Operating Profit Drivers for Large Companies in the Spanish Hotel Sector: A Temporal Analysis (2004-2017)

Abstract

The improvement of performance and profitability in hotel companies is backed by a continuous and progressive management process. However, research methodologies have generally adopted a static perspective that does not specify the concrete trajectories used by companies to improve management performance and profitability. This article proposes a dynamic methodological approach to analyze the profit drivers of the largest Spanish hotel companies throughout the period 2004 to 2017. This period is characterized by a recession and subsequent economic recovery, and therefore, it supplies a long and varying perspective for evaluating the management trajectories used by these companies to push their profits. Findings show that the composition of profit drivers is neither uniform nor time invariant and can depend on both external and internal factors.

Keywords

Spanish hotel industry large companies strategy profit drivers principal component analysis temporal analysis

Introduction

The analysis of management performance and profitability is one of the most widespread topics in studies of the hospitality industry (i.e., Fu et al., 2019; X. Li et al., 2017; Mohammed et al., 2015). Overall, the literature in this field has mainly focused on efficiency in resource management as a performance indicator (Arbelo-Pérez et al., 2017; Assaf & Tsionas, 2018). Moreover, performance management analysis has usually been associated with cost efficiency. However, profit is the main performance goal of companies¹ and depends on cost and income management. Therefore, efficient management of income can be as important as efficient management of cost. In fact, given that hospitality services are a perishable product that is not storable (Parasuraman & Varadarajan, 1988) and that hospitality companies dispose of limited capacity to meet demand, revenue management has become a relevant strategy for hospitality companies (Queenan et al., 2011; Rodríguez-Algeciras & Talón-Ballestero, 2017).

Considering profit as the main management objective, profit efficiency has been proposed as an indicator that best reflects the management performance of a hotel company (Arbelo et al., 2015). In this line, profit efficiency is defined as the ratio of the current profit to a hypothetical maximum profit that a company could obtain, where the hypothetical maximum profit is calculated using the best performance of hotel companies as a benchmark. However, maximizing profits can be considered a hypothetical management behavior (Simon, 1991). A realistic approach to evaluate managerial performance would require taking into account issues such as the manager’s objective, which can differ from those of the shareholders (Y. Li & Signal, 2018; Upneja & Ozdemir, 2014), the bounded rationality and incomplete information of managers in decision making, the resource restrictions of companies in the short and medium term, or the impact of the external environment.

The aim of this article is to propose a methodological approach to analyze the profit evolution of a hotel company by breaking it up into a set of drivers (Brea-Solis et al., 2015). These drivers draw a dynamic perspective about how profit is improved by a hotel company based on its own trajectory, and they are a consequence of the interrelation between managerial decisions and the external environment. From a strategic perspective, these drivers summarize how a company’s resources and capabilities are aligned with the external environment and how this fit determines a company’s performance (Dikova et al., 2017; González-Rodríguez et al., 2018). In methodological terms, the proposed approach presents some advantages but has limitations, which will be discussed throughout the article. First, this approach includes management variables directly related to profits. Therefore, all technical and economic aspects affecting profit are considered, which enables an analysis of the specific impact of each variable on profit and of separate technical and economic aspects. Second, the profitability evaluation of each company is based on the trajectory of the company itself. Thus, the heterogeneity impacts correspond only to changes within a company.

The methodological approach is applied to clarify the profit drivers of the largest Spanish hotels during the period 2004 to 2017, which is characterized by a recession and a subsequent economic recovery. Therefore, the article’s results are consistent in the long term, and they provide an update compared with previous studies on the Spanish hotel sector. The study analyzes the profile differences of profit drivers in the hotel sector and their temporal evolution. The results show that profit drivers are not uniform and invariant and can depend on both external and internal factors. Specifically, the analysis reveals significant differences in the trajectory that a company follows to improve its profits depending on its size and the external economic context.

The article is structured as follows: The next section provides a literature review on the analysis of management performance and profitability, the third section develops and discusses the proposed methodological approach, the fourth section presents the data and the variables included in the analysis, the analysis results are summarized in the fifth and sixth sections, and finally, the last section presents the conclusions and limitations of the present analysis and proposes future lines of research.

Literature Review

Several methodological approaches have been proposed for the analysis of performance management and profitability with common methodologies based on financial ratios and their relationships with profitability being often used as practical approaches (i.e., Lee & Jang, 2007; Mia & Patiar, 2001; Singh & Schmidgall, 2002). These approaches are simple, but their ability to control for the impact of environmental variables is limited. Additionally, they present a limited perspective on the relationship between economic and financial indicators. As an alternative, econometric approaches have been used to measure the impact of particular hotel characteristics and competitive environments on hotel profitability (i.e., Lado-Sestayo et al., 2018; Pan, 2005). However, econometric approaches usually do not incorporate internal management aspects into the analysis, or they should be introduced more specifically (Ben Aissa & Goaied, 2016).

From a strategic management perspective, it is necessary to take into account both the internal and the external environments to understand a hotel company’s performance (González-Rodríguez et al., 2018; Köseglu et al., 2013). Academics have highlighted the need for a good knowledge of the external environment to understand the opportunities and threats that a company faces (external analysis) and have emphasized knowledge of the internal environment to understand a company’s strengths and weaknesses (internal analysis). In this sense, researchers have proposed a series of paradigms to help evaluate how external and internal environments determine a company’s potential performance (i.e., Bain, 1951; Barney, 1991; Brandenburger & Nalebuff, 1996; Durand et al., 2017; Porter, 2008). Additionally, the integration of internal and external factors is essential for understanding how a company’s resources and capabilities align with the external environment and how this fit determines a company’s performance (Dikova et al., 2017; González-Rodríguez et al., 2018) and the logic of its business model (Casadesus-Masanell & Ricart, 2010). The basic guidelines for these approaches are the competitive advantage and value creation concepts (Hoopes et al., 2003; Peteraf & Barney, 2003). Therefore, these perspectives are centered on how companies can manage their own resources and capabilities to obtain a sustainable competitive advantage within a given external environment in order to create a greater economic value than their competitors. Understanding the formulation and implementation of strategies aimed at generating greater value is a basic aspect of a company’s performance but is not the only one. Companies must also be able to capture the generated value so that their strategies are translated into better performance and profitability (Gans & Ryall, 2017). Moreover, a global strategic analysis requires a comprehensive perspective on how a company generates its profits, which requires an understanding of how economic value is created and how a company captures a proportion of this economic value (Bowman & Ambrosini, 2000; Lepak et al., 2007). Very few empirical works have endeavored to study how companies drive profits by creating and capturing value (McWilliams & Siegel, 2011).

Without directly evaluating profitability, frontier methods are a common approach to evaluating cost efficiency and managerial performance. Frontier methods have been widely applied in the hotel sector (for recent reviews of the literature on hotel efficiency, see Arbelo-Pérez et al., 2017; Assaf & Josiasson, 2015). These methods use data from hotels to build a benchmark frontier, based on a hypothetical technological relationship between inputs and outputs. The efficiency of a hotel is measured by the distance of the observed data from the benchmark frontier. These methods have been extended to directly analyze the profit efficiency in the hotel industry (Arbelo et al., 2015; Arbelo-Pérez et al., 2017). Two types of frontier methodologies are usually distinguished: parametric and nonparametric (Assaf & Josiasson, 2015). Parametric approaches consider a specific functional form to obtain the relationship between multiple outputs and multiple inputs, which draws the benchmark efficiency frontier. By contrast, nonparametric approaches do not consider any functional form that relates multiple outputs to multiple inputs. Frontier methods offer a set of advantages, as well as limitations (Assaf et al., 2012; Assaf & Josiasson, 2015).

Methodological Approach

The operating profit of company $i$ in period $t$ ( $Π_{i t}$ ) is defined as the difference between the operating revenues and the costs. Only operational and current company activities are considered; profits from asset or liability operations are not considered. The company output is homogenized and defined as $q_{i t}$ , the number of accommodation services, and $p_{i t}$ is the sale price per accommodation service. According to convention, two inputs are introduced (Chatzimichael & Liasidou, 2019): labor and capital. Operating costs are labor costs, capital costs, and other operating costs (supply costs, external service costs, etc.). The term $c_{i t}$ corresponds to the other operating costs per unit of output. Labor and capital inputs are normalized by unit of output, and vector $x_{i t} = [x_{i t}^{l}; x_{i t}^{c}]$ is the input used (labor and capital, respectively) per unit of output. Finally, the price vector of labor and capital inputs per unit is $r_{i t} = [r_{i t}^{l}; r_{i t}^{c}]$ . Consequently, company profit is defined as follows:

Π_{i t} = p_{i t} q_{i t} - c_{i t} q_{i t} - r_{i t} x_{i t}^{T} q_{i t} = m_{i t} q_{i t} - r_{i t} x_{i t}^{T} q_{i t}

(1)

where $m_{i t} = p_{i t} - c_{i t} .$

Therefore, $m_{i t}$ is the gross margin per accommodation service. By comparing consecutive annual periods $t$ and $t + 1$ , company profit difference is expressed as follows:

Π_{i t + 1} - Π_{i t} = [m_{i t + 1} q_{i t + 1} - m_{i t} q_{i t}] - [r_{i t + 1} x_{i t + 1}^{T} q_{i t + 1} - r_{i t} x_{i t}^{T} q_{i t}]

(2)

By adding and subtracting the expressions $m_{i t} q_{i t + 1}$ and $r_{i t} (x_{i t + 1}^{T} - x_{i t}^{T}) q_{i t + 1}$ in Equation (2), the profit difference can be disaggregated into the following components:

q_{i t + 1} [m_{i t + 1} - m_{i t}] Gross margin variation

x_{i t + 1}^{T} q_{i t + 1} [r_{i t + 1} - r_{i t}] Input prices variation

[m_{i t} - r_{i t} x_{i t}^{T}] [q_{i t + 1} - q_{i t}] Output variation

r_{i t} q_{i t + 1} [x_{i t + 1}^{T} - x_{i t}^{T}] Input efficiency variation

(3)

The first expression above values the effect of the gross margin difference between both periods on the profit variation. The second expression calculates the effect of the price input variation on the profit variation. In this case, it calculates the effect for each input price variation. The third expression values the effect of the output variation between both periods on the profit variation. Thus, this variation is a sales-level effect. The fourth expression calculates the effect of the difference in used inputs per unit sold on the variation of profits. Therefore, it is considered a technical efficiency variation and can be calculated for each input.

From the expressions in Equation (3), we can identify a set of drivers to analyze the operating profit variations for a company. These drivers show the path followed by a company to push its profits.

Caution should be taken when interpreting the input efficiency variations; it is common to assume that the relationship between outputs and inputs is linear (technology presents constant returns to scale [CRS])²; in this case, technical efficiency variations are determined only by the company’s ability to use its inputs efficiently; however, when the technology is not CRS, the input efficiency variation can also be explained by scale effects. It is possible to isolate the scale effect by drawing a function that represents the technological relationship between the inputs and the outputs (Chatzimichael & Liasidou, 2019). Taking into account time, another efficiency variation resource is the technological progress (Chatzimichael & Liasidou, 2019; Orfila-Sintes et al., 2005). The approach considers all technical efficiency variations in an aggregated way. However, using the CRS interpretation is reasonable in this case for two reasons (Metters et al., 1999). First, CRS is appropriate when the units compared are homogeneous. The approach evaluates each company based on its own trajectory between two consecutive years. Therefore, there exists a clear homogeneity among the compared units. Second, CRS is appropriate when the temporal perspective adopted is long.

Data

The data come from the SABI (Iberian Balance sheet Analysis System) database, developed by INFORMA D&B in collaboration with Bureau Van Dijk. These data correspond to the annual accounts presented in the official records.

The analysis is aimed at large Spanish hotel companies. The European Union defines a small company³ as a firm in which annual turnover does not exceed €10 million, asset values do not exceed €10 million, and the number of employees does not exceed 50. Large companies are defined by the European Union as firms with 250 employees or more. On the basis of these definitions, the selection criteria used are hotel companies with an annual turnover of not less than €10 million and no fewer than 250 employees. The database uses the year 2017 as a reference; this is the most recent year with all available information for almost all the largest Spanish hotel companies. In 2017, a total of 118 companies in the Spanish hotel sector⁴ presented annual revenues of at least €10 million and had at least 250 employees.

The temporal scope of the database is selected taking into account two opposing aspects: first, the longer the time series, the more accurate the comparison of the temporal evolution; second, the longer the time series, the smaller the number of companies with complete available information. To balance these two aspects, the analysis covers the period 2004 to 2017. The database corresponds to 57 large companies that operated uninterruptedly throughout 2004 to 2017 and comprises 798 observations. However, the profit variations are analyzed using 741 observations, as profit variations are calculated for 13 annual periods.

The annual performance measure is operating profit, which is obtained by subtracting the cost of supplies, labor costs, other operating costs (mainly costs of services provided by other firms), and capital expenses from operating revenues.

The annual output for company $i$ in the current year $t$ is calculated by deflating the current revenues. We use the annual index of hotel prices provided by the Spanish Statistical Office to deflate the annual revenues. Thus, all operating revenues are expressed in revenues from the year 2004 (the baseline year). The annual index of hotel prices is the ratio of average hotel prices in the current year ( ${\bar{p}}_{t}$ ) to the average hotel prices in the baseline year ( ${\bar{p}}_{0}$ ). Let us define the price of company $i$ in year $t$ in relation to the average hotel prices in this year. Therefore, $p_{i t} = (1 + z_{i t}) {\bar{p}}_{t}$ , where $z_{i t}$ is the overprice or underprice of company $i$ in year $t$ in relation to the average hotel prices in year $t$ . Moreover, when the revenues of company $i$ are deflated, the following output is obtained:

{\bar{q}}_{i t} = \frac{p_{i t} q_{i t}}{{\bar{p}}_{t} / {\bar{p}}_{0}} = {\bar{p}}_{0} q_{i t} (1 + z_{i t})

(4)

From Equation (4), the output is the volume of accommodation services sold in the current year evaluated at the price of the baseline year, taking into account the company upper price or lower price in relation to the average price in the current year. Therefore, the output variation for the two periods corresponds to the difference in the quantity of accommodation services sold and the differences in the company upper price or lower price. If the upper/lower prices do not change in the time period, then output variations reflect only the differences in the quantity of accommodation services sold. In the other case, temporal differences in the upper/lower prices charged by a company are collected. Unfortunately, the companies’ prices per service sold are not available, and it is not possible to disaggregate quantity variations and upper-price/overprice variations. However, it is reasonable to consider that upper-price/overprice variations are associated with increases/decreases in the quality of accommodation services, given that the hotel industry is a competitive sector and the effect of an increase in the general level of hotel price is already taking into account. Therefore, in this case, the output variations are fit to quality variations in the accommodation services. Thus, the output variation is the result of a variation in the number of accommodation services sold and a variation in their quality. On the other hand, gross margin per unit is defined as the added value per output unit (deflected operating revenues), where the added value is operating revenues minus the cost of supplies and other operating costs (it does not include labor and capital costs).

The explicit inputs are labor and capital. The labor quantity per output unit is calculated as the ratio of the number of employees to the deflated operating revenues. The price of a labor unit is the ratio of labor costs to the number of employees. As usual, the quantification of capital as an input is made using the firm’s assets (assets are not temporarily revalued). Thus, the capital quantity per output unit is defined as the ratio of a firm’s assets to the deflated operating revenues.⁵ The price of capital per unit is the ratio of the sum of the current asset depreciation and the financial expenses to the firm’s assets. The output definition implies that the effects of quality changes in the accommodation services on the inputs used are incorporated in the analysis.

Table 1 reports the average, standard deviation, and maximum and minimum values for each variable in the period 2004 to 2017.

Table 1

Descriptive Statistics of Variables

Year	Statistics	Revenues	Assets	Employees	Gross Margin	Labor Cost	Capital cost	Operating profits
2004	Average	49,864.5	148,055.81	716.9	26,561.06	17,390.2	7,510.7	1,660.1
	SD	95,144.4	307,439.5	1,360.7	50,446.9	35,148.6	17939.56	8,482.7
	Maximum	572,047	1,853,834	7,324	299967	207,243	127,419	31,513
	Minimum	4,489.4	5,958	22	1,329.9	939.5	63	-34,695
2005	Average	51,099.6	158,333.1	722.6	26,834.7	18,464.9	7,849.4	520.5
	SD	94,512.1	318,787.7	1295	50,346.4	35,693.2	18,923.3	10,493.3
	Maximum	567,035	1,880,588	7,082	296,432	212,88	136,755	37,454
	Minimum	3,933.6	7,933	17	781.6	841.8	69	−53,203
2006	Average	56,467.6	180,785.4	766.9	29,892	20,607.9	8,408.7	875.3
	SD	99,955.3	349,004.7	1,308.5	53,662.3	36,890.9	17,400.4	9,562.9
	Maximum	590,809	1,882,527	7,099	314,398	214,915	120,089	39,922
	Minimum	3,963.3	11,531	20	791.3	854.5	76.1	−36,189.7
2007	Average	59,639.1	192,611.5	786.6	31,070	21,491.9	8,952.6	625.5
	SD	104,127.7	368,582.5	1,328.4	55,090.8	38,837.2	16,895.3	9,526.6
	Maximum	611,241	1,987,673	7,218	322,147	228,405	112,437	44,353
	Minimum	3,767.9	12,613	25	1,058.1	1,053.1	555.8	−31,676
2008	Average	60,084.7	202,062	780.2	31,511.2	22,042.7	9,720.6	−252
	SD	99,953	403,263.6	1,244.7	53,729	38,228.2	18,287.2	11,275.7
	Maximum	564,064	2,320,224	6,781	293,194	221,988	121,64	46,951
	Minimum	3,851	12,607	24	932	981	222	−50,434
2009	Average	52,002.3	214,244.1	719.6	24,416.1	21,452.1	8,449.4	−5,485.3
	SD	83,788.1	433,914.7	1,160.8	41,233.1	37,038	15,129.1	16551.3
	Maximum	460,935	2,378,223	6,072	221,699	210,687	105,038	39,663
	Minimum	3,739	13,231	26	1,027	1,077	88	−94,026
2010	Average	55,354.4	222,393.5	717.4	26,722.7	21,521.4	8,584.2	−3,382.9
	SD	87,076.2	450,182.1	1,173.5	42,525.5	37,313.4	16,835.1	17,708.9
	Maximum	486,953	2,479,899	6,148	217,336	211,021	119,962	46,624
	Minimum	4,051	11,697	27	1,040	1,114	78	−113,647
2011	Average	60,208	222,747	759.5	29,496.9	22,668.9	9,264.7	−2,436.7
	SD	93,698.2	442,930.4	1,239	45,549.1	37,813.1	18,986.7	18,338.6
	Maximum	555,016	245,1857	6,170	246,514	215,665	134,154	61,829
	Minimum	8,215.8	11,475	103	3,988.2	3,231.6	268	−103,305
2012	Average	59,090.5	222,314.3	709.4	27,721.1	22,224.7	9,735.8	−4,239.4
	SD	86,738.6	447,105.3	1,068.3	42,688.7	35,896.6	20,801.4	22,140.9
	Maximum	523,426	2,498,435	5,839	248,467	205,448	144,284	62,234
	Minimum	7,633.2	11,146	101	3,299.6	2,664.5	234	−101,265
2013	Average	59,267.4	223,422.2	720.2	28,358.3	21,480.4	10,455.6	−3,577.7
	SD	82,047.6	454,472.9	1,064.4	39,850.9	32,743.9	22,709.9	23,168.1
	Maximum	507,445	2,356,948	5,693	229,063	198,433	152,169	58,091
	Minimum	7.670.8	11,584	67	3,224.3	2,440.9	207	−121,539
2014	Average	62,889.2	23,6618	738.7	30,655.6	22,266.8	9,552.1	−1,163.3
	SD	86,294.6	470,842.5	1,057.5	41,979.6	33,779.1	18,857.9	21,990.1
	Maximum	523,713	2,569,737	5,627	231,536	206,168	126,596	71,042
	Minimum	7,556.6	12,495	64	-458	2,122.4	158	−101,228
2015	Average	70,318.6	244,508.4	746.1	35,110.3	23,452.1	8,926.1	2,732.1
	SD	94,028.3	481,372.2	973.1	44,187.6	32,804.5	15,628	21,634.5
	Maximum	576,721	2,626,719	5,327	228,816	195,156	97,9	90,912
	Minimum	10,473.8	15,157	160	-2,772	4,580.9	112	−99,938
2016	Average	78,722.9	262,853.7	764.2	40,328.4	25,216.7	8,103.8	7,008
	SD	101,348.6	515,987.9	932.3	48,203.1	34,745.1	13,776.5	24,208.3
	Maximum	603,016	2,831,382	5,129	284,217	210,105	78,067	137,525
	Minimum	13,981.6	14,783.1	237	7,333.7	6,120.7	138.6	−74,534
2017	Average	85,275.6	271,384.1	832.2	45,390.9	26,468.5	8,262.3	10,660.1
	SD	112,515	520,231	1,072.4	56,584	35,608.2	13,452	21,015.9
	Maximum	695,311.5	2,976,536	6,073	303,781	21,2578.5	79,124	139,084
	Minimum	12,343.2	16,893	260	5,526.7	5,363.7	144	−50,103

Note: Data are in thousands of Euros except the number of employees.

The data from Table 1 clearly show the effects and consequences of the crisis that erupted in 2008. For all companies, the decline of mean revenues did not clearly recover until 6 years later. However, the most recent data show a significant increase in revenues and their dispersion. The mean of the value of gross margin (gross added value) followed a similar trend as revenues; even the recovery of precrisis levels was later than that of revenues. Prior to the 2008 crisis, operating profits were positive but in a declining trend. The 2008 crisis deepened this evolution, and operating profits become negative. The worst figures were observed in 2009, when the mean losses reached almost €5.5 million. The initial adjustment of labor and capital costs at the start of the crisis, especially the last one, was not enough to prevent a strong decrease in the operating profit average. The recovery in recent years has resulted in an increase in the average number of employees, and its value has even exceeded the precrisis levels in the past year. Therefore, adjustment of labor costs was, in part, due to a reduction in the average workforce. On the other hand, the mean of the assets’ value decreased at the start of the crisis. Furthermore, while activity volumes have recovered in recent years, the adjustment of capital costs has continued.

Data Analysis

Using the expressions in Equation (3) and values from Table 1, operating profit variations were disaggregated in the corresponding components. The decomposition of operating profit variations required a calculation of deflated revenues (output), margin per output unit, prices per employee and capital unit, and labor and capital used per output unit. Supplemental Table 1 (available online) collects the mean and the standard deviation of each variation component and the total profit variation through the period 2004 to 2017.

In interpreting the results, it should be remembered that positive variations of gross margin and output are associated with positive variations of the operating profit, whereas a positive variation of inputs, in terms of price or efficiency, is associated with a negative variation of the operating profit. For the overall period, the positive variations of operating profit have been associated with the positive variations in gross margin, which have been combined with a positive variation of capital price and a positive variation of the labor input efficiency. These positive variations have offset the negative variations in terms of output, labor price, and capital efficiency.

It is relevant to distinguish between variations from income (gross margin and output) and variations from costs (price and efficiency of inputs). In the period as a whole, the variations from income accounted for 65.8% of the positive variation in operating profit; the remaining 34.2% was associated with the positive impact of the variations in costs. Therefore, improvements in income efficiency have had a greater impact on profit efficiency than improvements in cost efficiency. These results are consistent with Arbelo et al. (2015). These authors confirm that the greatest margin for improvement in profit efficiency by the Spanish hotel industry in the years 2007 to 2011 was precisely in revenue efficiency.

Given the high dispersion observed in the data from Supplemental Table 1, which has increased over time, a factorial analysis can summarize this dispersion in a few dimensions. Principal component analysis (PCA) is a multivariate statistical methodology used to reduce a set of variables into a few components that collect the maximum variability of the original data (Jolliffe, 2011). Therefore, a PCA application allows for reduction of the components of the operating profit variation into a few company profile factors.

With the aim of normalizing the data according to company size, the variations are divided by the number of employees of the company. The input variations are multiplied by (−1) to facilitate the interpretation of the results. Thus, positive (negative) values show variations of components that increase (decrease) operating profit. Table 2 shows the results of PCA on the normalized data. The usefulness of this approach has been tested previously using the usual criteria. The Kaiser-Meyer-Olkin test, a measure of the proportion of variance among variables that might be a common variance, is used to verify the sampling adequacy for the analysis. The results of the PCA analysis show that the Kaiser–Meyer–Olkin is 0.65, which is a sufficient value. Bartlett test of sphericity, χ²(15) = 1,275.9, p < .0000, presents a high value, which reveals that the correlations among variables are adequate. Table 2 presents the results of the PCA with varimax rotation used to reduce the number of variables by a factor.

Table 2

PCA Results

Factors and Variables	Loading F1	Loading F2	Loading F3	Eigenvalue	Variance Explained
F1: Capital cost management				2.25	27.72%
Capital price variation/employee	−0.929	−0.008	−0.040
Capital efficiency variation/employee	0.796	0.406	0.036
F2: Market strategy				1.66	26.21%
Gross margin variation/employee	0.248	0.761	−0.042
Quantity variation/employee	−0.030	−0.862	0.089
F3: Labor cost management				0.89	26.13%
Labor price variation/employee	0.263	0.104	0.892
Labor efficiency variation/employee	0.190	0.272	−0.872

Note: Total variance extracted = 80.06%.

Three factors are obtained to ensure that the variance of each profit component is correctly collected, and the total data variance extracted by these factors is 80.1%.

The second factor contrasts the positive variations from the gross margin (and the negative variations from the output) versus the positive variations from the output (and the negative variations from the gross margin). This factor can be identified as the market strategic approach used by a company to capture value from consumers. Enz et al. (2016) show that there is a trade-off between prices (gross margins) and occupancy (output). When a hotel company adopts a strong tight–gross margin policy, it captures less value for each unit of revenue but aims to stimulate demand and obtain a greater overall value. By contrast, if a hotel company adopts a leaky–gross margin policy, then the hotel company captures more value for each unit of revenue but risks discouraging demand. The best strategy depends on the characteristics of the demand (i.e., the price elasticity of the demand for the company’s services) and the company’s circumstances (i.e., the service capacity of the company in reference to demand). Therefore, it is reasonable to associate a positive variation in gross margin with the first strategic path and a positive variation in output with the second strategic path. Moreover, Factor 2 positions companies in terms of the strategic path adopted (market strategy).

Factor 3 draws a negative association between the positive operating profit variations from the labor price variations and the positive variations from the labor efficiency variations. Factor 1 is similar to Factor 3 for the case of capital input, but the direction of the variations is opposite. Therefore, both factors are associated with cost input management and control. The cost per unit of output depends on the inputs used and their price. A company can focus the control of input cost on two perspectives. One approach is focused on the inputs used; this can be considered a technical approach. The second approach is focused on the price inputs, which can be considered a more economic approach. Factors 1 and 3 position companies in terms of which approach is followed to better control capital and labor costs.

Operating Profit Driver Profiles and Internal and External Factors

The temporal evolution of the average values of the three factors is shown in Supplemental Figure 1 (available online); in addition, the evolution in the variation of the operating profit average per employee is also shown. The temporal differences cannot be tested using analysis of variance because a normality test on the factors reveals that the normality hypothesis does not fully hold. However, a nonparametric test is a reasonable alternative. The Friedman test, which analyzes the difference in distribution among several related samples (a variable is measured for the same subject at different times or conditions), is used. The results presented in Table 3 confirm that the null hypothesis of equal population distributions can be rejected for the three factors. Supplemental Figures 2, 3, and 4 (available online) present the evolution of the quartile values (Quartile 1, Quartile 2—median—and Quartile 3) for the annual distribution of each factor. In conclusion, the distribution function of each factor varies over time, meaning that the drivers of operating profit changed throughout the period.

Table 3

Friedman Test Results

Periods	Capital Cost Management			Market Strategy			Labor Cost Management
Periods	Mean	Median	Mean Rank	Mean	Median	Mean Rank	Mean	Median	Mean Rank
2004-2005	−0.005	−0.005	6.56	−0.135	−0.100	6.12	0.060	0.049	7.37
2005-2006	−0.108	0.059	6.84	−0.026	−0.067	6.65	0.026	−0.039	6.6
2006-2007	0.028	0.077	7.63	−0.079	−0.084	6.63	0.016	−0.003	6.89
2007-2008	0.147	0.029	6.82	0.031	−0.123	6.46	0.071	0.026	7.4
2008-2009	−0.442	−0.101	4.89	−0.293	−0.287	3.39	−0.142	−0.049	6.32
2009-2010	0.064	0.068	7.91	0.137	−0.031	6.86	−0.366	−0.096	5.67
2010-2011	0.311	0.121	8.44	−0.060	−0.050	7.25	−0.135	−0.069	5.75
2011-2012	0.179	0.082	7.96	−0.132	−0.172	4.98	0.145	0.012	7.61
2012-2013	0.201	0.119	8.75	−0.229	−0.160	5.21	−0.043	−0.012	6.32
2013-2014	−0.207	0.071	7.75	−0.154	0.011	8.18	0.088	−0.017	6.79
2014-2015	−0.027	−0.034	6.25	0.225	0.092	10.04	−0.030	0.000	7.21
2015-2016	−0.120	−0.002	6.04	0.502	0.085	9.35	0.017	0.113	8.11
2016-2017	−0.021	−0.062	5.14	0.211	0.154	9.89	0.292	0.164	8.96
Friedman test
χ²	65.83			173.77			38.66
Degrees of freedom	12			12			12
Asymptotic significance	0.00			0.00			0.00

As shown in Supplemental Figure 1, the fall of the operating profits in the crisis period was associated with a reduction of the gross margin, with labor management focused on efficiency and capital management focused on capital price. There was a short-lived recovery pushed up by gross margins and an improvement in capital efficiency. However, the gradual deterioration of capital efficiency and the return to tighter gross margins disrupted this slight recovery. Operating profit growth in the past years of the period was based on a market strategy more focalized on gross margins, as well as on greater control of capital price, and to a lesser extent on control over labor prices. However, the negative variation of the operating profits in the past years was associated with a reduction of the gross margin and a deterioration of labor efficiency. These results support previous evidence of the effect of the crisis on the Spanish hotel industry. Alonso-Almeida and Bremser (2013) analyzed a sample of Madrid hotels in the past crisis period. They concluded that higher financial performance was associated with the ability to maintain or increase prices and improve efficiency management. Moreover, hotels that were forced to decrease prices and unable to improve efficiency had the worst performance. In a similar fashion, Arbelo-Pérez et al. (2017) find evidence that Spanish hotels offering higher quality service also present better performance. Therefore, the extra cost of higher quality hotel service is more than offset by the higher revenues, and a market strategy focused on gross margins presents better performance.

Based on Supplemental Figures 1 to 4 and Table 3, the hypothesis that the external environment can affect the profit drivers’ trajectories that hotel companies use to push their profits is supported. Köseglu et al. (2013) show the empirical relationship between companies’ strategies and the adversity of the external environment. In our framework, we test this hypothesis considering real gross domestic product growth as an indicator of a favorable or adverse external environment. Perles-Ribes et al. (2017) show the empirical bidirectional relationship between the growth of tourism activity and economic growth in Spain. Annual gross domestic product growth ratios in Spain were positive in the period 2004 to 2008, negative in the period 2009 to 2013, and positive again from 2014⁶ on. Therefore, we define the period from 2009 until 2013 as crisis years. Because none of the three factors meets the normal hypothesis, Wilcoxon signed-ranks test is a reasonable nonparametric alternative to test if the profiles of profit drivers were significantly different in the period of crisis (Wilcoxon, 1945). For each company, the average value of each variable is calculated for both crisis and no-crisis periods. Thus, the differences between these average values are tested. Additionally, the relationships between the determinants of the economic environment and the deviations in income and costs (income and cost efficiency), the operating benefit per employee, and the assets are also tested using the same procedure. The results presented in Table 4 show significant differences between the crisis and no-crisis years. Thus, in the stage of economic recession, the largest Spanish hotel companies protected their profits with a market strategy based on stronger, tight gross margins to stimulate output. This is consistent with the usual approaches of the industrial economy (Enz et al., 2016). Price elasticity of market demand is higher (lower) in crisis stages (growth periods), so a strong tight–gross margin (leaky–gross margin) strategy is better suited to an adverse (favorable) external economic environment. On the other hand, crisis stages are characterized by greater control over capital price (interest rates are usually lower in recession periods) and labor efficiency (i.e., employment adjustments are usually more frequent in crisis periods). There is no evidence that the assets of these companies were resized throughout the crisis stage. Logically, the operating profit per employee is significantly lower in periods of crisis. Finally, significant evidence has been found supporting cost efficiency as the main component of profit efficiency in recession periods, whereas income efficiency is the main component in growth periods.

Table 4

Wilcoxon Signed-Ranks Test Results

Variable	Positive Ranks			Negative Ranks			Test Statistics
Variable	N	Mean Rank	Sum of Ranks	N	Mean Rank	Sum of Ranks	Z	p
Capital management (crisis–no-crisis periods)	37	29.78	1102	20	27.55	551	−2.19^b	.029*
Market strategy (crisis–no-crisis periods)	7	31.14	218	50	28.7	1,435	−4.83^a	.000**
Labor management (crisis–no-crisis periods)	14	24.93	349	43	30.33	1304	−3.79^a	.000**
Income efficiency (crisis–no-crisis periods)	6	21	126	51	29.64	1527	−5.57^a	.000**
Cost efficiency (crisis–no-crisis periods)	14	27.07	379	43	29.63	1274	−3.56^a	.000**
Assets (crisis–no-crisis periods)	24	30.00	720	33	28.27	933	−0.85^a	−.397
Operating profit_employee (crisis–no-crisis periods)	4	8.75	35	53	30.53	1618	−6.29^a	.000**

^aBased on negative ranks. ^bBased on positive ranks.

p < .05. ^**p < .01.

Internal factors also can affect the profit drivers that hotel companies use to push their profits. There exists evidence that the internal structure of companies in the hospitality sector exhibits a number of distinctive features that can affect their management strategies and their performance (Singal, 2015). One of the most analyzed internal factors is the size of hotel companies, which is related to their power and acting capacity and the presence of scale economies or diseconomies (i.e., Ben Aissa & Goaid, 2016; De Jorge & Suárez, 2014; Lado-Sestayo et al., 2018). In our framework, we test if the profiles of profit drivers show significant differences, taking into account company size. As usual, companies’ assets are considered as a proxy variable of size (i.e., Lado-Sestayo et al., 2018). Because company asset is a quantitative variable, and none of the three factors meet the normal hypothesis, the linear relationships cannot be tested using the usual Pearson coefficient. Therefore, we have to perform the rank Spearman test that is a nonparametric test (Spearman, 1904). For each company, the average value of the entire period is calculated for each variable, and the correlations among the variables are tested. Table 5 shows the test results when operating profit variation for employees is also included. The results do not identify a significant relationship between hotel company size and the factor associated with the management of capital or with the operational profit per employee. On the other hand, there is significant positive relationship between hotel companies’ size and market strategies focused on gross margins and between hotel companies’ size and labor management focused on efficiency. However, these relationships do not lead to greater profitability.

Table 5

Spearman Test Results

Assets	Capital Cost Management	Market Strategy	Labor Cost Management	Profit Variation to Employee
Spearman ρ correlation	−0.054	0.275*	−0.310*	−0.058
Significance (two tailed)	0.69	0.038	0.019	0.67
N	57	57	57	57

p < .05.

Conclusions

The operating profits of hotel companies are subject to continuous change. The external environment and internal management decisions are catalysts for this change. In fact, operating profit variations are the result of the interactions between these catalysts. Therefore, an accurate analysis of operating profit variations can provide insight into the strategic management drivers that companies use to improve performance. However, the analysis of profitability usually adopts a static perspective, focusing on a specific time and market position of a company according to fixed coordinates. This article presents a methodological approach to disaggregate operating profit variations into a set of components (drivers). These drivers have the ability to draw the trajectory followed by hotel companies to push their profits. Therefore, the analysis offers a dynamic perspective on the management guidelines followed by companies to boost their profit efficiency.

The analysis was applied to the largest Spanish hotel companies, but this methodology can be easily extended to other tourist sectors (restaurants, airlines, etc.). The largest Spanish hotel companies provide an ideal application framework because tourism is an important economic sector in Spain, and the largest companies have a great operating capacity to act and react to their external environment. Moreover, the largest Spanish hotel companies have had to face an adverse external environment derived from the past period of economic recession, and they have taken advantage of the recent economic recovery.

Fifty-seven companies provided the basis of the study and were analyzed throughout the period 2004 to 2017. All are companies for which complete information is available for the global period, and they represent almost half of the total largest hotel companies in 2017. For the overall period, the results of the analysis show that income efficiency is the variable with the greatest positive impact on profit efficiency. This result is consistent with Arbelo et al. (2015), who note that income efficiency is the path with the greatest potential to improve profit efficiency for Spanish hotels. Moreover, the result is also consistent with the notoriety achieved by revenue management systems and their effects on hotel profitability (Queenan et al., 2011). Additionally, in terms of cost efficiency, the main impact on profit has come from improvements in technical efficiency.

The research has shown that the profile of profit drivers is not uniform and time invariant. Therefore, internal and external conditions faced by companies not only affect the magnitude and the sign of the total operating profit variations—which has already been widely analyzed in the literature—but they also can have a significant effect on how these variations are configured. In particular, a test showed that in recession periods the largest Spanish hotel companies protected their profits by focusing their market strategy on output and controlling the price of capital and labor efficiency. By contrast, in the recovery periods, these companies have looked for their profit through a market strategy focused on gross margins, with control of costs focused on capital efficiency and labor prices. In a similar sense, the article shows that company size is also a significant internal factor, not only affecting the magnitude of operating profit variations but also having a significant impact on how profit variations are configured.

The research should be considered with caution—the analysis identifies the paths that companies have followed to push their profits but not the concrete internal and external factors that are behind these trajectories. For instance, external factors affecting the intensity of competition in the industry or internal factors such as the location or category of establishments could intensify or soften the profit drivers’ trajectories described. A future extension of this research is to incorporate internal companies’ characteristics and external environment issues into the analysis in a more comprehensive way. Additionally, the data used were collected exclusively from annual financial statements, which limit strategic insights.

Finally, the article considers that the relationship between output and inputs in the hotel industry is defined by a CRS technology. Although the use of this approach is usual and justified, it does not allow an accurate efficiency analysis.

Supplemental Material

Online_Supplement_Files_3_6_20 – Supplemental material for Operating Profit Drivers for Large Companies in the Spanish Hotel Sector: A Temporal Analysis (2004-2017)

Supplemental material, Online_Supplement_Files_3_6_20 for Operating Profit Drivers for Large Companies in the Spanish Hotel Sector: A Temporal Analysis (2004-2017) by Miquel Carreras-Simó in Journal of Hospitality & Tourism Research

Supplemental Material

OpProfit_Drivers_JHTR_ed_final_03_6_20 – Supplemental material for Operating Profit Drivers for Large Companies in the Spanish Hotel Sector: A Temporal Analysis (2004-2017)

Supplemental material, OpProfit_Drivers_JHTR_ed_final_03_6_20 for Operating Profit Drivers for Large Companies in the Spanish Hotel Sector: A Temporal Analysis (2004-2017) by Miquel Carreras-Simó in Journal of Hospitality & Tourism Research

Footnotes

Author’s Note:

I wish to thank the associate editor and three anonymous referees for their valuable comments. My sincere thanks to Germà Coenders, his comments on previous versions of the article have been invaluable, and to Nela Filimon and Andreas Kyriacou, for their help in the grammar review. I also wish to express my gratitude for financial support from the Universitat de Girona program 2017/031330/001 and to the Economics Department of the West Virginia University for its hospitality during the research stay where this work started, especially to Brad R. Humphreys and Roger Congleton.

ORCID iD

Miquel Carreras-Simó

Notes

Supplemental Material

Supplemental material for this article is available online.

Miquel Carreras-Simó (email: miquel.carreras@udg.edu), PhD, is full professor of Business Economics in the Business Department of Universitat de Girona.

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