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To cite this article:
Turner, M. J., & Guilding, C. (2012). Factors affecting biasing of capital budgeting Cash flow
forecasts: evidence from the hotel industry. Accounting and Business Research, 42(5), 519-
545.
FACTORS AFFECTING BIASING OF CAPITAL BUDGETING CASH FLOW
FORECASTS: EVIDENCE FROM THE HOTEL INDUSTRY
MICHAEL J. TURNER (corresponding author)
University of Queensland
UQ Business School
St. Lucia Campus
QLD 4072
AUSTRALIA
Phone: +61 7 3346 8071
Fax: +61 7 3346 8199
Email: m.turner@business.uq.edu.au
and
CHRIS GUILDING
Griffith University
Griffith Business School
Gold Coast Campus
QLD 4222
AUSTRALIA
Phone: + 61 7 5552 8790
Fax: +61 7 5552 8507
Email: c.guilding@griffith.edu.au
Author’s note: This paper has benefitted from anonymous referee comments provided in the
course of the paper’s review process. The authors would also like to acknowledge the helpful
suggestions provided by attendees at the AFAANZ Conference Darwin, Australia. Valuable
feedback was also received from attendees at seminars hosted by the School of Accounting
and Business Information Systems at the Australian National University as well as the School
of Accounting, Economics and Finance at Deakin University. Finally, the paper has profited
from useful audience comments provided at the University of Queensland Research Forum.
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FACTORS AFFECTING BIASING OF CAPITAL BUDGETING CASH FLOW
FORECASTS: EVIDENCE FROM THE HOTEL INDUSTRY
Abstract
This study contributes to a neglected aspect of the capital budgeting process, namely, the
proposal development stage, which is primarily concerned with project cash flow estimation.
Given that the deployment of sophisticated selection techniques is severely undermined when
directed to input data suffering from bias, it is surprising that minimal empirical research has
sought to explore for antecedent factors associated with biasing of capital budgeting cash flow
forecasts. This paper reports the findings of a survey concerned with determining factors
associated with biasing of capital budget cash flow forecasts in hotels that are mediated by a
management contract. Statistically significant support is provided for the view that higher
levels of biasing of capital budget cash flow forecasts occur in the presence of: high emphasis
attached to the payback investment appraisal method; deficient reserve funds for furniture,
fittings, and equipment (FF&E); low operator accessibility to reserve funds for FF&E; shorter
periods of time to management contract expiry; and high emphasis attached to non-financial
factors in capital budgeting appraisal.
Keywords: capital budgeting; cash flow forecasting; FF&E reserve; hotel management
contract; payback; power
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FACTORS AFFECTING BIASING OF CAPITAL BUDGETING CASH FLOW
FORECASTS: EVIDENCE FROM THE HOTEL INDUSTRY
1. Introduction
The corporate finance literature sees capital budgeting as comprising four stages: (1) project
identification; (2) project development; (3) project selection; and (4) project control (see
reviews by Burns and Walker 2009, Gordon and Pinches 1984, Mukherjee and Henderson
1987). This paper reports the findings of a study focussed on biasing of capital budget cash
flow forecasts (CBCFFs), which falls within the project development stage.
Biasing of capital budgeting cash flow forecasts has its foundations with asymmetric
information, where the agency model is central (Bohlin 1997). The presence of information
asymmetry can facilitate agents’ biasing of CBCFFs for those projects that will benefit their
self-interest relative to projects that may have better fulfilled the goals of the principal (see
Haka 2007 for a review).
Given the extensive literature concerning agents’ biasing of operational budgets (e.g. Davila
and Wouters 2005, Dunk 1993, Lukka 1988, Merchant 1985, Van der Stede 2000), one is
struck by the scant attention directed to the potential for biasing cash flow projections in
connection with capital budgeting. An extensive literature search has identified only four
empirical studies that have expressly examined the issue (see Guilding 2003, Guilding and
Lamminmaki 2007, Lazaridis 2006, Pruitt and Gitman 1987). None of these studies,
however, sought to develop a framework concerning variables that might affect the extent of
biasing of CBCFFs. This appears as surprising on three counts. Firstly, there would appear to
be considerable scope for biasing of CBCFFs, as, unlike the context of operational budgeting,
managers reviewing capital expenditure proposals generally do not have the benefit of prior
years’ data to use as a gauge in appraising the accuracy of a forecast. Secondly, it is notable
that efforts directed towards deploying increasingly sophisticated project selection techniques
will be rendered futile if input data suffers from bias. Thirdly, there has been a large number
of studies concerning the control stage of capital budgeting that have uncovered significant
deviations between forecasted and realised cash flows. Commenting on this literature, Linder
(2005) notes that a large proportion of these deviations derive from optimistic cash flow
forecasts and that this issue warrants research enquiry directed towards determining the
causes of such biasing.
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The lack of prior attention directed to CBCFF prompted the pursuit of the following
objectives in the study reported herein:
(1) to appraise the incidence of biasing of CBCFFs in hotels mediated by a management
contract; and
(2) to develop and test hypotheses concerned with factors that might affect the extent of
biasing of CBCFFs in hotels mediated by a management contract.
The remainder of the paper is structured as follows. The next section provides a review of
pertinent literature and includes a rationale for this study’s focus on the hotel industry. After
this, hypotheses concerned with factors that might affect biasing of CBCFFs are developed.
Subsequent sections address, in turn, the survey research method employed, the study’s
findings and a conclusion that discusses implications arising.
2. Literature review
Four reasons for biasing of CBCFFs have been advanced in the normative literature:
technical, economic, psychological, and political (see Flyvbjerg et al. 2002). Technical
causes of biasing are sometimes referred to as ‘forecasting errors’ (Cantarelli et al. 2008, p.
2). Such errors are unintentional and stem from imperfect techniques, inadequate data, honest
mistakes, inherent problems in predicting the future, or a lack of forecaster experience
(Flyvbjerg et al. 2003). Sub-explanations of these problems can include: poor project design
and implementation; incompleteness of estimates; deficient organisation structure; poor
decision-making or planning processes; and uncertainty (Cantarelli et al. 2008). Technical
error can be minimised through development of better forecasting models, better data, and
more experienced forecasters (Flyvbjerg 2007).
Economic causes of biasing of CBCFFs arise where approval of a project can have a positive
economic effect, either directly or indirectly, on stakeholders who have a capacity to
influence CBCFFs (Flyvbjerg et al. 2002). If involved in the CBCFF process, it would be
rational for such stakeholders to inflate projected revenues and deflate projected costs, in
order to raise the likelihood of project acceptance (Cantarelli et al. 2008). A lack of an
incentive to formulate a CBCFF accurately, combined with ample scope to bias a CBCFF,
provides fertile ground for biasing of CBCFFs if the forecaster stands to derive economic
benefit from a project proposal securing approval (Cantarelli et al. 2008).
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Psychological causes of biasing of CBCFFs relate to the mental state of the forecaster.
Forecasters can hold an innate optimistic predisposition towards the outcomes of a proposed
project (Flyvbjerg 2007). The extent to which such psychological biasing is intended appears
unclear, however. Overoptimism can be traced to cognitive biases which are essentially errors
in the way the human mind processes information (Flyvbjerg et al. 2002, Lavallo and
Kahneman 2003). The human psyche is distinguished by a significant ability to learn from
experience and such experience would result in the reduction of unintentional biasing of
CBCFFs (Flyvbjerg et al. 2002).
Political causes of biasing of CBCFFs relate to a forecaster using the formulation of a
CBCFF as an opportunity to pursue interests related to their career and power base (Flyvbjerg
1998). Overstating the net benefits of a projected capital expenditure in order to gloss over
the potential for failure can be rational for a manager concerned with jockeying for
hierarchical position in the face of a limited capital expenditure budget (Flyvbjerg 2007).
Securing insights into the extent of this phenomenon is bound to be difficult, however, as
forecasters would be reluctant to provide information that betrays the extent to which they
use the development of CBCFF as an opportunity to pursue their personal political agenda
(Flyvbjerg 1996).
Despite theoretical explanations for biasing of CBCFFs, empirical examination of the
phenomenon has been minimal. Consistent with the view of Miller and O’Leary (2007) that
the accounting literature concerned with capital budgeting practice has exhibited little
development in recent decades, a literature review has identified just four prior empirical
studies focused on biasing of CBCFFs. Pruitt and Gitman (1987) surveyed Fortune 500 firms
and found that 80% of high-ranking financial officers perceived profitability forecast error to
be compounded by a pronounced upward bias in revenue forecasts and a less-pronounced
downward bias in cost forecasts. Two-thirds of the sample believed that biases occurred as a
result of intentional overstatement or a lack of experience. The other third felt that bias was
psychologically based (e.g. myopic euphoria, mass psychology, group polarisation, or
salesman optimism), or the result of erroneous information provided by upper level
management. They also observed that managers involved in reviewing CBCFF tend to adjust
cash inflow estimates downward to compensate for a projected net cash inflow inflation
propensity.
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Guilding (2003) conducted a series of field study interviews within the context of the
Australian hotel industry. He found that, consistent with the expectations of agency theory,
capital budgeting systems in hotels mediated by a management contract have a greater degree
of capital budgeting formalisation and a higher propensity for biasing of CBCFFs than hotels
that are not managed under a management contract.
In a study of small and medium sized Mediterranean-based companies, Lazaridis (2006)
found detailed cash flow estimates to be required for almost all types of investment and a
large proportion of his sample adopted standardised procedures for estimating items such as
taxes, depreciation, and salvage values. Of particular note, the majority of Greek firms
surveyed were found to adjust CBCFFs in order to compensate for overoptimistic biasing.
Based on Australian survey data, Guilding and Lamminmaki (2007) benchmarked the degree
of biasing of CBCFFs in hotels relative to other industries. A significantly greater propensity
to inflate project cash inflows relative to deflating projected cash inflows was observed
across the whole sample. Contrary to the researchers’ expectation, less biasing of CBCFFs
was observed in the hotel sector relative to the other industrial sectors examined.
Somewhat surprisingly, no study pursuing an explicit focus on the causes of biasing of
CBCFFs has been found. However, some empirical studies concerned with the control phase
of capital budgeting provide some useful insights into biasing of CBCFFs. For example, Van
Vleck (1976) found that businesses representing the electronics and home appliance,
chemical, glass, concrete, metals, mining and textile sectors exhibit the highest deviations
between planned and actual cash flows. The pharmaceutical industry produced the smallest
deviation. No significant association between CBCFF error and company size or profitability
was observed. Merrow et al. (1981) focused on pioneer plant investments in chemical process
industries and found that the extent of deviations between actual cash flows and CBCFF
could be explained by three context specific factors: plant technology, plant site
characteristics and plant complexity. Merrow et al. (1981) also noted a positive relationship
between new technology projects and the size of CBCFF deviations. Flyvbjerg et al. (2002)
found CBCFF cost underestimation to be significantly affected by project type. They found
that compared to road construction, rail projects have a much greater deviation between
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actual cash flows and CBCFFs. Soares et al. (2007) found no association between business
size, industry, region, or investment incentives and CBCFF error.
As specific facets of an industry can be expected to carry the potential to affect the propensity
for biasing of CBCFFs, the study reported on herein was framed in an industry specific
manner. We believe that the hotel sector provides a particularly rich context to study biasing
of CBCFFs for a number of reasons. Firstly, the hotel industry is characterised by high capital
intensity (Collier and Gregory 1995). This underscores the importance of hotel capital
budgeting processes (Guilding and Hargreaves 2003). A large US study found hotel capital
expenditure equating to 5.4% of gross revenue (ISHC 2007). Secondly, capital budgeting is
rendered more complex in hotels that operate with a management contract. Under a hotel
management contract, one party (the owner) owns the hotel and its associated assets and a
second party (the operator) manages the hotel’s day-to-day operational activities (Rushmore
2002). In this situation, the key agent responsible for developing capital expenditure
proposals for consideration by an owner is the general manager, who is an employee of the
operator, not the entity owning the hotel (Eyster 1997). Although the owner is the party that
stands to gain most from instilling a culture supportive of high quality investment proposal
formulation, it is also the contracting party with a muted capacity to affect the organisational
culture experienced by that individual who has a particular capability to influence the
initiation of a capital expenditure proposal (Guilding 2003, 2006). As management contracts
generally remunerate hotel operators using a percentage linked to hotel revenue and profit,
there appears to be an incentive for operators to engage in biasing of CBCFFs in a manner
consistent with promoting greater owner capital expenditure (see Turner and Guilding
2010b).
3. Developing hypotheses concerning CBCFF biasing
3.1. Locus of power between hotel owner and operator
The study of organisational power branches into three distinct but overlapping areas: (1) the
intra-organisational power literature, which investigates power between individuals within
the same organisation (see Brass 2002 for a review); (2) the organisational power literature,
which studies the power of groups in connection with their relationship with, or dependency
on, an organisation (see Ocasio 2002 for a review); and (3) the interorganisational power
literature, which concerns power between organisations (see Mizruchi and Yoo 2002 for a
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review). The main point of difference between these three literatures concerns the unit of
analysis. The unit of analysis in the intra-organisational power literature is an individual, the
unit of analysis in the organisational power literature is a group of people, and the unit of
analysis in the inter-organisational power literature is an organisation.
In all three literatures, power is viewed as ‘a change in the belief, attitude, or behavior of [an
actor] … which results from the action or presence of another [actor]’ (Raven 1990, p. 495).
Within this definition, it is important to note that the ‘intentionality’ of power needs to be
present (Fairholm 1993, pp. 8-9). This concept outlines that the term ‘power’ should only be
used in connection with those actions that are carried out intentionally. To include the
unintended outcomes of wielding power would render the term too broad, as it would capture
every conceivable action (Krause and Kearney 2006).
A further issue to recognise in a consideration of power is that although the words ‘power’
(i.e. actual power) and ‘influence’ (i.e. the ability to wield power) are sometimes used
interchangeably (Pfeffer 1992), Krause and Kearney (2006) feel it is important to distinguish
between the two terms. Despite this, power researchers tend to define ‘power as an ability’
(e.g. Fairholm 1993). Such a definition, however, fails to recognise that power is always a
reciprocal interaction between an agent (A) and another party (B) (Mintzberg 1983). For
example, by not recognising this reciprocal interaction, it would lead to a failure to appreciate
that the power of A not only depends on the available resources of A, but also on the demand
for those resources by B, as well as the opportunity of B to obtain those desired resources
from another party (Thibaut and Kelley 1959). In this way, B plays an active role in their
relationship with A (Krause and Kearney 2006). This aspect of power relations is often
referred to as the ‘relationality’ of power relations (Hardy and O'Sullivan 1998, pp. 462-463).
Krause and Kearney (2006) also note that another important concept in power relations is the
level of ‘dependency’ (Hardy and O'Sullivan 1998, p. 462). Emerson (1962, p. 32) explains
that:
‘… the dependence of actor A upon actor B is (1) directly proportional to A’s
motivational investment in goals mediated by B, and (2) inversely proportional to the
availability of those goals to A outside the A-B relation.’
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Elaborating on this quote, Emerson (1962, p. 32) notes that ‘goals’ are taken to mean the
‘gratifications consciously sought as well as rewards unconsciously obtained through the
relationship’. With respect to the ‘availability of those goals to A outside of the A-B relation’,
this refers to the ‘alternative avenues of goal-achievement’ (Emerson 1962, p. 32). Therefore,
the greater is B’s dependency on A, the greater will be A’s power over B. Where B seeks to
pursue alternative avenues of goal-achievement, however, the costs of such an approach must
be included in the assessment of dependency (Emerson 1962).
The locus of power between a hotel owner and operator in a relationship mediated by a
management contract clearly relates to the inter-organisational power literature referred to
above. Several factors have been documented as affecting the relative locus of power
between a hotel owner and operator. These include: (1) the level of competition among
operators, with more competition leading to lower operator power (Bader & Lababedi 2007);
(2) the relative size of the hotel owner, with larger hotel owners holding more power (Beals
and Denton 2005); and (3) the strength of the hotel operator’s brand, with stronger brands
handing the operator greater power (Armitstead 2004, Forgacs 2003). An earlier study
conducted by the research team suggests further that the locus of power between a hotel
owner and operator is also influenced by the extent to which the owner’s hotel property is in
an area highly sought after by hotel operators (e.g. close to a tourism icon), the condition of
the property and the extent to which it is located in close proximity to similar competing
properties. If a well-appointed hotel property is located close to a tourism icon and there are
few competing properties in close proximity, the owner would likely have the capacity to
wield considerable power.
It is to be expected that where a hotel owner enjoys significant power in a relationship, the
owner will use this power to impose conditions on the way that a hotel operator develops
CBCFF in a manner consistent with limiting their scope to introduce bias to CBCFF.
Guilding (2003) notes that in this capital budgeting context, it is the operator that tends to
initiate capital budgeting proposals. Following the initiation of a proposal, focused
deliberations between the owner and operator will ensue, as it is the owner who has to
finance any asset purchase (Guilding 2003). Such deliberations will give ample scope for the
dynamics of the locus of power between the two parties to play out in the course of the
finalisation of the CBCFFs in advance of them being subjected to whatever suite of capital
expenditure appraisal techniques are deployed by the owner. In addition to these factors, it
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would appear inconsistent for an operator that has a high dependency on an owner (relative to
the owner’s dependency on the operator), to potentially jeopardise the smooth running of the
relationship by consistently seeking to inject bias into CBCFF. Hypothesis 1 has been
formulated in a manner consistent with this rationale:
H1. Higher owner locus of power is associated with less biasing of capital budgeting
cash flow forecasts.
3.2. Emphasis attached to the payback investment appraisal method
There have been many surveys of capital budgeting techniques (e.g. Gitman and Vandenberg
2000, Graham and Harvey 2001, Ryan and Ryan 2002). Most find payback, net present value
(NPV) and the internal rate of return (IRR) to be the most popular capital budgeting appraisal
techniques. Payback is considered simple, while NPV and IRR are viewed as more
sophisticated due to the discounted cash flow methodology that they entail (Ballantine and
Stray 1999).
Haka (2007) notes that there has been a steady increase in the use of sophisticated capital
budgeting techniques since the 1950s. Many commercial contexts may not provide enough
predictability and certainty for the full benefits of sophisticated techniques to be reaped,
however (Mouck 2000). Both techniques require the assignation of subjectively determined
risk premiums (most common) or modelling, such as the capital asset pricing model or
sensitivity analysis (Shao 1994). This signifies that the application of discounted cash flow
techniques provides more than one outlet for introducing biasing of CBCFFs. In the case of
NPV analysis, the party seeking to bias the analysis could massage cash flows or modify the
risk adjusted discount rate. Similarly, if applying the IRR approach, the party seeking to bias
an analysis could massage cash flows or modify the risk adjusted required rate of return. The
scope for such behaviour appears broad when one considers that Bierman (1993) found that
the capital budgeting discount rate used in 72% of Fortune 100 industrial firms is based on
the risk or the nature of a specific project. In a similar vein, Graham and Harvey (2001)
reported that 51% of their surveyed firms would always, or almost always, use a risk-matched
discount rate when appraising capital expenditure proposals. These observations signify that
the required rate of return does not tend to be fixed in organisations, rather, it is modified in
line with management’s somewhat subjective appraisal of a particular project’s risk. The
significant amount of subjectivity in calculating inputs to sophisticated capital budgeting
appraisal techniques can therefore lead to managerial self-serving bias (Tole et al. 1997).
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Marino and Matsusaka (2005) note that such bias represents a source of increased
information corruption.
In those situations where a hotel owner applies a risk adjusted discount rate in NPV and IRR
analysis, there would appear to be considerable scope for the operator to influence the risk
profile assigned to capital expenditure proposals. Partial support for this view derives from
Guilding’s (2003) observation that it is the hotel operator that acts as the main instigator of
hotel capital expenditure projects.
1
The opportunity to introduce bias appears to be much
more constrained when applying the payback technique, however, as it does not require the
development of a risk adjusted discount rate or required rate of return. This signifies that all
of the biasing intent would need to be channelled into cash flow massaging if high emphasis
is placed on the payback method. It is therefore expected that high use of the payback method
will result in more biasing of CBCFFs. Hypothesis 2 is worded in a manner consistent with
this expectation:
H2. Higher emphasis attached to the payback method in capital budgeting appraisal is
associated with more biasing of capital budgeting cash flow forecasts.
3.3. Adequacy of funds allocated to the FF&E reserve account
Turner and Guilding (2010a) provide a detailed examination of the manner in which most
hotel management contracts require owners to establish a reserve for the replacement of
furniture, fittings and equipment (FF&E). This account is designed to fund all capitalised
hotel asset expenditures other than real estate (Bader and Lababedi 2007). The amount to be
allocated to the FF&E reserve is generally stipulated in the hotel management contract and is
moderated by the rolling nature of FF&E capital expenditure (Rushmore 2002). Owners
generally require approval of competitive bids on all FF&E reserve funded requests from
operators as well as the provision of a well formulated capital budgeting proposal for projects
of any substance in terms of size (Eyster 1997). A hotel’s FF&E can account for up to 25 per
cent of the value of a hotel property (Rushmore and Baum 2001). Determining what
constitutes a sufficient allocation to the FF&E reserve, however, represents a considerable
challenge (Mellen et al. 2000).
Allocations to the FF&E reserve are most commonly based on a predetermined percentage of
gross revenue (Turner and Guilding 2010a). Since the 1930s, the general rule of thumb has
been that annual contributions to the FF&E reserve should be set at around three per cent of
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annual gross revenues (Brooke and Denton 2007). This amount, however, is typically
insufficient to cover the true cost of FF&E expenditure because it ignores plant life cycles,
routine maintenance costs and hotel building ageing (Reichardt and Lennhoff 2003). This is
why some management contracts allow more than the ‘rule of thumb’ to be contributed to the
FF&E reserve (see Turner and Guilding 2010a). Nevertheless, Turner and Guilding (2010a)
found that FF&E reserves remain, for the most part, significantly underfunded.
The extent to which a hotel’s FF&E reserve is underfunded would appear to be an important
contextual factor in hotel capital budgeting, particularly given the relative size of capital
expenditure on FF&E in a typical hotel. Based on the premise that operators will find it more
difficult to secure owner funding for capital expenditure once the FF&E reserve fund has
been expended, operators can be expected to perceive a greater need to demonstrate the
worthiness of proposed capital expenditures when FF&E reserves are limited. This
incremental need to demonstrate the worth of capital expenditure will be absent in hotels that
maintain relatively high adequacy of the FF&E reserve account. Hypothesis 3 is postulated in
a manner consistent with this view.
H3. Lower FF&E reserve adequacy is associated with more biasing of capital
budgeting cash flow forecasts.
3.4. Challenge in accessing FF&E reserve account funds
Operation of the FF&E reserve account has the potential to be a source of significant tension
between hotel owners and operators, particularly in connection with the manner in which
funds are released from the reserve (Corgel 2007). Release of funds is generally achieved
only following the provision of owner approval (Turner and Guilding 2010a). A lessening of
operator access to FF&E funds can be expected to result in operators perceiving a heightened
need to demonstrate the worthiness of capital expenditure proposals. This heightened need
for capital expenditure proposals to provide a persuasive case supportive of greater capital
expenditure can be facilitated via the preparation of optimistically biased cash flow forecasts.
Hypothesis 4 is formulated in a manner consistent with this rationale.
H4. Higher degrees of challenge experienced in accessing FF&E reserve account
funds is associated with more biasing of capital budgeting cash flow forecasts.
3.5. Remaining length of management contract
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It has long been claimed that long-term contracts can mitigate inconsistent principal/agent
interests (see Haka 2007 for a review). As capital budgeting cash flow forecasts can be used
by an owner as a basis for monitoring subsequent performance, there is an incentive for an
operator to refrain from biasing cash flow forecasts. This incentive will lessen as the maturity
date of a management contract approaches, due to the diminution of the period that the
forecast can be used as a performance benchmark.
2
Further, Guilding (2003) suggests that
where an operator is in a long-term relationship with an owner, there is a greater likelihood
that the operator will seek to instill and maintain trust in the relationship. The submission of
biased cash flow forecasts to an owner would appear to be an operator action that is
inconsistent with an ethos of engendering trust in a relationship, as it raises the spectre of
owner disappointment arising from failure to achieve forecast cash flows. Hypothesis 5 is
promulgated in a manner consistent with this rationale.
H5. Shorter periods of time to management contract expiry are associated with more
biasing of capital budgeting cash flow forecasts.
3.6. Emphasis on financial versus non-financial factors in investment appraisal
To assist in the capital budgeting decision-making process, financial and non-financial capital
budgeting appraisal techniques are often used (Moyer et al. 2001). Although there has been a
substantial focus within the literature on the use of different financial capital budgeting
appraisal techniques such as NPV, IRR and payback (e.g. Graham and Harvey 2001), limited
research attention has been given to the relative importance of non-financial factors in
investment appraisal (Chen 2008).
There appear to be two reasons suggesting that the relative degree of emphasis attached to
financial and non-financial investment appraisal factors might represent a potentially
important factor affecting biasing of CBCFFs. If high emphasis is attached to financially
oriented investment appraisal approaches, there would appear to be a greater incentive to bias
cash flow forecasts, as this data would be carrying a relatively high influence when seeking to
determine a proposal’s merit. On the other hand, where high emphasis is attached to
financially oriented investment appraisal approaches, an owner would likely more closely
monitor the propensity for bias in cash flow forecasts and this greater monitoring might
lessen the extent of biasing of CBCFFs. This rationale signifies that high emphasis attached
to financially oriented investment appraisal approaches could give rise to conflicting effects
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on biasing of CBCFFs. In light of this, hypothesis 6 has been developed in a non-directional
manner.
H6. Relative emphasis attached to financial versus non-financial factors in investment
appraisal is associated with biasing of capital budgeting cash flow forecasts.
4. Sampling method
A mailed questionnaire survey was sent to general managers in Australian and New Zealand
hotels with twenty or more rooms and a minimum star-rating of three. This provided a total
sample size of 664, comprising 463 Australian hotels and 201 New Zealand hotels. It is only
hotels operating with a management contract that evidence four of the factors invoked in the
analysis: locus of power between owner and operator; adequacy of funds allocated to the
FF&E reserve account; challenge in accessing FF&E reserve account funds; and remaining
length of management contract. However, no database could be identified that was limited to
hotels operating with a management contract only. The questionnaire was therefore sent to all
hotels in the sample frame and included a screening question that elicited the operating modal
type for each respondent’s hotel.
Three weeks subsequent to the initial mailing, a follow-up mailing was sent to the entire
sampling frame. Two weeks after the second mailing, a number of hotel owner
representatives known to the research team agreed to circulate the questionnaire to general
managers with whom they had close contact. This generated a further 51 responses. Two
weeks subsequent to the owner representatives’ distribution of questionnaires, the sample was
contacted by email and encouraged to complete the questionnaire which was provided as an
attachment. Finally, two weeks after the email approach, random telephone calls were made
to 31 general managers. The objective of these phone calls was threefold: to thank the general
manager if they had already completed the survey; to ascertain the main reasons for non-
participation in the study; and to encourage participation in the study. The survey response
pattern is reported in Table 1.
INSERT TABLE 1 ABOUT HERE
Two investigations for non-response bias were undertaken. Non-response reasons provided
by the hotels contacted by phone included ‘completing questionnaires contravenes company
policy’, ‘too busy’ and ‘the general manager was away on holiday’. No factors cited
15
suggested the presence of any systematic non-response bias. Secondly, an investigation for
profile differences between the first mailing respondents and the remainder of the
respondents was undertaken. Although a Mann-Whitney U Test revealed some differences,
the statistical strength of association between the two groups (r value) was small (i.e. r < .2)
(see Cohen 1988). These investigations suggest the issue of non-response bias does not
constitute a strong threat to the validity of the study’s findings.
5. Variable measurement
5.1. Locus of power between hotel owner and operator
Given that two distinct organisations are involved in a hotel management contract, the owner
and the operator, it appeared appropriate to guide the current study’s operationalisation of a
power measure by the inter-organisational power literature. Inter-organisational power survey
research is usually framed according to two common power perspectives: (1) an assessment
of the alternative choices available to both organisations in the negotiation phase; and (2) the
strategic importance of the relationship to the organisations at the time of contract
negotiation. Difficulties arise when attempting to apply these conceptualisations of power to
the context of the current study, however. These relate to the fact that the best placed party to
comment on the locus of power between hotel owner and operator is the hotel general
manager. There is a low likelihood, however, that a general manager responding to the survey
questionnaire would be in a position to comment on the conditions existing at the time that
the hotel management contract was negotiated. This is because of general managers’
notoriously high job mobility (Akrivos et al. 2007) and also the fact that management
contracts are typically entered into for ten or more years (Haast et al. 2005). Despite these
concerns, the hotel general manager would appear to be in a very strong position to provide a
well-informed perspective on the locus of power between owner and operator. This is
because while the operator employs the general manager, the general manager’s appointment
typically requires approval of the owner, and it is the owner that funds the general manager’s
salary (Guilding 2003). Guilding (2006, pp. 403-405) comments:
‘… the general manager … can be seen to be well placed to observe any ‘cross-fire’
between a hotel owner and operator. [general managers are strategically placed] …
with respect to mediating the relationship between hotel owner and operator.’
16
Based on the view that a general manager is motivated to maintain a strong working
relationship with both the owner and operator, it is argued that for the purposes of gauging
power in the current study, a general manager’s relationship with both the owner and the
operator can be viewed as being similar to a ‘within organisation’ relationship. This is
because a hotel owner and operator are in an enduring organisational relationship. In light of
this, the well-established intra-organisational power literature can be drawn upon to inform
the development of a measure of the locus of power between hotel owners and operators.
Within the intra-organisational power literature, Krause and Kearney (2006) explain that
much empirical questionnaire survey based research has been conducted within a broad range
of organisational contexts and that a classical five power base typology developed by French
and Raven (1959) has been used. This typology comprises: (1) reward power; (2) coercive
power; (3) legitimate power; (4) referent power; and (5) expert knowledge power. French and
Raven (1960, pp. 612-613) describe their five bases of power in which an agent, O, can exert
influence over a person, P, as follows:
‘(a) Reward power, based on P’s perception that O has the ability to mediate rewards
for him; (b) coercive power, based on P’s perception that O has the ability to mediate
punishments for him; (c) legitimate power, based on the perception by P that O has a
legitimate right to prescribe behaviour for him; (d) referent power, based on P’s
identification with O; (e) expert power, based on the perception that O has some
special knowledge or expertise.’
Within the current study, it was determined that the locus of power between hotel owner and
operator could be most effectively elucidated by determining which of the two contracting
parties exerted the greatest influence over the general manager. Following a review of the
intra-organisational power literature, Krause and Kearney (2006) recommend that: (1) power
bases be measured through the use of multi-item measurement; (2) that responses be rated
rather than ranked; and (3) that future studies measure the wielding of power in its
dependency on the particularities of the situation (i.e. context specific). Krause and Kearney’s
(2006) recommendations were heeded in developing the current study’s power measure. This
has resulted in three separate context specific questions being posed for each of French and
Raven’s (1959) five power bases, using a seven-point Likert-type scale. The 15 items are
preceded by the introductory wording: ‘In terms of your hotel owner and your hotel operating
company, which entity is in a stronger position to:’. Column four of Table 2 presents the 15
17
items posed together with an identifying code for each item. The Table’s first three columns
capture the origin of each item and the Table’s final three columns in respective order
provide each item’s mean, standard deviation and number of observations in the sample.
INSERT TABLE 2 ABOUT HERE
In addition to these fifteen items, one question was posed as a holistic measure of the locus of
power between hotel owner and operator. Respondents were asked: ‘In terms of influencing
the hotel’s objectives/goals, which is more powerful?’ A Likert scale was provided that
ranged from ‘1’ (the operator) to ‘7’ (the owner). This question generated a mean of 3.21 and
a standard deviation of 1.53 (n = 100).
An assessment of the suitability of applying factor analysis to the data collected via the 15
items was undertaken, consistent with the view that power is a multidimensional variable
(French and Raven 1960). An independent factor analysis was conducted for each of the three
sets of items comprising the five dimensions of power. In all cases, correlations between the
three items were highly statistically significant (p < .01). Kaiser Meyer Olkin and Bartlett’s
Test indicated favourable sampling adequacy. Communalities were greater than the
recommended 0.5 threshold in all but one case (item L1 at .382). Hair et al. (2006) indicate
that items yielding communalities below 0.5 can be ignored. As the L1 item derives from a
measure that has been used in prior studies, it was deemed preferable to retain the item within
the factor solution. For each of the five dimensions of power, a single factor solution with an
Eigenvalue greater than 1 was extracted, with a variance explained greater than the 60%
recommended threshold (Hair et al. 2006). All item loadings were above the recommended
.55 threshold and were statistically significant (p < .05). These findings support the
measurement of the five dimensions of power by calculating the mean of the three underlying
items for each of the five item groupings.
The degree of association between the five dimensions of power and also the holistic measure
of power was examined using correlation analysis (see Table 3). This table shows that all
items are highly statistically significantly correlated at p < .01. The strong correlation
between the holistic item and all five dimensions of power constitutes a strong affirmation as
to the reliability of the holistic measure. In light of this, the holistic item has been employed
as the measure of power in hypothesis testing.
18
INSERT TABLE 3 ABOUT HERE
5.2. Emphasis attached to the payback investment appraisal method
Rather than using an absolute measure of payback usage, it was felt that a more refined
payback emphasis indicator would result if the use of payback were gauged relative to the use
of other popular investment appraisal techniques. Following the question ‘To what extent are
the following capital budgeting investment appraisal techniques used in your hotel?’, the
NPV, IRR and Payback techniques were listed. For each of these techniques, the respondent
recorded a score on a Likert scale that ranged from ‘1’ (‘not at all’) to ‘7’ (‘to a large extent’).
Table 4 provides the mean and standard deviation statistics for these three items.
INSERT TABLE 4 ABOUT HERE
Emphasis attached to the payback investment appraisal method has been measured by taking
a respondents’ score for the highest ranking item out of NPV or IRR and subtracting it from
their score for the payback item (i.e. Payback – higher of NPV or IRR). This measure
generated a mean of .76 and a standard deviation of 1.71 (n = 101). The rationale for using
only the score for the most dominant item of NPV or IRR stems from the view that the
highest scoring item represents the most appropriate benchmark to use when seeking to gauge
the relative emphasis attached to the payback investment appraisal method.
5.3. Adequacy of funds allocated to the FF&E reserve account
Two approaches were taken to measure the adequacy of funds allocated to the FF&E reserve
account. Firstly, respondents were asked ‘To what extent do you consider the funds allocated
to the FF&E reserve in your hotel are sufficient to fund FF&E expenditure?’ Responses were
recorded on a Likert scale ranging from ‘1’ (‘not sufficient’) to ‘7’ (‘very sufficient’). This
measure yielded a mean of 3.81 and a standard deviation of 1.55 (n = 88). For the second
measure, the respondents were asked the following two questions: (1) ‘What percentage of
gross revenue is allocated annually to the FF&E reserve account in your hotel? ____%’; and
(2) ‘What percentage of gross revenue would be required to cover the true cost of reasonable
annual FF&E expenditure in your hotel?____%’. An index was then computed by taking a
subject’s score for the first question (actual allocation to the FF&E reserve) and deducting
their score on the second question (true cost of FF&E reserve). This second measure yielded
19
a mean of -2.14% and a standard deviation of 1.74 (n = 70). The two measures were highly
significantly correlated (p < .01) suggesting high measurement reliability. In the hypothesis
testing below, the first of the two measures has been used as the gauge of the adequacy of
funds allocated to the FF&E reserve account.
3
5.4. Challenge in accessing FF&E reserve account funds
Three items were employed to measure the extent of challenge associated with accessing
FF&E reserve account funds. The first question asked respondents to indicate their
affirmation with the statement: ‘In my hotel it can be hard to get the owner to release funds
from the FF&E reserve’ on a Likert scale ranging from ‘1’ (‘strongly disagree’) to ‘7’
(‘strongly agree’) (mean 3.33, std. dev. 1.85). The second question asked: ‘How often does
your hotel owner refuse to release funds from the FF&E reserve?’, with responses provided
on a Likert scale ranging from ‘1’ (‘never’) to ‘7’ (‘frequently’) (mean 2.72, std. dev. 1.51).
The final question asked ‘How much do you have to pressurise your hotel owner in order to
get funds released from the FF&E reserve?’ with responses recorded on a Likert scale
ranging from ‘1’ (not at all) to ‘7’ (very much) (mean 3.17, std. dev.1.83). A correlation
analysis revealed statistically significant positive associations between all three items (p <
.01). A factor analysis revealed all items having a communality greater than .8 with a single
factor solution explaining 83.02% of the variance. In light of this, the challenge in accessing
FF&E reserve account funds has been measured by calculating the mean of the three items.
The three items yielded a strong Cronbach Alpha of .895. The factor developed had a mean
of 3.07 with a standard deviation of 1.58 (n = 88).
5.5 Remaining length of management contract
No study employing a measure of remaining length of management contract was found in the
literature. Given the relatively simple nature of the information sought, respondents were
asked ‘Approximately how long is it until your management contract expires? (please
specify) ____ year(s)’. The mean was 8.53 years with a standard deviation of 5.63 years (n =
85).
5.6. Emphasis on financial versus non-financial factors in investment appraisal
Butler et al. (1993) see three distinct orientations in non-financial approaches to investment
appraisal: (1) strategically-oriented investment appraisal, which is evident when high
importance is attached to a project’s capacity to deliver competitive advantage (Lefley 2004);
20
(2) politically-oriented investment appraisal, which is evident when an organisation has
highly self-interested individuals who employ guile and strategies such as coalition building
to enhance the likelihood of their preferred project proposal being sanctioned by senior
management (Hickson et al. 1986), and (3) intuition-based investment appraisal, which is
evident when high importance is attached to the exercise of senior management’s intuition
and judgment (Chami and Fullenkamp 2002). Drawing on this categorisation, three questions
were developed relating to each of these non-financial investment appraisal orientations. In
addition, three questions were developed to gauge the extent to which financial
considerations influence whether a capital expenditure proposal is supported. Table 5
provides an overview of the 12 items developed. The first column identifies the
categorisation in Butler et al. (1993). The second column documents the 12 items developed
together with an identifying code for each question. The final column provides the mean and
standard deviation for each item. For each item, a Likert scale was used ranging from ‘1’ (not
at all) to ‘7’ (to a large extent). The 12 items were preceded by the wording: ‘In your hotel, to
what extent do the following factors influence whether an investment proposal is given the go
ahead?’
INSERT TABLE 5 ABOUT HERE
A factor analysis was undertaken, justified on the grounds of the four thematic origins of the
12 questions.. Kaiser Meyer Olkin and Bartlett’s Test indicated favourable sampling
adequacy. The factor analysis generated three factors with eigenvalues greater than 1 (3.830,
2.608 and 1.128). The relative association of the items with these three factors is evident from
Table 6, which presents output from a principal component analysis. The thematic origin of
each of the items is provided in the table’s second column. While this initially suggested a
three factor result, inspection of the eigenvalue scree plot revealed a significant kink in the
curve occurring at the locus of the third highest eigenvalue (the fourth and fifth highest
eigenvalues were .888 and .798, respectively). Accordingly, and consistent with Hair et al’s
(2006) recommendations, it appeared most appropriate to consider two strong underlying
factors:
4
1. Non-financial emphasis (items I2, P2, P3, P1, I1, I3); and
2. Financial emphasis (items F2, F3, F1).
INSERT TABLE 6 ABOUT HERE
21
The three items associated with financial emphasis (the second factor in Table 6) were
consolidated by computing their mean, which was 6.04 with a standard deviation of .78 (n =
101). These three variables yielded a Cronbach Alpha of .726. The six items associated with
non-financial emphasis (the first factor in Table 6) were consolidated by computing their
mean, which was 3.85 with a standard deviation of 1.23 (n = 99). These six items yielded a
Cronbach Alpha of .850. The measure for emphasis on financial versus non-financial factors
in investment appraisal has been calculated by deducting the non-financial investment
appraisal emphasis indicator from the financial emphasis indicator. The resultant measure
produced a mean of 2.18 with a standard deviation of 1.44 (n = 99).
5.7 Extent of biasing of capital budgeting cash flow forecasts
Two prior questionnaire survey based studies (see Guilding and Lamminmaki 2007, Pruitt
and Gitman 1987) have sought to measure the extent of biasing of CBCFFs. Drawing on this
prior research, Table 7 provides an overview of the items used to measure this dependent
variable. Column one shows the source of the original item drawn from prior literature, the
second column displays the current study’s adapted item and the final three columns show the
mean, standard deviation and number of observations in the sample respectively. A seven
point Likert scale was used for all three items in the current study, with ‘1’ corresponding to
‘strongly disagree’ and ‘7’ corresponding to ‘strongly agree’.
INSERT TABLE 7 ABOUT HERE
The three items were found to be statistically significantly correlated (p < .01).
Communalities were computed and item loadings were found to be well above the
recommended .50 threshold (Hair et al. 2006) and statistically significant for the first two
items listed in Table 7, but not for the third item (.497). As the communality for this third
item was close to the recommended .5 threshold, the decision was made to retain the item in
the factor analysis. A factor analysis was undertaken yielding one factor with an eigenvalue
exceeding 1 (71% variance explained). The factor loadings were found to be well above the
recommended .55 threshold (p < .05) for a sample size of 100. In light of this, it was
determined that the extent of biasing of CBCFFs would be calculated as the mean of the three
items. These three items yield a strong Cronbach Alpha of .784.
5
22
6. Results
Table 8 presents a frequency distribution and the mean scores for the three items ‘extent of
biasing of capital budgeting cash flow forecasts’. The means ranged from 2.63 to 2.87. The
fact that the mean scores for all three items were below the mid-point (4) of the measurement
scale signified that the extent of biasing of capital budgeting cash flow forecasts may be
relatively muted. This observation should be qualified, however. The frequency distribution
reveals that the proportion of the sample scoring at the mid-point or above is 24% for the first
measure, 21.8% for the second measure, and 30.8% on the third measure. This underscores a
view that there is considerable variation in the degree to which hotels are biasing CBCFF. It
also signifies that approximately one-quarter of the sample are engaging in significant
degrees of CBCFF biasing.
INSERT TABLE 8 ABOUT HERE
To test the study’s six hypotheses, the following multiple regression equation was applied:
Y = β0 + β1POWER_LOCUS + β2EMPHASIS_PAYBACK + β3ADEQUACY_FF&E +
β4CHALLENGE_FF&E_RELEASE + β5CONTRACT_LENGTH +
β6FINANCIAL_EMPHASIS + e
where:
Y = Extent of biasing of capital budgeting cash flow forecasts.
POWER_LOCUS = Locus of power between hotel owner and operator (hypothesis 1).
EMPHASIS_PAYBACK = Emphasis attached to the payback investment appraisal
method (hypothesis 2).
ADEQUACY_FF&E = Adequacy of funds allocated to the FF&E reserve account
(hypothesis 3).
CHALLENGE_FF&E_RELEASE = Challenge in accessing FF&E reserve account funds
(hypothesis 4).
CONTRACT_LENGTH = Remaining length of management contract (hypothesis 5).
FINANCIAL_EMPHASIS = Emphasis on financial versus non-financial factors in
investment appraisal (hypothesis 6).
Table 9 provides a correlation matrix of all variables in the regression equation. All
independent variables are statistically significantly correlated with the dependent variable (p
< .05). None of the independent variables exhibit statistically significant association with one
23
another (p > .05). All variable inflation factors in the model (see Table 10) are also well
below the suggested threshold of 5 (Kutner et al. 2004).
INSERT TABLES 9 AND 10 ABOUT HERE
Table 10 presents the results of the multiple regression analysis. The adjusted R2 reveals that
32.2% of the dependent variable’s variation is explained by the independent variables. The
model is statistically significant (F = 6.614, p < .000, df = 6, 65).
6
Hypothesis 1 posited that high owner locus of power is associated with less biasing of capital
budgeting cash flow forecasts. No support was found for this hypothesis (p = .150; one-
tailed).
7
Hypothesis 2 posited that higher emphasis attached to the payback method in capital
budgeting appraisal is associated with more biasing of capital budgeting cash flow forecasts.
Support was found for this hypothesis at the p < .01 level of confidence (p = .008; one-
tailed).
Hypothesis 3 posited that low FF&E reserve account adequacy is associated with more
biasing of capital budgeting cash flow forecasts. Support was found for this hypothesis at the
p < .05 level of confidence (p = .011; one-tailed).
Hypothesis 4 posited that higher degrees of challenge experienced in accessing FF&E reserve
account funds is associated with more biasing of capital budgeting cash flow forecasts.
Support was found for this hypothesis at the p < .05 level of confidence (p = .026; one-
tailed).
Hypothesis 5 posited that shorter periods of time to management contract expiry are
associated with more biasing of capital budgeting cash flow forecasts. Support was found for
this hypothesis at the p < .05 level of confidence (p = .043; one-tailed).
Hypothesis 6 posited that relative emphasis attached to financial versus non-financial factors
in investment appraisal is associated with biasing of capital budgeting cash flow forecasts.
This hypothesis was framed in a non-directional manner as arguments could be made for
24
either a positive or negative relationship. Support was found for a negative relationship at the
p < .01 level of confidence (p = .000; two-tailed).
7. Discussion and conclusion
With respect to examining the incidence of biasing of CBCFFs, while at a general level of
abstraction it appears biasing of CBCFFs in hotels mediated by a management contract is not
high, considerable variability is in evidence and around 25% of the sample were found to be
engaging in significant degrees of CBCFF biasing. The significance of this observation for
hotel owners becomes apparent when the size of the cash flows associated with the capital
budget as well as the long term implications arising from capital expenditure committal are
recognised. It is notable that the recent past has seen an increase in hotel owners engaging the
services of ‘asset managers’ whose job entails ensuring that the hotel operator is taking
actions that are consistent with the hotel owner’s interests (Armitstead 2004). It appears
reasonable to expect that the increased engagement of hotel asset managers has been
instigated by the desire of hotel owners to lessen hotel operator dysfunctional behaviour such
as the biasing of CBCFF. This development may well signify that, compared to other sectors,
the hotel sector now has relatively low levels of biasing of CBCFFs. Recognition that
industry specific factors will likely affect biasing of CBCFFs underscores the need for the
research initiative reported herein to be replicated in other industrial contexts.
Hypothesis 1 posited that high owner power is associated with less biasing of CBCFFs. No
support was found for this hypothesis (p = .150; one-tailed). It is notable, however, that a
statistically significant association (p < .05) was found between high owner locus of power
and biasing of CBCFFs (see Table 9). This observed relationship is directionally consistent
with hypothesis 1. While care should be taken not to overstate any inferred relationship
between locus of power between hotel owner and operator and biasing of CBCFFs (given
failure to find support for hypothesis 1 in the multiple regression analysis), it appears
reasonable to suggest that power between a principal and agent could constitute a useful
variable to examine in further research, particularly in light of the fact that no prior survey-
based empirical accounting study that invokes the power construct has been found in the
literature. The power measure developed in this study would appear to be especially useful in
any subsequent empirical research that seeks to determine the locus of power between hotel
owners and operators and may also provide a platform upon which to build further context
specific power measures between principals and agents.
25
Support was provided for hypothesis 2 that higher use of the payback method in capital
budgeting appraisal is associated with more biasing of CBCFFs. Normative commentaries
that outline the relative merits of different investment appraisal methods will need to consider
whether they should be broadened to include the view that a downside of the payback method
is that it can promote greater biasing of CBCFFs. Consistent with all new research findings,
our confidence over the sustained existence of this observed relationship can only be attained
through research replication and examination of the relationship in other commercial
contexts.
Support was found for hypothesis 3 that lower adequacy of FF&E reserve is associated with
more biasing of CBCFFs. Support was also provided for hypothesis 4 which postulated that
higher degrees of challenge experienced in accessing FF&E reserve account funds is
associated with more biasing of CBCFFs. The novelty of these findings derive from the fact
that there has been no prior empirical examination of implications arising from hotel FF&E
reserve account management practices. Considered together, these two findings carry a
resonance with prior studies that report a heightened potential for biasing in capital budgeting
contexts characterised by tight capital rationing regimes (see e.g. Mukherjee and Hingorani
1999). These findings can also be seen to provide an extension to Turner and Guilding’s
(2010a) examination of FF&E reserve accounting practice.
Support was also provided for hypothesis 5 that shorter periods of time to management
contract expiry are associated with more biasing of CBCFFs. With respect to agency theory
grounded perspectives on contracting, this finding provides support for the view that longer-
term contracts carry the benefit of promoting greater principal-agent goal congruency (see
Haka 2007). The finding also signifies that the interest of principals may be served by
expending greater resources monitoring agents in the latter stages of a contract, relative to the
early stages of a contract. The extent to which such increased monitoring is justified would,
however, be dependent on the resultant benefits relative to higher monitoring costs incurred.
This finding suggests that ‘stage in a contract’s life’ could be a useful contextual factor to
consider in future capital budgeting research.
Finally, support was provided for hypothesis 6 which posited that relative emphasis attached
to financial versus non-financial factors in investment appraisal is associated with biasing of
26
CBCFFs. Evidence suggesting a negative relationship between relative emphasis attached to
financial factors in investment appraisal and biasing of CBCFFs was found. This observation
is particularly notable given the minimal prior accounting research interest in the relativity of
financial versus non-financial investment appraisal method applications. This lack of interest
appears as surprising as custodians of the capital expenditure budget in all organisations need
to calibrate the degree of importance that they will attach to financial relative to non-financial
investment appraisal techniques. The significance that has been found for this variable in the
current study may well spur further enquiry into the relative importance attached to financial
versus non-financial investment appraisal methods, particularly as it can be seen as
constituting an ‘accounting’ variable. At the very least, a greater focus on this variable will
shed light on an under-appreciated aspect of the way capital budgeting systems are
operationalised in organisations.
This study’s focus on biasing of CBCFFs has highlighted a variable that would appear worthy
of greater attention from academics and practitioners alike. The degree of research endeavour
that has been invested in examining biasing of CBCFFs is meagre relative to the large
quantum of research enquiry directed to appraising the relative incidence of different capital
budgeting appraisal techniques.
A distinguishing facet of this study has been its focus on a particular industry, the hotel
industry. Four of the six variables investigated in this study could not have been examined
had a generic, cross-industry, survey approach been taken. The locus of power between
owner and operator, adequacy of funds allocated to the FF&E reserve account, challenge in
accessing FF&E reserve account funds, and remaining length of management contract are
factors particular to hotels mediated by a management contract. It is particularly telling that
three of these four variables have been found to have a significant effect on the extent of
biasing of CBCFFs. This lends support to the view that significant insights can derive from
conducting management accounting surveys that focus on particular issues and facets that are
specific to a particular industrial sector.
The study’s singular industry focus has drawn out another interesting facet of hotel capital
budgeting. As a hotel operator is remunerated based on revenue and profitability, there
appears little financial incentive for the operator to focus on NPV, IRR or payback when
promoting capital expenditure proposals, rather, their remuneration basis suggests they will
27
simply be interested in projects that maximise absolute sales and profit levels. As capital is
effectively free for operators that have a revenue and profitability linked remuneration basis,
one might expect that greater biasing of CBCFFs will occur in the presence of such a
remuneration basis.
This study suffers from all the limitations generally associated with social scientific research
that is based on mailed questionnaire survey data. In particular, when interpreting the study’s
results, it should be born in mind that some of the variable measures used had to be
developed due to negligible prior attention directed toward the constructs of interest. This
signifies that some of the measures do not carry the benefit of extensive prior validation.
Although there is no strong reason to suggest that the measures have failed to adequately
measure the intended constructs, the possibility of this occurring should be acknowledged. In
light of the study’s relative novelty, further research directed towards replicating aspects of
the work contained within should be welcomed.
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32
Table 1
Questionnaire survey reply pattern
Country
Australia
(n)
New Zealand
(n)
Total
(n)
% of final
responses
First mailing
55
28
83
41.5%
Second mailing
36
11
47
23.5%
Industry distribution
41
10
51
25.5%
Emailing distribution
10
6
16
8.0%
Telephone
3
0
3
1.5%
Total number of responses
145
55
200
100.0%
Total number in sample
437
184
621
Total response rate
33.18%
29.89%
32.21%
Hotels with a management
contract
85
16
101
50.5%
34
Table 2
Overview of items used to measure locus of power between hotel owner and operator
Power base
of French
and Raven
(1959)
Authors
Author’s original item
Current study’s adapted itema
Mean
Standard
deviation
n
Reward
Power
Hinkin and
Schriesheim
(1989)
My supervisor can increase my
pay level.
RW1: Which entity is in a
stronger position to provide you
with increased pay?
2.68
1.74
101
Hinkin and
Schriesheim
(1989)
My supervisor can influence
my getting a promotion.
RW2: Which entity is in a
stronger position to influence
your next promotion?
2.23
1.45
100
Bachman et
al.(1966)
My supervisor can give special
help and benefits to those who
cooperate with him.
RW3: Which entity is in a
stronger position to give you
special help and benefits in
return for your co-operation?
3.18
1.28
99
Coercive
power
Bachman et
al. (1966)
My supervisor can apply
pressure or penalize those who
do not cooperate [with them].
C1: Which entity is in a stronger
position to put pressure on you if
they perceive you to not be
supportive of their wishes?
3.33
1.39
101
Hinkin and
Schriesheim
(1989)
My supervisor can make things
unpleasant here.
C2: Which entity is in a stronger
position to make things
unpleasant for you at the hotel?
3.96
1.17
97
Hinkin and
Schriesheim
(1989)
My supervisor can make my
work difficult for me.
C3: Which entity is in a stronger
position to make your work
difficult?
3.79
1.25
97
Legitimate
power
Bachman et
al. (1966)
My supervisor has a legitimate
right, considering his position,
to expect that his suggestions
will be carried out.
L1: Which entity is in a stronger
position to require that their
suggestions are carried out?
3.63
1.28
101
35
Hinkin and
Schriesheim
(1989)
My supervisor can give me the
feeling that I have
responsibilities to fulfil.
L2: Which entity in a stronger
position to give you a sense of
importance associated with
fulfilling your responsibilities?
3.62
1.19
100
Hinkin and
Schriesheim
(1989)
My supervisor can make me
feel like I should satisfy my job
requirements.
L3: Which entity is in a stronger
position to make you want to
achieve a high level of
performance?
3.50
1.40
100
Expert
power
Yukl and
Falbe (1991)
My supervisor has the
experience and knowledge to
earn my respect, and for me to
defer to his/her judgment in
some matters.
E1: Which entity is in a stronger
position to command your
respect?
3.91
1.19
101
Hinkin and
Schriesheim
(1989)
My supervisor can give me
good technical suggestions.
E2: Which entity is in a stronger
position to provide you with
good technical suggestions?
2.77
1.26
101
Hinkin and
Schriesheim
(1989)
My supervisor can provide me
with sound job-related advice.
E3: Which entity is in a stronger
position to provide you with
sound job-related advice?
2.59
1.30
100
Referent
power
Bachman et
al. (1966)
I admire my supervisor for his
personal qualities, and want to
act in a way that merits his
respect and admiration.
RF1: Which entity is in a
stronger position to expect you
to act in order to win their
respect and admiration?
3.80
0.98
98
Hinkin and
Schriesheim
(1989)
My supervisor can make me
feel valued.
RF2: Which entity is in a
stronger position to make you
feel valued?
3.43
1.36
99
Hinkin and
Schriesheim
(1989)
My supervisor can make me
feel personally accepted.
RF3: Which entity is in a
stronger position to provide you
with a sense of being personally
accepted?
3.46
1.31
100
aThese items were introduced with the wording ‘In terms of your hotel owner and your hotel operating company, which entity is in a stronger position to:’.
All items were measured on a Likert scale where ‘1’ signifies high operator power and ‘7’ signifies high owner power.
36
Table 3
Correlation analysis of holistic power and the five sub-dimensions of power
Holistic
power
Reward
power
Coercive
power
Legitimate
power
Expert
power
Referent
power
Holistic power
1.000
.518*
.252*
.627*
.617*
.499*
Reward power
.518*
1.000
.267*
.650*
.623*
.577*
Coercive power
.252*
.267*
1.000
.442*
.277*
.335*
Legitimate power
.627*
.650*
.442*
1.000
.667*
.746*
Expert power
.617*
.623*
.277*
.667*
1.000
.666*
Referent power
.499*
.577*
.335*
.746*
.666*
1.000
* Significant (p < .01) one-tailed.
The correlations reported in this table signify that all of the variables are highly inter-correlated.
35
Table 4
Relative use of NPV, IRR and payback investment appraisal techniques
NPV
IRR
payback
Mean
4.67
4.47
5.78
Std. dev.
1.62
1.67
1.29
n
101
100
101
All questions were posed on a 7-point Likert scale where ‘1’ signifies low use and ‘7’ signifies high use.
36
Table 5
Questionnaire items used to measure emphasis on financial versus non-financial factors in investment appraisal
Categories
of Butler et
al. (1993)
Current study’s measurement statementsa
Mean
Standard
deviation
n
Financial
F1. The proposal is justifiable on financial grounds.
6.15
0.82
101
F2. Cash flow and profitability forecasts support the proposal’s viability.
5.96
1.06
101
F3. The project’s budget provides a good financial return.
6.02
1.03
101
Strategic
factors
(non-
financial)
S1. The proposal can be justified on the basis of gaining marketplace
competitive advantage.
5.30
1.23
101
S2. The proposal is justified by a thoroughly conducted strategic analysis
(e.g. competitive positioning analysis, SWOT analysis).
5.16
1.30
101
S3. The project represents an opportunity to pre-empt the competition.
4.50
1.44
101
Political
factors (non-
financial)
P1. The proposal’s main sponsor (i.e. the manager most closely associated
with the project’s initiation and development) has a strong company track
record.
3.90
1.73
101
P2. The manager acting as the proposal’s sponsor is a shrewd negotiator.
3.43
1.61
99
P3. The manager acting as the proposal’s sponsor understands internal
politics and uses this to their advantage in seeking company approval.
3.50
1.85
101
Intuitive
factors
(non-
financial)
I1. The proposal appears justifiable on intuitive grounds.
3.00
1.53
101
I2. Experience suggests that the project will be successful.
4.13
1.54
101
I3.On the face of it, the proposal makes sound commercial sense.
5.19
1.49
101
a Prior to posing the questions, the questionnaire stated: ‘In your hotel, to what extent do the following factors influence whether an investment proposal is given the go
ahead?’ All items were posed using a 7-point Likert scale: 1 = ‘not at all’; 7 = ‘to a large extent’
38
Table 6
Factor analytic pattern matrix for emphasis on financial versus non-financial factors in
investment appraisal
Questionnaire
item
Thematic origina
Intuitive and
political factor
Financial
factor
Strategic
factor
I2
Intuitive
.863
P2
Political
.830
P3
Political
.786
P1
Political
.743
I1
Intuitive
.687
I3
Intuitive
.598
F2
Financial
.869
F3
Financial
.795
F1
Financial
.640
S1
Strategic
.904
S2
Strategic
.497
S3
Strategic
.481
Eigenvalue
3.830
2.608
1.128
a The ‘strategic factor’, ‘political factor’ and ‘intuitive factor’ are each distinct orientations concerning non-
financial approaches to investment appraisal. The ‘financial factor’ gauges the extent to which financial
considerations influence whether a capital expenditure proposal is supported.
39
Table 7
Extent of biasing of capital budgeting cash flow forecasts measurement items: derivation and descriptive statistics
Original item from prior literature
Current study’s itema
Mean
Standard
deviation
n
‘In my hotel, there is a tendency for investment
project sponsors (i.e. the managers most closely
associated with the project’s initiation and
development) to inflate projected cash inflow
estimates in order to increase the likelihood of
the project gaining senior management support’
(Guilding and Lamminmaki 2007, p. 492).a
In my hotel, there is a tendency for investment
project sponsors (i.e. the managers most closely
associated with the project’s initiation and
development) to inflate projected cash inflow
estimates in order to increase the likelihood of
the project gaining the hotel owner’s support.
2.66
1.49
100
‘Revenue forecasts of capital budgeting
proposals (not necessarily accepted projects)
are typically overstated’ (Pruitt and Gitman
1987, p. 48).b
In my hotel, revenue forecasts of capital
budgeting proposals (not necessarily accepted
projects) are typically overstated.
2.63
1.38
101
‘Cost forecasts of capital budgeting proposals
(not necessarily accepted projects) are typically
understated’ (Pruitt and Gitman 1987, p. 48).b
In my hotel, cost forecasts of capital budgeting
proposals (not necessarily accepted projects) are
typically understated.
2.87
1.52
101
a All questions in the current study were posed on a 7-point Likert scale: 1 = ‘strongly disagree’; 7 = ‘strongly agree’.
40
Table 8
Extent of biasing of capital budgeting cash flow forecasts: frequency distribution and mean scores
Likert scale
Strongly
disagree
Strongly
agree
Mean
Standard
deviation
n
Measurement itema
1
2
3
4
5
6
7
In my hotel there is a tendency for
investment project sponsors (i.e. the
managers most closely associated with
the project’s initiation and
development) to inflate projected cash
inflow estimates in order to increase the
likelihood of the project gaining the
hotel owner’s support.
23%
33%
20%
11%
6%
6%
1%
2.66
1.49
101
In my hotel, revenue forecasts in capital
budgeting proposals (not necessarily
accepted projects) are typically
overstated.
20.8%
33.7%
23.8%
9.9%
7.9%
3.0%
1.0%
2.63
1.38
101
In my hotel, cost forecasts in capital
budgeting proposals (not necessarily
accepted projects) are typically
understated.
17.8%
32.7%
18.8%
14.9%
8.9%
5.0%
2.0%
2.87
1.52
101
a All questions in the current study were posed on a 7-point Likert scale: 1 = ‘strongly disagree’; 7 = ‘strongly agree’.
41
Table 9
Pearson r correlation matrix of multiple regression analysis variables
CBCFF
_BIASING
POWER_
LOCUS
EMPHASIS_
PAYBACK
ADEQUACY_
FF&E
CHALLENGE_
FF&E_RELEASE
CONTRACT_
LENGTH
FINANCIAL_
EMPHASIS
CBCFF_BIASING
1.000
-.204
.224
-.270
.238
-.269
-.349
POWER_LOCUS
-.204*
1.000
-.080
.063
-.178
-.021
.077
EMPHASIS_PAYBACK
.224*
-.080
1.000
.027
.074
-.128
.157
ADEQUACY_FF&E
-.270*
.063
.027
1.000
-.161
.096
-.028
CHALLENGE_FF&E_
RELEASE
.238*
-.178
.074
-.161
1.000
-.140
.154
CONTRACT_LENGTH
-.269*
-.021
-.128
.096
-.140
1.000
.035
FINANCIAL_EMPHASIS
-.349*
.077
.157
-.028
.154
.035
1.000
* Significant (p < .05) one-tailed.
CBCFF_BIASING = Extent of biasing of capital budgeting cash flow forecasts.
POWER_LOCUS: Locus of power between hotel owner and operator
EMPHASIS_PAYBACK: Emphasis attached to the payback investment appraisal method
ADEQUACY_FF&E: Adequacy of funds allocated to the FF&E reserve account
CHALLENGE_FF&E_RELEASE: Challenge in accessing FF&E reserve account funds
CONTRACT_LENGTH: Remaining length of management contract
FINANCIAL_EMPHASIS: Emphasis on financial versus non-financial factors in investment appraisal
42
Table 10
Results from regression analysis
Regression equation
Y = β0 + β1POWER_LOCUS + β2EMPHASIS_PAYBACK + β3ADEQUACY_FF&E +
β4CHALLENGE_FF&E_RELEASE + β5CONTRACT_LENGTH +
β6FINANCIAL_EMPHASIS + e
Variable
βi standardised coefficient
t-statistic
Constant
0
7.238
POWER_LOCUS
-.105†
-1.045
EMPHASIS_PAYBACK
.250†*
2.484
ADEQUACY_FF&E
-.231†**
-2.325
CHALLENGE_FF&E_RELEASE
.203†**
1.970
CONTRACT_LENGTH
-.174†**
-1.740
FINANCIAL_EMPHASIS
-.412‡
-4.074
Adj. R2
32.2%
F (df = 6, 65)
6.614
p
.000
Key:
† One-tailed test
‡ Two-tailed test
* p < 0.01
** p < 0.05
POWER_LOCUS: Locus of power between hotel owner and operator
EMPHASIS_PAYBACK: Emphasis attached to the payback investment appraisal method
ADEQUACY_FF&E: Adequacy of funds allocated to the FF&E reserve account
CHALLENGE_FF&E_RELEASE: Challenge in accessing FF&E reserve account funds
CONTRACT_LENGTH: Remaining length of management contract
FINANCIAL_EMPHASIS: Emphasis on financial versus non-financial factors in investment
appraisal
1
Senior hotel personnel interview observations made by the research team over several years of research
enquiry, suggest it is commonplace for a capital expenditure proposal submitted by a hotel operator to a hotel
owner to be accompanied by a quantitatively based justification of the proposal. Further, in hotels where an
owner emphasises discounted cash flow analysis in investment appraisal, the operator tends to initiate a
discounted cash flow analysis in submitted proposals. This provides operators with scope to influence the
discount rate used in an owner's appraisal of a capital expenditure proposal's merit.
2
This hypothesised relationship may be somewhat negated in those situations where both the owner and
operator have an expectation that the contract will be renewed. It is notable, however, that many management
contracts specify a maximum number of renewals (typically one or two) between an owner and an operator,
with respect to a particular hotel (Bader and Lababedi 2007).
43
3
An investigation has been conducted into the sensitivity of the results with respect to which measure of FF&E
reserve adequacy is taken. It was found that no significant changes in the findings result if the alternative
measure were adopted.
4
Additional justification for the removal of the three items associated with strategic factors is that such factors
are sometimes viewed as a hybrid of both financial and non-financial factors (see e.g. Fleisher 2007).
5
Means and standard deviations for each of the three items as well as the factor developed are discussed in
further detail in the results section.
6
Table 10 reports the results of a regression analysis where missing cases were excluded listwise, signifying
that the analysis was based on 71 cases (i.e. 30 cases had one or more missing values). A missing values
analysis revealed that missing data were missing completely at random (MCAR) (Little’s MCAR test: Chi-
Square = 32.204, df = 32, p = .457). Using the ‘Missing Values Analysis’ function in SPSS, an Estimation
Maximisation (EM) dataset was derived and the multiple regression re-run based on the EM dataset. Consistent
with the model reported in Table 10, the results of this EM multiple regression model were statistically
significant (F = 6.626, p < .000, df = 6, 94). Likewise, the results of hypothesis testing remained largely the
same. Statistically significant support for hypotheses 3 and 5 increased from p < .05 to p < .01, while support for
hypothesis 2 decreased from p <.01 to p < .05. Given that the missing data were MCAR, there is no
disadvantage in reporting the results of the listwise model in the hypothesis testing results noted in the main
body of the paper.
7
In order to test for the robustness of this finding, the five distinct dimensions of power were each substituted
for the holistic power measure in five further iterations of the multiple regression model. In each of these five
further multiple regression models, the variables reported as significant in Table 10 did not change. This
signifies that our findings do not appear to be sensitive to the particular power measure used in the regression
model formulated to test the study’s hypotheses.
