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Does the asset-light and fee-oriented strategy create value?
International Journal of Hospitality Management 32 (2013) 270–277
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International Journal of Hospitality Management journal homepage: www.elsevier.com/locate/ijhosman
Does the asset-light and fee-oriented strategy create value? Jayoung Sohn a,∗ , Chun-Hung (Hugo) Tang b,1 , SooCheong (Shawn) Jang c,2 a b c
School of Hospitality and Tourism Management, Purdue University, 900 W. State Street, Marriott Hall, Room 206, West Lafayette, IN 47907-2115, United States School of Hospitality and Tourism Management, Purdue University, 900 W. State Street, Marriott Hall, Room 253, West Lafayette, IN 47907-2115, United States School of Hospitality and Tourism Management, Purdue University, 900 W. State Street, Marriott Hall, Room 245, West Lafayette, IN 47907-2115, United States
a r t i c l e Keywords: Fixed assets Fee business Firm value Profitability Operating risk Hotel
i n f o
a b s t r a c t During the past couple of decades, many hotel chains in the U.S. have shifted in their business strategy: whittling down properties while expanding the management or franchising business. We labeled such strategic shift as an “Asset-Light and Fee-Oriented (ALFO)” strategy and examined the theoretical and empirical effectiveness of the strategy. Theoretically, resource-based view and corporate finance theory predict competing implications of the ALFO strategy, calling for a study for validating the net benefits of the strategy. Our results indicate that expanding fee business and decreasing fixed asset intensity have a positive impact upon firm value. Using path analysis, we further verified the working mechanism of the ALFO strategy. The strategy is effective in lifting profitability, mitigating earnings volatility, and thereby contributing to firm’s market premium. © 2013 Elsevier Ltd. All rights reserved.
1. Introduction During the past couple of decades, many hotel chains in the United States have shifted their business strategy. In the 1980s, brands such as Marriott International Inc. and Hilton Hotels Worldwide began whittling down properties on their balance sheets to concentrate on the more lucrative and less capital-intensive business of operating properties for fees (Hudson, 2010). Most property sales are followed by management contracts: the seller still operates the property and the property owner pays fees for the service provided by the seller, now the operator. For example, at year-end 2010, Starwood Hotel and Resorts Worldwide Inc. operated 463 properties under long-term management agreements and 502 hotels under franchising contracts. This is a more than a 50% increase from the end of 2000. In contrast, the decline in the number of properties owned or leased by Starwood was also dramatic: from 162 in 2000 to 62 in 2010. This represents a decrease of more than 60%. Table 1 summarizes room affiliation structure of four major hotel chains. The proportion of rooms they actually own or lease accounts for less than 18% while more than 80% of rooms under their flags are operated via fee-generating contract business. As seen in Table 1, the movement to an asset-light and feeoriented (hereafter ALFO) company is significant among hotel
∗ Corresponding author. Tel.: +1 517 420 4744; fax: +1 765 494 0327. E-mail addresses: [email protected] (J. Sohn), [email protected] (C.-H. Tang), [email protected] (S. Jang). 1 Tel.: +1 765 494 4733; fax: +1 765 494 0327. 2 Tel.: +1 765 496 3610; fax: +1 765 494 0327. 0278-4319/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijhm.2012.07.004
firms. Yet the value of the ALFO strategy has not been empirically tested in order to validate this strategy. More importantly, the net benefits of the ALFO strategy are theoretically uncertain due to the competing impacts of the strategy on firm value. Fee business allows firms to expand their market share with limited capital investment. In terms of profitability and operating risk, fee business is also favorable. Since operators are reimbursed the cost of operating hotels by property owners, the cost of feebased revenue is lower than that of sales from owned properties. Fee income also has less variance than income from operating owned properties (Roh, 2002). Additionally, by selling properties, hotel firms can further reduce operating risk caused by high operating leverage. There are, however, potential pitfalls of the ALFO strategy as well. Turning over ownership means losing control in that property, implying that maintaining identical service quality across properties would not be as effective as when the hotels are all owned by the same company. Inconsistencies in quality and operations might have an adverse impact on firm value in the longer term. Moreover, decreased asset tangibility is another factor inflating the financial distress costs, and thus lowering firm value (Brealey and Myers, 2002). Seeing that many hotel firms have substantially expanded fee business, we believe the time is ripe to do a reality check of whether it is really effective in raising firm value. This study goes one step further to explain how the value of the ALFO strategy is realized. To achieve the second objective, we examine the mediating roles of profitability and operating risk. We expect this study to contribute to both industry and academia. Above all, it provides practitioners with an opportunity to empirically evaluate the effectiveness of the ALFO strategy.
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Table 1 Room affiliation structure of four major hotel firms.
Marriott International Inc. Starwood Hotel and Resorts Worldwide Inc. Intercontinental Hotels Group Hyatt Hotels Corporation
Owned or leased (%)
Managed (%)
Franchised (%)
2.0 6.8 0.8 17.2
44.7 51.6 25.1 68.2
53.4 39.3 74.1 13.1
Source: Mintel (2011).
Further, it allows practitioners to obtain a more concrete understanding of the strategy’s working mechanism. From an academic perspective, this study examines advantages and risks of the ALFO strategy from different theoretical viewpoints: strategic management and capital structure theories. The resource-based view predicts that the strategy could create value through operational and financial improvements. However, capital structure theories warn that reduced asset tangibility may inflate financial distress costs, and thereby lower firm value. This theoretical incongruence calls for an empirical study into the valuation effect of the ALFO strategy. In addition, by applying path analysis for our mediation model, which is a relatively new approach for analyzing financial data, we expect to make methodological contributions as well. By infusing theoretical foundations into an industry’s strategic practice, this study could be a catalyst for future studies that aim to bring the industry and academia closer. Implications, limitations, and suggestions for future studies are discussed along with the findings.
2. Hypothesis development 2.1. Dynamics of fee business A fee business is implemented using one of two contract modes: management or franchise contracts. In terms of fee structure, the two modes are similar. Franchise fees consist of two main components: an initial fee and continuing fees. The initial fee is paid upon a submission of franchising application and is used to cover the franchisor’s costs to process the application, survey market demand, provide service during pre-opening stages, and so on. Continuing fees are proportionally linked to the sales revenue of a franchisee. Royalty fees, marketing and advertising fees, and reservation fees are included in this category. For 2010, franchisees pay 3–7% of rooms revenue as royalty payments and 1.0–4.3% as marketing and advertising fees (HVS, 2011). In addition, owners pay fees for using the franchisor’s centralized reservation system. Reservation fees are either 0.4–10.0% of rooms revenue or $1.1–$10.0 per available room per month (HVS, 2011). Management fees, on the other hand, comprises a base management fee, which is a percentage of sales revenue of a managed hotel, plus an incentive fee based upon achieving certain levels of cash flows or certain levels of profitability of the hotel (deRoos, 2010). On average, a basic fee is 3.25% of gross revenue for full service provided by brand operators like Marriott International and 4% by independent operators such as Interstate Hotels and Resorts. An incentive fee is approximately 6–10% of the gross operating profit of the managed hotel (Eyster and deRoos, 2009). The major difference between franchising and management contracts resides in the degree of control. While franchisees are put in charge of daily operation of outlets, management contracts impose a much greater role on the management companies. Management companies take overall responsibility for managing the property, from hiring and training employees to nationwide marketing and promotional services. This difference explains why franchising is more common for economy and mid-scale hotels, whereas management contracts are more popular among upscale
and luxury hotels. Management of economy hotels usually relies on tangible criteria, such as building design and facilities. Meanwhile, for upscale and luxury brand hotels, nonphysical factors including service quality and atmospherics are more crucial, which can be properly delivered by expertly trained staff who are familiar with the spirit of the brand (Hsu and Jang, 2009). Chen (2005) distinguished the two modes in terms of ownership of intangible assets. Under management contracts, the hotel operator maintains claim over intangible resources such as transaction-specific data and computer reservation system. Franchising, however, grants franchisees the right to use these intangibles as part of the “franchise package.” Despite such differences, we do not distinguish the two modes insofar as both are ways to produce income based on intangible assets instead of fixed assets and grow without heavy capital commitment. 2.2. Fee business and firm value The greatest advantage of the fee business would probably be the growth with limited capital without the risk of owning properties. Traditionally, hotels had to build or purchase properties to enlarge their business, which required them to commit a huge capital investment to fixed assets. However, by letting contracted properties fly the flag of the franchisor or the management hotel firm, fee business enables the hotel firms to expand their portfolio at a faster rate, which boosts brand values. Meanwhile, risk arising from owning properties is shifting onto the owners. The resource-based view literature has shown that firms that are able to draw on common core competences (Rumelt, 1982) or share resources across businesses (Chatterjee and Wernerfelt, 1991) perform better. Fee business enables hotel firms to transfer the core competences, such as property management skills and human resources, across properties. In addition, the resource-based view claims that companies equipped with strategic competitive advantages tend to earn above-normal profits in the long run. Intangible strategic assets that gradually accumulate over a long period and cannot easily be copied by followers are likely to be inelastic in supply (Dierickx and Cool, 1989; Barney, 1991). Firms engaged in fee business mostly do so by leveraging their brand power, management expertise, established distribution channels, etc., all of which are intangible strategic assets expected to bring abnormal returns. Hence, we posit that fee-business would enable hotel firms to create firm value through faster portfolio growth and better operational performance. H1a.
Fee business improves profitability.
Another positive impact of fee business is stabilization of earnings. Roh (2002) demonstrated that the operating cash flows of franchising firms have lower volatility since royalties received from a franchised outlet show less variance than the revenue and profit of the outlet itself. The more stable and secure cash flow stream from fee-based income would provide a buffer to economic downturns. Less volatility in earnings would also lower certain financial costs, such as financial distress (Smith and Stulz, 1985), underinvestment (Bessembinder, 1991), and taxes (Graham and Rogers, 2002), and eventually lead to higher firm value.
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H1b.
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Fee business reduces earnings volatility.
However, potential pitfalls should be noted as well. As both franchising and property management are non-equity modes of development, the management would lose control over operations and pricing policy. In the case of franchising, maintaining standards can be an issue because service is mostly delivered by property owners. This problem is more critical for upscale and luxury hotels that distinguish themselves by intangible service features. In a similar vein, disagreements between the operator and the property owner over investing in properties can be an issue under management contracts (Mintel, 2007). Moreover, as competition among chains intensifies and more sophisticated groups of owners enter the market, owners are becoming more demanding and aggressive in terms of their expected returns on investments (Beals and Denton, 2005). Contracts for short durations also work against management/franchising companies too (Mintel, 2007). However, most of these potential problems are related to specific segments or uncertainties (i.e. the contract can be negotiated). Thus, we believe the magnitude of the costs would be relatively smaller than the certain and quantifiable benefits. These costs would not be considered as significant risks that outweigh the company-wide advantages of fee business. Given that, we hypothesize that fee-business has a positive effect on firm value. H1c.
Fee business has a positive effect on firm value.
2.3. Going asset-light and firm value Decreasing fixed assets primarily involves firm risk. Firms that own many fixed assets cannot easily adjust themselves to economic conditions since a large portion of their capital was already committed to illiquid assets. Hence, firms with a higher fixed-asset ratio are more vulnerable to economic ups and downs, increasing the systematic risk of the firm. Higher risk, in turn, obliges risk-averse investors to demand higher return, increasing the cost of capital (Brealey and Myers, 2002). Traditional firm valuation formula estimates the value of a firm as the sum of discounted future free cash flows to the firm using its cost of capital. Thus, all other things being equal, the higher the cost of capital is, the lower the firm value would be. The impact of fixed assets on firm value has been examined by a myriad of studies. Lev (1974) and Mandelker and Rhee (1984) showed that operating leverage, which is the ratio of fixed to variable costs, is positively related to the systematic risk of common stock. Tuzel (2010) explained why firms holding large fixed assets are more susceptible to economic shocks and thus are riskier than their counterparts. In a similar vein, Titman et al. (2004) examined how the stock market reacts to firms’ announcements of capital investments. They observed a negative return on the firms that considerably increase capital investments. In addition, lower operating leverage mitigates the sensitivity of earnings to fluctuations in sales revenue. Hotel firms, by nature, record heavy fixed costs, mostly incurred from depreciation plus maintenance, whereas variable costs are very low (the marginal cost of selling a room-night is quite small). Since fixed costs mostly do not vary with sales revenue, they have a bullwhip effect on the bottom line. Skalpe (2001) argued that for firms with high operating leverage, even a decent amount of sales variance may bring greater volatility upon the bottom line. A high operating risk would fuel the debt and equity yields and thus results in a higher cost of capital, which in turn lowers firm value. In this regard, we propose the second hypothesis as follows: H2a.
Fixed-asset ratio is positively related to earnings volatility.
However, disposition of properties might bring about adverse effects. Diminished asset tangibility can inflate financial distress costs (Brealey and Myers, 2002). Based on corporate finance
theories, firms consisting of mostly intangible assets would face higher financial distress costs, and thus lower firm value. This is because the value of tangible assets (e.g. commercial real estate) can emerge from a bankruptcy process mostly unscathed. However, the value of intangible assets is contingent on the company being a going-concern. If the company goes bankrupt, the value of intangible assets (e.g. expertise, brand) would mostly cease to exist (Brealey and Myers, 2002). In sum, a lower fixed-asset ratio reduces operational risk (i.e. operating leverage and earnings volatility) but increases financial risk. For a financially healthy firm, the decrease of asset tangibility is not expected to significantly inflate financial distress costs because stable and healthy cash flows can substitute for the safety brought by fixed asset collaterals. Empirically, Lev (1974) and Mandelker and Rhee (1984) also observed lower systematic risk in firms with lower levels of fixed assets. Therefore, we can reasonably assume that the net effect is lower (total) risk in an average sample. In this regard, we propose the last hypothesis as follows: H2b.
Fixed-asset ratio is negatively related to firm value.
We do not propose a relationship between the fixed-asset ratio and operating margin. There are two reasons. First, the main goal of going asset-light lies in reducing operating risk but profitability is more closely related with operating efficiency than operating risk. In addition, while increasing fee-income ratio is more directly linked to income statement items, decreasing fixed assets is more related to balance sheet items. 3. Data and methodology 3.1. Data collection Most accounting data were retrieved from the Compustat database based on the Standard Industrial Classification (SIC) code. We used data of firms under the SIC code 7011, which is Hotels and Motels. As for the management and franchising fee income, we referred to 10-K reports filed to the Securities and Exchange Committee. First we read the Item 1. Business section to confirm that a sample hotel is engaged in management or franchise business. Then we searched the reports using several keywords: management fee; franchise (franchising) fee; management contract; and franchise (franchising) contract. The data collection period spanned from 2002 to 2010 due to the availability of fee-based income data. In 2002; many firms started reporting their managementand franchise-fee income separate from other revenue sources. For a firm to be included in the dataset; it has to report both revenue and fee income from management or franchise business. Out of 472 total firm-quarter observations; after deleting outliers (26); 446 observations were used. 3.2. Variables ALFO strategy: We used fee-income ratio, which is the sum of fee income from franchising and management contracts against total sales revenue, as the measure of the degree of fee business. Since not every hotel firm reports quarterly fee income, we divided the yearly fee income by four to obtain approximate quarterly data. There are two reasons behind this: First, the mean sales revenue was not significantly varied across quarters, (F-value = 0.10, p-value = 0.962) and fee income is highly correlated to sales revenue (correlation coefficient = 0.885, p-value < 0.001). It might come surprising that quarterly sales revenue is not significantly different each other. When we checked the quarterly sales revenue of the ALFO firms, we observed mild fluctuation. Third and fourth quarters recorded higher sales revenue than the others. However, the difference was not statistically significant. We suspect the reason is
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the use of calendar quarters. For example, the fourth quarter covers October, November, and December. November and December have the biggest holiday seasons, but October does not. Relatively low sales in October may partially cancel out the high demand in late winter. In a similar vein, third quarter includes July, August, and September. Summer season usually starts from mid-May or June and ends with August. Thus, decreased demand in September may calm down the summer rush. In addition, as most management and franchising contracts are long term, we assume that the number of contracted hotels would not greatly fluctuate within a year, implying that the flow of fee income would be rather stable throughout a year. For these reasons, we could assume that our method reasonably approximates quarterly data. To capture the other dimension of the ALFO strategy, we include fixed-asset ratio, which is the ratio of the amount of net property, plant and equipment to total assets, to measure the extent to which it is asset-light. Since the original fee-income ratio shows high skewness (3.058) and kurtosis (14.317), we took a natural log transformation to attain more normality. After the transformation, its skewness (−.641) and kurtosis (4.515) reached an acceptable level (Kline, 1998). Fee-income ratio (Fee) = Ln
Fixes-asset ratio (PPE) =
Management fee + Franchise fee Sales revenue
Net property, plant, and equipment Total asset
Firm value: Q, the ratio of the market value of the firm to the replacement value of its assets, has long been used as a firm’s market premium measure in a myriad of studies (Lang and Stulz, 1994; Wernerfelt and Montgomery, 1988; Park and Jang, 2010). By incorporating the stock market’s perception of the firm’s present and future cash flow and growth potential, Q implicitly uses a correct risk-adjusted discount rate (Wernerfelt and Montgomery, 1988). In this study, approximate version of Q developed by Chung and Pruitt (1994) was used for three reasons: 1. low computational cost; 2. data availability on Compustat; 3. high degree of correlation with more rigorously constructed Q. Q =
MVE + PS + DEBT TA
where MVE = share price × number of common stock outstanding, PS = liquidating value of the firm’s preferred stock, DEBT = book value of long-term debt + short-term liability − short-term assets, and TA = book value of total assets. Profitability: This study adopted operating profit margin as the operating profitability measure. Operating income, a measure of a company’s earning power from its main business activities, takes the gross margin and subtracts operating expenses and depreciation. Operating expenses include the cost of sales and other overhead expenses and is the most general and comprehensive measure of a firm’s total cost of operation (Mitra and Chaya, 1996). Hence, the operating profit margin, which is the ratio of operating income scaled by sales revenue, is an indicator of the company’s overall operating efficiency. Operating margin (opmargin) =
Operating income after depreciation Sales revenue
Earnings volatility: We measured earnings volatility as the coefficient of variation of operating income after depreciation over four quarters: three previous and one current quarters. Coefficient of variation is a standard deviation standardized by the mean. As with the fee-income ratio, the original earnings volatility showed significantly high skewness (13.33) and kurtosis (203.83). Hence, we took a natural log transformation again. Transformation reduced its skewness to .761 and kurtosis to 3.757, both of which came to an acceptable level (Kline, 1998).
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3.3. Models We examined the relationship of the ALFO strategy and firm value. In order to investigate potential channels through which the strategy is translated into firm value we selected two mediating variables from the return and risk framework: operating profit margin and earnings volatility. To analyze the model with multiple independent variables and multiple mediators, we used the structural equation modeling (SEM) approach. SEM is a comprehensive and flexible method for specifying relations among variables (Hoyle and Smith, 1994). Due to its greater flexibility and ability to simultaneously capture multiple relationships among variables, the SEM approach is regarded as the best method for estimating multiple mediators (Preacher and Hayes, 2008; Iacobucci et al., 2007). Estimating components simultaneously has statistical advantages over doing so in piece-meal fashion since it enables the researcher to control for other relationships in one model (Iacobucci et al., 2007). Especially in the case of a model with multiple mediators, it is difficult to test the effects of multiple mediators on the same output variable using the multiple regression method. A system of equations would need to be formulated to test the effect of each mediator individually, and it would be cumbersome to merge all the statistics to assess the overall mediation effect (Li, 2011). For these reasons, we adopted the SEM approach rather than multiple regression approach. Specifically, as we used only observed variables, the model is called path analysis. Path analysis is a special case of SEM in that path analysis deals only with measured variables, whereas SEM handles both observed and latent variables. Preacher and Hayes (2008) suggested that error terms of mediators be allowed to covary rather than be fixed to zero. Constraining the mediator residual covariance to zero implies that the covariance between mediators can be completely accounted for by their mutual dependence on the exogenous variables. However, it is usually hard to defend theoretically (Preacher and Hayes, 2008). In this regard, we permitted the residuals of mediators to covary because operating margin and earnings volatility are outcomes of operation influenced by a number of factors. The basic conceptual model is visualized in Fig. 1.
4. Results 4.1. Descriptive analysis Fig. 2 depicts historical trends for the average industry feeincome ratio and the average fixed-asset ratio. The industry average fee-income ratio is the ratio of the sum of fee income (which is the sum of fee income) over the sum of revenue (which is the sum of the ALFO firms’ sales revenue). The average fixed-asset ratio is the ratio of the average net property, plant and equipment against the average total assets of the ALFO firms. The trends show a strong inverse relationship, implying that firms engaged in fee business have increased fee-income ratio and decreased fixed-asset ratio. The fact that hotel firms have changed to be more resilient with less fixed assets is also supported by the opposite movements of current assets and fixed assets. As seen in Fig. 3, during the sample period the current assets of the sample hotel firms have constantly increased from $355 M to $818 M—a more than 130% increase. However, fixed assets shrank by nearly a half from $2009 M to $1057 M. It seems that hotel firms have been prepared for unexpected economy shocks by reducing their exposure to real estate risk and promoting liquidity. More information about sample statistics is summarized in Table 2. On average, Q is slightly greater than 1, implying that the capital market assigns a premium to intangible assets. The average
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Fig. 1. Path diagram.
70%
16%
60%
14% 12%
50%
10%
40%
confirms the opposite movement of fee income and fixed-asset size evidenced in Fig. 2. Fixed-asset ratio has a negative correlation with Q but a positive with earnings volatility. However, there seems to be no significant linear relationship between fixed-asset ratio and operating margin.
8% 30%
6%
20%
4.2. Path analysis
4%
10%
2% 0%
0% 2002
2003
2004
2005
2006
Mean of fixed-asset rao
2007
2008
2009
2010
Mean of fee-income rao
Fig. 2. Historical trend of fixed-asset ratio and fee-income ratio.
Fig. 3. Historical trend of fixed assets and current assets.
fixed-asset ratio is 55% for sample hotels and operating profitability is quite low, at less than 10%. Table 3 reports pair-wise correlation coefficients. Almost every correlation is significant at ˛ = .01with an expected sign. Feeincome ratio is positively related to Q and operating margin but negatively related to fixed-asset ratio and earnings volatility. The negative correlation between fee-income ratio and fixed-asset ratio
Path analysis based on maximum likelihood was utilized and the results are reported in Table 4 and Fig. 4. The goodness-of-fit of the model was evaluated using five indices: 2 , the root mean squared error of approximation (RMSEA: Steiger et al., 1985), the comparative fit index (CFI: Bentler and Bonett, 1980), the Tucker–Lewis index (TLI: Tucker and Lewis, 1973), and the standardized root mean square residual (SRMR). The 2 statistic is for testing the difference between the proposed model and the saturated model. The smaller the statistic, the less discrepancy between the saturated and our hypothesized model, and hence the better fit. RMSEA values less than .05 and CFI values greater than .90 indicate good fit. Similarly, TLI values greater than .90 and SRMR less than .05 signify good fit. The fit indices indicate that the hypothesized model reasonably fits the data (2 (1) = 1.385, RMSEA = .033, CFI = .999, TLI = .991, SRMR = .011) (see Table 5). All hypotheses were consistent with our prior expectations. The effect of fee-income ratio on profitability was estimated as .136 (H1a, z = 2.63, p-value = .009), meaning that fee business is effective in improving profitability. In addition, the path coefficient between fee-income ratio and earnings volatility is also significant as −.273 (H1b, z = −4.21, p-value < .001), meaning that fee business mitigates earnings volatility. The positive impact of feeincome ratio on Q is supported by a coefficient estimate of .285 (H1c, z = 5.03, p-value < .001), suggesting that fee-business contributes to ultimate firm value. Hypothesis testing on fixed-asset ratio confirmed our prior expectations as well. Decreasing fixedasset ratio is shown to attenuate the volatility of earnings. The path coefficient was estimated as .176 (H2a, z = 2.84, p-value = .005). The proposed relationship between fixed-asset ratio and Q is also supported by an estimate of −.271 (H2b, z = −4.87, p-value < .001), indicating that decreasing fixed-asset ratio has a positive impact
Table 2 Sample characteristics. Variable
Definition
Mean
Std. dev.
Min
Max
Skewness
Kurtosis
Q Fee PPE opmargin EarVol
The ratio of MV to BV of assets Ln(fee incomet /sales revenuet ) Net property, plant and equipmentt /total assett OIADPt /sales revenuet Ln(coeff. of variation of OIADP over four quarters)
1.12 −3.14 .55 .07 −.30
.55 1.08 .24 .10 1.12
.22 −8.97 .05 −.39 −2.61
3.75 −.62 .91 .41 4.14
1.70 −.64 −.57 −1.03 .76
6.32 4.52 2.24 6.32 3.76
OIADP: operating income after depreciation.
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Table 3 Sample correlation. Q Q Fee PPE opmargin EarVol ***
Fee
PPE
opmargin
EarVol
1 −.6634*** .2410*** −.3896***
1 −.0669 .3455***
1 −.4448***
1
1 .5372*** −.5005*** .3994*** −.4421***
p < .01.
Table 4 Estimates of path coefficients. Hypothesized path
Standardized path coefficients
Results
H1a: Fee-income ratio → profitability H1b: Fee-income ratio → earnings volatility H1c: Fee-income ratio → Q H2a: Fixed-asset ratio → earnings volatility H2b: Fixed-asset ratio → Q Profitability → Q Earnings volatility → Q
.136*** −.273*** .285*** .176*** −.271*** .299*** −.094** 2 (1) = 1.385 RMSEA = .033 CFI = .999 TLI = .991 SRMR = .011
Reject the null Reject the null Reject the null Reject the null Reject the null
Note: RMSEA = root mean square error of approximation; CFI = comparative fit index; TLI = Tucker–Lewis index; SRMR = standardized root mean square residual. ** p < .05. *** p < .01.
Fig. 4. Path analysis: standardized coefficients.
upon firm value. Other paths from profitability and earnings volatility to Q were consistent to previous research. The path coefficients from operating margin to Q and from earnings volatility to Q are estimated as .299 (z = 7.03, p-value < .001) and −.094 (z = −2.00, pvalue = .046), respectively. The correlation between two error terms of operating margin and earnings volatility was estimated as −.445 (p-value < 001), implying that other hidden factors that influence those two factors are negatively correlated each other.
4.3. Mediation analysis According to Baron and Kenny’s (1986) approach, for a model to have mediating effect, it has to meet four conditions: (1) the predictor (or exogenous) variable should have a significant relationship to the outcome (or endogenous) variable; (2) the predictor (or exogenous) variable should have a significant relationship to the presumed mediator; (3) the presumed mediator should have a
Table 5 Mediation analysis results. Coefficients of fee-income ratio Full
opmargin constrained ***
2 2 ** ***
p < .05. p < .01.
.285 1.39 0
***
.303 46.06 44.67*** (df = 1)
Coefficients of fixed-asset ratio EarVol constrained ***
.306 5.33 3.94** (df = 1)
Full
EarVol constrained
−.271 1.39 0
***
−.287*** 5.33 3.94** (df = 1)
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significant relationship to the outcome variable; (4) given the mediator in the model, the direct effect of the predictor on the outcome variable should be substantially reduced. Following Baron and Kenny’s (1986) criteria, we tested mediation effect of operating profitability and earnings volatility. The first three conditions were satisfied in the original model. Fee-income ratio and fixed-asset ratio had significant effects on mediators and Q. The direct impacts of mediators (operating margin and earnings volatility) on Q were also significant. To test for the fourth condition, we constrained the direct effect of operating margin on Q to zero and reestimated the model. In that case, 2 (44.67, df = 1) between the full (2 (1) = 1.39) and the constrained model (2 (2) = 46.06) was significant (p-value < .001), suggesting that the model fit significantly got worse in the constrained model. Change in the coefficients of exogenous variables should also be noted. While the direct effect of fee-income ratio in the full model was .285 (z = 5.03, p-value < .001), in the constrained model it was .303 (z = 5.05, p-value < .001). A significant increase in 2 and change in the coefficient indicate that operating margin is a mediator in the relationship between fee-income ratio and Q. Following the same logic, we tested the mediating role of earnings volatility. When we reran the model while constraining the direct effect of earnings volatility on Q to zero, 2 increased by 3.94 (df = 1) from 1.39 (full model) to 5.33 (constrained model) and it was statistically significant (p-value = .047). The parameter estimate of the path between fee-income ratio and Q was estimated as .306 (z = 5.46, p-value < .001) in the constrained model, which was larger than that in the full model. Fixed-asset ratio showed a similar pattern. The direct effect of fixed-asset ratio on Q was −.271 (z = −4.87, p-value < .001) in the full model and it decreased to −.287 (z = −5.21, p-value < .001) in the constrained model. Changes in 2 and coefficients of fee-income ratio and fixed-asset ratio signified that earnings volatility acts as a mediator in the model. However, both of them are partial mediators, implying that there are probably other factors conveying the effect of the ALFO strategy to firm value.
5. Discussion and conclusions The hotel industry, by nature, has been regarded as having a higher level of business risk compared to other industries (Skalpe, 2001; Tsai et al., 2011). Performance is closely tied to the state of the economy and thus sensitive to external shocks. In addition, a great deal of capital used to be committed to fixed assets, which hinders the ability of hotel firms to accommodate unexpected market changes. Confronted with such risks, many hotel firms have carried out mutually supportive policies: decreasing fixed assets while expanding management or franchising business. First, our results indicate that the capital market assign premiums to hotel firms that go asset-light. The results show that decreasing fixed-asset ratio mitigates operating risk and elevates firm value, suggesting that investors’ concerns about the firms going asset-heavy outweigh benefits provided by fixed assets. In the meantime, our analysis also shows that fee-business is effective in improving operating profitability, earnings stability, and eventually the firm value. In sum, the results suggest the two sides of the ALFO strategy have worked in sync to create value for investors. This is consistent with the main motivation for sale-and-leaseback transactions (SLBT) (Whittaker, 2008). Interviews with 15 practitioners in hotel-related businesses revealed that the market pressure to “separate bricks from brain” is one of the major drivers of SLBT.
5.1. Theoretical implications This study contributes to the hospitality literature by not only verifying the value of a popular strategy practiced in the industry but also developing and testing a theoretical framework to explain the ALFO strategy. We started with the risk-return framework and incorporated the concepts of the value of stable cash flows, operating leverage, and systematic risk sensitivity to diagnose the benefits and potential risks of the strategy. The resource-based view and capital structure theories predict opposing effects of the ALFO strategy and the present study reconciled these two theories’ conflicting predictions on firm value, providing a comprehensive picture of the strategy. Based on the theoretical framework, we further investigated the value realization mechanism of the strategy. This was accomplished by using a powerful statistical procedure that is popular with marketing studies but rarely used in the finance literature: structural equation modeling. By applying path analysis, we captured concurrent impacts of the ALFO strategy on the firm’s profitability, risk, and market premium. We hope this cross-pollination of methodologies from different disciplines could encourage future interdisciplinary studies in hospitality management.
5.2. Managerial implications The findings of this study provide some meaningful implications to practitioners as well. As evidenced by the results, the ALFO strategy increases firm value. The continuing worldwide economic recession has led the lodging industry into intense price-based competition. To maintain cash flows, many hotels are selling surplus rooms on online booking sites at heavily discounted prices (IBISWorld, 2011). Such price-based competition could decrease a firms’ profitability. However, fee business can provide a buffer to the bottom line. Moreover, it helps firms to create stable cash flows and reduce earnings fluctuation. Thus, aware of the benefits of fee business, management firms and franchisors should keep sharpening their core competences in hotel operation and nationwide marketing to increase their value to property owners and thereby attain more contracts. In the meantime, to prevent the brand value from being diluted, franchisors should closely supervise whether franchised units are properly operated under the criteria defined by the brand. Our results also show that disposing fixed assets has a positive impact on firm value. The stock market recognizes the advantages of being lean and flexible. Decreasing the portion of assets committed to illiquid assets reduces the exposure to real estate risk and makes the company more resilient to economic shocks. This has proven to be a substantial advantage in the recent real estate downturn. Cash inflows from disposition can also be used to manage financial leverage or for more promising investment opportunities in the future. Hence, the management ought to keep it mind the importance of enriching substance rather than increasing in size. However, this poses a question: Should hotel firms simply sell their properties to decrease the operational risks? There is no clear-cut answer because fixed assets provide some positive impacts. Maintaining ownership grants the hotel firm control of the properties, which also can be a source of collateral and reduce the probability of distress. Therefore, the management should carefully consider potential risks of disinvestment and find the optimal mix between owned and contracted properties in view of unique characteristics of each hotel segment. For instance, Accor applies dissimilar strategies to different business segments, the “asset-right” strategy rather than the “asset-light” strategy. Accor usually retains ownership for economy hotels, where return on investment is generally the highest, while steadily disposing upscale brand Sofitel and
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Pullman properties under a sale and manage-back agreement (Mintel, 2009). 5.3. Limitations Despite the significance of this study, it is not free from limitations. Since the sample consisted of only hotel firms with fee income, the data did not represent all the companies in the industry. Hence the results and discussions may not be generalized to the whole lodging industry. Another drawback is associated with a small number of mediators. Though we used two mediators for the sake of parsimony, there can be other areas linking the ALFO strategy and firm value as hinted by the mediation analysis results. Future studies may investigate other areas influenced by the ALFO strategy or use different output measures other than Q. Use of estimates of quarterly fee income should be noted in this section. It was an inevitable choice due to the unavailability of the data but might introduce potential errors. Lastly, our model does not take into consideration the macroeconomic conditions. Economic meltdown triggered by the subprime mortgage crisis in of 2007 hit the hospitality industry. Nevertheless, as we use quarterly data, it was not feasible to include time dummies. However, it would be an interesting study to weave the effect of the economic environment into the dynamics of the ALFO strategy. References Baron, R.M., Kenny, D.A., 1986. The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51 (6), 1173–1182. Barney, J., 1991. Firm resources and sustained competitive advantage. Journal of Management 17 (1), 99–120. Beals, P., Denton, G., 2005. The current balance of power in North American hotel management contracts. Journal of Retail and Leisure Property 4 (2), 129–145. Bentler, P.M., Bonett, D.G., 1980. Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin 88 (3), 588–606. Bessembinder, H., 1991. Forward contracts and firm value: investment incentive and contracting effects. Journal of Financial and Quantitative Analysis 26 (4), 519–532. Brealey, R.A., Myers, S.C., 2002. Principles of Corporate Finance, 7th ed. McGraw Hill Higher Education. Chatterjee, S., Wernerfelt, B., 1991. The link between resources and type of diversification: theory and evidence. Strategic Management Journal 12 (1), 33–48. Chen, J.J., 2005. Expansion strategy of international hotel firms. Journal of Business Research 58, 1730–1740. Chung, K.H., Pruitt, S.W., 1994. A simple approximation of Tobin’s q. Financial Management 23 (3), 70–74. deRoos, J., 2010. Hotel management contracts—past and present. Cornell Hospitality Quarterly 51 (1), 68–80. Dierickx, I., Cool, K., 1989. Asset stock accumulation and sustainability of competitive advantage. Management Science 35 (12), 1504–1511. Eyster, J., deRoos, J., 2009. The Negotiation and Administration of Hotel Management Contracts, 4th ed. Pearson Custom Publishing, London. Graham, J.R., Rogers, D.A., 2002. Do firms hedge in response to tax incentives? Journal of Finance 57 (2), 815–839. Hoyle, R.H., Smith, G.T., 1994. Formulating clinical research hypotheses as structural equation models: a conceptual overview. Journal of Consulting and Clinical Psychology 62 (3), 429–440.
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Hsu, L.-T., Jang, S.C., 2009. Effects of restaurant franchising: does an optimal franchise proportion exist? International Journal of Hospitality Management 28 (2), 204–211. Hudson, K., 2010. Hotels poised to shed properties. The Wall Street Journal September, C11. HVS Global Hospitality Services, 2011. Hotel franchise fee guide. Retrieved from: http://www.hvs.com/Bookstore/#2011ResFranFee (15.01.12). Iacobucci, D., Saldanha, N., Deng, X., 2007. A meditation on mediation: evidence that structural equations models perform better than regressions. Journal of Consumer Psychology 17 (2), 140–154. Samadi, N., 2011. IBISWorld Industry Report 72111. Hotels and Motels in the US. Retrieved from: IBISWorld database (10.10.11). Kline, R.B., 1998. Principles and Practice of Structural Equation Modeling. Guilford, New York. Lang, L.H.P., Stulz, R.M., 1994. Tobin’s q, corporate diversification, and firm performance. Journal of Political Economy 102 (6), 1248–1280. Lev, B., 1974. On the association between operating leverage and risk. Journal of Financial and Quantitative Analysis 9 (4), 627–641. Li, S.D., 2011. Testing mediation using multiple regression and structural equation modeling analyses in secondary data. Evaluation Review 35 (3), 240–268. Mandelker, G.N., Rhee, S.G., 1984. The impact of the degrees of operating and financial leverage on systematic risk of common stock. Journal of Financial and Quantitative Analysis 19 (1), 45–57. Mintel, 2007. International Growth Strategies of Major Hotel Chains—March 2007. Retrieved from: Mintel Market Research Reports database (05.08.11). Mintel, 2009. Hotels—International—December 2009. Retrieved from: Mintel Market Research Reports database (05.08.11). Mintel, 2011. International Hotel Industry–September 2011. Retrieved from: Mintel Market Research Reports database (15.01.12). Mitra, S., Chaya, A.K., 1996. Analyzing cost-effectiveness of organizations: the impact of information technology spending. Journal of Management Information Systems 13 (2), 29–57. Park, K., Jang, S.C., 2010. Insider ownership and firm performance: an examination of restaurant firms. International Journal of Hospitality Management 29 (3), 448–458. Preacher, K., Hayes, A., 2008. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods 40 (3), 879–891. Roh, Y.S., 2002. Size, growth rate and risk sharing as the determinants of propensity to franchise in chain restaurants. International Journal of Hospitality Management 21 (1), 43–56. Rumelt, R.P., 1982. Diversification strategy and profitability. Strategic Management Journal 3 (4), 359–369. Skalpe, O., 2001. Hotels and restaurants—are the risks rewarded? Evidence from Norway. Tourism Management 24 (6), 623–634. Smith, C.W., Stulz, R.M., 1985. The determinants of firms’ hedging policies. Journal of Financial and Quantitative Analysis 20 (4), 391–405. Steiger, J.H., Shapiro, A., Browne, M.W., 1985. On the multivariate asymptotic distribution of sequential Chi-square statistics. Psychometrika 50 (3), 253– 263. Titman, S., Wei, K.C.J., Xie, F., 2004. Capital investments and stock returns. Journal of Financial and Quantitative Analysis 39 (4), 677–700. Tsai, H., Pan, S., Lee, J., 2011. Recent research in hospitality financial management. International Journal of Contemporary Hospitality Management 23 (7), 941–971. Tucker, L.R., Lewis, C., 1973. A reliability coefficient for maximum likelihood factor analysis. Psychometrika 38 (1), 1–10. Tuzel, S., 2010. Corporate real estate holdings and the cross-section of stock returns. Review of Financial Studies 23 (6), 2268–2302. Wernerfelt, B., Montgomery, C.A., 1988. Tobin’s q and the importance of focus in firm performance. The American Economic Review 78 (1), 246–250. Whittaker, C., 2008. Hotel operator motives in UK sale and leaseback/managementback transactions. International Journal of Hospitality Management 27 (4), 641–648.
Contents lists available at SciVerse ScienceDirect
International Journal of Hospitality Management journal homepage: www.elsevier.com/locate/ijhosman
Does the asset-light and fee-oriented strategy create value? Jayoung Sohn a,∗ , Chun-Hung (Hugo) Tang b,1 , SooCheong (Shawn) Jang c,2 a b c
School of Hospitality and Tourism Management, Purdue University, 900 W. State Street, Marriott Hall, Room 206, West Lafayette, IN 47907-2115, United States School of Hospitality and Tourism Management, Purdue University, 900 W. State Street, Marriott Hall, Room 253, West Lafayette, IN 47907-2115, United States School of Hospitality and Tourism Management, Purdue University, 900 W. State Street, Marriott Hall, Room 245, West Lafayette, IN 47907-2115, United States
a r t i c l e Keywords: Fixed assets Fee business Firm value Profitability Operating risk Hotel
i n f o
a b s t r a c t During the past couple of decades, many hotel chains in the U.S. have shifted in their business strategy: whittling down properties while expanding the management or franchising business. We labeled such strategic shift as an “Asset-Light and Fee-Oriented (ALFO)” strategy and examined the theoretical and empirical effectiveness of the strategy. Theoretically, resource-based view and corporate finance theory predict competing implications of the ALFO strategy, calling for a study for validating the net benefits of the strategy. Our results indicate that expanding fee business and decreasing fixed asset intensity have a positive impact upon firm value. Using path analysis, we further verified the working mechanism of the ALFO strategy. The strategy is effective in lifting profitability, mitigating earnings volatility, and thereby contributing to firm’s market premium. © 2013 Elsevier Ltd. All rights reserved.
1. Introduction During the past couple of decades, many hotel chains in the United States have shifted their business strategy. In the 1980s, brands such as Marriott International Inc. and Hilton Hotels Worldwide began whittling down properties on their balance sheets to concentrate on the more lucrative and less capital-intensive business of operating properties for fees (Hudson, 2010). Most property sales are followed by management contracts: the seller still operates the property and the property owner pays fees for the service provided by the seller, now the operator. For example, at year-end 2010, Starwood Hotel and Resorts Worldwide Inc. operated 463 properties under long-term management agreements and 502 hotels under franchising contracts. This is a more than a 50% increase from the end of 2000. In contrast, the decline in the number of properties owned or leased by Starwood was also dramatic: from 162 in 2000 to 62 in 2010. This represents a decrease of more than 60%. Table 1 summarizes room affiliation structure of four major hotel chains. The proportion of rooms they actually own or lease accounts for less than 18% while more than 80% of rooms under their flags are operated via fee-generating contract business. As seen in Table 1, the movement to an asset-light and feeoriented (hereafter ALFO) company is significant among hotel
∗ Corresponding author. Tel.: +1 517 420 4744; fax: +1 765 494 0327. E-mail addresses: [email protected] (J. Sohn), [email protected] (C.-H. Tang), [email protected] (S. Jang). 1 Tel.: +1 765 494 4733; fax: +1 765 494 0327. 2 Tel.: +1 765 496 3610; fax: +1 765 494 0327. 0278-4319/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijhm.2012.07.004
firms. Yet the value of the ALFO strategy has not been empirically tested in order to validate this strategy. More importantly, the net benefits of the ALFO strategy are theoretically uncertain due to the competing impacts of the strategy on firm value. Fee business allows firms to expand their market share with limited capital investment. In terms of profitability and operating risk, fee business is also favorable. Since operators are reimbursed the cost of operating hotels by property owners, the cost of feebased revenue is lower than that of sales from owned properties. Fee income also has less variance than income from operating owned properties (Roh, 2002). Additionally, by selling properties, hotel firms can further reduce operating risk caused by high operating leverage. There are, however, potential pitfalls of the ALFO strategy as well. Turning over ownership means losing control in that property, implying that maintaining identical service quality across properties would not be as effective as when the hotels are all owned by the same company. Inconsistencies in quality and operations might have an adverse impact on firm value in the longer term. Moreover, decreased asset tangibility is another factor inflating the financial distress costs, and thus lowering firm value (Brealey and Myers, 2002). Seeing that many hotel firms have substantially expanded fee business, we believe the time is ripe to do a reality check of whether it is really effective in raising firm value. This study goes one step further to explain how the value of the ALFO strategy is realized. To achieve the second objective, we examine the mediating roles of profitability and operating risk. We expect this study to contribute to both industry and academia. Above all, it provides practitioners with an opportunity to empirically evaluate the effectiveness of the ALFO strategy.
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Table 1 Room affiliation structure of four major hotel firms.
Marriott International Inc. Starwood Hotel and Resorts Worldwide Inc. Intercontinental Hotels Group Hyatt Hotels Corporation
Owned or leased (%)
Managed (%)
Franchised (%)
2.0 6.8 0.8 17.2
44.7 51.6 25.1 68.2
53.4 39.3 74.1 13.1
Source: Mintel (2011).
Further, it allows practitioners to obtain a more concrete understanding of the strategy’s working mechanism. From an academic perspective, this study examines advantages and risks of the ALFO strategy from different theoretical viewpoints: strategic management and capital structure theories. The resource-based view predicts that the strategy could create value through operational and financial improvements. However, capital structure theories warn that reduced asset tangibility may inflate financial distress costs, and thereby lower firm value. This theoretical incongruence calls for an empirical study into the valuation effect of the ALFO strategy. In addition, by applying path analysis for our mediation model, which is a relatively new approach for analyzing financial data, we expect to make methodological contributions as well. By infusing theoretical foundations into an industry’s strategic practice, this study could be a catalyst for future studies that aim to bring the industry and academia closer. Implications, limitations, and suggestions for future studies are discussed along with the findings.
2. Hypothesis development 2.1. Dynamics of fee business A fee business is implemented using one of two contract modes: management or franchise contracts. In terms of fee structure, the two modes are similar. Franchise fees consist of two main components: an initial fee and continuing fees. The initial fee is paid upon a submission of franchising application and is used to cover the franchisor’s costs to process the application, survey market demand, provide service during pre-opening stages, and so on. Continuing fees are proportionally linked to the sales revenue of a franchisee. Royalty fees, marketing and advertising fees, and reservation fees are included in this category. For 2010, franchisees pay 3–7% of rooms revenue as royalty payments and 1.0–4.3% as marketing and advertising fees (HVS, 2011). In addition, owners pay fees for using the franchisor’s centralized reservation system. Reservation fees are either 0.4–10.0% of rooms revenue or $1.1–$10.0 per available room per month (HVS, 2011). Management fees, on the other hand, comprises a base management fee, which is a percentage of sales revenue of a managed hotel, plus an incentive fee based upon achieving certain levels of cash flows or certain levels of profitability of the hotel (deRoos, 2010). On average, a basic fee is 3.25% of gross revenue for full service provided by brand operators like Marriott International and 4% by independent operators such as Interstate Hotels and Resorts. An incentive fee is approximately 6–10% of the gross operating profit of the managed hotel (Eyster and deRoos, 2009). The major difference between franchising and management contracts resides in the degree of control. While franchisees are put in charge of daily operation of outlets, management contracts impose a much greater role on the management companies. Management companies take overall responsibility for managing the property, from hiring and training employees to nationwide marketing and promotional services. This difference explains why franchising is more common for economy and mid-scale hotels, whereas management contracts are more popular among upscale
and luxury hotels. Management of economy hotels usually relies on tangible criteria, such as building design and facilities. Meanwhile, for upscale and luxury brand hotels, nonphysical factors including service quality and atmospherics are more crucial, which can be properly delivered by expertly trained staff who are familiar with the spirit of the brand (Hsu and Jang, 2009). Chen (2005) distinguished the two modes in terms of ownership of intangible assets. Under management contracts, the hotel operator maintains claim over intangible resources such as transaction-specific data and computer reservation system. Franchising, however, grants franchisees the right to use these intangibles as part of the “franchise package.” Despite such differences, we do not distinguish the two modes insofar as both are ways to produce income based on intangible assets instead of fixed assets and grow without heavy capital commitment. 2.2. Fee business and firm value The greatest advantage of the fee business would probably be the growth with limited capital without the risk of owning properties. Traditionally, hotels had to build or purchase properties to enlarge their business, which required them to commit a huge capital investment to fixed assets. However, by letting contracted properties fly the flag of the franchisor or the management hotel firm, fee business enables the hotel firms to expand their portfolio at a faster rate, which boosts brand values. Meanwhile, risk arising from owning properties is shifting onto the owners. The resource-based view literature has shown that firms that are able to draw on common core competences (Rumelt, 1982) or share resources across businesses (Chatterjee and Wernerfelt, 1991) perform better. Fee business enables hotel firms to transfer the core competences, such as property management skills and human resources, across properties. In addition, the resource-based view claims that companies equipped with strategic competitive advantages tend to earn above-normal profits in the long run. Intangible strategic assets that gradually accumulate over a long period and cannot easily be copied by followers are likely to be inelastic in supply (Dierickx and Cool, 1989; Barney, 1991). Firms engaged in fee business mostly do so by leveraging their brand power, management expertise, established distribution channels, etc., all of which are intangible strategic assets expected to bring abnormal returns. Hence, we posit that fee-business would enable hotel firms to create firm value through faster portfolio growth and better operational performance. H1a.
Fee business improves profitability.
Another positive impact of fee business is stabilization of earnings. Roh (2002) demonstrated that the operating cash flows of franchising firms have lower volatility since royalties received from a franchised outlet show less variance than the revenue and profit of the outlet itself. The more stable and secure cash flow stream from fee-based income would provide a buffer to economic downturns. Less volatility in earnings would also lower certain financial costs, such as financial distress (Smith and Stulz, 1985), underinvestment (Bessembinder, 1991), and taxes (Graham and Rogers, 2002), and eventually lead to higher firm value.
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H1b.
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Fee business reduces earnings volatility.
However, potential pitfalls should be noted as well. As both franchising and property management are non-equity modes of development, the management would lose control over operations and pricing policy. In the case of franchising, maintaining standards can be an issue because service is mostly delivered by property owners. This problem is more critical for upscale and luxury hotels that distinguish themselves by intangible service features. In a similar vein, disagreements between the operator and the property owner over investing in properties can be an issue under management contracts (Mintel, 2007). Moreover, as competition among chains intensifies and more sophisticated groups of owners enter the market, owners are becoming more demanding and aggressive in terms of their expected returns on investments (Beals and Denton, 2005). Contracts for short durations also work against management/franchising companies too (Mintel, 2007). However, most of these potential problems are related to specific segments or uncertainties (i.e. the contract can be negotiated). Thus, we believe the magnitude of the costs would be relatively smaller than the certain and quantifiable benefits. These costs would not be considered as significant risks that outweigh the company-wide advantages of fee business. Given that, we hypothesize that fee-business has a positive effect on firm value. H1c.
Fee business has a positive effect on firm value.
2.3. Going asset-light and firm value Decreasing fixed assets primarily involves firm risk. Firms that own many fixed assets cannot easily adjust themselves to economic conditions since a large portion of their capital was already committed to illiquid assets. Hence, firms with a higher fixed-asset ratio are more vulnerable to economic ups and downs, increasing the systematic risk of the firm. Higher risk, in turn, obliges risk-averse investors to demand higher return, increasing the cost of capital (Brealey and Myers, 2002). Traditional firm valuation formula estimates the value of a firm as the sum of discounted future free cash flows to the firm using its cost of capital. Thus, all other things being equal, the higher the cost of capital is, the lower the firm value would be. The impact of fixed assets on firm value has been examined by a myriad of studies. Lev (1974) and Mandelker and Rhee (1984) showed that operating leverage, which is the ratio of fixed to variable costs, is positively related to the systematic risk of common stock. Tuzel (2010) explained why firms holding large fixed assets are more susceptible to economic shocks and thus are riskier than their counterparts. In a similar vein, Titman et al. (2004) examined how the stock market reacts to firms’ announcements of capital investments. They observed a negative return on the firms that considerably increase capital investments. In addition, lower operating leverage mitigates the sensitivity of earnings to fluctuations in sales revenue. Hotel firms, by nature, record heavy fixed costs, mostly incurred from depreciation plus maintenance, whereas variable costs are very low (the marginal cost of selling a room-night is quite small). Since fixed costs mostly do not vary with sales revenue, they have a bullwhip effect on the bottom line. Skalpe (2001) argued that for firms with high operating leverage, even a decent amount of sales variance may bring greater volatility upon the bottom line. A high operating risk would fuel the debt and equity yields and thus results in a higher cost of capital, which in turn lowers firm value. In this regard, we propose the second hypothesis as follows: H2a.
Fixed-asset ratio is positively related to earnings volatility.
However, disposition of properties might bring about adverse effects. Diminished asset tangibility can inflate financial distress costs (Brealey and Myers, 2002). Based on corporate finance
theories, firms consisting of mostly intangible assets would face higher financial distress costs, and thus lower firm value. This is because the value of tangible assets (e.g. commercial real estate) can emerge from a bankruptcy process mostly unscathed. However, the value of intangible assets is contingent on the company being a going-concern. If the company goes bankrupt, the value of intangible assets (e.g. expertise, brand) would mostly cease to exist (Brealey and Myers, 2002). In sum, a lower fixed-asset ratio reduces operational risk (i.e. operating leverage and earnings volatility) but increases financial risk. For a financially healthy firm, the decrease of asset tangibility is not expected to significantly inflate financial distress costs because stable and healthy cash flows can substitute for the safety brought by fixed asset collaterals. Empirically, Lev (1974) and Mandelker and Rhee (1984) also observed lower systematic risk in firms with lower levels of fixed assets. Therefore, we can reasonably assume that the net effect is lower (total) risk in an average sample. In this regard, we propose the last hypothesis as follows: H2b.
Fixed-asset ratio is negatively related to firm value.
We do not propose a relationship between the fixed-asset ratio and operating margin. There are two reasons. First, the main goal of going asset-light lies in reducing operating risk but profitability is more closely related with operating efficiency than operating risk. In addition, while increasing fee-income ratio is more directly linked to income statement items, decreasing fixed assets is more related to balance sheet items. 3. Data and methodology 3.1. Data collection Most accounting data were retrieved from the Compustat database based on the Standard Industrial Classification (SIC) code. We used data of firms under the SIC code 7011, which is Hotels and Motels. As for the management and franchising fee income, we referred to 10-K reports filed to the Securities and Exchange Committee. First we read the Item 1. Business section to confirm that a sample hotel is engaged in management or franchise business. Then we searched the reports using several keywords: management fee; franchise (franchising) fee; management contract; and franchise (franchising) contract. The data collection period spanned from 2002 to 2010 due to the availability of fee-based income data. In 2002; many firms started reporting their managementand franchise-fee income separate from other revenue sources. For a firm to be included in the dataset; it has to report both revenue and fee income from management or franchise business. Out of 472 total firm-quarter observations; after deleting outliers (26); 446 observations were used. 3.2. Variables ALFO strategy: We used fee-income ratio, which is the sum of fee income from franchising and management contracts against total sales revenue, as the measure of the degree of fee business. Since not every hotel firm reports quarterly fee income, we divided the yearly fee income by four to obtain approximate quarterly data. There are two reasons behind this: First, the mean sales revenue was not significantly varied across quarters, (F-value = 0.10, p-value = 0.962) and fee income is highly correlated to sales revenue (correlation coefficient = 0.885, p-value < 0.001). It might come surprising that quarterly sales revenue is not significantly different each other. When we checked the quarterly sales revenue of the ALFO firms, we observed mild fluctuation. Third and fourth quarters recorded higher sales revenue than the others. However, the difference was not statistically significant. We suspect the reason is
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the use of calendar quarters. For example, the fourth quarter covers October, November, and December. November and December have the biggest holiday seasons, but October does not. Relatively low sales in October may partially cancel out the high demand in late winter. In a similar vein, third quarter includes July, August, and September. Summer season usually starts from mid-May or June and ends with August. Thus, decreased demand in September may calm down the summer rush. In addition, as most management and franchising contracts are long term, we assume that the number of contracted hotels would not greatly fluctuate within a year, implying that the flow of fee income would be rather stable throughout a year. For these reasons, we could assume that our method reasonably approximates quarterly data. To capture the other dimension of the ALFO strategy, we include fixed-asset ratio, which is the ratio of the amount of net property, plant and equipment to total assets, to measure the extent to which it is asset-light. Since the original fee-income ratio shows high skewness (3.058) and kurtosis (14.317), we took a natural log transformation to attain more normality. After the transformation, its skewness (−.641) and kurtosis (4.515) reached an acceptable level (Kline, 1998). Fee-income ratio (Fee) = Ln
Fixes-asset ratio (PPE) =
Management fee + Franchise fee Sales revenue
Net property, plant, and equipment Total asset
Firm value: Q, the ratio of the market value of the firm to the replacement value of its assets, has long been used as a firm’s market premium measure in a myriad of studies (Lang and Stulz, 1994; Wernerfelt and Montgomery, 1988; Park and Jang, 2010). By incorporating the stock market’s perception of the firm’s present and future cash flow and growth potential, Q implicitly uses a correct risk-adjusted discount rate (Wernerfelt and Montgomery, 1988). In this study, approximate version of Q developed by Chung and Pruitt (1994) was used for three reasons: 1. low computational cost; 2. data availability on Compustat; 3. high degree of correlation with more rigorously constructed Q. Q =
MVE + PS + DEBT TA
where MVE = share price × number of common stock outstanding, PS = liquidating value of the firm’s preferred stock, DEBT = book value of long-term debt + short-term liability − short-term assets, and TA = book value of total assets. Profitability: This study adopted operating profit margin as the operating profitability measure. Operating income, a measure of a company’s earning power from its main business activities, takes the gross margin and subtracts operating expenses and depreciation. Operating expenses include the cost of sales and other overhead expenses and is the most general and comprehensive measure of a firm’s total cost of operation (Mitra and Chaya, 1996). Hence, the operating profit margin, which is the ratio of operating income scaled by sales revenue, is an indicator of the company’s overall operating efficiency. Operating margin (opmargin) =
Operating income after depreciation Sales revenue
Earnings volatility: We measured earnings volatility as the coefficient of variation of operating income after depreciation over four quarters: three previous and one current quarters. Coefficient of variation is a standard deviation standardized by the mean. As with the fee-income ratio, the original earnings volatility showed significantly high skewness (13.33) and kurtosis (203.83). Hence, we took a natural log transformation again. Transformation reduced its skewness to .761 and kurtosis to 3.757, both of which came to an acceptable level (Kline, 1998).
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3.3. Models We examined the relationship of the ALFO strategy and firm value. In order to investigate potential channels through which the strategy is translated into firm value we selected two mediating variables from the return and risk framework: operating profit margin and earnings volatility. To analyze the model with multiple independent variables and multiple mediators, we used the structural equation modeling (SEM) approach. SEM is a comprehensive and flexible method for specifying relations among variables (Hoyle and Smith, 1994). Due to its greater flexibility and ability to simultaneously capture multiple relationships among variables, the SEM approach is regarded as the best method for estimating multiple mediators (Preacher and Hayes, 2008; Iacobucci et al., 2007). Estimating components simultaneously has statistical advantages over doing so in piece-meal fashion since it enables the researcher to control for other relationships in one model (Iacobucci et al., 2007). Especially in the case of a model with multiple mediators, it is difficult to test the effects of multiple mediators on the same output variable using the multiple regression method. A system of equations would need to be formulated to test the effect of each mediator individually, and it would be cumbersome to merge all the statistics to assess the overall mediation effect (Li, 2011). For these reasons, we adopted the SEM approach rather than multiple regression approach. Specifically, as we used only observed variables, the model is called path analysis. Path analysis is a special case of SEM in that path analysis deals only with measured variables, whereas SEM handles both observed and latent variables. Preacher and Hayes (2008) suggested that error terms of mediators be allowed to covary rather than be fixed to zero. Constraining the mediator residual covariance to zero implies that the covariance between mediators can be completely accounted for by their mutual dependence on the exogenous variables. However, it is usually hard to defend theoretically (Preacher and Hayes, 2008). In this regard, we permitted the residuals of mediators to covary because operating margin and earnings volatility are outcomes of operation influenced by a number of factors. The basic conceptual model is visualized in Fig. 1.
4. Results 4.1. Descriptive analysis Fig. 2 depicts historical trends for the average industry feeincome ratio and the average fixed-asset ratio. The industry average fee-income ratio is the ratio of the sum of fee income (which is the sum of fee income) over the sum of revenue (which is the sum of the ALFO firms’ sales revenue). The average fixed-asset ratio is the ratio of the average net property, plant and equipment against the average total assets of the ALFO firms. The trends show a strong inverse relationship, implying that firms engaged in fee business have increased fee-income ratio and decreased fixed-asset ratio. The fact that hotel firms have changed to be more resilient with less fixed assets is also supported by the opposite movements of current assets and fixed assets. As seen in Fig. 3, during the sample period the current assets of the sample hotel firms have constantly increased from $355 M to $818 M—a more than 130% increase. However, fixed assets shrank by nearly a half from $2009 M to $1057 M. It seems that hotel firms have been prepared for unexpected economy shocks by reducing their exposure to real estate risk and promoting liquidity. More information about sample statistics is summarized in Table 2. On average, Q is slightly greater than 1, implying that the capital market assigns a premium to intangible assets. The average
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Fig. 1. Path diagram.
70%
16%
60%
14% 12%
50%
10%
40%
confirms the opposite movement of fee income and fixed-asset size evidenced in Fig. 2. Fixed-asset ratio has a negative correlation with Q but a positive with earnings volatility. However, there seems to be no significant linear relationship between fixed-asset ratio and operating margin.
8% 30%
6%
20%
4.2. Path analysis
4%
10%
2% 0%
0% 2002
2003
2004
2005
2006
Mean of fixed-asset rao
2007
2008
2009
2010
Mean of fee-income rao
Fig. 2. Historical trend of fixed-asset ratio and fee-income ratio.
Fig. 3. Historical trend of fixed assets and current assets.
fixed-asset ratio is 55% for sample hotels and operating profitability is quite low, at less than 10%. Table 3 reports pair-wise correlation coefficients. Almost every correlation is significant at ˛ = .01with an expected sign. Feeincome ratio is positively related to Q and operating margin but negatively related to fixed-asset ratio and earnings volatility. The negative correlation between fee-income ratio and fixed-asset ratio
Path analysis based on maximum likelihood was utilized and the results are reported in Table 4 and Fig. 4. The goodness-of-fit of the model was evaluated using five indices: 2 , the root mean squared error of approximation (RMSEA: Steiger et al., 1985), the comparative fit index (CFI: Bentler and Bonett, 1980), the Tucker–Lewis index (TLI: Tucker and Lewis, 1973), and the standardized root mean square residual (SRMR). The 2 statistic is for testing the difference between the proposed model and the saturated model. The smaller the statistic, the less discrepancy between the saturated and our hypothesized model, and hence the better fit. RMSEA values less than .05 and CFI values greater than .90 indicate good fit. Similarly, TLI values greater than .90 and SRMR less than .05 signify good fit. The fit indices indicate that the hypothesized model reasonably fits the data (2 (1) = 1.385, RMSEA = .033, CFI = .999, TLI = .991, SRMR = .011) (see Table 5). All hypotheses were consistent with our prior expectations. The effect of fee-income ratio on profitability was estimated as .136 (H1a, z = 2.63, p-value = .009), meaning that fee business is effective in improving profitability. In addition, the path coefficient between fee-income ratio and earnings volatility is also significant as −.273 (H1b, z = −4.21, p-value < .001), meaning that fee business mitigates earnings volatility. The positive impact of feeincome ratio on Q is supported by a coefficient estimate of .285 (H1c, z = 5.03, p-value < .001), suggesting that fee-business contributes to ultimate firm value. Hypothesis testing on fixed-asset ratio confirmed our prior expectations as well. Decreasing fixedasset ratio is shown to attenuate the volatility of earnings. The path coefficient was estimated as .176 (H2a, z = 2.84, p-value = .005). The proposed relationship between fixed-asset ratio and Q is also supported by an estimate of −.271 (H2b, z = −4.87, p-value < .001), indicating that decreasing fixed-asset ratio has a positive impact
Table 2 Sample characteristics. Variable
Definition
Mean
Std. dev.
Min
Max
Skewness
Kurtosis
Q Fee PPE opmargin EarVol
The ratio of MV to BV of assets Ln(fee incomet /sales revenuet ) Net property, plant and equipmentt /total assett OIADPt /sales revenuet Ln(coeff. of variation of OIADP over four quarters)
1.12 −3.14 .55 .07 −.30
.55 1.08 .24 .10 1.12
.22 −8.97 .05 −.39 −2.61
3.75 −.62 .91 .41 4.14
1.70 −.64 −.57 −1.03 .76
6.32 4.52 2.24 6.32 3.76
OIADP: operating income after depreciation.
J. Sohn et al. / International Journal of Hospitality Management 32 (2013) 270–277
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Table 3 Sample correlation. Q Q Fee PPE opmargin EarVol ***
Fee
PPE
opmargin
EarVol
1 −.6634*** .2410*** −.3896***
1 −.0669 .3455***
1 −.4448***
1
1 .5372*** −.5005*** .3994*** −.4421***
p < .01.
Table 4 Estimates of path coefficients. Hypothesized path
Standardized path coefficients
Results
H1a: Fee-income ratio → profitability H1b: Fee-income ratio → earnings volatility H1c: Fee-income ratio → Q H2a: Fixed-asset ratio → earnings volatility H2b: Fixed-asset ratio → Q Profitability → Q Earnings volatility → Q
.136*** −.273*** .285*** .176*** −.271*** .299*** −.094** 2 (1) = 1.385 RMSEA = .033 CFI = .999 TLI = .991 SRMR = .011
Reject the null Reject the null Reject the null Reject the null Reject the null
Note: RMSEA = root mean square error of approximation; CFI = comparative fit index; TLI = Tucker–Lewis index; SRMR = standardized root mean square residual. ** p < .05. *** p < .01.
Fig. 4. Path analysis: standardized coefficients.
upon firm value. Other paths from profitability and earnings volatility to Q were consistent to previous research. The path coefficients from operating margin to Q and from earnings volatility to Q are estimated as .299 (z = 7.03, p-value < .001) and −.094 (z = −2.00, pvalue = .046), respectively. The correlation between two error terms of operating margin and earnings volatility was estimated as −.445 (p-value < 001), implying that other hidden factors that influence those two factors are negatively correlated each other.
4.3. Mediation analysis According to Baron and Kenny’s (1986) approach, for a model to have mediating effect, it has to meet four conditions: (1) the predictor (or exogenous) variable should have a significant relationship to the outcome (or endogenous) variable; (2) the predictor (or exogenous) variable should have a significant relationship to the presumed mediator; (3) the presumed mediator should have a
Table 5 Mediation analysis results. Coefficients of fee-income ratio Full
opmargin constrained ***
2 2 ** ***
p < .05. p < .01.
.285 1.39 0
***
.303 46.06 44.67*** (df = 1)
Coefficients of fixed-asset ratio EarVol constrained ***
.306 5.33 3.94** (df = 1)
Full
EarVol constrained
−.271 1.39 0
***
−.287*** 5.33 3.94** (df = 1)
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significant relationship to the outcome variable; (4) given the mediator in the model, the direct effect of the predictor on the outcome variable should be substantially reduced. Following Baron and Kenny’s (1986) criteria, we tested mediation effect of operating profitability and earnings volatility. The first three conditions were satisfied in the original model. Fee-income ratio and fixed-asset ratio had significant effects on mediators and Q. The direct impacts of mediators (operating margin and earnings volatility) on Q were also significant. To test for the fourth condition, we constrained the direct effect of operating margin on Q to zero and reestimated the model. In that case, 2 (44.67, df = 1) between the full (2 (1) = 1.39) and the constrained model (2 (2) = 46.06) was significant (p-value < .001), suggesting that the model fit significantly got worse in the constrained model. Change in the coefficients of exogenous variables should also be noted. While the direct effect of fee-income ratio in the full model was .285 (z = 5.03, p-value < .001), in the constrained model it was .303 (z = 5.05, p-value < .001). A significant increase in 2 and change in the coefficient indicate that operating margin is a mediator in the relationship between fee-income ratio and Q. Following the same logic, we tested the mediating role of earnings volatility. When we reran the model while constraining the direct effect of earnings volatility on Q to zero, 2 increased by 3.94 (df = 1) from 1.39 (full model) to 5.33 (constrained model) and it was statistically significant (p-value = .047). The parameter estimate of the path between fee-income ratio and Q was estimated as .306 (z = 5.46, p-value < .001) in the constrained model, which was larger than that in the full model. Fixed-asset ratio showed a similar pattern. The direct effect of fixed-asset ratio on Q was −.271 (z = −4.87, p-value < .001) in the full model and it decreased to −.287 (z = −5.21, p-value < .001) in the constrained model. Changes in 2 and coefficients of fee-income ratio and fixed-asset ratio signified that earnings volatility acts as a mediator in the model. However, both of them are partial mediators, implying that there are probably other factors conveying the effect of the ALFO strategy to firm value.
5. Discussion and conclusions The hotel industry, by nature, has been regarded as having a higher level of business risk compared to other industries (Skalpe, 2001; Tsai et al., 2011). Performance is closely tied to the state of the economy and thus sensitive to external shocks. In addition, a great deal of capital used to be committed to fixed assets, which hinders the ability of hotel firms to accommodate unexpected market changes. Confronted with such risks, many hotel firms have carried out mutually supportive policies: decreasing fixed assets while expanding management or franchising business. First, our results indicate that the capital market assign premiums to hotel firms that go asset-light. The results show that decreasing fixed-asset ratio mitigates operating risk and elevates firm value, suggesting that investors’ concerns about the firms going asset-heavy outweigh benefits provided by fixed assets. In the meantime, our analysis also shows that fee-business is effective in improving operating profitability, earnings stability, and eventually the firm value. In sum, the results suggest the two sides of the ALFO strategy have worked in sync to create value for investors. This is consistent with the main motivation for sale-and-leaseback transactions (SLBT) (Whittaker, 2008). Interviews with 15 practitioners in hotel-related businesses revealed that the market pressure to “separate bricks from brain” is one of the major drivers of SLBT.
5.1. Theoretical implications This study contributes to the hospitality literature by not only verifying the value of a popular strategy practiced in the industry but also developing and testing a theoretical framework to explain the ALFO strategy. We started with the risk-return framework and incorporated the concepts of the value of stable cash flows, operating leverage, and systematic risk sensitivity to diagnose the benefits and potential risks of the strategy. The resource-based view and capital structure theories predict opposing effects of the ALFO strategy and the present study reconciled these two theories’ conflicting predictions on firm value, providing a comprehensive picture of the strategy. Based on the theoretical framework, we further investigated the value realization mechanism of the strategy. This was accomplished by using a powerful statistical procedure that is popular with marketing studies but rarely used in the finance literature: structural equation modeling. By applying path analysis, we captured concurrent impacts of the ALFO strategy on the firm’s profitability, risk, and market premium. We hope this cross-pollination of methodologies from different disciplines could encourage future interdisciplinary studies in hospitality management.
5.2. Managerial implications The findings of this study provide some meaningful implications to practitioners as well. As evidenced by the results, the ALFO strategy increases firm value. The continuing worldwide economic recession has led the lodging industry into intense price-based competition. To maintain cash flows, many hotels are selling surplus rooms on online booking sites at heavily discounted prices (IBISWorld, 2011). Such price-based competition could decrease a firms’ profitability. However, fee business can provide a buffer to the bottom line. Moreover, it helps firms to create stable cash flows and reduce earnings fluctuation. Thus, aware of the benefits of fee business, management firms and franchisors should keep sharpening their core competences in hotel operation and nationwide marketing to increase their value to property owners and thereby attain more contracts. In the meantime, to prevent the brand value from being diluted, franchisors should closely supervise whether franchised units are properly operated under the criteria defined by the brand. Our results also show that disposing fixed assets has a positive impact on firm value. The stock market recognizes the advantages of being lean and flexible. Decreasing the portion of assets committed to illiquid assets reduces the exposure to real estate risk and makes the company more resilient to economic shocks. This has proven to be a substantial advantage in the recent real estate downturn. Cash inflows from disposition can also be used to manage financial leverage or for more promising investment opportunities in the future. Hence, the management ought to keep it mind the importance of enriching substance rather than increasing in size. However, this poses a question: Should hotel firms simply sell their properties to decrease the operational risks? There is no clear-cut answer because fixed assets provide some positive impacts. Maintaining ownership grants the hotel firm control of the properties, which also can be a source of collateral and reduce the probability of distress. Therefore, the management should carefully consider potential risks of disinvestment and find the optimal mix between owned and contracted properties in view of unique characteristics of each hotel segment. For instance, Accor applies dissimilar strategies to different business segments, the “asset-right” strategy rather than the “asset-light” strategy. Accor usually retains ownership for economy hotels, where return on investment is generally the highest, while steadily disposing upscale brand Sofitel and
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Pullman properties under a sale and manage-back agreement (Mintel, 2009). 5.3. Limitations Despite the significance of this study, it is not free from limitations. Since the sample consisted of only hotel firms with fee income, the data did not represent all the companies in the industry. Hence the results and discussions may not be generalized to the whole lodging industry. Another drawback is associated with a small number of mediators. Though we used two mediators for the sake of parsimony, there can be other areas linking the ALFO strategy and firm value as hinted by the mediation analysis results. Future studies may investigate other areas influenced by the ALFO strategy or use different output measures other than Q. Use of estimates of quarterly fee income should be noted in this section. It was an inevitable choice due to the unavailability of the data but might introduce potential errors. Lastly, our model does not take into consideration the macroeconomic conditions. Economic meltdown triggered by the subprime mortgage crisis in of 2007 hit the hospitality industry. Nevertheless, as we use quarterly data, it was not feasible to include time dummies. However, it would be an interesting study to weave the effect of the economic environment into the dynamics of the ALFO strategy. References Baron, R.M., Kenny, D.A., 1986. The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51 (6), 1173–1182. Barney, J., 1991. Firm resources and sustained competitive advantage. Journal of Management 17 (1), 99–120. Beals, P., Denton, G., 2005. The current balance of power in North American hotel management contracts. Journal of Retail and Leisure Property 4 (2), 129–145. Bentler, P.M., Bonett, D.G., 1980. Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin 88 (3), 588–606. Bessembinder, H., 1991. Forward contracts and firm value: investment incentive and contracting effects. Journal of Financial and Quantitative Analysis 26 (4), 519–532. Brealey, R.A., Myers, S.C., 2002. Principles of Corporate Finance, 7th ed. McGraw Hill Higher Education. Chatterjee, S., Wernerfelt, B., 1991. The link between resources and type of diversification: theory and evidence. Strategic Management Journal 12 (1), 33–48. Chen, J.J., 2005. Expansion strategy of international hotel firms. Journal of Business Research 58, 1730–1740. Chung, K.H., Pruitt, S.W., 1994. A simple approximation of Tobin’s q. Financial Management 23 (3), 70–74. deRoos, J., 2010. Hotel management contracts—past and present. Cornell Hospitality Quarterly 51 (1), 68–80. Dierickx, I., Cool, K., 1989. Asset stock accumulation and sustainability of competitive advantage. Management Science 35 (12), 1504–1511. Eyster, J., deRoos, J., 2009. The Negotiation and Administration of Hotel Management Contracts, 4th ed. Pearson Custom Publishing, London. Graham, J.R., Rogers, D.A., 2002. Do firms hedge in response to tax incentives? Journal of Finance 57 (2), 815–839. Hoyle, R.H., Smith, G.T., 1994. Formulating clinical research hypotheses as structural equation models: a conceptual overview. Journal of Consulting and Clinical Psychology 62 (3), 429–440.
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