The Nature of Technological Change in the Chinese Hotel Sector

Abstract

This article investigates the role of technological change in the Chinese hotel sector over the period 2005 to 2015. The analysis is conducted at the provincial level and on the star-rating hotel basis. A three-step approach is adopted. First, the Malmquist productivity index and its decomposition into efficiency and technological change is estimated. Second, the significance of each component is statistically tested. Third, the technological change is decomposed to analyze the Hicks neutrality assumption. The findings show that the Chinese hotel sector experienced an overall productivity growth, which is mainly and significantly driven by a technological progress. In most of the cases, the technological change is not neutral, and input and output biases are characterized.

Keywords

star-rated hotels China productivity biased technological change

Introduction

The sources or decompositions of productivity and economic growth are rooted in economic theory (Grosskopf, 2003). Among them, the impact of technological change on productivity growth is of great importance in economics as well as its nature and possible bias (Acemoglu, 2002). This is particularly relevant in the hotel sector that has experienced a continuous increase in domestic and foreign investment.

In China, star-rated hotels represent the main type of accommodation for tourists and have an important impact on the development of the hotel sector as well as on the development of tourism. Star-rated hotels are facing intense competition due to the overall slowing down of macroeconomic growth, the environmental impact of controlling public funds consumption and opposing waste, and so on (Walheer et al., 2020; Walheer & Zhang, 2018). In this context, efficiency optimization and productivity evaluation of star-rated hotels have received a lot of attention from scholars (Assaf & Josiassen, 2016; Assaf & Tsionas, 2019). In production frontier theory, efficiency is the link between resources used and results obtained at a given time period. The recognition of possible inefficiency in the economic literature started in the 1950s with the works of Debreu (1951), Farrell (1957), and Koopmans (1951). The changes over time are characterized by productivity changes, and this measurement can be decomposed in both efficiency and technological changes (Färe et al., 1989; Färe, Grosskopf, Norris, et al., 1994).

Regarding the Chinese hotel sector, the literature shows that specific contributions devoted to this case appeared recently (Liu & Tsai, 2018; Walheer & Zhang, 2018). Research devoted to the Chinese hotel sector mainly focuses on the macrolevel with regional and provincial scales by adopting nonparametric methods, such as data envelopment analysis (DEA). The study of this sector is based on the operating efficiency by including contextual characteristics and related factors (see e.g., Chaabouni, 2019; Dong et al., 2019, for recent surveys on this topic). The research results are rich and the methods used are diversified (Liu & Tsai, 2018). Despite the profound interest of scholars in hotel technological change issues, research to date shows that some challenges are not yet investigated. Indeed, the nature of technological growth in hotel sector remains largely unexplored. However, this is an important issue in economics (Acemoglu, 2002) and for the tourism and hospitality sector regarding the design and implementation of adequate policies (Assaf et al., 2017; Santos et al., 2016; Walheer et al., 2020).

With the rapid development of tourism in China, hotel industry, as an important part of tourism and the main source of income, has also made considerable progress (Walheer & Zhang, 2018; Yang et al., 2017). The hotel industry has become the pillar of the regional tourism industry and has promoted its development by providing necessary accommodation, entertainment, catering, and conference facilities (Yang et al., 2012). However, in recent years, the number of hotels, rooms, employees, rental rates, and operating income in China’s hotel industry have declined to a certain extent (Dong et al., 2020; Tsai, 2009; Yang et al., 2017). China’s hotel industry is facing unprecedented challenges. In an increasingly competitive market environment, reliable efficiency assessment is a prerequisite for effective strategic decision making and sustainable planning (Luo et al., 2014). Furthermore, regarding the investments in the hotel industry, the recent consensus in the literature is that the technological change has been “capital-biased” in that the marginal product of capital increases more than that of labor for a given capital–labor ratio. As a consequence, more and more hotels tend to use capital to substitute labor during the production process (Chatzimichael & Liasidou, 2019; Mao & Yang, 2016; Sun et al., 2015). In the Chinese tourism industry, Su and Sun (2017) show how the hotel sector has attracted the major part of the investment made. More capital has been put into high-end (four- and five-star) hotels, while low-end (below three-star) hotels have more focused on labor usage.¹ Since the increase in investment often leads to an increase in innovation and therefore a technological improvement (Desmarchelier & Zhang, 2018; Fu et al., 2011), the biased technological change between inputs and outputs are possible for different hotel categories with different types. Hence, the nature and sources of the technological change should be investigated. The identification of this information becomes crucial for the relevant decision makers to adopt the appropriate measures.

Therefore, accurately measuring the efficiency of the hotel industry, analyzing the causes of inefficiency, revealing its potential optimal resource allocation, and enhancing competitiveness are important issues to be solved urgently in the Chinese hotel industry. In addition, the nature of the technological progress remains largely unexplored with important implications when it comes to the design and implementation of effective policies. At the destination level, both an overall assessment and a cross-regional/time comparison of the technological change are required to gain insights into the overall and relative competitiveness of the hotel sector. To provide decision support for formulating development strategy of the Chinese hotel sector, this article makes an in-depth analysis of the changing trend, regional characteristics, and the nature of technological change in the Chinese hotel sector. From a methodological point of view, the nonparametric approach adopted in this article overcomes the issues raised by the use of parametric approaches. From a practical point of view, this study provides valuable information by investigating the nature of technological change in the hospitality industry in China. For managers and stakeholders, this information is particularly important to quantify the performance and to test the hypothesis whether technological change has been “capital-biased.” The findings of this study can therefore provide essential recommendations for strategic decisions.

The rest of this article unfolds as follows. Section 2 is devoted to the research gap in the study of technological change in the tourism literature. In Section 3, the methodology is presented. Section 4 describes the data used in this study. The findings are presented and discussed in Section 5. Section 6 concludes.

Efficiency, Productivity, and Technological Change in Tourism and Hospitality Research

Two main approaches exist in the literature to identify the nature of technological progress, that is, neutral or biased. On the one hand, by following the economic literature, the production function can be used; see, for example, Chambers (1988). Acemoglu (2002, 2007) proposed important contributions toward the effect of biased technical change by adopting specific forms for the production function. On the other hand, the production frontier theory can be considered in which both parametric (Kumbhakar & Lovell, 2003) and nonparametric methods (Färe, Grosskopf, Norris, et al., 1994) can be used to analyze efficiency and productivity with an emphasis on technological change. For instance, Barros (2006) used stochastic frontier analysis to study the rate of technical change in the Portuguese hotel sector. Chen and Soo (2007) analyzed the productivity growth of the international hotels in Taiwan between 1997 and 2001 by using a parametric approach based on a translog cost function. They find that productivity is affected by a technological change that is not neutral. However, the recent surveys by Assaf et al. (2012) and Assaf and Josiassen (2016) show that many contributions investigated productivity changes by using nonparametric measures and their decompositions to study the tourism and hospitality sector and to identify the technological change. The key advantages of these approaches are linked to the fact that it is not necessary to suppose a priori a functional form for the production technology, and mutioutput production technologies can be characterized.

The performance of the tourism industry evaluated by productivity and efficiency has been a common subject in tourism studies. The research objects in current literature mostly focus on the efficiency analysis by using DEA at the microlevel such as restaurants, tourism enterprises, and hotels. One of the initial contributions using DEA was carried out by Banker and Morey (1986) on restaurants and Anderson et al. (2000) on hotels. To consider the productivity changes over time, the Malmquist productivity index (MPI) has been widely used. Caves et al. (1982) introduced this index, and Färe, Grosskopf, Norris, et al., (1994) extended it. Cho and Wang (2017) adopted a cost metafrontier MPI model on Taiwan hotel industries and concluded that the international chain hotel is significantly superior to independent hotels. Cordero and Tzeremes (2017) applied the MPI approach to two main sun and sand tourism markets in Spain. They found that the economic downturn had major negative effects on hotel productivity in 2008 and 2009.

The case of tourism in China using the MPI was investigated more recently. Sun et al. (2015) have used the MPI to analyze the productivity of Chinese tourism industry between 2001 and 2009. They find that technological change is the main factor of productivity changes. Walheer and Zhang (2018) applied a Malmquist–Luenberger index from 2005 to 2015 on 30 Chinese provinces and found that star-rated hotels performed better over time in China, but not for every activity they operated. Dong et al. (2020) have adopted a two-stage DEA model to study the influence of enviromental variables on the hotels’ performance. Furthormore, dynamic aspects have also been also investigated to reflect productivity changes over time and the sources of the changes.

To the best of our knowledge, the tourism literature seems restricted to only one contribution by Assaf and Barros (2011) that further decomposes the technological change component in the nonparametric framework with a radial form. They followed the empirical framework of Weber and Domazlicky (1999) who studied the input bias of the technological change in the manufacturing sector by using the approach of Färe and Grosskopf (1996) based on linear programming (LP). In a tourism context, Assaf and Barros (2011) studied the Gulf hotel sector by using the MPI. However, the limitation of this study from an empirical viewpoint is that it “mechanically” decomposes the technological change component without justification. This gap in the literature can be also addressed by similar contributions applicable to other sectors: Barros and Weber (2009) for the airports and Barros et al. (2009) in the banking sector. Many decompositions are available in production frontier theory (Grosskopf, 2003), and a clear justification is needed to study further and decompose the technological component.

In this article, we apply the recent permutation test introduced by Asmild et al. (2018) on the MPI and its components to test their significance and justify further decomposition. This article innovates with a three-step approach in terms of empirical perspective. First, the MPI is estimated with its standard decomposition. Second, the test by Asmild et al. (2018) is applied to test the significance of each component of the productivity changes. Third, if the technological change is significant, the technological change is decomposed by following Färe and Grosskopf (1996) to analyze its nature and characteristics.

Methodology

The methodology used in this article is about production frontier theory and more precisely the MPI and its decompositions. First, we defined the production technology and the distance functions. Second, we present the MPI and the decompositions adopted in this article.

The production technology transforms inputs $x$ in outputs $y$ . Let $x = (x_{1}, \dots, x_{n}) \in ℝ_{+}^{n}$ an input vector and $y = (y_{1}, \dots, y_{p}) \in ℝ_{+}^{p}$ an output vector. In the rest of this section, $t$ represents a given time period.

The production technology $G R_{t}$ is then:

G R_{t} = {(x_{t}, y_{t}) \in ℝ_{+}^{n + p} : x_{t} can produce y_{t}} .

(1)

Färe et al. (1985) and Färe and Grosskopf (1996) show that the production technology $G R_{t}$ satisfies several axioms: A1: $(0, 0) \in G R_{t}, (0, y_{t}) \in G R_{t} \Rightarrow y_{t} = 0$ , that is, no fixed costs and it is impossible to produce an output without inputs (no free lunch).

A2: The set is bounded, that is, a finite input vector cannot produce infinite outputs.

A3: $G R_{t}$ is closed.

A4: $\forall (x_{t}, y_{t}) \in G R_{t}, (x_{t}, - y_{t}) \leq (u_{t}, - v_{t}) \Rightarrow (u_{t}, v_{t}) \in G R_{t}$ , that is, strong disposability of inputs and outputs.

A5: $G R_{t}$ is convex.

By following Varian (1984) and Banker and Maindiratta (1988), the nonparametric technology at the time period $t$ is:

G R^{t} = {z_{t} \in ℝ_{+}^{n + p} : x_{t} \geq \sum_{i}^{k} λ_{i} x_{t}, y_{t} \leq \sum_{i}^{k} λ_{i} y_{t}, λ_{i} \geq 0, i = 1 \dots k} .

(2)

For the empirical analysis, constant returns to scale (CRS) are adopted² to characterize the intense competition in the Chinese hotel sector. Furthermore, separate analyses are implemented for each hotel category (star-rating). Second, the calculations of the distance functions will be output-oriented. This is a common choice in tourism and hospitality research as the objective is to optimize the results (outputs) for a given level of resources (inputs).

At the time period $t$ , the output distance function (Shephard, 1970) $D_{o}^{t} : ℝ_{+}^{n} \times ℝ_{+}^{p} \to ℝ_{+} \cup {\infty}$ is defined by:

D_{o}^{t} (x_{t}, y_{t}) = \inf_{θ} {θ : (x_{t}, y_{t} / θ) \in G R^{t}}

(3)

The output distance function is the inverse of the Farrell (1957) measure. Under weak free disposability, the output distance function permits a full characterization of the production technology:

G R^{t} = {(x_{t}, y_{t}) : D_{o}^{t} (x_{t}, y_{t}) \leq 1} .

(4)

The MPI was introduced by Färe et al. (1989) and can be defined as follows:

M_{o} (x_{t}, y_{t}, x_{t + 1}, y_{t + 1}) = {[\frac{D_{o}^{t + 1} (x_{t + 1}, y_{t + 1})}{D_{o}^{t + 1} (x_{t}, y_{t})} . \frac{D_{o}^{t} (x_{t + 1}, y_{t + 1})}{D_{o}^{t} (x_{t}, y_{t})}]}^{1 / 2}

(5)

One of the main interests of the MPI is provided by its decompositions. By following Färe, Grosskopf, Norris, et al. (1994), this article follows the decomposition into two components under CRS: efficiency change and technological change.

M_{o} (x_{t}, y_{t}, x_{t + 1}, y_{t + 1}) = \frac{D_{o}^{t + 1} (x_{t + 1}, y_{t + 1})}{D_{o}^{t} (x_{t}, y_{t})} . {(\frac{D_{o}^{t} (x_{t + 1}, y_{t + 1})}{D_{o}^{t + 1} (x_{t + 1}, y_{t + 1})} . \frac{D_{o}^{t} (x_{t}, y_{t})}{D_{o}^{t + 1} (x_{t}, y_{t})})}^{1 / 2}

(6)

The first component is the efficiency change ( $E F F C H$ ) and represents the changes in resources management. The second component is the technological change ( $T E C H$ ) and characterizes the frontier shifts.

Each distance function is calculated by LP. For instance, the LP for $D_{o}^{t} (x_{t}, y_{t})$ is given by:

\begin{array}{l} D_{o}^{t} (x_{t}, y_{t}) = \min θ \\ s . t . x_{i}^{t} \geq \sum_{i = 1}^{k} λ_{i} x_{i}^{t} \\ (1 / θ) y_{i}^{t} \leq \sum_{i = 1}^{k} λ_{i} y_{i}^{t} \\ λ_{i} \geq 0, i = 1 \dots k . \end{array}

(7)

To identify the real impact of the $M P I$ and its components $E F F C H$ and $T E C H$ , the recent permutation test proposed by Asmild et al. (2018) is used. This test permits to check the significance of the impacts of productivity changes between two time periods. The implementation of this test is to permute randomly the data and to recalculate the components of the productivity change. Then, the empirical distribution of the components obtained with the permutations is compared with the components estimated on the observed data. Details of the procedure can be found in Asmild et al. (2018). The calculations of this test are made by using the R software (R Core Team, 2016) and the codes provided by Asmild et al. (2018).

This article also considers the decomposition of the technological change component. By following Färe and Grosskopf (1996), the $T E C H$ component can be decomposed in the following way:

\begin{array}{l} T E C H = {(\frac{D_{o}^{t + 1} (x_{t}, y_{t})}{D_{o}^{t} (x_{t}, y_{t})} . \frac{D_{o}^{t} (x_{t + 1}, y_{t})}{D_{o}^{t + 1} (x_{t + 1}, y_{t})})}^{1 / 2} \\ \times {(\frac{D_{o}^{t} (x_{t + 1}, y_{t + 1})}{D_{o}^{t + 1} (x_{t + 1}, y_{t + 1})} \frac{D_{o}^{t + 1} (x_{t + 1}, y_{t})}{D_{o}^{t} (x_{t + 1}, y_{t})})}^{1 / 2} \times \frac{D_{o}^{t} (x_{t}, y_{t})}{D_{o}^{t + 1} (x_{t}, y_{t})} \end{array}

(8)

where the first term is the input bias technological change ( $I B T E C H$ ), the second term is the output bias technological change ( $O B T E C H$ ), and the last term is the magnitude of the technological change ( $M A T E C H$ ).

This decomposition requires the computation of specific distance functions in the $I B T E C H$ and $O B T E C H$ components where the time period is different both for the technology and the variable. For instance, for the distance function $D_{o}^{t + 1} (x_{t + 1}, y_{t})$ , the LP is (Färe & Grosskopf, 1996, p. 93):

\begin{array}{l} {(D_{o}^{t + 1} (x_{t + 1}, y_{t}))}^{- 1} = \max δ \\ s . t . x_{i}^{t + 1} \geq \sum_{i = 1}^{k} λ_{i} x_{i}^{t + 1} \\ δ y_{i}^{t} \leq \sum_{i = 1}^{k} λ_{i} y_{i}^{t + 1} \\ λ_{i} \geq 0, i = 1 \dots k . \end{array}

(9)

All the calculations of the MPI and its components are made by using the R package “productivity” by Dakpo et al. (2018).

Data

To construct a production technology, the choice of inputs and outputs is an important preliminary step (Coelli et al., 2005). In the tourism literature, the identification of inputs and outputs for production technologies is now well established. Recent works by Assaf et al. (2012) or Assaf and Josiassen (2016) present complete surveys on this topic. By following these contributions and with respect to the constraint of data availability, the choice of the variables is the following. Two inputs, total fixed assets and employees, represent capital and labor factors, respectively. The number of employees reflects the core inputs of hotels, which indicate the capacity of hotels to offer all the services (Assaf & Cvelbar, 2010; Roh & Choi, 2010; Walheer & Zhang, 2018). Total fixed assets represent the capital that an hotel can use for its daily operation and future development (Barros, 2005; Walheer et al., 2020). Regarding the outputs, three kinds of hotel revenues are selected: rooms revenue, catering revenue, and other revenue. They reflect the revenue generated by the different activities (Walheer & Zhang, 2018; Yang et al., 2017).

The data are extracted from the Wind Database and Supplement of China Tourism Statistical Yearbooks. A sample of 30 Chinese provinces³ in mainland China is considered, and the data cover a period from 2005 to 2015. Table 1 presents the descriptive statistics of the inputs and outputs used to construct the production technology.

Table 1

Descriptive Statistics of Inputs and Outputs (2005-2015)

Variable	Total Fixed Assets	Employees	Rooms Revenue	Catering Revenue	Other Revenue
Two stars
M	127408.42	7802.13	26179.74	21197.54	8398.84
SD	172833.50	6806.41	25131.30	20785.44	11254.34
Min	885.40	79.00	380.36	1.44	0.00
Max	1487315.10	31051.00	125642.89	116742.26	63719.98
Three stars
M	425582.40	19500.69	75089.49	68464.83	25685.53
SD	480268.08	14907.78	64481.15	61831.18	27951.56
Min	40739.57	1255.00	7267.76	6439.81	1237.45
Max	3861317.58	76079.00	267333.23	319843.86	147116.01
Four stars
M	474006.46	14384.41	81699.45	70755.31	29404.09
SD	466786.65	12067.14	84887.96	76449.47	39694.54
Min	29013.30	936.00	3985.24	2078.65	635.23
Max	2512056.60	54551.00	429922.51	361476.76	214738.50
Five stars
M	500807.27	8562.81	90472.12	69612.35	26252.32
SD	641166.93	9700.96	137717.17	103051.04	42599.92
Min	640.10	231.00	1782.69	452.56	239.13
Max	2971243.30	45187.00	625181.70	469654.09	235867.99

The information about the correlation analysis between the inputs and the outputs selected in this study is provided in the online supplement (see Table 5). First, the correlation between the two inputs is not an issue in the sense that they reflect capital and labor, which is essential to model a technology in production economics (Coelli et al., 2005). Second, the high correlation between inputs and outputs ensures the fitness of the model (Assaf & Barros, 2011).

Results

Dynamic Analysis of Hotel Productivity Change in China

As an initial step, Table 2 presents the results of the test by Asmild et al. (2018) for the productivity changes over the period 2005 to 2015.

Table 2

Significance Probabilities of Productivity Changes

	Two Star	Three Star	Four Star	Five Star
MPI	1.4428 (.0047)	1.3388 (0)	1.3566 (.003)	1.1666 (.3204)
EFFCH	0.8167 (.2434)	0.9970 (.9793)	1.0463 (.5847)	1.1037 (.2879)
TECH	1.7666 (.0502)	1.3428 (.0098)	1.2966 (.0077)	1.0570 (.8195)

Note: MPI = Malmquist productivity index; EFFCH = efficiency change; TECH = technological change. Values are geometric means and significance are in parentheses.

The overall productivity changes ( $M P I$ ) are significant at 1% level for two-, three- and four-star hotels. The result for five-star hotels is not significant, and five-star hotels will be excluded from our discussion.⁴ The technological change( $T E C H$ ) presents the same findings, except for two-star hotels where the level of significance is lower. Efficiency changes ( $E F F C H$ ) are never significant in this analysis. The first conclusion is that, for two-, three- and four-star hotels, productivity changes are driven by technological change. In average, these Chinese star-rated hotels experienced a productivity growth between 2005 and 2015, which is largely explained by a significant technological progress.

The average results of the Malmquist index and its standard decomposition for star-rated hotels in Chinese provinces over the period 2005 to 2015 are presented in Table 3 (Columns 2-4).⁵ Values greater than 1 indicate an improvement in productivity ( $M P I$ ), an increase in efficiency ( $E F F C H$ ), and an advancement in technology ( $T E C H$ ). The first observation is that, on average, hotels with different stars have all improved their productivity during the observation period. However, the growth patterns for hotels with different stars are different. In average, the two- and three-star hotels suffered an efficiency deterioration during the period. This is the consequence of a bad management of resources or a weak customer relationship management. At the same time, the improvement of productivity of all hotels mainly comes from the contribution of technological progress. In words, the technological innovation of the booking system, the application of the latest technology such as big data analytics, attract customers. The second observation shows that, among all the hotels, two-star hotels appear to have achieved the highest productivity improvement, followed by four-star hotels, while three-star hotels have experienced the lowest productivity advancement. At the same time, we also find a negative connection between the level of technological improvement and the number of stars. This could be explained by the fact that high-end hotels in China were established initially in the form of a cooperation with foreign capital in China. We generally believe that these hotels have capital and technical advantages over other hotels (Yang & Mao, 2017); thus, they were born with certain advantages on their productivity level. At the same time, while these hotels bring some new core competitiveness such as management system, corporate culture, and service consciousness, local lower star hotels can learn from the management mode of foreign-funded enterprises and carry out reform and innovation. Thus, there is no surprise that in terms of productivity and technological growth, lower star hotels perform better than higher star hotels. At the same time, higher star hotels can improve their management system, adapt their operational strategy to local culture concept, and thus elevate their efficiency level.

Table 3

Average Results of Malmquist Productivity Index and Its Components

	MPI	EFFCH	TECH	OBTECH	IBTECH	MATECH
Two-star hotels
M	1.4428	0.8167	1.7666	1.0336	1.1223	1.5229
Three-star hotels
M	1.3388	0.9970	1.3428	1.0478	1.0734	1.1939
Four-star hotels
M	1.3566	1.0463	1.2966	1.0536	1.0422	1.1807
Five-star hotels
M	1.1666	1.1037	1.0570	1.0427	1.1136	0.9103

Note: MPI = Malmquist productivity index; EFFCH = efficiency change; TECH = technological change; OBTECH = output-biased technological progress; IBTECH = input-biased technological progress; MATECH = magnitude of technological progress.

In Table 3 (Columns 5-7), the decomposition about output-biased technological change and input-biased technological change, and the magnitude of technological change are also presented. A detailed look at these results at the provincial level (see Table 6 in online supplement) shows that there is no Hicks-neutral technological change for most of the hotels at the provincial level during the research period since most of the scores estimated ( $O B T E C H$ and $I B T E C H$ ) are different from each other.

Technological Change Analysis of Hotels in China

To well investigate the nature of technological change of Chinese star-rated hotels, the research period is divided into two subperiods: 2005 to 2008 and 2008 to 2015. This choice can be justified by the following arguments.

The first period corresponds to the takeoff stage of Chinese star-rated hotels. During this period, in less than 10 years, more than 8,000 economy hotels have been opened in China. In March 2007, the State Council promulgated a number of recommendations on accelerating the development of the service industry. On March 13, 2008, the General Office of the State Council issued the “ State Council issued 2008 No. 11” to emphasize again on how to implement relevant policies. Also, important international events, such as the 2008 Beijing Olympics, have also brought China’s hotel industry into a path of rapid development. As a result, the higher star hotel numbers have increased by 57.88%, and the total hotel numbers have increased by 19.2%.

The second period begins after 2008. After the global and Chinese economic slowdown in 2009, China’s hotel industry is gradually facing the dilemma of saturation of supply and demand and stagnation of revenue after 2008. Furthermore, in 2012, Chinese government has introduced several policies to reduce expenditures (Dong et al., 2020). As a result, the total profit of star-rated hotels in China declined considerably to −.2 billion in 2009 as a result of the global financial crisis in 2008; total profits recovered to 6.143 billion in 2011, but this number downed sharply to −2.09 billion in 2013, and it has not restored peak as before until 2016 (Yearbooks of China Tourism Statistics, 2017). By contrast, the total investment in hotels constantly rose in the past decade except for 2016 (Walheer & Zhang, 2018).

Table 4 illustrates the decomposition of technological change and the comparison between each input pairs and output pairs for these two subperiods. For the input bias technological change, first, the average $I B T E C H$ scores for all the hotel categories are all above 1, indicating that the substitution between labor and capital indeed exists in the Chinese hospitality industry. This finding is in line with both the economic literature (Acemoglu, 2002) and the research on tourism and hospitality (Assaf & Barros, 2011; Chen & Soo, 2007). Second, when discriminating between hotels with respect to their number of stars, we find that before 2008 there were distinct changes among different hotel categories. Two- and three- star hotels focused on increasing labor usage and capital saving, while four-star hotels focused on increasing capital usage and labor saving. However, after 2008, all the hotels showed more attention on labor usage. The results fit well with the reality in Chinese hotel development. For the four-star hotels, at the first stage of their development, they heavily relied on capital accumulation (through the form of cooperation with foreign hotel brand). After 2008, they have obtained enough market share by using their capital advantage compared with lower star hotels. The focus of their work shifts to relying on local workers to adapt to the Chinese business environment in order to complete the localization. As for the lower star hotels, during the first period, they have focused on adopting a large number of lower cost labors. While after 2008, with the dramatic changes in the supply and demand structure of hotel industry in China, the fierce competition between hotels led them all to rely more on labor usage. But during this time, higher star hotels became more important in terms of using labor (the share of labor for lower star hotels was initially 64.41% at 2005, but dropped to 55.66% in 2008 and 42.71% in 2015). It is also worth to note that 20% of the two-star hotels at the provincial level show a Hicks-neutral technological change in the use of inputs between 2008 and 2015. For these hotels, the time does not affect the marginal rate of technical substitution between labor and capital, and the efficiency of both inputs increases with the technological progress.

Table 4

Input- and Output-Biased Technological Change

Number of Hotels for Which	Period	I BTECH > 1		I BTECH < 1		I BTECH = 1
Number of Hotels for Which	Period	$X^{t + 1} > X^{t}$ ( $x_{2}$ -using)	$X^{t + 1} < X^{t}$ ( $x_{1}$ -using)	$X^{t + 1} > X^{t}$ ( $x_{1}$ -using)	$X^{t + 1} < X^{t}$ ( $x_{2}$ -using)	I BTECH = 1
Two star	2005-2008	20	3	2	4	1
Two star	2008-2015	16	2	1	5	6
Three star	2005-2008	12	4	1	8	5
Three star	2008-2015	19	3	5	3	0
Four star	2005-2008	1	16	11	1	1
Four star	2008-2015	27	0	0	3	0
Five star	2005-2008	8	13	6	1	0
Five star	2008-2015	21	1	0	6	0
Number of Hotels for Which	Period	OBTECH > 1		OBTECH < 1		OBTECH = 1
Number of Hotels for Which	Period	${(\frac{y_{1}}{y_{2}})}^{t + 1} > {(\frac{y_{1}}{y_{2}})}^{t}$ (y1-producing)	${(\frac{y_{1}}{y_{2}})}^{t + 1} < {(\frac{y_{1}}{y_{2}})}^{t}$ (y2-producing)	${(\frac{y_{1}}{y_{2}})}^{t + 1} > {(\frac{y_{1}}{y_{2}})}^{t}$ (y2-producing)	${(\frac{y_{1}}{y_{2}})}^{t + 1} < {(\frac{y_{1}}{y_{2}})}^{t}$ (y1-producing)	OBTECH = 1
Two star	2005-2008	2	15	8	3	2
Two star	2008-2015	16	7	1	6	0
Three star	2005-2008	3	18	4	3	2
Three star	2008-2015	6	13	4	5	2
Four star	2005-2008	2	20	5	1	2
Four star	2008-2015	8	8	5	9	0
Five star	2005-2008	4	18	3	3	0
Five star	2008-2015	4	8	6	9	1
Number of Hotels for Which	Period	OBTECH > 1		OBTECH < 1		OBTECH = 1
Number of Hotels for Which	Period	${(\frac{y_{1}}{y_{3}})}^{t + 1} > {(\frac{y_{1}}{y_{3}})}^{t}$ (y1-producing)	${(\frac{y_{1}}{y_{3}})}^{t + 1} < {(\frac{y_{1}}{y_{3}})}^{t}$ (y3-producing)	${(\frac{y_{1}}{y_{3}})}^{t + 1} > {(\frac{y_{1}}{y_{3}})}^{t}$ (y3-producing)	${(\frac{y_{1}}{y_{3}})}^{t + 1} < {(\frac{y_{1}}{y_{3}})}^{t}$ (y1-producing)	OBTECH = 1
Two star	2005-2008	8	9	4	7	2
Two star	2008-2015	10	13	5	2	0
Three star	2005-2008	5	16	5	2	2
Three star	2008-2015	6	13	6	3	2
Four star	2005-2008	7	15	3	3	2
Four star	2008-2015	8	8	7	7	0
Five star	2005-2008	8	14	5	1	0
Five star	2008-2015	8	4	10	5	1
Number of Hotels for which	Period	OBTECH > 1		OBTECH < 1		OBTECH = 1
Number of Hotels for which	Period	${(\frac{y_{2}}{y_{3}})}^{t + 1} > {(\frac{y_{2}}{y_{3}})}^{t}$ (y2-producing)	${(\frac{y_{2}}{y_{3}})}^{t + 1} < {(\frac{y_{2}}{y_{3}})}^{t}$ (y3-producing)	${(\frac{y_{2}}{y_{3}})}^{t + 1} > {(\frac{y_{2}}{y_{3}})}^{t}$ ( $y_{3}$ y3-producing)	${(\frac{y_{2}}{y_{3}})}^{t + 1} < {(\frac{y_{2}}{y_{3}})}^{t}$ (y2-producing)	OBTECH = 1
Two star	2005-2008	9	8	4	7	2
Two star	2008-2015	7	16	5	2	0
Three star	2005-2008	8	13	4	3	2
Three star	2008-2015	6	13	7	2	2
Four star	2005-2008	13	9	2	4	2
Four star	2008-2015	5	11	7	7	0
Five star	2005-2008	14	8	4	2	0
Five star	2008-2015	8	4	10	5	1

Note: IBTECH = input-biased technological progress; OBTECH = output-biased technological progress; $X^{t} = {(\frac{x_{1}}{x_{2}})}^{t}$ and $X^{t + 1} = {(\frac{x_{1}}{x_{2}})}^{t + 1}$ where x1: assets and x2: employees. y1: rooms revenue, y2: catering revenue and y3: other revenue.

For the output-biased technological change, nearly all the hotels are in favor of nontraditional services (catering and other services) between 2005 and 2008. In the same time period, higher star hotels appear to have focused on catering revenue. As for the period 2008 to 2015, two-star hotels have focused more on traditional room services; this is mainly due to their commercial position in the market (most of their customers are seeking accommodation services). On the contrary, three-star hotels have focused on other services. Four-star hotels have not distinguished between the traditional (accommodation) and nontraditional services. The best-performing hotels produced revenue related to catering services. These findings provide recommendations to identify which kind of revenues are affected by the technological change. It can be useful for decision makers in the hotel sector where revenues are generated from multiactivities and services (Walheer & Zhang, 2018).

Conclusion

In this article, we have adopted a three-step approach to measure the dynamic performance of Chinese star-rated hotels, investigate the nature of their technological change, as well as identify the sources of the input and output bias in technological change. The findings suggest that, first, different hotel categories performed differently during the study period, and only technology change is the significant contributor toward the overall productivity improvement for the whole industry. Second, Hick’s neutrality in the production of outputs and the use of inputs is not respected in our context; different hotels show different paths in terms of their technological advancement. The research framework proposed in this article provides a good complement to the literature devoted to tourism efficiency and productivity. The nature of technological change in the star-rated hotel sector is investigated, which is largely ignored by the past research. In terms of empirical perspectives in tourism and hospitality research, the use of recent tools available in the literature is strongly recommended to test and check the significance of productivity changes and its components.

The findings of this article can be used by policy makers as well as hotel managers to make their future strategic plans. They provide guidelines on how technological progress affects productivity changes over the time in the Chinese hotel sector. The results show a heterogeneous performance among different star hotels. For lower star hotels, the assumption of capital intensity biased does not hold, which means, at least at this stage, that cheap labor is still highly attractive for this subgroup of hotels. For higher star hotels, the requirements for employee quality are higher, which drive up the labor cost. Hence, higher star hotels show a preference for capital. These findings could be useful for both practitioners and researchers. First, for the recommendations of potential investors. The investment on lower star hotels should be labor-oriented, whereas for the investors who are looking for opportunities in higher star hotels, a large amount of capital investment will be required. Second, for decision makers in the hotel sector of other countries with a similar economy to implement strategic decisions and tourism policies. Third, these results present also an interest for scholars to investigate empirical comparisons with other case studies.

The limitations of this study necessitate to also underline paths for possible future research regarding the Chinese hospitality sector. First, luxury hotels experienced nonsignificant findings, and thus, more research is needed on this segment. Second, another limitation of this research is related to the dataset. The firm level is not available, and more detailed data could provide more insights for future research.

Supplemental Material

Online_Supplement_JHTR_PeypochSongZhang – Supplemental material for The Nature of Technological Change in the Chinese Hotel Sector

Supplemental material, Online_Supplement_JHTR_PeypochSongZhang for The Nature of Technological Change in the Chinese Hotel Sector by Nicolas Peypoch, Yuegang Song and Linjia Zhang in Journal of Hospitality & Tourism Research

Footnotes

Authors’ Note:

The authors thank two anonymous reviewers for helpful comments, which greatly improved the exposition of this article. Any remaining errors are solely due to the authors.

ORCID iDs

Nicolas Peypoch

Linjia Zhang

Supplemental Material

Supplemental material for this article is available online.

Notes

Nicolas Peypoch (corresponding author; e-mail: peypoch@univ-perp.fr) is a professor at the University of Perpignan, Perpignan, France. Yuegang Song (e-mail: sygang112@163.com) is an associate professor at the Henan Normal University, Xinxiang, China. Linjia Zhang (e-mail: linjia.zhang@xjtlu.edu.cn) is an assistant professor at the Xi’an Jiaotong-Liverpool University, Suzhou, China.

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