allocation of scare resources among
competing demands. Capital budgeting is a vital part in financial
planning and decision making since capital budgeting tools leads
better decision making and would be able to justify selection of specific
capital investments among competing alternatives (Sekwat, 1999).
Classification of Capital Budgeting
Capital budgeting practices help managers to select n out of N
investment projects with the highest profits at an acceptable ‘risk of ruin’
(Verbeeten, 2006). Literature has generally distinguished among simple
(or naive) and advanced capital budgeting practices (Haka, 1987; Haka,
Gordon & Pinches, 1985). Simple or naive capital budgeting practices
include payback (PB) and accounting rate of return (ARR) (Pike, 1988)
which generally do not use cash flows, do not consider the time value of
money and do not incorporate risk in a systematic manner. These are based
on the accounting income, they do not include cash flows from a project
(Pike, 1988).
Advanced/sophisticated capital budgeting practices include
Discounted Cash Flow (DCF) methods, internal rate of return (IRR), net
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Capital Investment Decision Making Under Uncertainty
present value (NPV) and profitability index (PI) that consider cash flows,
risk, and the time value of money (Pike, 1988, Klammer, 1973). Further,
Pike (1988) classified the capital investment evaluation methods were
risk analysis techniques that included sensitivity analysis, analysis under
different assumptions (best/worst), reduced payback periods, increased
hurdle rates, probability analysis and beta analysis. Besides these analysis,
they also covered the management science techniques that included
mathematical programming, computer simulation, decision theory and
critical path analysis. Both Pike (1988) and Haka, Gordon and Pinches
(1985) mentioned in their studies that companies employing sophisticated
capital budgeting techniques and controls (such as NPV, probability analysis
and post completion audits) should, theoretically, be more effective in capital
investment decision making than those employing naive methods (PB) with
little way of control mechanism. Pike (1996) conducted longitudinal survey
on capital budgeting practices and classified two groups of sophisticated
capital budgeting practices which are financial techniques (including IRR,
NPV and sensitivity analysis) and management science techniques (including
probability analysis, beta analysis, computer simulation, decision theory,
mathematical programming and critical path analysis). Farragher, Kleiman
and Sahu (2001) suggested that degree of sophistication is represented by the
use of the DCF techniques and incorporating risk in the analysis. Dixit and
Pindyck (1994) and Trigeorgis (1993) have indicated that these discounted
cash flow methods have serious shortcomings in analyzing investment
projects when information concerning future investment decisions are not
available.
One of the empirical studies in finance (Verbeeten, 2006) classified
the capital budgeting practices into three group by performing exploratory
factor analysis and the results presented as naive/simple, NPV based
advanced and sophisticated capital budgeting practices. According to his
findings, naive/ simple capital budgeting practices include PB, adaptation
of required payback and ARR. NPV based advanced capital budgeting
practices include Sensitivity analysis/break-even analysis, Scenario analysis,
Adaptation of required return/discount rate , IRR, NPV and Uncertainty
absorption in cash flows. Sophisticated capital budgeting practices include
Monte Carlo simulations, Game Theory, Real option Reasoning, Using
certainty equivalents, Decision trees, CAPM analysis / ß analysis and
Adjusting expected values.
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Asia-Pacific Management Accounting Journal, Volume 12 Issue 2
Recently Wolffsen (2012) conducted a survey on ‘modification of
capital budgeting under uncertainty’ and from his study he has grouped
capital budgeting practices into three as sophisticated, advanced and simple/
naive. Findings of his study is consistent with findings of Verbeeten’s
(2006) classification of capital budgeting practices. Wolffsen’s (2012)
sophisticated capital budgeting practices include Monte Carlo Simulation,
real option method, using certainty equivalents, Binomial Lattice, CAPM
analysis/β analysis and Value at Risk (VAR). The advanced capital budgeting
practices include sensitivity analysis/ breakeven analysis, scenario analysis,
adaptation of hurdle rates, NPV, APV, IRR, MIRR and PI. Likewise in the
simple/naive capital budgeting practices include PB, DPB, ARR, earnings
multiplier and other equivalents multipliers.
Uncertainty and Application of Capital Budgeting Practices
Uncertainty and risk are the major influence in making investment
decision and thus Mao (1970) says ‘A central aspect of any theory of capital
budgeting is the concept of risk’ (p.352). Presently, there are number of
risk analysis tools and investment assessment methods. Analysis of risk
is a straightforward adaptation of Markowitz’s quadratic programming
model of portfolio selection (Mao, 1970). In this regard, portfolio theory
is concerned with optimal diversification problem and assets allocation
problem (Cuthbertson & Nitzsche, 2008). However, modern portfolio
theory’s tool for the better investment decisions are Efficient Frontier,
Single Index Model (Sharpe, 1963), Capital Assets Pricing Model (CAPM)
(Sharpe, 1964) and Arbitrage Pricing Theory (APT) (Ross, 1976). Despite
the age of these tools, they are currently useful to manage investment risk
and detect mispriced securities among other things (e.g., Trahan & Gitman,
1995; Graham & Harvey, 2001; Alkaraan & Northcott, 2006). A condition
of uncertainty usually exists in the capital budget because investment
decisions imply that the uncertain long-term results are important for the
survival of the company and for which no information is available (e.g.,Zhu
& Weyant, 2003; Simerly & Li, 2000; Smit & Ankum, 1993; McGrath,
1997; Bulan, 2005; Emmanuel, Harris & Komakech, 2010; Bock & Truck,
2011; Ghahremani, Aghaie & Abedzadeh, 2012).
Pike (1996) conducted a study on the application of tools for
uncertainty analysis in capital budgeting practices that companies had lack
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Capital Investment Decision Making Under Uncertainty
of information on macroeconomic factors, the reactions of competitors
and trends in technology development, political information and public
opinion. Therefore, he suggested capital investment decisions were taken
under uncertainty. Investment decisions involve the allocation of resources
of a company with plans to recover the initial investment plus sufficient
earnings (or other income) from cash flows (or other benefits) generated
during the economic life of an investment Macmillan (2000). Thus, such
decisions are difficult to reverse without seriously disturbing the company
economically and otherwise. Miller (2000) states that ‘in the real world,
virtually all numbers are estimates. The problem with estimates, of course,
is that they are frequently wrong’. Therefore, a capital budgeting decision
requires systematic and careful analysis in the current uncertain global
environment for making capital investment.
on the basis of the previous literature, the following hypothesis were
developed to carry out the survey
H1: An increase in specific uncertainty will lead to the application of
sophisticated capital budgeting practices.
H1.1: If specific uncertainty factor lead to the application of sophisticated
capital budgeting techniques, all dimensions/variables of specific
uncertainty factor will lead to application of sophisticated capital
budgeting practices
Still most of the companies over the world are using NPV based
capital budgeting practices and they are also treated as sophisticated capital
budgeting practices in the previous literature (e.g: Farragher, Kleiman &
Sahu, 1999; Bennouna, Meredith & Marchant, 2010). Thus further current
study leads to following hypothesis for an emerging market context.
H2: An increase in specific uncertainty will be associated with the
application of NPV Based/Advanced capital budgeting practices.
H2.1: If specific uncertainty factor associated with the application of
NPV based/advanced capital budgeting techniques, all dimensions
of specific uncertainty factor will be associated to application of
advanced capital budgeting techniques
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Asia-Pacific Management Accounting Journal, Volume 12 Issue 2
MATERIALS AND METHODS
Participants
The samples were selected from 287 companies listed on Colombo
Stock Exchange (CSE) in Sri Lanka, of which 64% of the CFoS responded
to the survey.
Data Collection
Field work was carried to collect the primary data from June to
November 2013. The self reporting structured questionnaire was used to
collect the data from all listed companies. The questionnaire included a
cover letter to the Chief Financial Officers of the companies to emphasize
confidentiality, reason for conducting survey and beneficial nature of the
research to practitioners and academics.
Measurement Variables
Uncertainty
Miller (1992) uncertainty framework was selected to conduct the
current study on capital investment decision making under uncertainty. This
framework provided an opportunity to analyze the impact of uncertainty
factors on capital budgeting practices and this frame work covers uncertainty
from external environment (competition, exchange rates, etc.) and internal
environment (behavior, research and development, etc.). Moreover, it also
provide the opportunity to cover the general, industry related and firm
specific uncertainties factors. The purpose of adopting this framework, is
for its possibility to distinguish between the uncertainties that are addressed
in the investment decision and, therefore, uncertainties that are managed
by operational decisions, financial or other decisions in an organization.
Miller’s (1992) framework applied by Verbeeten (2006) which offered the
opportunity to investigate specific uncertainties that have an impact on
practices of capital budgeting, apparently springboard for future research.
According to Miller (1992), practitioners would be perceived as in categories
of (1) the general environment, (2) the industry, or (3) organizational factors.
Each of these categories encompasses a number of uncertain components,
which is presented below:
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Capital Investment Decision Making Under Uncertainty
Table 1: Uncertainty and Its Components
General
environment
uncertainty
Political Terrorism, War, Changes in Government,
Political instability,
Government
policy
Fiscal and monetary policies, Trade restrictions,
Regulations affecting the business sector, Tax
policy
Macro
Economic
Exchange rate, Interest rate, Inflation, Terms
of trade
Social Social unrest, Shift in social concerns , (beliefs,
values and attitudes reflected in current
government policy or business practice)
Natural Variations in weather, Natural disaster
Industry
specific
uncertainties
Input market Quality of inputs, Supply relative to industry
demand
Product market Consumer preferences, Market demand,
Availability of substitutes and complements
Competition Pricing and other forms of rivalry, New entrants,
Product and process innovation, Technological
uncertainty
Firm specific
uncertainties
Operations Labor relations, Availability of inputs, Production
variability and downtime
Liability Product liability, Emission of pollutants
R & D R & D activities, Regulatory approval of new
product
Credit & fraud Problems with collectibles, Fraudulent behavior
of employees
Cultural Cultural friction
Behavioral Agency problems, Emotions, Overconfidence
Source: Adopted from Miller, (1992) pp.314-319
The participants were asked to indicate on a 5-point Likert scale
(ranging from 1= not at all important, to 5 = very important) to what extent
they consider a number of uncertainties relevant for their company within
the time frame of an investment decision.
Capital Budgeting Practices
Capital budgeting practices were measured with questions originally
developed and validated by (Graham & Harvey, 2001, Brounen, deJong &
Koedijk, 2004; Verma, Gupta & Batra, 2009). The respondents were asked
to indicate on a 5-point Likert scale (ranging from 1 = never, to 5 = always)
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Asia-Pacific Management Accounting Journal, Volume 12 Issue 2
to what extent they consider several capital budgeting techniques useful or
important in the investment process.
Control Variable
Size
Size of the firm is one of the major determinants in capital budgeting
practices (e.g., Ho & Pike, 1992; Graham & Harvey, 2001; Farragher,
Kleiman & Sahu, 2001; Brounen, deJong & Koedijk, 2004; Verbeeten,
2006). Researches supported that large firms adopts more innovative
sophisticated capital budgeting methods to a large extent than smaller
firms do (e.g., Rogers, 1995; Williams & Seaman, 2001) since the larger
firms have the capacity and resources to use sophisticated capital budgeting
practices (Ho & Pike, 1992). Payne, Heath and Gale (1999) and Ryan and
Ryan (2002) documented that large firms were more inclined to use more
sophisticated capital budgeting practices. This is due to the fact that larger
firms involves larger projects and the use of sophisticated capital budgeting
practices become less costly (Payne, Heath & Gale, 1999; Hermes, Smid,
& Yao, 2007). The larger firms are much more likely to have full time staff
members for capital budgeting (Verbeeten, 2006) and make considerable
capital expenditure for new plant and equipment, which require the use of
more sophisticated capital budgeting practices.
DATA ANALYSIS
Exploratory Factor Analysis to Identify the Uncertainty
Factors
Factor analysis was performed to confirm the validity of the variables
to measure the uncertainty factors and capital budgeting practices. Factor
analysis has the ability to produce descriptive summaries of data matrices,
which aids in detecting the presence of meaningful patterns among a set of
variables (Dess & Davis, 1984). In this study, Principal Component Analysis
(PCA) had been employed to test the discriminant validity of the dimensions
in an emerging market context uncertainties and to verify whether the three
uncertainty categories mentioned by Miller (1992) are actually present.
Miller’s framework was used by Verbeeten (2006) in Netherland, the results
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Capital Investment Decision Making Under Uncertainty
were categorized as finance uncertainty, input uncertainty, social uncertainty
and market uncertainty.
In order to employ PCA , it needs to be confirmed that the sampling
adequacy of the data for the analysis which is measured by Kaiser-Meyerolkin
(KMo) measure of sampling adequacy (Hair et al., 2010). The
individual variable’s KMo can be obtained from anti-image matrix, and if
any variable was found to be lower the level of acceptance (0.5) should be
excluded from the factor analysis, one at a time, smallest is first (e.g., Hair et
al., 2010). After removing the unsuitable variables from anti image matrix
(uncertainties about output market, natural uncertainties, fluctuating results
under research projects, uncertainties on payment behavior of customers and
behavioral uncertainties) the remaining variables were grouped into four
factors. Here KMo and Bartlett’s test of Sphericity measure of sampling
adequacy (George & Mallery, 2003) was used. A measure of sampling
adequacy of 0.713 with a value of Bartlett’s test of Sphericity (1168.502)
with a high significant level (P <0.01), indicates the suitability of factor
analysis.
Factor loadings of the items on a factor are greater than 0.5 ensure
that EFA has a practical significance to the analyzed data (Hair et al., 1998).
Eigen value greater than one suggests that the four factors explain a sizable
variation contained in the data. Since these four factors have Eigen values
greater than one, which together explains a variance of 73.69%; therefore,
the factors confirmed the factorial validity. Table 2 represents these results.
Table 2: Total Variance Explained for Factors Indicating to the Uncertainty
Variables
Component
Market
Uncertainty
Factor 1
Social
Uncertainty
Factor 2
Operational
Uncertainty
Factor 3
Financial
Uncertainty
Factor 4
Eigen Value 3.021 2.459 2.104 1.996
Proportion of variance explained (%) 23.24% 18.91% 16.17% 15.36%
Cumulative percentage explained 23.24% 42.15% 58.34% 73.69%
Cronbach’s Alpha – Reliability of the
factors
0.915 0.876 0.825 0.816
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Asia-Pacific Management Accounting Journal, Volume 12 Issue 2
The total variance explained for factors indicating to the uncertainty
has been summarized in Table 2; the variables of market uncertainty
(covering the components of competitive uncertainty, output market
uncertainty and input market uncertainty), social uncertainty (covering
the components of political uncertainty, policy uncertainty and social
uncertainty), operational uncertainty (covering the components of input
uncertainties, labour uncertainties and production uncertainties) and
financial uncertainty (covering the components of interest rate, inflation and
exchange rate uncertainties) are grouped into factors. The result illustrates
that the uncertainty variables are grouped in four factors as the reviewed
literature: market uncertainty, social uncertainty, operational uncertainty
and financial uncertainty. The findings of this study is closely consisting
with the study of Verbeeten (2006), with the exception that the input market
uncertainty variable rotated into market uncertainties in the current study
which was not in the Verbeeten’s findings. However this finding is closely
consistent with Miller’s (1992) industry specific uncertainty factors (variable
of input market) and the operational uncertainty factor is also consistent
with the Miller’s (1992) findings.
Principal Component Analysis for Capital Budgeting
Practices
PCA was carried out to extract the capital budgeting practice as grouped
in the literature. After removing the unsuitable variables (profitability index,
economic internal rate of return, Monte Carlo Simulation, adjusting the
required return, modified internal rate of return and complex mathematical
model) from anti image matrix, the remaining variables are grouped into
three factors. Here, KMo and Bartlett’s test of Sphericity measure of
sampling adequacy (George & Mallery, 2003) were used. A measure of
sampling adequacy of 0.888 with a value of Bartlett’s test of Sphericity
(1221.845) with a high significant level (P <0.01), indicates the suitability
of factor analysis.
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Capital Investment Decision Making Under Uncertainty
Table 3: Total Variance Explained for the Factors
Indicating to the Capital Budgeting Practices
Factors
Advanced / NPV
based Capital
Budgeting
Practices
Sophisticated
Capital
Budgeting
Practices
Simple/
Naïve Capital
Budgeting
Practices
Eigen Value 5.822 2.108 1.365
Proportion of Variance Explained 38.815% 14.052% 9.101%
Cumulative Percentage Explained 38.815% 52.867% 61.968%
Cronbach’s Alpha – Reliability of
factors
0.890 0.809 0.744
The total variance explained for the factors indicating to the capital
budgeting practices is shown in Table 3, the variables of capital budgeting
practices are grouped into related factor. The variables that are grouped
in three factors as the reviewed literature: Advanced/ NPV based capital
budgeting practices include probability analysis, IRR, scenario analysis,
break-even analysis, uncertainty absorption in cash flows, sensitivity
analysis and NPV. Sophisticated capital budgeting practices consist of
real option, CAPM/B analysis, game theory decisions and decision trees.
Simple / Naive capital budgeting practices comprise DPB, ARR and PB.
The findings of this study underpinning the theoretical base were consistent
with the studies of Verbeeten (2006) and Wolffsen (2012).
Descriptive Analysis
Table 4 shows the descriptive statistics of the variables which consist
the minimum, maximum, mean value and the standard deviation of the
independent, dependent variables. As indicated, the measure of uncertainty
(ranging from 1= not at all important, to 5 = very important) and capital
budgeting variables (ranging from 1 = never to 5= always) were measured
by 5 point Likert scale ranging from 1-5. The mean value of the financial
and market uncertainty factors were 4.39 and 4.30 respectively which
indicated that those two uncertainty factors were very important factors that
affect the company’s investment decision as it had a mean value of above
The mean value of uncertainty factors of social and operational factors
were 3.56 and 3.06 respectively to impact the capital budgeting practices
and when compared to the social and operational uncertainty factors, the
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Asia-Pacific Management Accounting Journal, Volume 12 Issue 2
social uncertainty factors were significantly important than the operational
uncertainty as it had lowest mean value among four uncertainty factors.
Table 4: Descriptive Statistics of the Variables
Descriptive Statistics
N Minimum Maximum Mean Std.
Deviation
Financial uncertainty 186 1.33 5.00 4.3996 .73507
Market uncertainty 186 2.00 5.00 4.3047 .67678
Social uncertainty 186 1.00 4.00 3.5627 .80908
Operational uncertainty 186 1.67 4.00 3.0699 .70724
Sophisticated capital budgeting practices 186 1.00 3.25 1.3091 .47916
Advanced/NPVBased capital budgeting
practices 186 1.57 5.00 3.9209 .64694
Simple/NAÏVE capital budgeting practices 186 1.67 4.67 3.1900 .63131
Size of the company 186 6.90 11.59 9.3385 .70759
When considering the mean values of capital budgeting practices, the
mean value of sophisticated capital budgeting practices was 1.30 which
indicated that the application of sophisticated capital budgeting practices
were very rare but it was not concluded that the sophisticated practices were
not in practice in Sri Lanka. The mean value of the advanced/NPV based
capital budgeting practices was nearly 4 which indicated that often they are
in the practices. The mean value of the naive capital budgeting practices
was 3.20 which indicated that second important capital budgeting practices
next to NPV based. It was concluded that majority of the Sri Lankan firms
were using NPV based advanced capital budgeting practices followed by
simple capital budgeting practices. However, smaller number of companies
were attempting to use sophisticated capital budgeting practices. Finally,
the size of the company was calculated by logarithm of total assets which
was measured with an average value of the 5 years total assets.
Correlation Analysis
In order to evaluate the relationship between variables correlation
analysis was performed. The results of the analysis presented in Table 5.
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Capital Investment Decision Making Under Uncertainty
Table 5: Correlations Matrix
1 2 3 4 5 6 7 8
Size of the company (1) Pearson Correlation 1
Sig. (2-tailed)
Market uncertainty (2) Pearson Correlation .046 1
Sig. (2-tailed) .531
Social uncertainty (3) Pearson Correlation .044 .101 1
Sig. (2-tailed) .555 .170
Operational uncertainty
(4)
Pearson Correlation -.087 -.140 -.015 1
Sig. (2-tailed) .239 .057 .844
Financial uncertainty (5) Pearson Correlation .151* .014 .095 -.003 1
Sig. (2-tailed) .040 .855 .195 .966
Sophisticated capital
budgeting practices (6)
Pearson Correlation .211** .126 -.004 -.003 .324** 1
Sig. (2-tailed) .004 .086 .959 .968 .000
Advanced capital
budgeting practices (7)
Pearson Correlation .156* -.038 .006 -.125 .248** .402** 1
Sig. (2-tailed) .033 .611 .932 .090 .001 .000
Simple capital
budgeting practices (8)
Pearson Correlation -.111 -.050 -.060 -.047 -.188* -.448** -.433** 1
Sig. (2-tailed) .131 .494 .417 .521 .010 .000 .000
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
Table 5 summarizes the results of the correlation analysis. There was
a positive relationship between the size of the company and application of
capital budgeting practices that: the size of the company positively related
to sophisticated capital budgeting practices (r = 0.211, P<0.01). This reveals that when the size of the company increases, application of sophisticated capital budgeting practices will also increase. Further, there was a positive significant relationship between the size of the company and advanced capital budgeting practices (r= 0.156, P 0.05) but
it was not statistically significant in the current study. Financial uncertainty
was positively, related to the application of sophisticated capital budgeting
techniques (r =0.324, p < 0.01) and the application of advanced capital
budgeting techniques (r = 0.248, p < 0.01). However, there was a negative
significant relationship between financial uncertainty and application of
simple capital budgeting techniques as simple capital budgeting practices are
not normally considered the uncertainty. There was no relationship between
other uncertainty factors (market, social and operational) and application of
capital budgeting practices (sophisticated, advanced and simple).
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Asia-Pacific Management Accounting Journal, Volume 12 Issue 2
Impact of Uncertainty Factors on the Application of Capital
Budgeting Practices
Impact of Uncertainty Factors on the Application of
Sophisticated Capital Budgeting Practices
Hierarchical multiple regression was performed to investigate the
impact of uncertainty factors to predict the application of sophisticated
capital budgeting practices, after controlling for size of the organization.
The results of the regression analysis were presented in Table 6.
Table 6: Hierarchical Regression Model
of Sophisticated Capital Budgeting Practices
R R2 R2 change df F F
change b SE β t P_
value
Model 1 0.211 0.045 (1,184) 8.593 0.004
Size 0.143 0.049 0.211 2.931 0.004
Model 2 0.385 0.148 0.104 (5,180) 6.276 5.487 0.000
Size 0.112 0.047 0.165 2.356 0.020
Financial
Uncertainty 0.197 0.046 0.302 4.329 0.000
Market
Uncertainty 0.088 0.049 0.124 1.771 0.078
Input
Operational
Uncertainty
0.020 0.047 0.029 0.414 0.679
Social
Uncertainty -0.031 0.041 -0.052 -0.747 0.456
R2 = amount of variance explained by IVs
R2 Change = additional variance in DV
B = Unstandardized coefficient
β = Standardized coefficient (values for each variable are
converted to the same scale so they can be compared)
SE = Standard Error
t = estimated coefficient (B) divided by its own SE. If t < 2
the IV does not belong to the model
In the first model of hierarchical multiple regression, size was entered
as it was control variable in this analysis. This model was statistically
significant F (1, 184) = 8.593; p < .01 and explained 5 % of variance in
sophisticated capital budgeting practices (Table 6). After entry of four
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Capital Investment Decision Making Under Uncertainty
uncertainty factors which were financial, market, operational and social
uncertainty at model 2, the total variance explained by the model as a whole
was 15% (F (5, 180) = 6.276; p < .01). The impact of uncertainty explained
additional 10 % variance in application of sophisticated capital budgeting
practices, after controlling for size of the organization (R2 Change = .10;
F (4,180) = 5.487; p < .01). In the final model, two out of five predictor
variables were statistically significant, with financial uncertainty recording
a higher beta value (β = .302, p < .000) than the size of the organization (β =
.165, p < .020). Hypothesis 1 stated that an increase in specific uncertainty
will lead to the application of sophisticated capital budgeting practices.
The results illustrated that the financial uncertainty factors had positive
significant impact on application of sophisticated capital budgeting practices.
Therefore, it could be concluded that increase in financial uncertainty lead
to the application of sophisticated capital budgeting practices and thus H1
has supported by study and there were no significant impact of market,
social and operational uncertainty factors on the application of sophisticated
capital budgeting practices.
From the analysis, it was concluded that financial uncertainty had
significant impact on application of sophisticated capital budgeting
techniques. Therefore, it is forced to a question if all dimensions/variables
of financial uncertainty factor have an impact on application of sophisticated
capital budgeting practices. Thus hierarchical multiple regression was
performed to investigate the impact of dimensions of financial uncertainty
factor to predict the application of sophisticated capital budgeting practices,
after controlling for size of the organization and the results were indicated
in the Table 7.
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Table 7: Hierarchical Regression Model
of Sophisticated Capital Budgeting Practices
R R2 R2 change df F F
change b SE β T P_
value
Model 1 0.211 0.045 0.045 (1,184) 8.593 8.593 0.004
Size 0.143 0.049 0.211 2.931 0.004
Model 2 0.372 0.138 0.094 (4,181) 7.253 6.548 0.000
Size 0.111 0.047 0.165 2.356 0.020
Interest
rate
Uncertainty
0.009 0.052 0.017 0.177 0.860
Inflation
Uncertainty 0.111 0.053 0.203 2.108 0.036
Exchange
rate
Uncertainty
0.076 0.051 0.131 1.486 0.139
In the first model of hierarchical multiple regression, size was entered.
This model was statistically significant F (1, 184) = 8.593; p < .0.01 and
explained 5 % of variance in capital budgeting practices as shown in Table
After entry of three dimensions of financial uncertainty factor which were
interest rate, inflation and exchange rate uncertainty at model 2, the total
variance explained by the model as a whole was 14% (F (4, 181) = 7.253;
p < 0.01). The impact of uncertainty explained additional 9 % variance in
application of sophisticated capital budgeting practices, after controlling
for size of the organization (R2 Change = .0.094; F (4,181) = 6.543; p <
0.001). In the final model, only one out of the three dimensions of financial
uncertainty was statistically significant, with inflation uncertainty recording
a beta value (β = .203, p < .05). H1.1 stated that If specific uncertainty factor
lead to the application of sophisticated capital budgeting techniques, all
dimensions of specific uncertainty factors will lead to application of capital
budgeting practices. The results illustrated that only one dimension of
financial uncertainty had an impact on the application of sophisticated capital
budgeting practices. Therefore, the hypothesis was not supported; only one
dimension of inflation uncertainty was associated with the application of
sophisticated capital budgeting practices.
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Capital Investment Decision Making Under Uncertainty
Impact of Uncertainty Factors on the Application of NPV
Based Capital Budgeting Practices
Table 8: Hierarchical Regression Model
of NPV based Capital Budgeting Practices
R R2 R2 change df F F
change b SE β t P_
value
Model 1 0.156 0.024 0.024 (1,184) 4.598 4.598 0.033
Size 0.143 0.067 0.156 2.144 0.033
Model 2 0.305 0.093 0.068 (5,180) 3.685 3.397 0.010
Size 0.104 0.066 0.114 1.580 0.116
Financial
Uncertainty 0.205 0.063 0.232 3.224 0.002
Market
Uncertainty -0.059 0.069 -0.061 -0.853 0.395
Input
Uncertainty -0.112 0.066 -0.123 -1.707 0.090
Social
Uncertainty -0.013 0.057 -0.016 -0.229 0.819
Hierarchical multiple regression was performed to investigate the
impact of uncertainty factors to predict the application of NPV based capital
budgeting practices, after controlling for size of the organization.
In the first model of hierarchical multiple regression, size was entered.
This model was statistically significant F (1, 184) = 4.598; p < .0.05 and
explained 2 % of variance in capital budgeting practices. After entry of
four uncertainty factors which were financial, market, operational and
social uncertainty at model 2, the total variance explained by the model as
a whole was 9% (F (5, 180) = 3.685; p <0.01). The impact of uncertainty
explained additional 7 % variance in application of NPV_Based capital
budgeting practices, after controlling for size of the organization (R2 Change
= .0.068; F (5,180) = 3.397.; p < 0.01). In the final model, one out of five
predictor variables were statistically significant, with financial uncertainty
recording beta value (β = .232, p < .002). Hypothesis 2 stated that an
increase in specific uncertainty will be associated with the application of
NPV Based/Advanced capital budgeting practices. The results illustrated
that the financial uncertainty factors had positive significant impact on
application of advanced/ NPV based capital budgeting practices. Therefore,
it could be concluded that increase in financial uncertainty associated to the
application of advanced/ NPV based capital budgeting practices and thus H2
was supported by the study and there was no significant impact of market,
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Asia-Pacific Management Accounting Journal, Volume 12 Issue 2
social and operational uncertainty factors on the application of advanced
capital budgeting practices.
From the previous analysis, it was concluded that financial uncertainty
had significant impact on application of NPV_Based capital budgeting
techniques. Therefore, it is also questioned if all dimensions of financial
uncertainty factors have an impact on application of NPVBased capital
budgeting techniques. Another hierarchical multiple regression was
performed to investigate the impact of dimensions of financial uncertainty
factor to predict the application of NPV based capital budgeting practices,
after controlling for size of the organization and the results were presented
in Table 9.
Table 9: Hierarchical Regression Model
of NPV based Capital Budgeting Practices
R R2 R2
change df F F
change b SE β t P_
value
Model 1 0.156 0.024 0.024 (1,184) 4.598 4.598 0.033
Size 0.143 0.067 0.156 2.144 0.033
Model 2 0.284 0.081 0.056 (4,181) 3.981 3.708 0.004
Size 0.111 0.066 0.122 1.689 0.093
Interest
Uncertainty
0.115 0.073 0.157 1573 0.118
Inflation
Uncertainty
0.078 0.074 0.105 1.057 0.292
Exchange
rate
Uncertainty
0.002 0.072 0.002 0.021 0.983
In the first model of hierarchical multiple regression, size was entered.
This model was statistically significant F (1, 184) = 4.598; p < .0.05 and
explained 2 % of variance in capital budgeting practices. After entry of
three dimensions of financial uncertainty factors; interest rate, inflation
and exchange rate uncertainty at model 2, the total variance explained by
the model as a whole was 8% (F (4, 181) = 3.981; p < 0.01). The impact of
uncertainty explained additional 6 % variance in application of sophisticated
capital budgeting practices, after controlling for size of the organization
(R2 Change = .0.056; F (4,181) = 3.708; p < 0.01). In the final model,
none of the variable was statistically significant. H2.1 stated that If specific
uncertainty factor associated with the application of NPV based advanced
capital budgeting techniques, all dimensions of specific uncertainty factor
will be associated to application of advanced capital budgeting techniques.
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Capital Investment Decision Making Under Uncertainty
The results illustrated that none of the dimensions of financial uncertainty
had an impact on the application of NPV based advanced capital budgeting
practices. Therefore, hypothesis H2.1 was not supported; none of the variable
of financial uncertainty had association with the application of NPV based
advanced capital budgeting practices.
DISCUSSION
Finance and strategic management theory suggest that organization specific
uncertainties in the investment decisions should respond by adopting
advanced capital budgeting practices. Having understood the importance of
uncertainty on investment decision making, the current study was conducted
to identify the uncertainty factors and its impact on application of capital
budgeting practices. Findings of the study identified several uncertainty
factors which were financial uncertainty (interest rate, inflation and exchange
rate), market uncertainty (input market, output market and competitive
factors), operational uncertainty (input, labor and production) and social
uncertainty (policy, political and social). Empirical evidence shows that the
theoretical application of the sophisticated capital budgeting involves the
use of multiple tools and procedures (e.g., Monte Carlo simulation, certainty
equivalents). In line with the theoretical underpinning and empirical
evidence, capital budgeting practices had been confirmed into three
groups; as sophisticated capital budgeting practices, NPV based/ advanced
capital budgeting and simple capital budgeting practices (Verbeeten, 2006;
Wolffsen, 2012). Among the four uncertainty factors, an increase in financial
uncertainty was significantly associated to the application of sophisticated
capital budgeting practices and advanced capital budgeting practices. The
findings are in line with the theoretical underpinning and empirical evidence
(Verbeeten, 2006). Size was considered as control variable as per empirical
evidence of Payne, Heath and Gale (1999) and Ryan and Ryan (2002)
documented that large firms were more inclined to use more sophisticated
capital budgeting practices. Size was accordance with the expectations,
that it was positively significantly related to sophisticated capital budgeting
practices and advanced capital budgeting practices. Further analysis was
extended that all dimensions / variables of financial uncertainty factors
impact on application of capital budgeting practices. The findings showed
that among the three variables of financial uncertainty, inflation was only
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influenced on the application of sophisticated capital budgeting practices
which was not influenced even in NPV based /advanced capital budgeting
practices.