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

Acknowledgements

This paper has benefitted from two anonymous reviewers’ 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.

Notes

Senior hotel personnel interview observations made by the research team over several years of research enquiry suggest that 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.

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).

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.

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).

Mean and standard deviations for each of the three items as well as the factor developed are discussed in further detail in the results section.

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 value analysis revealed that missing data were missing completely at random (MCAR) (Little's MCAR test: χ ² = 32.204, df = 32, p = 0.457). Using the ‘Missing Values Analysis’ function in SPSS, an Estimation Maximisation (EM) data set was derived and the multiple regression re-run based on the EM data set. Consistent with the model reported in Table 10, the results of this EM multiple regression model were statistically significant (F = 6.626, p < 0.000, df = 6, 94). Likewise, the results of hypothesis testing remained largely the same. Statistically significant support for hypotheses 3 and 5 increased from p < 0.05 to p < 0.01, while support for hypothesis 2 decreased from p < 0.01 to p < 0.05. 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.

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.