Measuring Business Performance
3.1 The Input/Output Transformation Model
The success of any business firm is a result of the interaction of two major sets of factors. The first major factors influencing the performance of a business enterprise emanate from inside the firm. They determine the firm’s ability to use its resources to adapt to and take advantage of the constantly changing environment. Those inputs that are controlled or determined by the manager are referred to as controllable inputs to the model (Andersonet al.,1997).
Controllable inputs define the manager’s decision alternatives and thus are also referred to as the decision variables or discretionary variables of the model (Fig. 3.1).
In any realistic situation,however,there may exist exogenously fixed or non-discretionary inputs that are beyond the control of a firm’s management, and are therefore uncontrollable.1These uncontrollable variables are either factors determined by a company’s market area (e.g. location of a hotel) or by physical characteristics of the property (e.g. number and mix of rooms). They are more or less exogenously fixed in the sense that they cannot be changed by management. In the flowchart in Fig. 3.1,the environmental factors are referred to as uncontrollable inputs to the model.2
There are several efficiency studies which have included uncontrollable variables. For example,Banker and Morey (1986b) illustrated the impact of exogenously determined inputs that are not controllable in an analysis of a
1 The terms ‘exogenously fixed’, ‘non-discretionary’ and ‘uncontrollable’ variables, as well as the terms ‘discretionary’ and ‘controllable’ variables, are used interchangeably through- out this text.
2 Although non-discretionary output is also conceivable, it is usually not addressed in performance studies.
network of fast-food restaurants. In their study,each of the 60 restaurants in the fast-food chain consumed six inputs to produce three outputs. The three outputs corresponded to breakfast,lunch and dinner sales. Only two of the six inputs,expenditures for supplies and expenditures for labour,are defined as discretionary. The other four variables (age of store,advertising level,urban/
rural location and drive-in capability) are beyond the control of the individual restaurant manager.
3.1.1 Multiple input–multiple output
As stated earlier,the definition of productivity is more complex than a single output measure. This is especially true in the public sector,where non-profit organizations (NPOs) provide social services particularly in health,education and defence. Profit is very rarely an objective. In the public sector in particular, the assessment of performance demands multiple objectives (e.g. Lewin and Morey,1981; Lewin et al.,1982; Sengupta and Sfeir,1986; Smith and Mayston,1987; Barrow and Wagstaff,1989; Ganley and Cubbin,1992;
Valdmanis, 1992).
In the private sector,profit seems to be the dominant measure of output.
However,the calculation of profit is hardly ever straightforward since it depends on sets of accounting conventions concerning the treatment of such factors as long-term investment,depreciation and tax-deferments (Norman and Stoker,1991). Furthermore,only considering profit gives no indication of the potential for improvement within an organization or firm and therefore of their level of productivity. It is a common misunderstanding that information on profit is used to describe productivity. A profit number on its own conveys little information. It needs to be compared with,or put into the context of,some other number,measuring either a similar quantity in another organization (or the same quantity for another time period) or a related quantity in the same organization.
There are additional issues which have to be discussed when hospitality operations are considered. Firstly,there is a very large number and variety of inputs/outputs that occur in the daily operation of a hotel. Secondly,in the Fig. 3.1. Process of transforming inputs into outputs (Andersonet al., 1997).
hotel industry,similar to the manufacturing industry,only the physical attrib- utes of the hotel room or the food items might be considered standardized, whereas many of the other features experienced during the stay,such as service and atmosphere,areintangible. Thus,each service transaction with each individual customer can be regarded as unique. Given that both inputs and outputs include tangibles and intangibles,and these are almost impossible to measure directly,the realization of productivity improvement will almost always be difficult and the results are likely to be imprecise.
Another approach to the measurement is based on productivity objectives being a result of the various responsibilities a hotel has in terms of producing performance results. First,the management must achieve certain market performance results such as target sales volume,desired sales growth size or a competitive market share to maintain and strengthen the hotel’s market position. Second,the owners (i.e. stockholders and creditors) expect the hotel to produce certain financial performance results in terms of profitability, growth and liquidity. Finally,a variety of other stakeholders in the business, such as employees,suppliers and the community,sometimes expect certain performance results in terms of employment stability and advancement, creditworthiness and good ‘corporate citizenship’ (for an overview of hotel stakeholders’ interests see also Huckestein and Duboff,1999). The degree to which a hotel meets the responsibilities can only be measured by several performance indicators simultaneously. Unfortunately,each individual num- ber gives only a partial or incomplete picture and sometimes objectives are even contradictory (e.g. Pickworth, 1987).
Suppose a hotel has identified the following four key performance ratios for its operations:
• accommodation revenue per (whole time equivalent) staff;
• percentage of return visitors;
• overall guest satisfaction evaluated on a scale from 1 to 5;
• occupancy rate (capacity utilization ratio).
It would be operationally meaningless to simply add the four ratios to produce a composite overall measure. Some way needs to be found to accommodate all of these individual measures so that some sort of comprehensive assessment can be made. One approach is to weight each individual factor with the relative importance of the individual ratios,which became well-known as z-score analysis in the bankruptcy prediction research area.
3.1.2 Z-score analysis
In an attempt to reduce multiple measures into a single measure,some econo- mists developed a viability indicator that has become known as the z-score (Altman,1968). The z-score is a composite measure comprising the weighted
sum of some of the key financial ratios. For example,a typical z-score might be computed as
z=b0+b1x1+b2x2+b3x3 (3.1)
where
x1=profits before tax x2= current liabilities
current a
, ssets
total liabilities
current liabilities total a , x3=
ssets andb0,b1,b2andb3are constants.
In 1968,Altman introduced a bankruptcy classification model that applied discriminant analysis to two groups of companies over a period of time (Altman,1968). The first group had either gone into receivership or voluntary liquidation,and the second group had remained solvent. The results of the z-score studies indicate potential significant application to credit-worthiness assessment and to external and internal performance analysis. Since this early work,there has been considerable interest in using quantitative models for bankruptcy classification,especially for credit-granting decisions. In fact, bankruptcy prediction has been a major research issue in accounting and finance since the early 1970s.
Most bankruptcy and related models are based on the concept of
‘z-scoring’ by use of weights usually determined as statistically significant coefficients of some linear statistical model,frequently the linear multiple discriminant model (Altman,1968; Blum,1974; Deakin,1976b; Altman et al.,1977; Sharma and Mahajan,1980; Karels and Prakash,1987; Messier and Hansen,1988) and recently also neural network models (Wilson and Sharda,1994; Wilsonet al.,1995; Lee et al.,1996; Serrano-Cinca,1996) and Data Envelopment Analysis (Barret al.,1994; Barr and Siems,1997).
Applications to the hotel industry have been reported by Olsenet al. (1983) who carried out a study on restaurant failure using univariate analysis. Using multiple discriminant analysis in the prediction of business failure in hospital- ity organizations was suggested by others (Adams,1991,1995; Adams and Kwansa, 1992).
This z-score approach is of interest because it attempts to give a compre- hensive assessment of a company’s viability that is comparable among a range of firms. However,there are several drawbacks in simple bankruptcy classification models. First,the dependent variable in such studies is whether a company went into liquidation or remained solvent and hence is defined as a discrete (qualitative,indicator) variable following a multinomial distribution.
The statistical model chosen to represent the data must take this property into account. Linear discriminant models,however,implicitly assume that the attribute measurements arise from multivariate normal populations such that the classes have identical covariance matrices,differing only in the value of their mean vectors. More recently,artificial intelligence approaches to bankruptcy prediction models seem to overcome these statistical distribution assumptions (Odom and Sharda,1990; Wilson and Sharda,1994; Leeet al.,
1996; Serrano-Cinca,1996). The main drawback of these measures is aimed at giving a single dimensional indication of the strength of a company – without regard for its standing with its competitors. Here the focus lies on the overall performance of an entity measured in comparison with the performance of several other entities.
Developments in the treatment of multiple objectives have also taken place in the broader context of performance assessment. Rusth and Lefever suggested:
Some sort of multidimensional performance evaluation is much more appropriate in the international setting than the combination of net income and return on investment typically used for domestic operations. (Rusth and Lefever, 1988: 72) This is consistent with the ‘generic performance dimensions’ proposed by Fitz- geraldet al. (1991) on service businesses,the benchmarking methodology of Morey and Dittman (1995),the performance pyramid (Lynch and Cross,1991), the integrated performance measurement proposed by Nanniet al. (1992) and with the work of Kaplan and Norton (1992,1993) and Brander-Brown and McDonnell (1995) on the balanced scorecard. These emphasize the relevance of qualitative and quantitative approaches to performance measurement.
3.1.3 The balanced scorecard
One performance measurement method proposed to overcome the lack of
‘balance’ in performance measures is the ‘balanced scorecard’ introduced by Kaplan and Norton (1992,1993). The balanced scorecard approach aims to provide management with a set of measures which combine to give a compre- hensive view of the business. It is based on the idea that managers have to eval- uate their business from at least four major perspectives: customers,internal business,innovation and learning,and financial (see Fig. 3.2). According to Kaplan and Norton,the performance measures developed to monitor these four perspectives should answer the following questions.
• How do customers view a firm?
• What business processes must the firm improve?
• Can the firm continue to learn and improve, and thereby create value?
• How does the firm appear to its shareholders?
The measures incorporated in the scorecard should provide a balance between external and internal measures,and thereby reveal the potential trade-offs between them. The balanced scorecard is intended to provide managers with a streamlined view of most major activities (Kaplan and Norton,1997). The ability of the balanced scorecard to provide this view depends on the construc- tion of a set of performance measures which will capture the pulse of a corpora- tion in a few focused indicators. The implementation of a balanced scorecard requires that an organization has a clear view of where it is going,and how
activities,individuals’ jobs and particular assets are linked to the overall objec- tives of the organization. The scorecard works via a process in which managers for each of the perspectives in Fig. 3.2 set goals,and specific measures for each are stipulated in order to achieve each goal. In this manner high-level goals are cascaded downwards into the organization through a process of tight specification while utilizing a consensus approach. In this way,the scorecard helps to translate and implement strategy.
Recently,Kaplan and Norton (2000) introduced the notion of the
‘strategy map’,showing how initiatives,resources and intangible assets will be converted into tangible outcomes. The ‘balanced scorecard strategy map’ is supposed to link the financial,customer,internal process,and learning and growth perspectives to the goal of improved shareholder value. The authors illustrate their approach with reference to Mobil North American Marketing and Refining,showing how the company moved from centrally controlled commodity product sales to become a decentralized,customer-driven organi- zation (Kaplan and Norton, 2000).
The handbook publication of Kaplan and Norton’s ideas on the balanced scorecard (Kaplan and Norton,1996) has also created enormous interest in the hotel sector (Brown and McDonnell,1995; Huckestein and Duboff,1999;
Schwärzler,1999; Denton and White,2000; Atkinson and Brown,2001;
Harris and Mongiello, 2001).
In general,the development of a balanced scorecard encourages the use of a broader set of measures. However,several problems associated with per- formance measurement remain unsolved. For instance,Gering and Rosmarin (2000) assert that although the balanced scorecard should empower decen- tralization,if badly implemented it can become a ‘centralized trap’ and part of corporate politics. The authors stress the importance of using the scorecard as
‘the language of ongoing strategic discussion’,driving it down to profit centre level and keeping the numbers of indicators manageable. They suggest that the learning and growth measures related to core competences are the only ones that can be dictated from the centre,and warn against incentivizing the scorecard directly.
Although Kaplan and Norton certainly spent considerable time and effort in the definition and selection of performance measures for a large number of Fig. 3.2. Perspectives of the balanced scorecard (Kaplan and Norton, 1992).
companies,the problem of the relative importance of performance measures remains unsolved. Moreover,Kaplan and Norton do not review existing methodologies which could be considered for analysing these weights.
In general,it seems that the success of the balanced scorecard concept is attributable to the managers’ wishes to overcome the information overload with which they are confronted in their daily operations rather than to have science-based findings. In this context,some recently published extensions to facilitate that are promising developments. For instance,Min and Min (1997) and Liberatore and Miller (1998) demonstrate the use of a multicriteria decision-making technique (Analytic Hierarchy Process) (AHP) and Larsen et al. (1997) propose an interactive simulation system incorporating expert judgements.