Total factor productivity growth and regional competitive analysis of China’s star-rated hotels

Abstract

This study investigates the total factor productivity (TFP) growth, technological progress, pure technical efficiency change, scale efficiency change, and mix efficiency change of star-rated hotels in China by employing a Hicks–Moorsteen index approach. The results show that the TFP of star-rated hotels in China had an annual average growth rate of 13.11%, mainly attributed to an annual average growth rate of operational efficiency of 21.85% and a mix efficiency growth rate of 13.52%. The growth rate of optimal production technology in the Western region markedly outperformed those in other regions and yet its growth rate of operational efficiency significantly underperformed. We also found that catch-up effects in the Central and Western regions were progressing in terms of operational efficiency and optimal production technology, respectively. The findings suggest that policy makers and practitioners should focus on TFP growth and its components, drawing the attention of star-rated hotels to upgrade their optimal production technology and enhance their operational efficiency as a means of improving TFP and competitiveness. Lastly, this study advances a new research perspective in efficiency assessment in the hotel industry by considering both financial and service production outputs.

Keywords

competitive analysis efficiency Hicks–Moorsteen index hotel total factor productivity

Introduction

China has become the second top destination for both international tourism receipts and tourist arrivals and is likely to continue playing an important role in the tourism industry worldwide benefiting from its strong currency and fast-growing economy (China National Tourism Administration (CNTA), 2016; United Nations World Tourism Organization, 2016). Its 2015 tourist arrivals reached 133.82 million, of which 56.88 million stayed overnight with an average stay of 3.26 days, increasing from 3.15 days in 2014 (CNTA, 2016). Total expenditure of inbound tourists amounted to US$114 billion in 2015, more than doubling from US$45.81 billion in 2010. One main driver supporting China’s strong tourism performance was the rapid development of the hotel industry, particularly of star-rated hotels (Tsai, 2009) which, from 2005 to 2014, accounted for an average of 78.7% of revenue from the hotel industry nationwide (National Bureau of Statistics of the People’s Republic of China, 2016).

While the hotel operational environment in China has changed dramatically since the early 1980s when the hotel industry started to develop (Zha and Li, 2017), the observed growth of star-rated hotels through extensive capital expansion may not be sustainable if demand does not catch up with supply, resulting in resources lying idle. The industries in China have transformed from extensive to intensive growth (Chen, 2009); quality improvement and efficiency of economic growth have been paid more attention. In particular, tourism was a highlight of economic reform and its development has contributed significantly to the national economy (Tsang and Hsu, 2011). Such a significant developing phenomenon of the hotel industry in China, under a rapidly changing economic environment, urges a need for a more precise approach that focuses on productivity evaluation and improvement (Huang et al., 2012). On the other hand, the government plays an important role in shaping the development of the tourism industry through policy setting and guidance (Zhang et al., 2008). In providing the star-rated hotel industry with effective management suggestions and policy, it is high time to measure the productivity growth and effectiveness of resource utilization in the star-rated hotel industry in China.

Productivity measurement is one important issue in the production of tourism activities, including hotel services (Luo et al., 2014; Phillips, 1999). In particular, total factor productivity (TFP) is a critical indicator, reflecting the magnitude of total output growth relative to that of the growth in traditionally measured inputs of labor and capital, or the ratio of output to input used in production (Cordero and Tzeremes, 2017). An accurate measurement of hotel productivity measurement in terms of TFP growth and its components is useful for not only effective industrial and business policy-making (O’Donnell, 2010) but also national and regional tourism planning and development (Huang et al., 2012), thus leading to increased profitability for the hotel industry (Peypoch and Solonandrasana, 2008; Witt and Witt, 1989).

Conventionally, hotel industrial performance could be measured using single dimensional indicators/ratios, such as average occupancy rates (Oses et al., 2016), relative efficiency (Arbelo et al., 2017), or partial productivity indicators (Assaf and Agbola, 2011; Sigala and Mylonakis, 2005). The majority of hotel productivity–related studies are microcosmic in nature and based on cross-sectional data at the individual level of hotels; a more holistic performance measure such as TFP growth at the industrial level has received relatively scant attention in the hotel industry in China. Therefore, our research aims to fill the research void and measure TFP growth of China’s star-rated hotels at the aggregate level of the regional hotel industry (Cracolici et al., 2008; Huang et al., 2012; Tsai, 2009) using a Hicks–Moorsteen index approach (O’Donnell, 2010) to monitor TFP growth, technological progress, and changes in various efficiencies over time. Moreover, the present study could provide both development suggestions to decision makers and practitioners and a practical example to other countries and regions undergoing rapid transition in the development of their hotel industry and fills the gap by focusing on the antecedents of TFP growth of the hotel industry at the regional level in China.

Performance assessment in the hotel industry

Previous studies assessed hotel performance by applying various financial indicators, mainly focusing on profitability measures such as return on assets (Chen, 2010; Kim et al., 2002; Rudež and Mihalič, 2007) or return on investment (Jang and Yu, 2002; Wu and Liang, 2009; Xiao et al., 2012), among others. Nevertheless, such indices are largely confined to the measurement of final operational outcomes, ignoring the efficiency of resource input utilization (Tsai et al., 2011) and neglecting the multidimensional aspects of hotel industry operations (Yu and Chen, 2016). To tackle such deficiency, some scholars introduced parametric approaches such as stochastic frontier analysis (SFA) by considering multiple inputs and outputs of a hotel and assessed its efficiency by measuring its deviation from an optimal frontier (Arbelo et al., 2017; Assaf, 2012; Barros, 2004; Barros, 2006; Chen, 2007; Lin, 2011). Additionally, another common approach in efficiency assessment is a nonparametric approach of data envelopment analysis (DEA) (Charnes et al., 1978), in which an efficiency frontier is constructed reflecting a firm’s minimum resource usage for a given level of output (Liu et al., 2018).

The DEA technique has been applied in a number of hotel efficiency studies (e.g. Aissa and Goaied, 2016; Barros and Dieke, 2008; Barros et al., 2010; Detotto et al., 2014; Huang, 2017; Oliveira et al., 2013; Yin et al., 2015). In particular, some studies such as Peng and Chen (2004), Pine and Phillips (2005), and Chan (2012) focused on China’s hotel industry; analogously these studies found that hotel performance in terms of efficiency is significantly different across different regions and star ratings. To our best knowledge, only Huang et al. (2012) carried out a dynamic efficiency assessment on China’s hotel industry during 2001–2006 at the aggregate level of the regional hotel industry. The techniques of SFA and DEA not only have been applied extensively in hotel efficiency evaluation research (Yin et al., 2015) but also could be used in deriving productivity measures.

Productivity refers to the ratio of outputs over inputs yielding an absolute measure of performance that may be applied to multiple inputs and outputs through aggregation (Bernini and Guizzardi, 2010); productivity growth is a relative measure of productivity changes between different years. On the basis of SFA, Barros (2006) used a translog frontier model and the maximum likelihood estimation technique to estimate the technology change in the Portuguese hotel industry. Chen and Soo (2007) employed a multiproduct translog cost function to investigate productivity growth of the hotel industry in Taiwan. Kim (2011) also employed a translog function to examine TFP growth of the hotel industry in Malaysia. While the SFA technique could handle random disturbance (Coelli et al., 2005), it has some major constraints on production function assumptions and sample size (Zha and Li, 2017).

To the contrary, the DEA technique does not require any specification on the functional relationship between input and output variables (Banker et al., 1984). Färe et al. (1994) investigated productivity development in Swedish hospitals using the Malmquist output index approach; Barros (2005) evaluated the performance of hotels in Portugal with a Malmquist TFP index and decomposed the index into technical progress and changes in technical efficiency. Furthermore, Assaf et al. (2010) and Assaf and Agbola (2011) employed a bootstrapped Malmquist TFP index approach to assess the efficiency of hotels in Taiwan and Australia, respectively. More recently, Yu and Chen (2016) also measured productivity growth of the hotel industry in Taiwan from 2008 to 2011 using the metafrontier Malmquist TFP index.

While the Malmquist TFP index has been applied in hotel studies, its properties are maintained under constant returns to scale rather than variable returns to scale, and it generally is less applicable in interpreting TFP (Grifell-Tatjé and Lovell, 1995; Kerstens and Woestyne, 2014). O’Donnell (2010) criticized the Malmquist TFP index for its limited ability in TFP decomposition and developed a DEA-based methodology to decompose the multiplicative Hicks–Moorsteen TFP index into technical change, pure technical eficiency (PTE) change, scale efficiency (SE) change, and mix efficiency change, which provides a more general notion of TFP as expressed by a ratio of change in aggregate output to that in aggregate input (Peyrache, 2014). The Hicks–Moorsteen TFP index could generally be derived through Shephard’s distance function because of the index’s multiplicative nature (O’Donnell, 2010). Even though the directional distance function (DDF) is a complete generalization of Shephard’s distance function, McFadden’s gauge function, and the directional input and output (Chambers and Chung, 1998), the DDF is more useful in calculating the TFP index when undesirable or negative output is present (Briec and Kerstens, 2004; Luenberger, 1992).

Therefore, to investigate the TFP change of star-rated hotels at the regional level in China in a more comprehensive manner and to offer relevant developmental directions for the hotel industry across different regions, we employed the Hicks–Moorsteen TFP index approach through Shephard’s distance function in this study.

Framework

Conventional measures of the TFP change could not completely distinguish between technological progress and changes in various efficiencies, even though progress and various changes are analytically distinct and may have quite different practical implications (Nishimizu and Page, 1982). The current research adopts an efficient frontier approach by employing a DEA-based Hicks–Moorsteen TFP index that breaks down changes in TFP into technological progress and changes in operational efficiency. To provide a framework for the productivity measurement of star-rated hotels in China, the measures developed in recent years and outlined in other papers are adopted by this study (Kerstens and Van de Woestyne, 2014; O’Donnell, 2010, 2012), described in the following section.

Technology level

In the case of a single-input, single-output firm, its technology level (TL) reflects its productivity, which is a ratio of the production output to the input factor that made the output possible (Fried et al., 1993). In contrast, if the firm uses multiple inputs to produce multiple outputs, then the inputs and outputs ought to be aggregated, so that its productivity measure remains a ratio of two scalars.

Let $x_{i j}^{t} = (x_{1 j}^{t}, x_{2 j}^{t}, \dots, x_{m j}^{t})$ and $y_{r j}^{t} = (x_{1 j}^{t}, x_{2 j}^{t}, \dots, x_{s j}^{t})$ denote the observed input vector and output vector, respectively, of hotel j in period t. $i \in (1, 2, \dots, m)$ is the ith input and $r \in (1, 2, \dots, s)$ is the rth output. Furthermore, let $X_{j} = X (x_{j})$ and $Y_{j} = Y (y_{j})$ denote the aggregate input and output, and $X (.)$ and $Y (.)$ the nondecreasing linearly homogeneous aggregator functions, which means that hotels in different periods have the same input and output vectors and the same production function. With this definition, a hotel’s TL could be expressed as

{TL}_{j}^{t} = Y_{j}^{t} / X_{j}^{t}

{TL}^{t *} = Y^{t *} / X^{t *}

where t = 1, 2, …, T; j = 1, 2,…, N; i = 1, 2,…, m; r = 1, 2,…, s. Equation (1) is an observed production TL of hotel j in period t, and equation (2) is the optimal production TL of hotel(s) in period t subject to the constraint of technology.

Hicks–Moorsteen TFP index

With the above definition, the index that compares the TFP of hotel j′ in period t′ with that of hotel j in period t is as follows

{TFP}_{j, j'}^{t, t'} = \frac{{TL}_{j'}^{t'}}{{TL}_{j}^{t}} = \frac{Y_{j'}^{t'} / X_{j'}^{t'}}{Y_{j}^{t} / X_{j}^{t}} = \frac{Y_{j, j'}^{t, t'}}{X_{j, j'}^{t, t'}}

where t′ = 1, 2,…, T; j′ = 1, 2,…, N; $Y_{j, j'}^{t, t'} = Y_{j'}^{t'} / Y_{j}^{t}$ is the output quantity index and $X_{j, j'}^{t, t'} = X_{j'}^{t'} / X_{j}^{t}$ is the input quantity index. Equation (3) demonstrates that the TFP change can be written as an index of output growth divided by an index of input growth.

O’Donnell (2010) asserts that the Hicks–Moorsteen TFP (TFP_HM) index based on DEA could be denoted as

{TFP}_{HM}^{j t, j' t'} = {(\frac{\frac{d_{O}^{t'} (x_{j'}^{t'}, y_{j'}^{t'}) d_{O}^{t} (x_{j}^{t}, y_{j'}^{t'})}{d_{O}^{t'} (x_{j'}^{t'}, y_{j}^{t}) d_{O}^{t} (x_{j}^{t}, y_{j}^{t})}}{\frac{d_{I}^{t'} (x_{j'}^{t'}, y_{j'}^{t'}) d_{I}^{t} (x_{j'}^{t'}, y_{j}^{t})}{d_{I}^{t'} (x_{j}^{t}, y_{j'}^{t'}) d_{I}^{t} (x_{j}^{t}, y_{j}^{t})}})}^{1 / 2}

Here, d is the Shephard’s distance function and the subscripts O and I are output-orientation and input-orientation, respectively.

Decomposition of TFP_HM index

According to Farrell (1957), the operational efficiency (E) of hotel j in period t could be denoted as

E_{j}^{t} = \frac{{TL}_{j}^{t}}{{TL}^{t *}} ≪ 1

${TL}_{j}^{t}$ and ${TL}^{t *}$ are denoted in equations (1) and (2) and could be measured in terms of aggregate quantities. Here, the observed production TL of hotel j in period t ( ${TL}_{j}^{t}$ ) is not more than the optimal production TL of hotels in period t ( ${TL}^{t *}$ ). The eficiency measures that feature in the decomposition of operational efficiency (E) are PTE, SE, and residual mix efficiency (RME), all of which are in line with Charnes et al. (1978) and Banker et al. (1984). On the basis of equation (5), equation (3) could be further decomposed

{TFP}_{j, j'}^{t, t'} = \frac{{TL}^{t' *}}{{TL}^{t *}} \times \frac{{PTE}_{j'}^{t'}}{{PTE}_{j}^{t}} \times \frac{{SE}_{j'}^{t'}}{{SE}_{j}^{t}} \times \frac{{RME}_{j'}^{t'}}{{RME}_{j}^{t}}

Finally, the TFP_HM index could be decomposed following equation (6). The detailed calculation process of the TFP_HM index and its decomposed efficiencies can be seen in O’Donnell (2010). When the value of TFP_HM is greater than, equal to, or less than 1, the TFP increased, remained unchanged, or decreased, respectively.

The term $\frac{{TL}^{t' *}}{{TL}^{t *}}$ on the right-hand side of equation (6) is a measure of technological progress (denoted as ΔTech), which reflects the difference between the maximum productivity possible using the period-t′ technology and the maximum productivity possible using the period-t technology. The other ratios on the right-hand side of equation (6) are measures of change in PTE (denoted as ΔPTE) representing the efficiency level achieved as a result of management effort, change in scale eficiency (denoted as ΔSE) representing the efficiency level of a hotel against one that is operating at an optimal scale over the long term, and change in residual mix eficiency (denoted as ΔRME) representing the efficiency level achieved as a result of a mix of various outputs (Joo et al., 2009; O’Donnell, 2012). The product of ΔPTE, ΔSE, and ΔRME is the change in operational efficiency (ΔE) representing how well star-rated hotels actually process various inputs to achieve outputs (Barros, 2005). In our research, the TFP_HM index of star-rated hotels in China is measured every 2 years during the observation period. Accordingly, period t′ = t + 1.

In our study, technological progress (ΔTech) is the consequence of innovation or the adoption of new technology by best practice hotels on the optimal production TL. The TFP change is the product of the growth rate of technological progress and changes in various efficiencies (ΔPTE, ΔSE, and ΔRME). We should note that a high growth rate of technological progress could coexist with deteriorating efficiencies, perhaps due to failures in achieving technological mastery that could lead to resources being wasted or insufficient outputs, and thus the overall growth rate of TFP could be either positive or negative. Similarly, a relatively low growth rate in technological progress could coexist with rapidly improving efficiencies. Therefore, on the basis of a detailed performance decomposition, suggestions to improve the rate of TFP growth could be better devised. For example, firms should focus on accelerating their rate of innovation in circumstances where slow hotel TFP changes were observed due to a low rate of diffusion of the optimal production technology.

Data and variables

The method used in this study requires the identification of input and output variables for inclusion in the TFP function. The selection of variables should represent the inputs and outputs used by China’s hotel industry in its service production process and the results obtained in empirical research performed prior to the investigation (Such Devesa and Mendieta Peñalver, 2013). For a TFP study, each input is classified as either an indicator of labor or capital reflecting resources such as staff, capital, and equipment deployed in producing tangible products and intangible services (Huang et al., 2012; Yasin et al., 1997; Zhou et al., 2008). As a result, we considered two inputs (i.e. number of employees and the amount of fixed assets) to denote human resources and capital deployment (Kim, 2011). Output variables could include those besides revenue and thus “total operating revenue” and “room nights sold” were selected to denote the financial and service production outcomes. The input and output indexes we selected were also adopted by Barros (2006), Kim (2011), and Yin et al. (2015).

In our study, since the hotel TFP change was analyzed from the perspective of both industrial and regional development, the hotels in China’s 31 respective provinces and municipalities were regarded as 31 unique decision-making units (Huang et al., 2012). According to the Seventh Five-Year Plan of the Chinese government considering the differences in regional economic development, these 31 provinces and municipalities in China can be divided into Eastern, Central, and Western regions (Liu et al., 2017), as shown in Table 1.

Table 1.

Regions and constituent provinces and municipalities.

Regions	Constituent provinces and municipalities
Eastern region (11 districts)	Beijing, Tianjin, Hebei, Liaoning, Shandong, Shanghai, Jiangsu, Zhejiang, Fujian, Guangdong, Hainan
Central region (10 districts)	Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan, Guangxi
Western region (10 districts)	Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, Tibet, Xinjiang, Xizang

Source: Liu et al. (2017).

To estimate the TFP change of star-rated hotels in China, provincial panel data obtained from the CEIC China Database (2016) and Statistical Yearbook of China were used. For the sample period 2001–2015, a total of 465 observations, including 31 provinces and municipalities in mainland China, were included. Table 2 shows the descriptive statistics of the input and output variables used for TFP change assessment.

Table 2.

Descriptive statistics of input and output variables (2001–2015).

Region	Index	Number of employees	Fixed assets	Total revenue	Room nights sold
Eastern region	Median	73.76	6537.51	2422.48	6,753,875.90
	Average	78.52	11,544.17	4856.04	9,985,549.65
	SD	43.56	12,354.77	5539.40	8,919,741.15
	Max	188.64	54,265.24	20,686.44	52,418,869.17
	Min	13.20	460.54	121.19	506,099.44
Central region	Median	31.74	6281.34	2352.37	6,665,310.99
	Average	33.34	10,704.64	4410.13	9,039,606.51
	SD	12.48	11,879.49	5151.69	7,368,342.43
	Max	60.19	57,538.01	22,405.24	30,390,492.03
	Min	9.79	316.62	81.47	210,646.46
Western region	Median	26.27	8171.95	3039.14	8,435,911.54
	Average	28.09	10,777.23	4293.27	9,736,672.95
	SD	18.39	8647.37	4109.65	6,678,036.83
	Max	86.74	44,390.79	26,198.52	33,687,023.55
	Min	1.44	665.35	218.20	640,978.40

Note: Number of employees is measured in thousands; fixed assets and total revenue are measured in million RMB (RMB stands for Renminbi, which is a Chinese currency); Room nights sold is measured in nights; TFP_HM: Hicks–Moorsteen total factor productivity; SD: standard deviation.

From Table 2, we can see that while the median of the number of employees in the Western region was the lowest, the median of the fixed assets was the highest and the standard deviation was the lowest in the Western region. The maximum fixed assets and total revenue in the Central region were higher than those in other regions, but the number of room nights sold in the Eastern region was the highest.

Results and analysis

According to our framework, the TFP_HM by year was calculated and then separated into technological progress (ΔTech) and operational efficiency changes (ΔE). The TFP_HM can be explained by its components ΔTech and ΔE, respectively. Moreover, ΔE was further decomposed into pure technology efficiency change (ΔPTE), scale efficiency change (ΔSE), and residual mix eficiency change (ΔRME). The results of TFP_HM and the various decomposed values are listed in Tables 3 and 4.

Table 3.

Evolutionary trend of star-rated hotels’ TFP_HM in China.

Year	TFP_HM	ΔTech	ΔE	ΔPTE	ΔSE	ΔRME
2002/2001	0.8835	0.9260	0.9231	1.0120	1.0887	0.8749
2003/2002	0.8036	0.7621	1.0913	1.0465	1.0793	0.9969
2004/2003	1.1078	1.2842	0.9257	0.9976	1.0382	0.9183
2005/2004	2.1608	1.2575	1.8314	1.1062	0.9393	1.8125
2006/2005	1.0241	1.0376	1.0826	0.9832	1.0933	1.0095
2007/2006	0.8890	0.8810	1.0346	1.0312	0.9263	1.0775
2008/2007	1.1682	1.0520	1.1401	0.9723	1.0842	1.0775
2009/2008	1.0718	1.3220	0.9060	1.0721	0.9894	0.8433
2010/2009	0.9174	0.7237	1.3495	0.9757	1.0193	1.3305
2011/2010	0.9550	0.8700	1.1204	0.9972	0.9733	1.1612
2012/2011	1.1620	1.1894	1.0116	1.0342	1.0844	0.9331
2013/2012	0.6027	0.8217	0.7575	0.8544	0.9117	0.9498
2014/2013	1.9139	0.7692	2.9107	1.3792	1.1214	1.9511
2015/2014	1.1750	1.3220	0.9740	0.9757	1.0728	0.9564
Average	1.1311	1.0156	1.2185	1.0312	1.0301	1.1352
Max	2.1608	1.3220	2.9107	1.3792	1.1214	1.9511
Min	0.6027	0.7237	0.7575	0.8544	0.9117	0.8433

Note: TFP_HM: Hicks–Moorsteen total factor productivity; ΔTech: technological progress; ΔE: operational efficiency; ΔPTE: pure technical eficiency; ΔSE: scale eficiency; ΔRME: residual mix eficiency.

Table 4.

Evolutionary trend of star-rated hotels’ TFP_HM in Eastern, Central, and Western regions.

Year	TFP_HM			ΔTech			ΔE			ΔPTE			ΔSE			ΔRME
Year	Est	Ctl	Wst	Est	Ctl	Wst	Est	Ctl	Wst	Est	Ctl	Wst	Est	Ctl	Wst	Est	Ctl	Wst
02/01	0.8003	0.9137	0.9419	0.8699	0.9232	0.9846	0.8909	0.9332	0.9468	0.9549	1.1481	0.9577	1.2300	1.0483	0.9805	0.7854	0.8099	1.0177
03/02	0.8570	0.7998	0.7532	0.7863	0.8151	0.6946	1.1159	1.0051	1.1373	1.1059	0.9351	1.0783	1.1412	1.1265	0.9788	0.8964	0.9853	1.1071
04/03	0.8978	0.8811	1.5033	0.8837	0.9966	1.92	1.0472	0.9152	0.8128	0.9084	1.0046	1.0811	1.0304	0.9966	1.08	1.1378	0.9516	0.6715
05/04	2.2315	2.2657	2.0044	1.2956	1.1648	1.2952	1.8252	2.0419	1.6654	1.2563	1.0664	0.9886	0.9508	0.8636	0.9899	1.5950	2.2133	1.702
06/05	1.1104	1.3188	0.6967	1.0973	1.0851	0.939	1.1385	1.304	0.8455	1.0004	1.1309	0.8452	1.0845	1.1754	1.035	1.0539	1.0158	0.96
07/06	0.8116	0.8753	0.9775	0.8832	0.7656	0.9733	0.9371	1.1659	1.0247	1.0096	1.0088	1.0712	0.9134	0.963	0.9092	0.9875	1.2001	1.0671
08/07	1.1662	1.3663	1.0081	1.1086	1.0595	0.9892	1.0621	1.3197	1.0711	0.9548	1.014	0.9557	1.1036	1.0979	1.0536	1.0396	1.151	1.0552
09/08	1.0233	0.7894	1.3514	1.2127	1.5178	1.271	0.9173	0.6429	1.11	1.0607	0.9536	1.1804	1.0110	0.9329	1.0141	0.8609	0.6889	0.9521
10/09	1.1711	1.0362	0.5665	0.7652	0.7227	0.6831	1.6395	1.4772	0.955	1.0298	1.0732	0.8418	1.0516	1.0421	0.9684	1.4788	1.3115	1.1979
11/10	0.7364	0.8461	1.2625	0.8163	0.9272	0.877	0.9037	0.925	1.4968	0.8436	0.905	1.2261	0.9476	0.9382	1.0278	1.1260	1.0813	1.2617
12/11	1.2687	0.9849	1.2002	1.3529	0.8991	1.2634	0.9569	1.0944	0.9986	1.0723	1.0471	0.9856	1.1460	1.0228	1.0732	0.8205	1.0094	0.9832
13/12	0.5084	0.3524	0.9018	0.7836	0.8979	0.7974	0.6435	0.4299	1.1395	0.7837	0.8469	0.9312	0.8807	0.7533	1.0724	0.9303	0.729	1.1499
14/13	2.2431	2.9252	0.7571	1.0373	0.636	0.6102	2.5175	4.86	1.709	1.6334	1.3896	1.1164	1.0356	1.3905	0.9869	1.4812	3.0809	1.4965
15/14	1.0473	0.682	1.7061	0.9770	1.4113	1.5938	1.1034	0.6093	1.143	0.8863	0.9335	1.0995	1.0852	1.1313	1.0126	1.1531	0.6368	1.0212
Average	1.1338	1.1455	1.1165	0.9907	0.9873	1.0637	1.1928	1.3374	1.1468	1.0357	1.0326	1.0256	1.0437	1.0345	1.013	1.0962	1.2046	1.1174

Note: Est, Ctl, and Wst are abbreviations for the Eastern, Central, and Western regions, respectively; TFP_HM: Hicks–Moorsteen total factor productivity; ΔTech: technological progress; ΔE: operational efficiency; ΔPTE: pure technical eficiency; ΔSE: scale eficiency; ΔRME: residual mix eficiency.

Regional performance analysis

Table 3 shows that the star-rated hotels had TFP growth (TFP_HM > 1) in 8 of 14 years but a reduction (e.g. TFP_HM < 1) in the other 6 years. The average value of TFP_HM exceeded 1, indicating that the star-rated hotels had gained an average of 13.1% TFP growth per annum during the sample period. Both the technological progress (1.56%) and efficiency change (21.85%) played a positive role in supporting the growth of star-rated hotels in China. From these results, we can see that the operational efficiency growth was the main driving force for TFP in star-rated hotels. In particular, the annual average growth rate of mix efficiency was 13.52%, outperforming those of PTE and SE. Generally speaking, the output productivity reflected by revenue and room nights sold demonstrated good growth momentum. More importantly, the high growth rate of mix efficiency reveals that the mixed output of revenue and room nights sold continually improves, which means that the coordination between commercial profit (i.e. revenue) and hospitality services (i.e. room nights sold) is also improving.

While the productivity of star-rated hotels shows a rising trend, the TFP_HM fluctuated. The TFP_HM showed a remarkable 116.08% growth from 2004 to 2005; however, it also experienced a significant drop in 2013 of 39.73%. This increase or decrease in hotel TFP may depend on different driving forces. Taking TFP_HM in 2008–2009 and 2013–2014 as an example, while the improvement of TFP_HM from 2008 to 2009 (i.e. 7.18%) was mainly attributed to positive technological progress (ΔTech = 32.2% and ΔE = −9.4%), from 2013 to 2014 (i.e. 91.39%) it was mainly derived from operational efficiency improvements (ΔTech = −23.08% and ΔE = 191.07%).

As stated earlier, regional economic development in China is uneven, which could have influenced the growth of the hotel industry and management decision-making (Chen, 2007). Table 4 shows the evolutionary TFP_HM trend in star-rated hotels in the Eastern, Central, and Western regions.

In the Eastern region, 8 of 14 TFP changes were progressive from 2001 to 2015. While the annual average TFP_HM had an increase of 13.38%, mild technological regression (ΔTech = 0.9907) impeded further improvement in TFP_HM. The average improvement in operational efficiency reached 19.28% and it was this unique impetus that stimulated the TFP_HM growth of star-rated hotels in the Eastern region. In particular, the PTE, SE, and mix efficiency had an annual increase of 3.57%, 4.37%, and 9.62%, respectively, meaning that the management level, returns to scale, and the mixture of outputs improved. The level of average ΔTech (i.e. 0.9907) indicates that the optimal production technology of star-rated hotels in the Eastern region did not progress, instead they regressed slightly to 0.93%. That is, the management level of star-rated hotels was basically at a standstill during the sample period, although the level of economic development in the Eastern region expanded rapidly.

Compared with the Eastern region, the evolutionary trend of star-rated hotels’ TFP_HM in the Central region shows an analogical but more conspicuous performance: only 5 of 14 TFP_HM changes were progressive from 2001 to 2015. While the annual average TFP_HM and technology change showed an increase of 14.55% and regression of 1.27%, respectively, the annual average increase of operational efficiency of star-rated hotels in the Central region was greater than that of the Eastern region, reaching 33.74%. That is, star-rated hotels in the Central region exhibited a stronger catch-up effect in terms of operational efficiency (Cook, 1989), meaning that star-rated hotels in the Central region transformed their resource inputs into production outputs more efficiently, and the diffusion of optimal production technology was better than those in the Eastern region. Specifically, the annual average ΔRME was equal to 1.2046, which means that the optional mixture of outputs played the most important role in the catching-up effect. However, the disappointing performance of technological progress (annual average ΔTech = 0.9873) discloses that the optimal production technology showed a regressive trend in star-rated hotels in the Central region, even worse than that in the Eastern region.

Though economically underdeveloped, the Western region was the only region with an annual average ΔTech exceeding 1 (i.e. 1.0637), meaning that the optimal production technology of star-rated hotels in the Western region progressed by 6.37% annually from 2001 to 2015. However, the average increases in TFP_HM (i.e. 11.65%) and operational efficiency (i.e. 14.68%) of star-rated hotels in the Western region were the lowest compared to those of the other two regions. In other words, while both the technological progress and operational efficiency improvement together drove the TFP_HM increase of star-rated hotels in the Western region, the Western region’s slower TFP_HM increase was mainly caused by inferior performance of its operational efficiency improvement. That is, the utilization and diffusion of optimal production technology in star-rated hotels in the Western region was subordinate to those in the other two regions.

Besides regional development characteristics, there are some commonalities in the performance of star-rated hotels among the three regions. First, the TFP_HM and operational efficiency of star-rated hotels in the three regions all averagely increased. Second, the growth rates of mix efficiencies of the star-rated hotels in the three regions were the highest among the various efficiency changes. Last, the level of TFP_HM was unstable both regionally and nationally. Meanwhile, as we expected, there was some difference in performance between regional and national perspectives. That is, the production technology progress of star-rated hotels nationally was best reflected by those in the Western region as those in other regions showed a regression.

Integrated performance analysis

Improving productivity is a long-term task in the hotel industry (Kilic and Okumus, 2005), which signifies that a time sequence analysis of star-rated hotels is necessary and could help managers better understand the development of the industry. Accordingly, on the basis of cumulative yearly values, we depict the TFP_HM and its components of star-rated hotels, nationally and regionally, in Figures 1 to 3. The intercept of the dotted line is equal to 1 in Figures 1 to 3, above this line means an increase of TFP_HM while below means otherwise.

Figure 1.

Analysis of cumulative TFP_HM from 2001 to 2015. TFP_HM: Hicks–Moorsteen total factor productivity.

Figure 2.

Analysis of cumulative ΔTech from 2001 to 2015. ΔTech: technological progress.

Figure 3.

Analysis of cumulative ΔE from 2001 to 2015. ΔE: operational efficiency.

From Figure 1, we can see that the trend lines of the cumulative TFP_HM of hotels in all regions are, more or less, mimicked by all of them. In general, the TFP_HM of star-rated hotels has progressed positively, but with some fluctuations in most years in the sample period, and only declining in a few years. After a short, sustained drop from 2001 to 2003, the TFP_HM of star-rated hotels in the Western region took the lead in 2004. From 2004 to 2012, the TFP_HM of star-rated hotels in all three regions showed positive growth in spite of different paces. It is worth noting that the TFP_HM of both Eastern and Central regions plummeted in 2013 and thereafter the TFP_HM of all the regions resumed a positive growth. The two noteworthy TFP declines of star-rated hotels happened in 2003 and 2013, respectively. In 2003, the SARS outbreak in China caused a sharp drop in visitors, contributing to a fatal blow to the hospitality industry in Asia, especially China (Chen, 2007; Chen et al., 2007). Compared with the cause of the TFP_HM drop in 2003, the underperformance of TFP_HM change in the star-rated hotels in 2013 was most likely due to a, then, new policy (Zhao, 2014) related to “the eight rules” and a nationwide petition about frugality in the hotel industry mandated by the Political Bureau of the Central Committee and China Tourist Hotels Association in December 2012 and January 2013 (People.cn, 2014), respectively.

As one of the two driving forces of TFP_HM, the performance of technological progress (ΔTech) is like a wave throughout the years in the sample period as shown in Figure 2. By the end of the sample period, the optimal production technology of star-rated hotels in all regions had virtually not progressed, except in the Western region. Meanwhile, the optimal production technology of star-rated hotels had a sharp decline in 2002–2003 and 2013–2014, respectively, which may not be related to the operational environment under the influence of emergent matters or changes in policy. In addition, it can be seen in Figure 2 that the optimal production technology progress of star-rated hotels in the Western region was the most noteworthy and the changes in optimal production technology in the Eastern and Central regions were under the dotted line every year except 2009. Combined with the previous regional analysis, we can infer that the continuous progress of optimal production technology played an important role in the catchup of the Western region.

Finally, there are some interesting findings worthy of note. First, the highest gain of cumulative ΔTech in star-rated hotels happened during 2008–2009 when the global financial crisis broke out. Second, the performance of cumulative ΔTech showed a rebound in 2015 after a decline from 2013 to 2014, this may be attributed to the fact that China’s star-rated hotels had completed their transition following the government’s corruption crackdown efforts, especially in the food and beverage industry (Zhao, 2014).

As the other driving force, the cumulative growth of operational efficiency (ΔE) depicted in Figure 3 has been above the dotted line since 2004, although some slower growths were observed in 2009 and 2013, respectively, meaning that operational efficiency has generally improved. Unlike the performance of optimal production technology, the operational efficiency improvement was affected in 2009 and 2013, through which the TFP_HM growth of star-rated hotels was also affected and appeared unstable as previously described. In addition, by combining Figures 1 and 2, we can see that the improvement of the TFP_HM growth rate in 2014 was mainly attributed to the astonishing increase in cumulative ΔE, as shown in Figure 3. It is also worth noting that the influence of the global financial crisis during 2008–2009 on star-rated hotels in China was not significant; the main reasons could be that the active policies in China not only offset the influence of the global financial crisis but also simultaneously stimulated the consumption of domestic tourism, which demonstrates the essential need for government to take an active role in the development of tourism (Chin et al., 2013).

Competitive analysis

The higher the operational efficiency and the more advanced the technology, the greater the competitiveness and the greater the development potential (Cracolici et al., 2008). In order to analyze the competitiveness and development potential of star-rated hotels at a provincial level, we measured the average values of ΔTech and ΔE, respectively, of all administrative regions during the sample period. Using the results, we depict a four-quadrant diagram in which the horizontal axis is ΔTech, with one being a division value and the vertical axis is ΔE, with the provincial average value as a division value. The 31 provinces and municipalities were divided into four groups and are depicted in quadrants accordingly, as shown in Figure 4.

Figure 4.

Competitive position analysis of star-rated hotels in administrative regions.

In Figure 4, we can see that in quadrant I, ΔE is greater than the average value and ΔTech is greater than 1. In other words, the development potential of star-rated hotels in the administrative regions in this quadrant was good and their competitiveness was improving because their diffusion of optimal technology was fast and their optimal production technology was progressing. In quadrant II where ΔE is greater than the average value and ΔTech is less than 1, development potential and competitiveness improvement in star-rated hotels in the administrative regions in this quadrant were observed but were limited because the diffusion of optimal technology was fast but their optimal production technology was regressing. In quadrant III where ΔE is less than the average value and ΔTech is less than 1, the development potential was limited and competitiveness of the star-rated hotels in the administrative regions in this quadrant was getting worse because the diffusion of optimal technology was slow and their optimal production technology was regressing. In quadrant IV where ΔE is less than the average value but ΔTech is greater than 1, the development potential for most of the hotels in this quadrant showed little change but the competitiveness of “best practice” hotels improved because the diffusion of optimal technology was slow but their optimal production technology was progressing.

Inconsistent with the status of regional economic development, we can see from Figure 4 that the performance of start-rated hotels in most of the Eastern administrative region was not promising in terms of technological progress. Take Beijing and Shanghai as an example, while they are undoubtedly the most developed cities in the Eastern region, or even in mainland China, even though their production technology diffusion and resource utilization for star-rated hotels show efficiency, their production technology regression (ΔTech < 1) was more obvious than in other provinces and cities and deteriorated gradually; such observations do not correspond to the continuous regional economic development in Beijing and Shanghai. In contrast, the technological progress performance in the Western region and the efficiency improvement performance in the Central region were surprising. While economic development was not as good as that in the Eastern region, the operational efficiency of star-rated hotels in most of the Central region (filled dots in Figure 4) and the optimal productivity of star-rated hotels in most of the Western region (triangles in Figure 4) outperformed those of the Eastern region.

Implications

Our study offers several theoretical and practical contributions. Theoretically, we applied the Hicks–Moorsteen index model (O’Donnell, 2010) to star-rated hotels in China to assess the TFP change along with changes in various efficiency measures. The findings confirmed that the approach could completely decompose the operational efficiency and provides an additional research perspective on efficiency assessment in the hotel industry. In addition, the findings of our study verified quantitatively the statement of Zhao (2014) that the policies of “the eight rules” and the “nationwide petition about frugality in the hotel industry” have a short-term and negative effect on industry performance but may have a positive effect on the industry-wide restructuring of star-rated hotels. Third, we also addressed a gap in the literature on TFP change assessment of star-rated hotels nationally and regionally in China by proposing a Hicks–Moorsteen index approach, based on DEA, considering financial and service production outputs, offering further research opportunities.

Practically, the findings on the annual increase of star-rated hotels’ TFP_HM (13.11%) in China, mainly relying on ΔE (21.85%) rather than ΔTech (1.56%), indicate that the diffused utilization of optimal production technology is superior to the pace of technological innovation. This should urge star-rated hotel owners and management companies to pay more attention to introducing and employing advanced production technology and service equipment to continue improving the TFP of the regional hotel industry. Second, in tackling low occupancy rates coupled with increasing amount of fixed asset investment and limited growth of SE, hotel industrial stakeholders including owners and government authorities in charge of hotel development should consider limiting and monitoring the size of fixed asset investment in star-rated hotels in China to improve economic efficiencies of scale. Third, from the results of long-term stagnant or slightly declining optimal production technology observed in star-rated hotels, practitioners in the Eastern and Central regions could strengthen their technological progress by learning and applying advanced management skills rather than being complacent about past achievements. In addition, the rapid development of the Western region’s star-rated hotels in optimal technology demonstrates that China’s Western Development Strategy (Yu, 2015) has been favorable for star-rated hotels. Furthermore, the result that the diffusion of optimal production technology for star-rated hotels in the Western region was inferior to those in other regions (accumulative ΔE in Figure 3) reminds managers that they should not only focus on benchmarking and learning from “best practice” hotels but also pay more attention to strengthening communication among hotels in the Western region to achieve optimal production technology from an industrial perspective. Fourth, practitioners of star-rated hotels in China should be sensitive to the influence of government policies such as “the eight rules” and respond quickly to develop new business opportunities suitable to the ever-changing operating environment. For example, hotels could alter their marketing strategies toward leisure travelers rather than relying on government-related income sources. Finally, decision makers in different provinces and municipalities could develop strategies to improve the competitiveness of their star-rated hotels in line with the competitive position analysis provided in our study. Take Shaanxi Province, located in the fourth quadrant in Figure 4, as an example. The focus of star-rated hotels in Shaanxi should be on appreciating and adopting the use of optimal production technology from best practice hotels within Shaanxi rather than bringing in advanced technology from other provinces. In other words, internal benchmarking against hotels within the province to improve the operational efficiency of star-rated hotels is the most important issue.

Conclusion

In this article, we proposed the Hicks–Moorsteen TFP index approach based on DEA by considering both financial and service production outputs to investigate the TFP change at the regional level. The index is then further decomposed into technological progress, PTE change, SE change, and mix efficiency change of star-rated hotels in China, both nationally and regionally. As such, the present study makes three major contributions. First, unlike previous tourism and hospitality studies that mainly used the Malmquist index model, the present study employed the Hicks–Moorsteen TFP index approach based on DEA in examining the TFP growth, technological progress, and changes in various efficiencies in particular the mix efficiency change, thus further enhancing the understanding of hotel TFP and technological progress. While previous studies generally analyzed hotel productivity in terms of relative efficiency, this study extends the scope of productivity assessment to macro socioeconomic environment at the regional level. Second, the present study introduces a novel two-dimensional technological progress and efficiency change–based matrix diagram to assess the competitive advantage of the hotel industry across different regions in China. Finally, the implications resulting from the present study allow regional government policy makers to benchmark the best regional hotel industry’s performance and offer policy guidelines.

Based on our assessment of star-rated hotels, the current study found that while the performance of TFP_HM had some fluctuations during the sample period, the TFP of star-rated hotels in China had an annual average growth rate of 13.11%, mainly attributed to an annual average growth rate of operational efficiency of 21.85% and, in particular, a mix efficiency growth rate of 13.52%. From a regional perspective, for star-rated hotels in the Eastern region, we found that while the average TFP had an annual increase of 13.38%, which was mainly supported by an annual improvement of 19.28% in operational efficiency, the technical efficiency regressed mildly (ΔTech = 0.9907); for star-rated hotels in the Central region, the annual average changes in TFP and in technology had an increase of 14.55% and a regression of 1.27%, respectively. Furthermore, operational efficiency had an annual average increase of 33.74% and revealed a strong catch-up effect in terms of operational efficiency. The Western region was the only region where star-rated hotels had an increasing TFP, technological progression, and operational efficiency simultaneously, and the growth rates were 11.65%, 6.37%, and 14.68%, respectively.

From the perspective of annual growth, we found that while the TFP of star-rated hotels in different regions had certain increases and their performances similarly plummeted in 2013, the growth rate of optimal production technology in the Western region markedly outperformed those in other regions and yet the growth rate of operational efficiency underperformed significantly. On the basis of the above analyses, we also found that the catch-up effects in the Central and Western regions were progressing in terms of operational efficiency and optimal production technology, respectively. From the perspective of competitiveness, while the competitiveness of star-rated hotels in the Eastern region may be currently strong, that of star-rated hotels in the Central and Western regions are improving, which is good news for the increase of TFP in the industry nationwide.

Finally, policy makers and practitioners should focus on TFP growth and its components, drawing attention to the importance of upgrading their optimal production technology and operational efficiency as a means of improving TFP and competitiveness in star-rated hotels.

Limitations and further research

There are limitations associated with our study to address. All of our findings were based on the sample and data we collected from the “CEIC China Database” from 2001 to 2015 in mainland China and therefore, the results and measures should be interpreted for star-rated hotels in the sample period only. However, our framework could be further employed not only to hotels in different countries but also to other service sectors.

Future studies are encouraged to include more years of data, covering various economic conditions, to see how macro-environmental parameters may have impacted the TFP in the hotel industry. Besides, future studies may focus on TFP growth or changes among hotels of different types (e.g. mid- and up-scale) or star ratings (e.g. three to five stars).

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge the funding support from Social Science Foundation of Anhui Province, China (Grant No. AHSKQ2017D12) and Humanities and Social Sciences Research Foundation, Education Department of Anhui Province, China (Grant No. Y05021831).

ORCID iD

Henry Tsai

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Total factor productivity growth and regional competitive analysis of China’s star-rated hotels

Abstract

Keywords

Introduction

Performance assessment in the hotel industry

Framework

Technology level

Hicks–Moorsteen TFP index

Decomposition of TFPHM index

Data and variables

Results and analysis

Regional performance analysis

Integrated performance analysis

Competitive analysis

Implications

Conclusion

Limitations and further research

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

References

Decomposition of TFP_HM index