Title: Profitability measurement of United Kingdom theme parks: An aggregate approach

Transcription

1 Tourism Management Manuscript Draft Manuscript Number: JTMA-D Title: Profitability measurement of United Kingdom theme parks: An aggregate approach Article Type: Research Note Keywords: Theme park; Profitability; Financial ratios; Data envelopment analysis; Window analysis Abstract: Traditionally, financial ratios were used for profitability measurement. This research attempts to apply data envelopment analysis (DEA) to aggregate various financial ratios into a composite index representing the overall profitability of theme parks. The research sample includes thirteen parks managed by ten leading theme park operators in the United Kingdom over the past ten years. To provide a greater degree of freedom to the sample and capture the variations of performance over time, a model called DEA window analysis was adopted. The result first reveals that multi-site operators are significantly more profitable than their single-site rivals; however, there is a non-systematic relationship between size and profitability. Furthermore, the consistency between DEA and ratio analysis indicates that DEA can be used as either an alternative or complementary to ratio analysis. It is hoped that this approach can provide new insights about financial measurement not available to managers through traditional ratio analysis.

2 Manuscript (remove anything that identifies authors) Profitability measurement of United Kingdom theme parks: An aggregate approach 1. Introduction The theme park industry represents one of the major components of the tourism industry. In the past two decades, the industry, especially in North America, has gone through a period of great expansion (Milman, 2001). However, expenditure on personal tourism increased by 30.2% between 2000 and In contrast, global theme park revenue grew by 21.6% during the same period, highlighting the fact that theme parks are finding it increasingly difficult to garner market share in the highly competitive tourism environment (Mintel, 2006a). According to Formica and Olsen (1998), the competition is fierce within the theme park industry. The power of substitutes is very strong and continuously increasing. The theme park business requires substantial capital investment and is particularly sensitive to financial leverage. Indeed, the initial investment only represents the first financial effort to enter the market; once operating, theme parks demand considerable capital funds to update the available attractions and implement new, more captivating rides to retain market share. In the United Kingdom, theme parks made their debut in the late 1970s. The park operators have risen to the challenge of matching the appeal of foreign theme parks by consistently investing in installing bigger and better rides. On the other hand, UK theme parks always face competition from other day-out attractions across the country because they appeal to a similar target market (Lewis & Clacher, 2001). A market research conducted by Mintel (2006b) further demonstrates the threats of the industry. Over the past five years, profitability in most UK theme parks was markedly low and sizeable 1

3 operating losses were reported by a few operators. Furthermore, the rapidly increasing cost of power supplies could become an even significant factor in the next few years. It is therefore important to measure the profitability of theme parks to assist managers in their operational and capital investment decisions. Traditionally, financial ratios were used for profitability measurement. However, the most criticized aspect of uni-dimensional ratio analysis is that the choice of a single ratio does not provide enough information about the various dimensions of performance of a firm. Even by considering many ratios, there may be conflicting signals emerging from competing ratios (Al-Shammari & Salimi, 1998). This deficiency has prompted researchers to find new ways of addressing the issue of financial analysis. One technique that has been used in the recent years is a mathematical programming technique known as data envelopment analysis (DEA). The aim of this research is to apply DEA to aggregate various financial ratios into a composite index representing the overall profitability of a theme park. It is hoped that this approach can provide new insights about financial measurement not available to managers through traditional ratio analysis. 2. Literature review Although theme park research is in its infancy, most of the empirical research on theme parks was published in the past two decades. As reported by Milman (2001), the majority of the studies addressed marketing implications like segmentation, perceptual mapping or segmentation strategies in addressing new market expectations. Some empirical studies looked at the economic aspects of theme parks and the future of these entertainment complexes. No attempt has been made to analyze the financial performance of theme parks. 2

4 Although extensive research has been conducted regarding DEA in tourism research, according to a literature survey conducted by Barros and Santos (2006), most of the studies have focused on the hotel industry and assessed single-period performance. The dearth of DEA in other tourism sectors is possibly because of the relatively small sample size, such as those obtained for the study of theme parks. To provide a greater degree of freedom to the sample and capture the variations of performance over time, Charnes et al. (1985) proposed a technique called DEA window analysis. In DEA, if the total number of input and output factors is greater than half of the number of firms, the discriminating power will decrease (Golany & Roll, 1989). However, window analysis treats the performance values of the same alternative in different periods as different firms. The number of firms, as a result, increases, and this can remedy the defect of the mathematical model in DEA. The moving average approach can also smooth the impact of possible anomalies in annual data (Bowlin, 2004). Till date, only a handful of papers have utilized the windows approach to DEA. To the author s knowledge, this technique has not been applied in any other tourism research. The joint use of DEA and financial ratio analysis also has limited applications. Yeh (1996) offered one of the earliest such comparisons. On the basis of DEA bank performance evaluation, she demonstrated that in conjunction with financial ratio analysis, DEA can effectively aggregate a large number of ratios into meaningful financial dimensions. In this research, the research model basically follows that of Halkos and Salamouris (2004), who took a modified DEA model to derive a composite index of performance in which the outputs are a set of financial ratios with no inputs specified (Equation 1). The advantage of this model is to allow an examination of the consistency between DEA and ratio analysis. 3

5 [Equation 1 about here] 3. Methodology Initially, a list of twenty major theme parks in the United Kingdom was obtained from Mintel (2006b). Constrained by incomplete panel data, the research sample only covers thirteen parks managed by ten leading theme park operators. The financial data was obtained from the FAME database and represented a ten-year period ( ). The DEA window analysis is a moving average analysis where each firm s performance in successive time periods is analyzed simultaneously. The window is then shifted ahead one period and the analysis is repeated. As there is no theory that underpins the definition of the window size, this paper utilizes a three-year window (a total of eight windows), which is consistent with the original work by Charnes et al. (1985). Table 1 illustrates that the first window incorporates years 1997, 1998 and When a new period is introduced into the window, the earliest period is dropped. In the second window, year 1997 is dropped and year 2000 is added to the window. As DEA window analysis treats a firm as a different entity in each year, a 3-year window with 10 firms is equivalent to 30 observations, providing a greater degree of freedom. [Table 1 about here] The choice of profitability ratios in the DEA model relies on what is most commonly used for financial analysis. Also, they should reflect the industrial characteristics under study. Because theme park operators are facing the challenge of rising operating costs (Mintel 2006b), the profit margin was selected to assess whether a company has better control over its costs compared to its competitors. Return of assets (ROA) and return of capital employed (ROCE) are the other two common ratios to 4

6 measure the efficiency of the management in using its assets and capital invested to generate earnings. Furthermore, to retain market share, theme parks require substantial capital investment to update the attractions and the profit might be adversely affected. Consequently, the capability of revenue generation was also considered. The theme park is also a capital and labour intensive industry (Formica & Olsen 1998), so two turnover ratios- fixed asset turnover and turnover per employee, were included in the model. The other common profitability ratio- return of equity (ROE), was not considered because, in some cases, stockholders equity had a negative value that makes the calculation of ROE unsuitable. Finally, DEA requires all variables to be greater than zero (Charnes et al., 1978). In this study, profit margin, ROA and ROCE have the potential to have negative values. According to the principle of translation invariance proposed by Ali and Seiford (1990), these negative values are handled by adding a constant to the specific output for each entity in the analysis to enable the value for that particular output be greater than zero for every firm. 4. Results The result first reveals that the overall long-run average efficiency was on a declining trend during the earlier part of the studies (from in to in ), before increasing dramatically during the later period. The overall efficiency level surged strongly in the final window ( ) to record the highest mean overall efficiency level (0.981) during the period of study. The profitability of operators is then reviewed cross-sectionally and longitudinally. Table 2 reports the average DEA scores across all window s and the associated standard deviations for the entire period. The ten operators were ranked according to the 5

7 efficiency levels. The features (in terms of the number of sites operated) and the sizes (in terms of average total assets) of firms were also shown. First, a low mean efficiency tended to be accompanied by high variance. Flamingoland, Legoland and Tussauds had the best practice and were the most consistent performers owning to their high means and low variances. In contrast, Pleasureland was found to be the worst performer, with relatively low efficiency score and large variance. The other two major operators Blackpool and Prime Resorts also reported lower mean overall efficiency compared to their counterparts Flamingoland and Legoland. Further, among the single-site operators, small firms generally outperformed larger firms, with Pleasureland as the only exception. [Table 2 about here] The aforementioned descriptive statistics reveal only performance and comparison of average values. A more effective conclusion on the determinants of efficiency requires further statistical tests. To investigate the distribution of efficiency, the nonparametric Kruskal-Wallis and Mann-Whitney U tests were used to test the null hypothesis that the firms with different sizes and features follow the same efficiency distribution. Nonparametric tests were used because DEA is a nonparametric method assuming the distributions of efficiency scores yielded are not normal (Sueyoshi & Aoki, 2001; Brockett & Golany, 1996). The result reveals significant differences in efficiencies at the 0.05 level owning to the number of sites operated, but there are no statistically significant differences because of the size of the firm. It implies that the multi-site operators are significantly more profitable than their single-site rivals. However, there is a non-systematic relationship between size and profitability. Finally, to examine the consistency between DEA and ratio analysis, the Pearson 6

8 correlation analyses were employed. The results, as shown in Table 3, suggest that the DEA scores are positively and significantly associated with four of the five profitability ratios, with fixed assets turnover as the only exception. The highest correlation coefficient was found between the results of DEA and profit margin, followed by turnover per employee, ROA and ROCE. It indicates that DEA can be used as either an alternative or complementary to ratio analysis for the evaluation of a firm s overall profitability. However, ratio analysis can be still used to supplement the DEA results to obtain additional details. [Table 3 about here] 5. Conclusion Theoretically, in comparison to the broadly employed financial ratio analysis, DEA forms a rounded judgment on firms performance, taking into consideration a variety of ratios simultaneously and aggregating them into a single measure representing the overall profitability of a firm. Furthermore, DEA accomplishes this aggregation without resorting to a priori specification of the variables weights, thereby permitting more flexibility in the financial production model employed by each firm, and enabling each firm to be put in the best possible light (Bowlin, 1995). On the other hand, although there have been various applications of DEA in tourism research based on cross-sectional data. When time is not considered, the efficiency results derived can be biased. To overcome this problem, this study applied DEA windows analysis. This technique can be also used when small sample sizes are under consideration. Empirically, the results of this study shows significant profitability difference between multi- and single-site operators. Moreover, Mintel (2006b) predicted that the gap between the major corporate, well-funded players in the industry and the smaller, 7

9 private single-site operators is almost certain to widen in the next five years, particularly those who have access to private equity funds for investment, such as Tussauds and Legoland. It appears that the competition between theme park operators is becoming fiercer. Some less profitable, single-site operators may not be able to make enough money to invest in new rides and attractions, the lifeblood of any park. At the other end, although the market continues to be dominated by one operator Tussauds, other sizeable multi-site operators (i.e. Flamingoland and Legoland) are undoubtedly the rivals to Tussauds. There are also signs of other international operators showing interest in the UK market. For example, the French operator Grévin and Cie made its first UK acquisition in 2004 when it purchased the regional park New Pleasurewood Hills (Mintel, 2006b). This may make minor players harder to compete. Finally, the limitations of this current research must be acknowledged. First, the five ratios adopted in the analysis are in common use for evaluating a firm s profitability; however, there is no single correct set of variables and other researchers might logically want to consider other variables. Consequently, further research is required to investigate the major concerns of theme park operators when assessing profitability. Second, the results stated herein only provide one reason for the (in)efficiency of the operations. A clearer assessment of the determinants of efficiency may be gleaned from the application of the two-stage DEA approach (Coelli et al., 1998), which incorporates a series of environmental variables into a Tobit regression analysis. 8

10 References Ali, I. & Seiford, L. (1990).Translation invariance in data envelopment analysis, Operations Research Letters, 9(6), Al-Shammari, M. & Salimi, A. (1998). Modelling the operating efficiency of banks: a nonparametric methodology, Logistics Information Management, 11(1), Barros, C. P. & Santos, C. A. (2006). The measurement of efficiency in Portuguese hotels using data envelopment analysis. Journal of Hospitality and Tourism Research, 30, Bowlin, W. F. (1995). A characterization of the financial condition of the United States aerospace-defence industrial base. Omega, 23(5), Bowlin, W. F. (2004). Financial analysis of civil reserve air fleet participants using data envelopment analysis. European Journal of Operational Research, 154, Brockett, P. L. & Golany, B. (1996). Using rank statistics for determining programmatic efficiency differences in data envelopment analysis. Management Science, 42, Charnes, A., Clark, C., Cooper, W. & Golany, B. (1985). A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the U.S. air forces. Annals of Operation Research, 2, Charnes, A., Cooper, W. W. & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), Coelli, T., Prasada Rao, D. & Battese, G. (1998). An Introduction to Efficiency and 9

11 Productivity Analysis. Boston: Kluwer Academic Publishers. Formica, S., & Olsen, M. (1998). Trends in the amusement park industry. International Journal of Contemporary Hospitality Management, 10(7), Golany, B. & Roll, Y. (1989). An application procedure for DEA. Omega, 17(3), Halkos, G., & Salamouris, D. (2004). Efficiency measurement of the Greek commercial banks with the use of financial ratios: A data envelopment analysis approach. Management Accounting Research, 15, Lewis, B. R. & Clacher, E. (2001). Service failure and recovery in UK theme parks: The employees perspective. International Journal of Contemporary Hospitality Management, 13(4), Milman, A. (2001). The future of the theme park and attraction industry: A management perspective. Journal of Travel Research, 40, Mintel (2006a). Theme Park Tourism - International. Market research report. London: Mintel. Mintel (2006b). Theme Parks - UK. Market research report. London: Mintel. Sueyoshi, T & Aoki, S. (2001). A use of a nonparametric statistic for DEA frontier shift: The Kruskal and Wallis rank test. Omega, 29(1), Yeh, Q. J. (1996). The application of data envelopment analysis in conjunction with financial ratios for bank performance evaluation. The Journal of the Operational Research Society, 47(8),

12 Table Equation 1. The linear program of DEA Max s.t. N 1 n 1 Z 0 n 1 Z 0 r n N n in z r 0 i0 ( i 1, 2,..., m) 0; 0; ( n 1, 2,..., N) n ; where Z 0 = DEA efficiency score of firm 0; n = the weights placed on each of the firms in forming the efficiency frontier for firm 0; r i = observed ratios for firm 0; N = number of firms which are appraised on m financial ratios. 1

13 Table 1. Illustration of DEA window analysis Window Window Window Window Window Window Window Window

14 Table 2. Operators features, sizes and results of window analysis Operators Theme parks Total assets Overall efficiency ( millions) Mean S.D. Flamingoland Flamingo Land; Pleasure Island Legoland * Legoland Tussauds Alton Towers; Chessington World of Adventures; Thorpe Park Paultons Paultons Oakwood Oakwood Helston Flambards Experience Lightwater Lightwater Valley Blackpool Blackpool Pleasure Beach Prime Resorts Camelot Pleasureland Pleasureland * The UK Legoland is a part of Blackstone, which also owns the other three Legoland Parks in Germany, Denmark and California. 3

15 Table 3. Correlation coefficient between DEA scores and five profitability ratios Profit margin ROA ROCE Fixed assets turnover Turnover per employee Correlation coefficient Sig