Conformity or differentiation? The effect of competitive strategy on hotel performance

Abstract

Hotels often stay close to each other to exploit the agglomeration benefit. However, geographical proximity also parallels competition. This study used annual longitudinal hotel data from the 2016–2019 Indonesian Accommodation Survey to examine the relationship between competitive strategy and hotel performance. We constructed a capacity and quality distance index to capture hotel strategies and used the panel fixed effect method to address possible sorting behavior. Our estimates broadly suggest that conformity corresponds to a higher total revenue per available room. We attribute the result to labor pooling and lower consumer searching cost mechanisms. The results show that hotels that implement a conformity strategy in capacity or quality with neighboring hotels tend to have a higher total revenue per available room. It implies that a hotel’s conformity strategy can be a reference for investors and hoteliers in planning a profitable hotel business and creating sustainable and quality tourism.

Keywords

competitive strategy conformity differentiation hotel performance Indonesia panel fixed effect

Introduction

Hotels often stay close to each other to exploit the agglomeration benefit, including knowledge spillover, proximity to consumers, efficient infrastructure allocation, and better matching of job opportunities and skills between firms and workers (Gan and Hernandez, 2013). However, that proximity is also in parallel with competition, which may diminish the benefit (Baum and Haveman, 1997; Kalnins and Chung, 2004). Hotels will experience gains or losses, depending on the strategy taken. Mazzeo (2002) stated that the effectiveness of a particular strategy might relate to the other firms’ strategies in the same market. It is also important to determine how similar hotel products should be to their competitors (Baum and Haveman, 1997; Kim et al., 2020).

Previous studies have examined the relationship between competitive strategy and hotel performance. Hotels could choose to implement a conformity or differentiation strategy. Several studies support hotels choosing a differentiation strategy to reduce the effect of price competition or gain a competitive advantage (Becerra et al., 2013; Firoz S. et al., 2019; S. K. Lee, 2015; S. K. Lee et al., 2017; Porter, 1990). Chung and Kalnins (2001) also found that hotels in Texas benefitted when differentiating capacity from competitors. In contrast, Song L. et al. (2005) and Kim et al. (2020) support hotels adopting a conformity strategy to enjoy lower learning costs and gain higher performance.

Although there was some previous research on the impact of hotel strategy on performance, very little has been discussed about the mechanisms. Chung and Kalnins (2001) discussed the mechanism for increasing hotel performance through one of the benefits of agglomeration: lower customer search costs. They found chain affiliation effectively reduced search costs for customers in rural markets. Our study extends the literature by adding other potential mechanisms that could describe how competitive strategy impacts hotel performance. We suspect that the similarity of hotel characteristics will lead to labor pooling and lower customer search costs.

We use Indonesia, one of the top five countries that dominate the supply of hotel rooms in the Asia Pacific region (HVS, 2019), as a case study. The country is experiencing a surge in the number of hotels in line with the increase in the number of visiting tourists. Therefore, it is interesting to study the hotel’s strategy in competing with surrounding hotels. We focus on studying the characteristics of nearby hotels, so that hotels can determine strategies that improve their performance even though new hotel construction is still happening in Indonesia.

The relationship between the hotel’s competitive strategy and performance might be due to self-selection. To increase performance, firms purposefully choose their strategies (Hamilton and Nickerson, 2003). A hotel would not randomly select to conform to or differentiate from competitors in the capacity and quality dimensions, but instead self-select based on their expectations of how their strategic decision might influence future performance. Likewise, the random selection of hotel locations can affect strategic business planning and hotel profitability. Hence, the study utilized a panel fixed effect method to purge the bias from time-invariant unobservable related to the self-selection process. Island-specific year-fixed effects were also introduced to capture different results at the island level that may correlate with hotel strategy.

Our study has broadly shown that conformity is associated with higher total revenue per available room. Assuming that hotels with the same capacity also have the same worker requirements, research shows that conformity in a worker is associated with an increase in per-worker revenue. From the demand side, the study’s results stated that the more similar the quality of the hotel, the higher the number of guests per available room.

The paper fills three research gaps in this field. First, most empirical studies focus on strategies: namely, the benefits of being similar or being different (Baum and Haveman, 1997; Kim et al., 2020). However, these studies have not provided evidence of potential mechanisms of how competitive strategies affect hotel performance. We suspect that the effect of competitive strategy on hotel performance is related to the agglomeration theory. Canina et al. (2005) stated that agglomeration benefits from increasing production and enhanced demand. Likewise, Marshall (1920) stated that the benefits of agglomeration include labor pooling and reduced consumer search costs. Therefore, we enriched the existing literature by examining the potential mechanisms by which competitive strategies affect hotel performance through labor pooling and lower consumer search costs.

Second, a recent study showed that the effects of conformity strategies were heterogeneous across hotel locations. Previous research has shown a positive relationship between hotels located in urban areas and airports with hotel performance (Kim et al., 2020). However, no studies explain the effects of conformity strategies for hotels in tourist areas, business centers, and other areas. This paper shows the effects of strategic competitive heterogeneity across these three regions.

Third, most empirical studies in determining the characteristic distance of hotels do not use the distance between hotels as a weight (Baum and Haveman, 1997; Kim et al., 2020). Tobler (2004) stated that everything is related to everything else, but things that are nearer are more related than those that are further away. Our study includes distance between hotels as a weight in calculating the distance of the hotel characteristics to demonstrate the distance decay effect better.

The remaining sections of the essay are structured as follows. The second section is a literature review while the third section provides a context: hotel sector development in Indonesia. The fourth section explains the method, including the estimation approach and data to answer the main research question. The fifth section briefly analyzes the empirical results obtained from the previous section, while the sixth section offers conclusions.

Literature Review

Hotels are usually located adjacent to other hotels to benefit from agglomeration (Yang et al., 2014). Some works demonstrate a positive relationship between hotel concentration and performance (Canina et al., 2005; Chung and Kalnins, 2001). However, higher density among similar hotels can also lead to higher competition to acquire guests or input factors (McCann and Folta, 2008). The advantage or disadvantage of location proximity between firms depends on the relative strength of the effects of agglomeration and competition (Yang et al., 2014).

Literature provides two competing hypotheses on the hotel strategy of facing competition. Competitive strategies theory suggests that geographical competition differentiates hotels from neighboring hotels to maximize profit (Porter, 1990). Hotels differentiate to reduce the effect of price competition or gain a competitive advantage (Firoz S. et al., 2019; S. K. Lee, 2015; Porter, 1990). On the contrary, Marshall–Arrow–Romer’s (MAR) theory suggests that firms may opt to be characteristically indifferent to neighboring hotels to enjoy a lower learning cost and thus offer similar or comparable products (Song L. et al., 2005). Empirical studies find that hotels that have similarities with their closest competitors will gain greater profits. Urtasun and Gutiérrez (2006) stated that hotels that are agglomerated with other hotels of the same size and service would have a greater advantage. Kim et al. (2020) also show that hotels with the same capacity and quality as nearby hotels can produce a higher performance. However, the literature discussing potential mechanisms regarding the relationship between hotel strategy and performance is still limited.

Canina et al. (2005) and Chung and Kalnins (2001) further discuss proximity to competitors, which may be beneficial. They refer to Marshall (1920) that describes two types of gains: production enhancements and heightened demand. One of the benefits of agglomeration obtained from the production side is through labor market pooling. Labor market pooling makes it easier for job seekers to find work quickly, and companies can find workers with specific skills relevant to the industry (Andini et al., 2013). Labor pooling facilitates the job-matching process, so that it might reduce labor turnover and then boost labor productivity. Regarding demand advantages, agglomeration reduces the consumers' cost of searching (Marshall, 1920). Physical collocation might increase the likelihood of visitation and then heighten demand (Chung and Kalnins, 2001).

Furthermore, Canina et al. (2005) state that the agglomeration effect is only seen in luxury and economy hotels. Kim et al. (2020) distinguish the effect on the airport, urban, and conference hotels. In another study, Cró and Martins (2017) stated that there is a historical tendency for hotels to cluster in certain locations, such as the Central Business District (CBD) and tourist centers, due to the ease of accessibility. Visitors to the two regions tend to have different characteristics. Tourists visit tourist areas to enjoy the beauty of tourist attractions. They expect a hotel with several criteria, such as a clean, safe, comfortable hotel with friendly staff (Bowie and Buttle, 2004). Meanwhile, tourists who visit the business center invariably choose a location that provides various services. They are willing to pay more for easy access to the city center, where the hotel area is centrally located, between the CBD and the commercial zone (Yokeno, 1968).

Context: hotel sector development in Indonesia

Tourism is one of the most important sectors in Indonesia. This sector has developed into one of the fastest-growing sectors and contributed almost 5% to the Indonesian GDP in 2019. Indonesia has welcomed more than 10 million international visitors since 2015 and achieved 16.11 million in 2019 (Statistics Indonesia, 2020b). From the domestic side, the number of domestic visitors experienced an increasing trend until reaching a peak in 2019 of 722.16 million visits (Statistics Indonesia, 2021b).

The positive trend of Indonesian tourism attracts investors to establish new hotels in Indonesia. As a result, the number of star hotels jumped from 2387 hotels providing 233,007 hotel rooms in 2016, to 3516 hotels with 363,749 rooms in 2019 (Statistics Indonesia, 2016, 2020a). Various types of accommodation, such as non-star hotels, youth inns, tourist lodges, villas, bungalows, cottages, and others, also thrive in Indonesia. There were more than 25,000 of these types of accommodations, providing 412,276 rooms in 2019 (Statistics Indonesia et al., 2020a). The rapid growth of hotels makes Indonesia one of the five countries that dominate the supply of hotel rooms in the Asia Pacific region (HVS, 2019). As a result, it is interesting to study the hotel’s strategy in competing with surrounding hotels.

Method

Estimation approach

We first formed a unique competition set for each observation to determine the impact of competition strategy on hotel performance. Following Baum and Haveman (1997) and Kim et al. (2020), this study defines the 10 closest geographic neighbors of the focus hotel as its competitors. This paper utilizes geocoded information for each hotel to measure the proximity between hotels using the Euclidean distance formula. We also established several competitive sets: five, 11, 12, 13, 14, and 15 nearest neighbor hotels for the robustness test. Then, we determined the competitive strategy from each observation: whether the hotel tends to choose a conformity or differentiation strategy. The paper used Capacity Distance (CD) as the first measurement of competitive strategy. Estimating CD develops a study by Kim et al. (2020) by adding location distance as a weight. So, we calculated the CD by averaging the difference in the number of guest rooms available between the focal hotels with 10 nearby competitors by using location distance as a weight. The formula is

{C D}_{i t} = \frac{1}{n} \sum_{j = 1}^{n} (| {r o o m}_{i t} - {r o o m}_{j t} | / L D_{i j})

(1)

where n is the number of neighboring hotels and room is the available guest rooms offered by each hotel. The study used the 10 closest neighboring hotels for the baseline.

Our second measurement of competitive strategy is Quality Distance (QD). The procedure to calculate QD is similar to the CD, except that we now use the type of hotels to replace the number of rooms. This formula uses the six types of hotel quality (class): non-star, one-star, two-star, three-star, four-star, and five-star hotels. The formula is

{Q D}_{i t} = \frac{1}{n} \sum_{j = 1}^{n} (| {c l a s s}_{i t} - {c l a s s}_{j t} | / L D_{i j})

(2)

Consequently, capacity/QD measures the degree of capacity/quality differentiation from a focal hotel to its 10 nearest competitors. A lower value of CD or QD indicates a firm has similar characteristics with its nearest neighboring hotels as its competitor, implying a conformity strategy.

Modeling the impact of competitive strategy on hotel performance is subject to endogeneity problems because the decision of a hotel’s location and strategy is not random. Hotel owners purposively have certain considerations when deciding on a hotel’s location and strategy. It involves observable factors and unobservable covariates, which may still lead to selection bias (McKenzie et al., 2010). Unobservable covariates that must be isolated include geographic and individual characteristics (for example, hotel owner’s vision and leadership characteristics). Therefore, this study implemented a panel fixed effect model to overcome these problems to achieve an unbiased and efficient inference. To reduce endogeneity issues, we included control variables at the hotel and region levels. The model also introduced individual fixed effects to account for unobserved heterogeneity at the hotel level. The model is

y_{i t} = β_{0} + β_{1} C D_{i t} + β_{2} Q D_{i t} + \sum γ^{k} X_{i t}^{k} + δ_{i} + ρ_{r} τ_{t} {+ ε}_{i t}

(3)

y_{i t}

is the total revenue per available room in a natural logarithm for hotel-i, year-t. The revenue captures all potential sources of hotel revenue, including revenue from restaurants, gift shops, rent, meetings, or other hotel activities, not only from room revenue (S. Lee et al., 2019). We chose total revenue per available room as the hotel’s performance over revenue per available room since the competitive strategy will likely affect all sources of the hotel’s revenue stream. This argument is even truer for star hotels that have a substantial portion of revenue from non-room activities. Hence, we expect this strategy to capture the relationship better than existing empirical literature that only relies on room revenue to capture hotel performance (Canina et al., 2005; Kim et al., 2020).

$C D_{i t}$ is the CD for hotel-i, year-t, $Q D_{i t}$ is the QD for hotel-i, year-t. $X_{i t}^{k}$ is a vector of k-time-varying controls that can influence competitive strategy and hotel performance, such as hotel quality, chain management, and location characteristics (Kim et al., 2016). The study used hotel quality, chain management, hotel location, and availability of meeting rooms, restaurants, and fitness centers as control variables representing the hotel’s attributes. In addition, the hospitality industry is vulnerable to economic conditions. Consumer spending on hotel accommodation is directly related to GDP performance (Oaten et al., 2015; Weerathunga et al., 2020). Therefore, we control annual gross regional domestic product per capita as regional characteristics (Kim et al., 2020). Furthermore, 4-year indicators were used to control the possibility of annual systematic effects for all hotels (Chung and Kalnins, 2001).

$δ_{i}$ is an individual-specific fixed effect. $ρ_{r} τ_{t}$ is an interaction term between the individual’s island and survey year. It captures nationwide and island-specific period shocks. Division of territory by island based on the Ministry of National Development Planning of the Republic of Indonesia (Bappenas) policy divides the archipelago in Indonesia into six priority areas. Finally, $ε_{i t}$ is the error term. The estimation of equation (3) is in practice, subject to a possibly severe serial correlation problem due to exogenous shock across hotels, which is not correlated with competitive strategies, but can affect the hotel’s total revenue per available room. To handle the potential serial correlation, we clustered all standard errors at the hotel level (Bertrand et al., 2004).

This paper also describes the potential mechanism of the relationship between competitive strategy and hotel performance. First, we regressed the variable worker distance and total revenue per worker based on equation (3). The method of calculating worker distance is also similar to the previous distance measures. We use the number of the workers in the formula

{W D}_{i t} = \frac{1}{n} \sum_{j = 1}^{n} (| {w o r k e r}_{i t} - {w o r k e r}_{j t} | / L D_{i j})

(4)

Then, to test the second potential mechanism, the research regressed the relationship between QD and the number of guests per available room.

The paper also tests the consistency of our baseline results by conducting several robustness checks on our model. First, the study applies different hotels’ competitive sets. We estimate the model in competitive sets that consist of the five, 11, 12, 13, 14, and 15 closest neighboring hotels. Second, we use the revenue per available room as the dependent variable to measure the hotel’s performance. Revenue per available room only calculates the revenue from the sales of hotel rooms. Appendix 1 presents a complete definition of all variables in this study.

Data

The research used data from the Annual Accommodation Survey by Statistics Indonesia. The survey collected information on the business characteristics of accommodation services, including star hotels, non-star hotels, tourist lodges, and other short-term accommodation provisions. We obtained data on hotel characteristics from the accommodation survey, such as the location, hotel quality, number of rooms sold, number of available rooms, room prices, chain management, availability of hotel facilities, and annual hotel revenue. The accommodation survey also provides precise information about the hotel’s location in the form of latitude and longitude. This information allows us to measure location distances between hotels, so that each hotel will have a unique competitive set. The research also utilized location distance information to make better measurements of capacity and QDs between hotels. The recent study sample is limited to the star and non-star hotels that had a series for 4 years (2016–2019) according to the limitations of accurate location data (geotagging) and minimizing bias due to the impact of COVID-19. Our panel data set was 28,024 firm-year observations from 7006 hotels. Figure 1 shows the distribution of hotels. There is a spatial tendency for hotels to be concentrated on the Java and Bali islands.

Figure 1.

Hotel distribution in Indonesia, 2016–2019. Source: The Annual Accommodation Survey by Statistics Indonesia.

Before proceeding to the econometric results, we present descriptive statistics in Appendix 2. We divided CD and QD values into two groups using the median as the central tendency to simplify the tabulation. A negative relationship exists between total revenue per available room with CD and QD. The value of total revenue per available room tends to be higher for hotels that have a CD or QD below the median.

Results and analysis

Effects of competitive strategies on the total revenue per available room

Table 1 presents the results of the panel fixed effect regression model. It estimates the association between competitive strategies and hotel performance. Competitive strategies are represented by conformity or differentiation in capacity and quality, while the hotel performance measure is the total revenue per available room.

Table 1.

The relationship between competitive strategies and total revenue per available room, baseline estimate.

	(1)	(2)	(3)
	Dependent variable: Total revenue per available room
Capacity distance (CD)	−0.024***	−0.022**	−0.026***
Capacity distance (CD)	(0.009)	(0.009)	(0.009)
Quality distance (QD)	−0.185**	−0.182	−0.190**
Quality distance (QD)	(0.111)	(0.110)	(0.110)
Control variables:
Hotel characteristics	Yes	Yes	Yes
Regional characteristics	No	Yes	Yes
Year-fixed effect	No	No	Yes
Year-island fixed effect	No	No	Yes
R-squared:	0.003	0.007	0.034
Observation	28,024	28,024	28,024

Notes: ** and *** indicate statistical significance at 5% and 1%, respectively. Standard errors clustered at the hotel level are reported in parentheses. Control variables include hotel characteristics: hotel quality, chain management, hotel location, availability of meeting rooms, availability of restaurants, and availability of fitness centers; regional characteristics: per capita GDRP, year-fixed effects, and year-island fixed effects.

We gradually introduced baseline controls in columns (1)–(3) based on equation (3) to determine the effect of competitive strategy on total revenue per available room. We begin the analysis using hotel characteristics as the control for individual characteristics in column (1). Column (2) includes regional characteristic as control variables. Column (3) adds the remaining island-year fixed effect to control for the unobserved effect of the island-year policy orientation. We grouped standard errors at the individual (hotel) level to address for potential heteroscedasticity and autocorrelation between hotels.

The coefficient on CDs in all columns is highly significant and remarkably stable. This coefficient only varies between 0.022 and 0.026 in negative signs. In column (1), it is equal to 0.024, which only includes the hotel’s characteristics as a control variable. Additionally, it equals 0.022 in column (2), which adds a regional characteristic, and 0.026 in column (3), which is a full specification. The coefficient on QDs ranges from 0.182 to 0.190 in negative signs. Although the coefficients tend to be stable, the coefficient in column (2) is insignificant.

The results in column (1) show a negative relationship between CD and total revenue per available room at a significance level of 1%. When the CD increases by 1%, it will cause the total revenue per available room to decrease by 0.02%. Our results also show a negative relationship between QD and total revenue per available room. It shows that every 1% increase in QD will decrease the total revenue per available room by 0.19%. The coefficient of QD is statistically significant at the 5% level. The conclusion drawn from column (1) is that when a hotel chooses to differentiate in capacity and quality, it will reduce its total revenue per available room.

The coefficient of CD in column (2) shows negative values and is significant at 5%. However, the coefficients of QD are negative and insignificant. It means insufficient evidence suggests a relationship between QD and total revenue per available room after controlling for the hotel and regional characteristics.

The result in column (3) shows a negative and significant coefficient of CD and QD of 0.03 and 0.19, respectively. It indicates that the total revenue per available room decreases by 0.03% and 0.19%, respectively, when the CD and QD increase by 1%. In other words, choosing different capacities or qualities from the 10 nearest competitors might decrease the total revenue per available room. The results are consistent with the Marshall–Arrow–Romer’s (MAR) theory, which states that companies will not benefit from innovating in a competitive environment. Companies will therefore achieve a higher performance by conformity with other hotels.

Potential mechanisms

The main result of this study is that having conformity in capacity and quality with the nearest hotel can increase the total hotel revenue per available room. We use Marshall’s theory of agglomeration to explain the potential mechanism that might occur in this relationship. Marshall (1920) stated that the benefits of agglomeration could occur through production enhancements and heightened demand.

The first mechanism to explain how competitive strategy affects hotel performance is through the benefits of agglomeration from the production side: labor pooling. Labor pooling increases the job-matching between firms and workers (Combes and Duranton, 2006). It could reduce labor turnover and increase labor productivity.

The paper assumes that hotels with similar capacities will have similar numbers of workers because the firm’s capacity is related to the number of workers. In equation (4), we calculate the labor distance between the focal hotel and its competitors as a variable representing labor pooling. Then we use the hotel’s total revenue per worker as a variable representing worker productivity.

The research regressed worker distance and total revenue per worker based on equation (3) to examine the relationship between labor pooling and productivity. Column (1) of Table 2 shows the results. There is a negative relationship between worker distance and total revenue per worker. The more similar the number of workers with the nearby rivals might increase the total revenue per worker. We conclude that similarity in hotel capacity reduces the probability of labor turnover between hotels and boosts worker productivity. The more productive the workforce, the higher the income per available room.

Table 2.

Potential Mechanism: agglomeration yielding production enhancement and heightened demand.

	(1)	(2)
	Per worker revenue	Guest per available room
Main independent variable:
Worker distance	−0.007***
Worker distance	(0.001)
Quality distance		−0.107*
Quality distance		(0.059)
Control variables:
Hotel characteristics	Yes	Yes
Regional characteristics	Yes	Yes
Year-fixed effect	Yes	Yes
Year-island fixed effect	Yes	Yes
R-squared:	0.029	0.002
Observation	28,024	21,018

Notes: * and *** indicate statistical significance at 10% and 1%, respectively. Standard errors clustered at the hotel level are reported in parentheses.

Samples in Column (2) are limited to the star and non-star hotels that had a series for 2016–2018.

Worker distance is defined as the difference in the average number of workers between the focus hotel and the 10 closest hotels.

The second mechanism stems from the notion that co-location allows consumers to evaluate a variety of offerings without leaving an area (Chung and Kalnins, 2001). They can easily compare all the nearby hotels. They will look for a hotel that they think fits their preferences. If customers find their initial hotel is fully booked, they tend to look for the closest hotel with the same quality as the reference hotel. To support this argument, we empirically tested the effect of QD on the number of guests per available room based on equation (3). We suspect that the more similar the quality of the hotel to its closest competitors, the more guests per available room. Thus, when the number of guests staying increases, the total revenue per available room will likewise rise.

Column (2) of Table 2 shows that the QD coefficient is negative and significant, so there is a negative relationship between QD and the number of guests per available hotel room. This result is in line with the main finding. The hotels that are of a comparable quality to their neighbors will gain more guests, which will then increase the hotel’s total revenue.

Heterogeneity analysis

Our models suggest that competitive strategies are negatively associated with total revenue per available room. However, the results might be heterogeneous between sub-samples. We checked some sub-samples: hotel age, location, island, and hotels’ membership association.

Older firms tend to accurately estimate their viability and operating capabilities (Calantone et al., 2002; Kim et al., 2018). The current study investigates the heterogeneous reactions of competitive strategy to hotel performance between hotels that have been in operation for more than 10 years, 5–10 years, and less than 5 years. Table 3, Columns (1)–(3) show that the CD coefficient is only significant in hotels that are more than 10 years old and 5–10 years old. The values are almost the same, namely, −0.0033 and −0.035. In contrast, the QD variable is only significant in the newest hotel group.

Table 3.

The impact of competitive strategies on total revenue per available room by hotels’ age and location.

	(1)	(2)	(3)	(4)	(5)	(6)
	Hotel age (in years)			Location
	>10	5-10	<5	Tourism areas	Shopping/Business areas	Others
Capacity distance	−0.033***	−0.035*	0.002	−0.004	−0.048***	−0.040**
	(0.011)	(0.020)	(0.012)	(0.013)	(0.012)	(0.016)
Quality distance	−0.108	−0.179	−0.880**	−0.392**	−0.017	−0.140
Quality distance	(0.159)	(0.208)	(0.422)	(0.172)	(0.200)	(0.204)
Control variables:
Hotel characteristics	Yes	Yes	Yes	Yes	Yes	Yes
Regional characteristics	Yes	Yes	Yes	Yes	Yes	Yes
Year-fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Year-island fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
R Squared	0.036	0.031	0.066	0.036	0.033	0.031
Observation	16,443	8563	3018	6432	10,212	11,380

Notes: *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively. Standard errors clustered at the hotel level are reported in parentheses.

We further estimate equation (3), which evaluates the impact of competitive strategy on hotel performance based on the hotel location to account for a certain degree of heterogeneity. We divide hotel locations into shopping/business, tourism, and others. Table 3, Columns (4)–(6) show that all CD coefficients are negative, but only significant for hotels located in shopping/business areas and others. In contrast, the QD coefficient is only significant for hotels in tourism areas. This difference in results may be due to differences in the characteristics of tourists visiting these locations.

We also examine the possibility of heterogeneity due to the different characteristics of the Indonesian islands. Development policies between islands may differ depending on natural resources, human resources, infrastructure, demand, etc. We divided hotels located on the islands of Java-Bali and non-Java-Bali. Table 4 shows that the competitive strategy is only significant for hotels in Java and Bali. The results showed that the CD and QD coefficients were negative and significant, respectively, 0.06 and 0.38. It means that the total revenue per available room decreases by 0.06% and 0.38%, respectively, when the capacity gap and quality gap increase by 1%. The proximity of hotels with similar characteristics will increase the total revenue per room available for hotels in Java and Bali.

Table 4.

The impact of competitive strategies on total revenue per available room by island and hotel’s association membership.

	(1)	(2)	(3)	(4)
	Islands		Hotel association membership
	Java and Bali	Non-Java and Bali	Yes	No
Main independent variable:
Capacity distance (CD)	−0.059***	0.000	−0.030***	−0.018
Capacity distance (CD)	(0.013)	(0.010)	(0.011)	(0.015)
Quality distance (QD)	−0.379*	−0.128	−0.424***	0.115
Quality distance (QD)	(0.206)	(0.138)	(0.145)	(0.157)
Control variables:
Hotel characteristics	Yes	Yes	Yes	Yes
Regional characteristics	Yes	Yes	Yes	Yes
Year-fixed effect	Yes	Yes	Yes	Yes
Year-island fixed effect	Yes	Yes	Yes	Yes
R-squared:	0.031	0.156	0.075	0.016
Observation	13,432	14,592	12,676	15,348

Notes: *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively. Standard errors clustered at the hotel level are reported in parentheses.

Possible explanations for these results are that in Java and Bali, the hotel is more accessible, and the availability of labor is greater than in other islands. Java and Bali are home to around 65% of Indonesia’s hotel employees (Statistics Indonesia, 2021a). As previously explained, a similar hotel capacity will require almost the same workforce. Ease of access and unification of the workforce facilitates knowledge spillover between hotels and encourages increased hotel productivity for hotels on the islands of Java and Bali.

This study checks the possibility of heterogeneity due to hotel association membership status. The Indonesian Hotel and Restaurant Association (IHRA) is a forum for businesspeople in the tourism sector. The common interest of this association is to create a business climate and maintain dignity and respect among business owners who focus on hospitality, restaurants, tours and travel, and hospitality education institutions in Indonesia.

The estimation results in Table 4 Columns (3) and (4) show that the competitive strategy is only significant for hotel group members of the IHRA. IHRA always coordinates with private business entities and the government to protect its members, so that hotel room occupancy rates remain controlled.

Robustness analysis: different competitive sets, hotel performance indicator alternative

We perform robustness checks concerning our results to determine whether unobservable factors influenced them by identifying relationships between competitive strategies and hotel performance in multiple aspects, indicating that our results consistently maintain a negative association. Table 5, Columns (1)–(6) show the estimation using different competitive sets: the 5, 11, 12, 13, 14, and 15 nearest hotels. The robustness test findings reveal that the capacity and QD coefficients are greater than the baseline finding and are in the same direction as the main finding.

Table 5.

Effect of competitive strategies on different competitive sets and hotel performance measurement.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	Total revenue per available room						Revenue per available room
	Number of … closest hotels
	5	11	12	13	14	15
Capacity distance	−0.010***	−0.035***	−0.038***	−0.040***	−0.043***	−0.057***	−0.044***
Capacity distance	(0.003)	(0.010)	(0.011)	(0.011)	(0.011)	(0.014)	(0.010)
Quality distance	−0.067*	−0.211*	−0.240*	−0.266**	−0.290**	−0.297*	−0.106*
Quality distance	(0.068)	(0.076)	(0.127)	(0.136)	(0.144)	(0.159)	(0.106)
Control variables:
Hotel characteristics	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Regional characteristic	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year-fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year-island fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes
R-squared	0.032	0.032	0.029	0.029	0.027	0.032	0.001
Observation	28,024	28,024	28,024	28,024	28,024	28,024	28,024

Notes: *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively. Standard errors clustered at the hotel level are reported in parentheses.

Revenue per available room is calculated by dividing the total room revenue by the total number of rooms available in a year.

The coefficients of CD ranged from −0.010 to −0.057, which are significant at the 1% level. Meanwhile, the coefficient of QD ranged from −0.067 to −0.297 at 5% and 10% significance levels. The results broadly suggest that capacity and QDs negatively affect the total revenue per available room.

A recent study also tests our estimation using a different outcome in the model, that is, revenue per available room (Canina et al., 2005; Kim et al., 2020). Revenue per available room only calculates the revenue from the sales of hotel rooms. Column (7) of Table 5 suggests that the estimates remain consistent with the baseline results. The capacity and QD have negative and significant coefficients.

This paper also presents the results of additional robustness tests in Table 7. Column (1) shows the results of using CD and QD with the squared LD denominator. The QD variable is insignificant even though it has the same direction as the baseline result. We also conducted a robustness check using the hotel performance measure: profit per available room in Column (2). We obtain insignificant results even though they have the same direction as the coefficients with the baseline results. Column (3) provides a robustness test of the relationship between competitive strategy and total revenue per available room by excluding the 10% of hotels with the farthest average distance from other hotels. Tests show that our estimates are relatively strong.

Conclusions

Several studies identify empirical evidence on how hotel strategy relates to hotel performance (Becerra et al., 2013; Chung and Kalnins, 2001; S. K. Lee & Jang, 2017). However, research exploring the impact of how hotel strategy influences its performance is rare, especially in Indonesia, one of the top five countries that dominate the supply of hotel rooms in the Asia Pacific region. This study elaborates on previous research by examining potential mechanisms by which competitive strategy influences hotel performance.

A recent study used annual longitudinal hotel data from the 2016–2019 Indonesian accommodation survey to examine the relationship between competitive strategy and hotel performance. We apply the fixed effects method to accommodate unobservable time-invariant factors. The results show that hotels implementing a capacity or quality conformity strategy with neighboring hotels tend to have a higher total revenue per available room. The results are consistent with previous research, which found that differentiated hotels tend to have a lower performance (Kim et al., 2020). In addition, our research provides empirical evidence that there is a relationship between hotel strategy and hotel performance through agglomeration benefits: lower consumer search costs and labor pooling. This study also shows that the effect of conformity strategy is heterogeneous across hotel locations.

Our findings have policy implications. A new hotel must consider having a similar quality and capacity to neighboring hotels. The strategy’s effectiveness also depends on the location due to differences in market segmentation. Tourists visiting tourist attractions prioritize comfort and quality, while visitors to business centers prioritize hotel capacity. Regarding human resources, hoteliers can take advantage of labor pooling due to the proximity between hotels. Labor pooling will make it easier for hotels to find workers according to their needs, reducing employee search costs. When setting up a hotel-based economic zone, this study recommends that policymakers prioritize hotel agglomerations with similar characteristics. Policymakers can maintain a competitive environment between hotels, so each hotel can take advantage of the spillover effect and increase its productivity. The government should encourage hotels to join the IHRA to facilitate coordination between hotels and the government or the private sector. IHRA can monitor market trends and share information on emerging spillover opportunities with hotels. IHRA’s role in creating a positive climate and mutual respect causes spillover effects between hotels to run smoothly. Finally, improving the infrastructure and labor availability is necessary to improve the performance of hotels outside Java and Bali.

The following are the limitations of this study and suggestions for further research. First, the data we use does not include other types of accommodation, such as new accommodation, accommodation that stopped operating between study periods, hotel apartments, shared accommodation, and other small seasonal accommodations. Therefore, to improve the accuracy of the findings, future research can develop a dynamic model that includes all types of accommodation, including occasionally open and closed hotels. Second, the short research time, only 4 years, is due to the limitation of accurate location data (geotagging) and minimizing bias due to the impact of COVID-19. Further research can use monthly data to estimate longer timelines and capture seasonal effects. Third, we use the dimension of hotel quality based on stars, although there may be non-star hotels with comparable facilities to star hotels. Therefore, future research could change the quality measure based on the availability of facilities at the hotel.

Supplemental Material

Supplemental Material - Conformity or differentiation? The effect of competitive strategy on hotel performance

Supplemental Material for Conformity or differentiation? The effect of competitive strategy on hotel performance by Beta Septi Iryani, Muhammad Halley Yudhistira, Khoirunurrofik Khoirunurrofik, Djoni Hartono in Tourism Economics

Footnotes

Acknowledgements

Iryani acknowledges the Statistics Indonesia for providing financial support for this study. The authors are also grateful for the support of the PUTI Grant NKB-060/2022 of Universitas Indonesia. The funders had no role in the study design, data collection, data analysis, data interpretation or preparation of the manuscript.

Author contributions

Beta Septi Iryani: conceptualization and design, methodology, analysis and interpretation, writing-original draft.

Muhammad Halley Yudhistira: conceptualization and design, methodology, analysis and interpretation, writing-original draft.

Khoirunurrofik: conceptualization and design, methodology, analysis and interpretation.

Djoni Hartono: conceptualization and design, methodology, analysis and interpretation.

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: This work was supported by the Universitas Indonesia (PUTI Grant NKB-060/2022 of University of Indonesia); (state budget scholarship BPS-Statistics Indonesia).

ORCID iD

Muhammad Halley Yudhistira

Supplemental Material

Supplemental material for this article is available online.

Author biographies

Beta Septi Iryani obtained a master’s degree in Economics from IPB University. Now, she is pursuing a doctoral degree at the Department of Economics, Faculty of Economics and Business, Universitas Indonesia. She has worked for the Directorate of Finance, Information Technology and Tourism Statistics, BPS-Statistics Indonesia, since 2010. She has been involved in statistical activities related to the tourism sector and has produced several Indonesian tourism statistics publications.

Muhammad Halley Yudhistira obtained his PhD in Public Economics from National Graduate Institute for Policy Studies, Japan. His fields of expertise are Theory and Empirics in Transportation Economics, Urban and Regional Economics, and Applied Econometrics. Currently, he is a research fellow of the Institute for Economic and Social Research (LPEM), Faculty of Economics and Business, Universitas Indonesia.

Khoirunurrofik received his PhD in Public Economics from the National Graduate Institute for Policy Studies- GRIPS (Tokyo, Japan) in 2014. He also obtained a Master of Arts in Policy Analysis from GRIPS in 2011, Master in Public Policy and Management (MPM) from Carnegie Mellon University-Australia Campus in 2007, and International Master in Regional Integration from Asia Europe Institute, University of Malaya, Malaysia – University of Autonoma Madrid, Spain in 2004. He joined LPEM FEB UI in 1999 after earning his Bachelor’s degree in Statistics from FMIPA, IPB University. Rofik has been a lecturer at the Faculty of Economics and Business, University of Indonesia, since 2000. His research interests are Public Finance and Fiscal Decentralization, Urban and Regional Economics, and Energy and Transportation.

Djoni Hartono is a Professor at the Department of Economics, Faculty of Economics and Business, Universitas Indonesia. He received his B.S. degree in mathematics from IPB University, while his Master and PhD degrees in economics were obtained from Universitas Indonesia. He teaches several subjects such as mathematical economics, econometrics, energy economics, regional economics, and economic modeling. His research interests cover the economy-wide impact of energy policies and regional and economic development. His other specialty is in computable general equilibrium modeling.

References

Andini

de Blasio

Duranton

, et al. (2013) Marshallian labour market pooling: evidence from Italy. Regional Science and Urban Economics 43(6): 1008–1022. DOI: 10.1016/j.regsciurbeco.2013.04.003

Baum

JAC

Haveman

(1997) Love Thy neighbor? Differentiation and agglomeration in the Manhattan hotel industry, 1898-1990. Administrative Science Quarterly 42(2): 304–338. DOI: 10.2307/2393922

Becerra

Santaló

Silva

(2013) Being better vs. being different: differentiation, competition, and pricing strategies in the Spanish hotel industry. Tourism Management 34: 71–79. DOI: 10.1016/j.tourman.2012.03.014

Bertrand

Duflo

Mullainathan

(2004) How much should we trust differences-in-differences estimates? The Quarterly Journal of Economics 119(1): 249–275. DOI: 10.1162/003355304772839588

Bowie

Buttle

(2004) Hospitality Marketing an Introduction. Oxford: Elsevier Butterworth-Heinemann (first). DOI: 10.4324/9781315445526-1

Calantone

Cavusgil

Zhao

(2002) Learning orientation, firm innovation capability, and firm performance. Industrial Marketing Management 31(6): 515–524. DOI: 10.1016/S0019-8501(01)00203-6

Canina

Enz

Harrison

(2005) Agglomeration efects and strategic orientations: evidence from the U.S. lodging industry. Academy of Management Journal 48(4): 565–581. DOI: 10.5465/AMJ.2005.17843938

Chung

Kalnins

(2001) Agglomeration effects and performance: a test of the Texas lodging industry. Strategic Management Journal 22(10): 969–988. DOI: 10.1002/smj.178

Combes

Duranton

(2006) Labour pooling, labour poaching, and spatial clustering. Regional Science and Urban Economics 36(1): 1–28. DOI: 10.1016/j.regsciurbeco.2005.06.003

10.

Cró

Martins

(2017) Hotel and hostel location in Lisbon: looking for their determinants. Tourism Geographies 20(3): 504–523. DOI: 10.1080/14616688.2017.1360386

(2019) The applicability of Porter’s generic strategies in pure online firms: a case study approach. Strategic Change 28(3): 167–176. DOI: 10.1002/jsc.2258

12.

Gan

Hernandez

(2013) Making friends with your neighbors? agglomeration and tacit collusion in the lodging industry. Review of Economics and Statistics 95(3): 1002–1017. https://www.jstor.org/stable/43554808

13.

Hamilton

Nickerson

(2003) Correcting for endogeneity in strategic management research. Strategic Organization 1(I): 51–78. DOI: 10.1177/1476127003001001218

14.

HVS (2019) Market snapshot: Asia pacific. HVS.com (Issue 1). Singapore: HVS. https://www.hvs.com/article/8626-market-snapshot-asia-pacific-2019

15.

Kalnins

Chung

(2004) Resource-seeking agglomeration: a study of market entry in the lodging industry. Strategic Management Journal 25(7): 689–699. DOI: 10.1002/smj.403

(2016) The effect of idiosyncratic price movements on short- and long-run performance of hotels. International Journal of Hospitality Management 56: 78–86. DOI: 10.1016/j.ijhm.2016.04.006

(2018) Competitive price interactions and strategic responses in the lodging market. Tourism Management 68: 210–219. DOI: 10.1016/j.tourman.2018.03.013

(2020) Different from or similar to neighbors? An investigation of hotels’ strategic distances. Tourism Management, 76, 103960(February 2019). DOI: 10.1016/j.tourman.2019.103960

(2005) Sectoral manufacturing productivity growth in Korean regions. Urban Studies 42(7): 1201–1219. DOI: 10.1080/00420980500121889

20.

Lee

(2015) Quality differentiation and conditional spatial price competition among hotels. Tourism Management 46: 114–122. DOI: 10.1016/j.tourman.2014.06.019

(2017) Early Mover or late Mover advantage for hotels? Journal of Hospitality & Tourism Research 41(1): 23–40. DOI: 10.1177/1096348013503995

(2019) RevPAR vs. GOPPAR: Property- and firm-level analysis. Annals of Tourism Research 76 (September 2018), 180–190.

23.

Marshall

(1920) Industry and trade Economics for Managers Second. London: Macmillan. DOI: 10.1007/978-1-349-17812-4_9

24.

Mazzeo

(2002) Product Choice and Oligopoly market structure. The RAND Journal of Economics 33(2): 221. DOI: 10.2307/3087431

25.

Mccann

Folta

(2008) Location Matters: where We have been and where We might Go in agglomeration research †. Journal of Management 34: 532–565. DOI: 10.1177/0149206308316057.

(2010) How important is selection? Experimental vs. non-experimental measures of the income gains from migration. Journal of the European Economic Association 8(4): 913–945. DOI: 10.1162/jeea_a_00008

(2015) Adapting to Uncertainty - the Global Hotel Industry. The Travel & Tourism Competitiveness Report, January 2010, Vols 47–51. Geneva: World Economic Forum. http://reports.weforum.org/travel-and-tourism-competitiveness-report-2015/chapter-1-2-adapting-to-uncertainty-the-global-hotel-industry/

28.

Porter

(1990) Competitive Strategy: Techniques for Analyzing Industries and Competitors: With a New Introduction. The Free Press.

29.

Statistics Indonesia . (2016) Hotel and Other Accommodation Statistics in Indonesia 2016 (Sub-directorate of Tourism Statistics, Ed.). Jakarta: BPS-Statistics Indonesia.

30.

Statistics Indonesia (2020a) Hotel and accommodation statistics in Indonesia 2019. In: Barudin

Suryani

(eds), BPS Statistics Indonesia. Jakarta: BPS-Statistics Indonesia.

31.

Statistics Indonesia (2020b) Number of Foreign Tourist Visits to Indonesia by Nationality (People), 2015-2019. Jakarta: BPS-Statistics Indonesia. https://www.bps.go.id/indicator/16/1821/3/jumlah-kunjungan-wisatawan-mancanegara-ke-indonesia-menurut-kebangsaan.html

32.

Statistics Indonesia (2021a) Hotel and Other Accommodation Statistics in Indonesia (Workers, Revenue, and Expenditure). Jakarta: BPS-Statistics Indonesia.

33.

Statistics Indonesia (2021b) Number of Nusantara Tourist Travels (People), 2015-2020. Jakarta: BPS-Statistics Indonesia. https://www.bps.go.id/indicator/16/1189/2/jumlah-perjalanan-wisatawan-nusantara.html

34.

Tobler

(2004) On the first Law of Geography: a reply. Annals of the Association of American Geographers 94(2): 304–310.

35.

Urtasun

Gutiérrez

(2006) Hotel location in tourism cities: Madrid 1936-1998. Annals of Tourism Research 33(2): 382–402. DOI: 10.1016/j.annals.2005.12.008

, et al. (2020) The relative effect of growth of economy, industry Expansion, and firm-specific factors on Corporate hotel performance in Sri Lanka. SAGE Open 10(2): 215824402091463. DOI: 10.1177/2158244020914633

(2014) Theoretical, empirical, and operational models in hotel location research. International Journal of Hospitality Management 36: 209–220. DOI: 10.1016/j.ijhm.2013.09.004

38.

Yokeno

(1968) La localisation de l’industrie touristique: Application de l’analyse de Thunen-Weber [The location of the tourism industry sector: Application of the Thunen-Weber analysis]. Universite’ d’Aix-Marseille Centre d’e’tudes du tourisme.

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