Agglomerations around natural resources in the hospitality industry: Balancing growth with the Sustainable Development Goals

Aragon-Correa, Juan Alberto; de la Torre-Ruiz, José; Vidal-Salazar, María Dolores Article Agglomerations around natural resources in the hospitality industry: Balancing growth with the Sustainable Development Goals BRQ Business Research Quarterly Provided in Cooperation with: Asociación Científica de Economía y Dirección de Empresas (ACEDE), Madrid Suggested Citation: Aragon-Correa, Juan Alberto; de la Torre-Ruiz, José; Vidal-Salazar, María Dolores (2023) : Agglomerations around natural resources in the hospitality industry: Balancing growth with the Sustainable Development Goals, BRQ Business Research Quarterly, ISSN 2340-9444, Sage Publishing, London, Vol. 26, Iss. 1, pp. 11-26, https://doi.org/10.1177/23409444221103283 This Version is available at: https://hdl.handle.net/10419/327009 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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Introduction The United Nations’ Sustainable Development Goals (SDGs) constitute an action plan designed to address pressing large-scale grand challenges (Sachs et al., 2019). Environmentally oriented targets found in, for instance, SDG 6 on clean water and sanitation, SDG 13 on climate action, SDG 14 on life below water, and SDG 15 on life on land were included to make the SDGs more ambitious, comprehensive, and respectful with regard to the environment than previous United Nations declarations, such as the Millennium Development Goals. Notably, these targets deal with the difficulty of achieving a balance between ecological and social aspirations and traditional economic priorities related to growth (Neumann et al., 2017). This article examines the evolution of business growth close to natural resources in the hospitality industry and its potential implications for the achievement of the SDGs. “Sustainable tourism” is one of the key businessrelated topics included in the SDGs of the United Nations’ 2030 Agenda. The tourism industry is explicitly named in three SDGs: sustainable economic growth (SDG 8), sustainable consumption and production (SDG 12), and life below water (SDG 14) (United Nations World Travel Organization [UNWTO], 2021). Moreover, the increasing human and environmental pressure in coastal areas resulting from the agglomeration of hotel facilities led to explicit consideration of coastal area conservation in both SDG targets 14.2 and 14.5 (Neumann et al., 2017). Not surprisingly, the recently launched One Planet Sustainable Tourism Programme (UNWTO, 2021) establishes that Agglomerations around natural resources in the hospitality industry: Balancing growth with the Sustainable Development Goals J Alberto Aragón-Correa , José M de la Torre-Ruiz and M Dolores Vidal-Salazar Abstract Many tourism agglomerations are situated near natural resources, which implies a need to balance business growth with environmental preservation. Our analysis of the location decisions of 295 luxury beach hotels in Spain between 1960 and 2015 reveals two main findings. First, we confirm the positive relationship between the existence of demandrelated urbanization services around natural resources and the attractiveness of agglomerations to new entrants. Second, we find that an agglomeration’s attractiveness negatively affects the density of firms in the agglomeration if that attractiveness hinders firms’ access to the same natural resources. Our results contribute to the strategy literature on agglomerations and provide a better understanding of how the tourism industry can work toward achieving the sustainable development goals (SDGs). JEL CLASSIFICATION: L83, M13, M14, Q50, Q26, R30 Keywords Natural environment, agglomeration, sustainable development goals, sustainable growth, hospitality industry Department of Business Management II, Faculty of Economics and Business, University of Granada, Granada, Spain Corresponding author: Prof. J. Alberto Aragón-Correa, Department of Business Management II, Faculty of Economics and Business, University of Granada, Campus Cartuja S/N, 18071 Granada, Spain. Email: [email protected] 1103283BRQ0010.1177/23409444221103283Business Research QuarterlyAragón-Correa et al. research-article2022 Regular Paper 12 Business Research Quarterly 26(1) sustainability must be the new norm for every part of the tourism sector. This prevailing vision and its focus on sustainability make it necessary to improve our understanding of those aspects of the sector’s development that may still be unsustainable, such as excessive business growth in certain fragile areas. Our research interest in this article lies in these three SDGs. We analyze how the agglomeration of hotels around valuable coastal resources evolves, and the implications of that evolution for the competitiveness of regions and firms. Although there is robust evidence on the benefits of colocation for firms (Alcacer & Zhao, 2016; Woo et al., 2019), recent work has called for more research into how agglomerations develop (Kim, 2016; McCann et al., 2016; Pe’er et al., 2016). We focus on the factors that influence how the attractiveness of agglomeration around natural resources dynamically evolves for new entrants. This issue is particularly relevant because it involves certain inherent tensions. On one hand, co-locating with competitors offers certain widely studied benefits, including access to a pool of competitive suppliers and customers (Kim, 2016). On the other hand, new entrants’ access to valuable natural resources may be limited when competitors are already located in a focal region (Lee & Jang, 2015). To extend the extant agglomeration literature, we analyze a sample of location decisions made by 295 luxury beach hotels between 1960 and 2015. We examine how the availability of urban demand-related services and the density of co-located competitors may influence where new entrants locate around valuable coastal resources, and the strategic implications of these factors for the emergence of new agglomerations. Our empirical analysis improves our understanding of why managers may have conflicting preferences over time with regard to co-locating in an agglomeration around natural resources, and why they should carefully consider the balance between sustainable consumption objectives and developments around natural resources. This article adds to the growing stream of research on the dynamic attractiveness of agglomerations (Alcacer & Chung, 2014; Kim, 2016; McCann et al., 2016; Pe’er et al., 2016; L. Wang et al., 2014). Specifically, our study makes three contributions to the strategic agglomeration literature. First, multiple works have suggested that a more comprehensive assessment of the benefits of agglomerations may help to explain agglomeration processes (Hervas- Oliver et al., 2017; Kim, 2016; Krugman, 1991; L. Wang et al., 2014). Reviews of the agglomeration management literature reveal that economic and technological factors have received a great deal of attention (Gur & Greckhamer, 2019; McCann & Folta, 2008, 2009) Meanwhile, the economics literature has focused on the strong relation between the initial availability of natural resources and the establishment of agglomerations in multiple industries (Hervas-Oliver et al., 2015). However, it has ignored its strategic implications over time. We bridge these perspectives by analyzing how an agglomeration’s attractiveness to new entrants is simultaneously affected by factors that influence opportunities to access both endogenous demand-related benefits and exogenous natural resource– oriented benefits. Second, it is tempting to assume that the limited mobility of natural resources provides relatively stable exogenous benefits (McCann & Folta, 2009). However, we extend recent dynamic perspectives on firms’ agglomeration (Alcacer & Chung, 2014; Kim, 2016; McCann et al., 2016; Pe’er et al., 2016; L. Wang et al., 2014) by showing that a higher density of co-located firms negatively affects an agglomeration’s attractiveness to new entrants owing to the difficulties of accessing the valuable but limited natural resources close to beaches in tourist regions. In this regard, changes over time reflect the importance of a dynamic perspective on the attractiveness of an agglomeration. Third, our results improve our understanding of the importance of ensuring progress toward sustainable growth as prioritized in the SDGs. The emergent management literature on the SDGs (Kolk et al., 2017; Montiel et al., 2021; Sachs et al., 2019) has emphasized the important role of business in achieving these goals. Our results contribute to this stream of literature by showing how new agglomerations may emerge when a proper balance among growth, consumption, and sustainability is present. Specifically, our results confirm that access to natural resources in an agglomeration becomes more difficult as the number of co-located firms increases. Consequently, even if agglomeration benefits in a new region increase, some firms will still favor the superior provision of agglomeration benefits in the original region. While theorists on ecological organizations would argue that a population of firms begins to decrease when it is overwhelmed by competition (Carroll & Hannan, 2000; Hanan & Freeman, 1989), our results show that the sustainable growth of the original agglomeration may offer benefits to both firms in the original agglomeration and firms located in different regions that provide better access to natural resources. Agglomerations around natural resources Supply-side and demand-side benefits: the attractiveness of an agglomeration Management literature on the SDGs (Kolk et al., 2017; Montiel et al., 2021; Sachs et al., 2019) highlights the importance of understanding the interactions between business and the natural resources. The nature of the benefits for new entrants from co-locating around valuable natural resources include both exogenous and endogenous Aragón-Correa et al. 13 ones. The exogenous benefits include the competitive gains derived from privileged access to the natural resources in a region—such as oil, fresh water, minerals, natural attractions, or soil, among others—and these are often the original reason for the agglomerative process in multiple industries (Hervas-Oliver et al., 2015, 2017; LaFountain, 2005; Rosenthal & Strange, 2001). The endogenous benefits arise from the existence of co-located firms and may encompass both supply-side benefits, such as the existence of specialized labor, suppliers, or knowledge spillovers (Alcacer & Chung, 2014; Alcacer & Zhao, 2016; Engel, 2015; Folta et al., 2006; Funk, 2014; McCann et al., 2016), and demand-side benefits, such as a reduction in consumer search costs (Canina et al., 2005; Chung & Kalnins, 2001; McCann & Vroom, 2010; Urtasun & Gutiérrez, 2017). While the endogenous benefits normally grow as the number of similar firms in the same location increases (Arthur, 1990; McCann & Folta, 2009), previous research mostly assumes that exogenous externalities are independent of the number of co-located firms (McCann & Folta, 2009). The benefits derived from agglomerations of firms in the same industry have attracted an exponential degree of attention in recent decades (Ryu et al., 2018); however, conflicting influences on the attractiveness to new entrants of co-locating around natural resources merit specific consideration from a managerial point of view. For example, a new winery may find it attractive to be co-located with other competitors in the Napa Valley because this would provide privileged opportunities for access to specialized services and to the high level of demand generated by this American Viticultural Area’s reputation for wine production (L. Wang et al., 2014). However, the existence of competitors may reduce the Valley’s attractiveness to new entrants by limiting the opportunities for access to soil in the best areas and, indirectly, this may encourage the development of new wineries in a peripheral land in the Napa County or in an alternative region, such as Central or North Coast, which offer cheaper access to similar land but fewer demand benefits from co-locating with competitors than in the Napa Valley (Hira & Swartz, 2014). The growing business concerns about limited natural resources (Bansal & Song, 2017; Berrone et al., 2013; Flammer, 2013) will increase the importance of knowing more about the dynamics of agglomerations around natural resources. We define an agglomeration’s attractiveness in this article as its effectiveness in influencing new entrants to co-locate as close as possible to the agglomeration. Thus, if the agglomeration’s attractiveness is high, the entrant will locate the new firm closer to the center of the agglomeration than if the agglomeration’s attractiveness is low. Our research interest in this article is to examine how two factors connected with the existence of agglomerations—the density of co-located competitors and the urbanization in the selected location—may have different impacts on the agglomeration’s attractiveness around natural resources and how this matters to the SDGs aspirations. Attractiveness of agglomerations in the hospitality industry Intuitively, one might expect that a manager would prefer their firm to be located as far as possible from its competitors to provide easy opportunities for acquiring customers and reinforced bargaining power with suppliers in the area. However, the hospitality industry used as our empirical setting provides a robust illustration of the co-location tendency (Lee & Jang, 2015; Woo et al., 2019). Although much of the management literature is focused on the supply-side positive externalities of agglomerations in industries (McCann & Folta, 2008), attention to demand-side externalities dominate analyses of agglomerations in the hospitality industry and services in general (Urtasun & Gutiérrez, 2017). Previous findings have highlighted the importance of agglomerations in reinforcing the strength of demand at a location in the hospitality industry (Baum & Haveman, 1997; Baum & Mezias, 1992; Canina et al., 2005; Chung & Kalnins, 2001; Kalnins & Chung, 2004; McCann & Vroom, 2010; Woo et al., 2019). In general, demandrelated externalities are connected to reduced search costs for customers as a consequence of the agglomeration of firms (Marshall, 1920). Demand-related externalities in the hospitality industry include all the advantages that agglomerations may provide over isolated locations for attracting tourists (e.g., reputation, airports, and transport in the region); however, an excessive growth of competitors in the area may erode or cancel out the potential benefits. A good balance is hard to get. For instance, Oahu’s natural scenery makes it the most visited of the islands in the Hawaiian archipelago. Oahu already offers 31,637 visitor units in 107 different hotels in an island just 44 miles (71 km) long (Hawai’i Tourism Authority, 2018). In this context, it is unrealistic to expect that any new hotel will be able to gain similar access to the privileged beaches enjoyed by the first hotels on the island. However, new entrants will get access to tourists hoping to visit one of the best-known tourist areas in the United States using any of the 27 domestic and international carriers, 4 commuter airlines, and 3 inter-island airlines that land at Honolulu International Airport. New entrants to hospitality agglomerations have to find a balance between providing similar services to businesses already established in the region and extending the offer. For example, Baum and Haveman (1997) show that new hotels tend to locate geographically close to incumbents who are similar in terms of price, quality, and class but different in terms of size. Similarly, Chung and Kalnins (2001) found that rural hotels had higher revenues when their local market was made up of a greater fraction of 14 Business Research Quarterly 26(1) hotels that were larger than they were. In general, new hotels are more likely to agglomerate when the region or existing hotels in the agglomeration are already well differentiated; for example, high-quality and larger hotels attract more new entrants than low-quality and smaller hotels (Kalnins & Chung, 2004). Interestingly, there is evidence that, in general, agglomerating is more attractive for firms that are more dependent on external factors (Kukalis, 2010; McCann & Folta, 2008). The acknowledged benefits that proximity to certain natural resources provide to multiple firms in the industry (e.g., ski areas, beaches, natural parks, rainfall, and so on; Canina et al., 2005: 568) contrast with the scant attention paid in the literature to the evolution of hospitality agglomerations that are close to these valuable natural resources in the industry. Hypotheses The baseline foundations: a firm’s access to valuable natural resources A firm may receive relevant gains from being located in places endowed with exclusive natural advantages that generate industry agglomerations (Ellison & Glaeser, 1999; Russo, 2003). Marshall’s (1920) pioneering work (p. 269) claimed that the location decisions of firms are highly influenced by physical conditions, such as “climate, soil, mines or quarries in nearby areas, or easy access by land or water.” Economic geographers have focused on showing that cost advantages related to easy access to natural benefits explain agglomerations in multiple industries (LaFountain, 2005; Rosenthal & Strange, 2001). Although natural resources have received limited attention in the management agglomeration literature so far, the growing interest in the managerial implications of a limited availability of natural resources (Bansal & Song, 2017; George et al., 2018) reinforces the need to integrate these literatures. Favorable proximity to attractions or natural settings is one of the single strongest factors for differentiation value in the hospitality industry (Canina et al., 2005). For example, beach hotels typically gain differentiation when they are located on the immediate beachfront, when compared to other similar hotels in the area that are located further away from the beach (e.g., sea views usually command a premium in beach hotels). Extending previous economic perspectives highlighting the connection between the existence of natural resources and the initial exogenous benefits of agglomerated firms (Arthur, 1990), we expect that the number of co-located competitors necessarily decreases the new entrants’ opportunities for accessing valuable but limited natural resources in the region. Firms located in agglomerations where valuable natural resources are strategically relevant will pay special attention to obtaining privileged access to those natural resources. Firms arriving early to an agglomeration will be able to guarantee privileged access to natural resources because of availability and limited causal ambiguity regarding the strategic role of natural resources in the industry (Kim, 2013, 2016) In our empirical setting, early hotels will look for the location with the best access to natural resources because it offers the opportunity to obtain the maximum level of benefits derived from their proximity to those resources (e.g., charging a premium for direct access from the premises, being located within a walkable distance, or having direct views). However, privileged access to strategic natural resources in the region will become more difficult as the density of the firms in the agglomeration grows while the natural resources remain the same. Each potential new entrant to the agglomeration will find that the existing hotels have already occupied some of the potential locations for accessing strategic natural resources in the industry. Even if there are still opportunities to access natural resources, new entrants will necessarily find fewer and typically less promising opportunities than previous entrants. In general, market competition is particularly intensive when the consumption of the product or service is local and there is rivalry between firms for scarce but valuable input resources (Kukalis, 2010: 455). As a consequence, new entrants to the agglomeration will get progressively less privileged access to the valuable natural resources in the region when the density of the agglomeration increases. Our baseline hypothesis in this article is: Hypothesis 1: Access to valuable natural resources in an agglomeration of competitors decreases as the density of firms in the agglomeration increases. The process: rise and fall in the attractiveness of an agglomeration around natural resources The rise: urbanization and demand-related services. Subsequent entrants in the lodging industry that choose the same location benefit from the size of agglomeration demand by reducing their costs or risks of attracting customers versus those in less developed regions from a tourism perspective (Urtasun & Gutiérrez, 2017). Regional variations in levels of urbanization may make a substantial difference in the opportunities for new entrants to access the endogenous demand-related benefits of agglomerations in the industry (Graham, 2009). The value of the natural resources in certain tourist regions does not take away from the significance of the demand-related benefits; in fact, the related services may become more significant when climbing, surfing, or diving schools, equipment rental, or boat hire, for example, are available. Urbanization may generate economies to firms from the scale of markets and from good infrastructure and public service provision (Graham, 2009). The popular idea that Aragón-Correa et al. 15 agglomerations reduce the search costs for customers in the hospitality industry has been traditionally related to additional opportunities for personal visual inspections (Chung & Kalnins, 2001; Kalnins & Chung, 2004). The use of the internet has increased the information economics (Williamson, 1991) of firms in urban areas versus their geographically isolated counterparts because the consolidated urban regions receive a larger number of online searches, and it is now easier to make comparisons between the offers of assets in different regions. Urbanization also creates opportunities for provision of demand-related services that are not available in rural areas. These services are particularly relevant for generating demand in the lodging because customers often prioritize convenient access and the facilities that are available around the hotel (UNWTO, 2019). The demand-related services in the hospitality industry usually involve the provision of infrastructure and facilities for visitors, such as airports, internal transport, restaurants, hospitals, or security services, among others. In general, subsequent entrants in the industry may prefer to be located close to competitors when that means a good provision of related services. Because urbanization generates a relevant package of extra and differentiated benefits to co-located firms (Rosenthal & Strange, 2004), we propose that urbanization increases the attractiveness to new entrants of agglomerations around valuable natural resources. Our hypothesis is: Hypothesis 2: There is a positive relationship between urbanization and the agglomeration’s attractiveness to new entrants around valuable natural resources. The fall: density and access to natural resources. Scholarly attention to the preservation of the generated value in agglomerations is more limited and recent (Kim, 2016), but different scholars have begun to suggest the possibility of a progressive loss of attractiveness of co-location (Kalnins & Chung, 2004). McCann and Folta (2008) have raised some doubts about the generalizability of this loss of attractiveness when related to the endogenous benefits of co-locating and suggest that high-resource firms will always enjoy reinforced capabilities to attract more benefits from agglomerations. However, attracting unlimited access to the exogenous benefits of agglomerations may be difficult when they are associated with valuable but limited natural resources. This factor is particularly relevant when access to the valuable natural resources is a key strategic asset in the industry. Proximity to well-preserved natural resources has been recognized as “the most powerful single factor” in providing differentiation in the hospitality industry (Canina et al., 2005: 568). Although limited research attention has been paid to the difficulties in accessing similar level of benefits derived from existing valuable natural resources once competitors are already located in the region (Lee & Jang, 2015), by and large, proximity to, utilization of, or even views over valuable natural resources are usually more limited and expensive for late entrants, or simply impossible because of physical conditions. Furthermore, the value of natural resources can easily be reduced by overcrowding. Traffic congestion, air pollution, and noise are particularly significant negative factors for consumers of leisure around natural resources. New entrants may find it difficult to deal with these issues and gain competitive access to valuable natural resources when the concentration of competitors grows. Difficulties in accessing valuable natural resources when the density of an agglomeration grows do not necessarily prevent new entrants from joining the agglomeration, but they limit its attractiveness. Some new entrants may accept suboptimum access to the valuable natural resources in the region (e.g., good views when the best ones are no longer available). This may be good enough for certain new entrants because of the endogenous benefits of co-location. However, when access to valuable natural resources plays a key role in an agglomeration, we claim that the growing density of co-located firms in the region and the resultant difficulties in accessing limited natural resources will mostly negatively influence the attractiveness of the region to new entrants. This situation may contrast with alternative regions where similar natural resources may be available. To summarize, when the density of firms increases in an agglomeration around valuable natural resources, the attractiveness of co-location to new entrants will decrease because of the exponential difficulties of accessing natural resources in the region. Our hypothesis is: Hypothesis 3: There is a negative relationship between the density of an agglomeration around valuable natural resources and the agglomeration’s attractiveness to new entrants. The implications: access to natural resources outside of the agglomeration Given the benefits of agglomerating close to valuable natural resources, new entrants’ location outside of the agglomeration may be mostly understood as a consequence of a relative degradation of the benefits in the agglomeration in comparison to different geographic areas (Folta et al., 2006; Malecki, 1985; McCann & Folta, 2009). When an agglomeration is unable to provide access to natural resources that generate reduced costs or differentiation in an industry, favorable access to natural resources outside the agglomeration may partially substitute the benefits of being co-located with similar firms. The inability of incumbents to prevent further access to an agglomeration around limited natural resources may 16 Business Research Quarterly 26(1) also reinforce the progressive degradation of the relative attractiveness of the agglomeration. Alcacer and Chung (2014) delimitate that the attractiveness of an agglomeration is higher for new entrants when they perceive that incumbent firms in the agglomeration will be able to preserve the exclusivity of the value related to the location. As a consequence, despite the fact that the original natural attractions of the agglomeration may have been well preserved, a high density of agglomerated firms may in time decrease the benefits for new entrants and progressively increase the relative attractiveness of areas outside the agglomeration. Baum and Mezias (1992) have shown that, in Manhattan, locating closer to other hotels increases a hotel’s chances of survival, but the failure rates are higher when neighboring hotels become too numerous in closely bounded areas. We claim that the provision in a new region of conditions superior to those of the original agglomeration may be particularly tempting when access to certain limited natural resources is a strategic but physically limiting factor in the region. The relative importance of access to valuable natural resources outside the original agglomeration will increase when the opportunities to access natural resources in the agglomeration are insufficient for new entrants. Using a sample of firms from the semiconductor and pharmaceutical industries, Kukalis (2010) showed that the financial performance of laggards located outside the industry cluster was higher than that of geographically clustered firms in a late stage of the industry life cycle. Similarly, laggards entering an agglomeration of hospitality firms around strategic natural resources may face difficulties in extracting value from a highly populated agglomeration in comparison with other locations that are some distance from the original agglomeration centers. This factor has implications for the emergence of new agglomerations. Once alternative regions are able to provide better access to valuable natural resources for new entrants than the established agglomerations, new regions for co-location will progressively emerge. Because the value generation potentiality of being located close to natural resources is quite explicit (i.e., causal ambiguity is not relevant), the specific distance of a firm from the natural resource increases its importance. Close proximity to the natural resources may be a physical impossibility because of the existence of previous competitors, or the high costs involved may deter new entrants from accessing the valuable natural resources in an agglomeration of competitors. Furthermore, the natural resources may also be finite or degrade when new firms arrive (e.g., a view, clean air, a quiet environment) or may involve property rights that bar new entrants (e.g., a mine, a private beach). We propose that access to natural resources outside the agglomeration becomes relevant when the density of competitors in the original agglomeration is high and prevents easy access. Our hypothesis is: Hypothesis 4: Access to valuable natural resources outside an agglomeration of competitors becomes more attractive to new entrants as the density of firms in the agglomeration increases. Methods Sample We focus our analysis on the locations made between 1960 and 2015 by a sample that includes all the new 295 luxury beach hotels in Andalusia (Spain). In this research, luxury hotels are considered to be those that have four or five stars according to Spanish legal regulations (i.e., the highest standards in the industry). Our regional focus is appropriate because Spain is the world’s second largest tourist destination, with 81.8 million international tourist arrivals and US$68 billion in international tourism receipts in 2017 (UNWTO, 2018), and Andalusia is the biggest region in the country. In addition, the beach context is relevant to an analysis of the agglomeration of competitors around valuable natural resources as beaches are the most popular category of tourism destination. Although Andalusia has a coastline of 945 kilometers, each subarea has had quite different levels of tourism development. Our agglomeration of interest in this region is the so-called “Costa del Sol” (Sunshine Coast), one of the biggest, earliest, and most long-standing agglomerations of beach hotels in Europe, receiving more than 900,000 tourists each year in a municipality with less than 8 km of coastline (Instituto Nacional de Estadística, 2018). We will analyze how the density of hotels in the agglomeration and the urbanization have influenced the locations of the new luxury beach hotels in the region. We will also analyze how access to the beachfront has evolved as more firms have co-located and the implications of this. Agglomerations in the hospitality industry have received considerable attention in the management literature. Most of the analyses have focused on how the different approaches and internal characteristics of US hotels may change the level of benefits obtained by agglomerated hotels in urban districts (Baum & Haveman, 1997), rural contexts (Chung & Kalnins, 2001), specific states (Kalnins & Chung, 2004; McCann & Vroom, 2010). or a country (Woo et al., 2019). In this article, we have used a homogeneous strategic orientation, quality category, and region to focus on how the attractiveness of an agglomeration for new entrants evolves and whether access to the beachfront may influence it. We initially included in our analysis all the new luxury hotels from 1960 to 2015 in Andalusia within 20 km of the coastline. We selected this distance of 20 km following consultation with industry representatives about how far beach hotels are usually located from the coastline in this area. We repeated our analysis with slightly different Aragón-Correa et al. 17 distances and obtained similar results. As our focus was on beach hotels, we excluded from the sample hotels located in cities considered to be administrative centers because of their different strategic orientation. The final sample is composed of 295 hotels, 256 of which are four-star hotels and the others five-star hotels. These are the two highest official rating categories for hotels in Spain. Public information from the regional Government of Andalusia was our data source for the hotels’ opening dates, categories, and locations. We began our analysis by entering each of the hotels into the ArcMap software. This software, developed by Esri and used by geographic information system professionals worldwide, is a popular geoprocessing application that allows individuals to create maps, edit and manage spatial data, and perform the analyses needed to turn raw geographic data into valuable information. This software has been broadly accepted and used in studies analyzing the environmental impact of human activities (Ding et al., 2021; Nautiyal & Sharma, 2021). We calculated the median center of the hotel coordinates in the municipality of Torremolinos (the unofficial capital of our analyzed “Costa del Sol” agglomeration) to delimitate the center of the agglomeration each year. We used Google Maps to find out the global positioning system (GPS) coordinates for each hotel, and each hotel’s geographic location was entered into ArcMap. Variables Agglomeration attractiveness. A new hotel’s decision to be located closer to the center of the agglomeration provides a proxy about the agglomeration’s attractiveness for new entrants (Baum & Haveman, 1997). We calculated the attractiveness of the agglomeration for new beach hotels in the analyzed region by measuring the distance from each new entrant’s selected location to the agglomeration center. We used the Generate Near Table tool in the Arc- GIS software to measure it as a straight line connecting the coordinate point for each hotel in the sample to the coordinate point of the agglomeration center when the hotel began its activity. We calculated this variable as the additive inverse value of the distance to the agglomeration center to provide a more natural interpretation of our measurement: the higher the value in our measurement, the greater the attractiveness of the agglomeration. Density of the agglomeration. To measure the influence of the density of the agglomeration on the distance of each new entrant to the agglomeration, we calculated the number of hotels operating in the agglomeration area during the year prior to each hotel opening. To measure this variable, the agglomeration area included the entire circumference space in a radius of 1 km drawn from the previously calculated median center in the agglomeration. This way of measuring hotel density in an agglomeration is similar to that used in previous research (Baum & Haveman, 1997). The utilization of slightly different distances for the radius in our measurement may change the density each year, but it did not significantly affect our final conclusions. Urbanization. The provision of general services and the infrastructure available to tourists (e.g., airports, internal transport, restaurants, security services, etc.) are relevant factors in their decision-making when selecting a hotel (UNWTO, 2019). The population size is a good metric to measure the level of urbanization (Graham, 2009) and an accurate proxy for the existence of demand-related services in the hospitality industry (Puciato, 2016; Zhang et al., 2013). In this sense, urbanization has been often represented by the total population or total employment of an urban area (Graham et al., 2010) and, consequently, we have adopted total population as a proxy for urbanization. The information about population size in our analysis was obtained from the Spanish Government Institute of Statistics. We obtained this information for each new hotel location in the year prior to its opening. Access to valuable natural resources. Because proximity to the coast is a key resource for beach hotels, we focused our calculation of the access to natural resources variable in our sample on the new entrant’s access to the coast. Specifically, we calculated the shortest distance from the coordinate point where each new hotel was located to the coastline. This distance is calculated as the perpendicular to the coastline or, if a perpendicular cannot be drawn within the end vertices of the line segment, then the distance to the closest end vertex is used as the shortest measured distance. The higher the value in our variable, the poorer the access to valuable natural resources. Low-category hotels. The existence of low-category hotels in an agglomeration may lessen the interest of new entrants among luxury hotels in being located in the area (Baum & Haveman, 1997; Kalnins & Chung, 2004). We controlled the percentage of hotels operating in the agglomeration in the year prior to the hotel opening in the category of three or fewer stars. Chain membership. Being part of a chain has been used as a proxy of resource availability and standardized professionalism in previous agglomeration literature for this industry (Canina et al., 2005; Kalnins & Chung, 2004; McCann & Vroom, 2010). A chain’s internal policies may also influence the location decisions of the sampled hotels (Woo et al., 2019). Although most of the hotels in the analyzed region belong to different firms, we controlled this variable using a dummy variable, where the value 0 implies that the hotel does not belong to a chain. 18 Business Research Quarterly 26(1) Hotel size. Hotel size may influence the hotel’s capacity to provide a more complete range of services and it has been used in the agglomeration literature to control for potentially different orientations of hotels depending on their internal level of resources (e.g., Baum & Haveman, 1997; Canina et al., 2005). We measured the hotel size as the natural logarithm number of available rooms in the hotel. Model specification and estimation We used a multilevel model for our analysis and our method of estimation was the iterative generalized least squares (IGLS). Specifically, we calculated a multilevel hierarchical linear model, assuming that level 1 was connected to the set of individual measurements for each hotel and level 2 was connected to the years when the analyzed hotels were founded. This approach is appropriate for controlling for the variation that the foundation year of the hotels could produce in our results. Although the variation between foundational years may be also modeled by incorporating a dummy variable for each year, this procedure would be inefficient because of the large range of analyzed years (i.e., it would require the estimation of a large number of coefficients) and would also be inadequate for the purpose of generalization because it does not treat years as a random sample (Rasbash et al., 2019). The selected multilevel models enabled us to understand whether and how the “year” effect may occur. Convergence in our iterative analysis is judged to have occurred when, for each of the parameter estimates, the relative differences between two iterations is less than a given tolerance, which is 10 − 2 = 0.01 (Rasbash et al., 2019). Results Table 1 presents descriptive statistics and correlations for all the variables in our analysis. According to the Kolmogorov–Smirnov and Shapiro–Wilk tests, data are not normally distributed, so we calculated Kendall’s tau-b correlations coefficients that are suggested for non-nor- mally distributed data (Kendall & Gibbons, 1990). In addition, for our multilevel model, we converted data to normal scores. In each case, our conversion assigns the value from the inverse of the standard (0.1) normal cumulative distribution for the estimated proportion of hotels from the data variable’s original distribution (Darlington & Hayes, 2017). In addition, we conducted some collinearity analysis using the SPSS statistical program to check whether there were any problems with multicollinearity. The condition indices, which are computed as the square roots of the ratios of the largest eigenvalue to each successive eigenvalue, show one value greater than 30, which implies a serious problem with collinearity (Mason & Perreault, 1991). To fix the collinearity problems, we used centered to the mean scores of the independent variables, and we confirmed that condition indices were vastly improved relative to the original model with values lower than four. Finally, as we have 13 missing values for some relevant control variables, we excluded these cases from our analysis, thus having a final sample of 282 hotels. We began our analysis by confirming that our multilevel methodology is appropriate to control the effects of the foundation year in our analysis. To do that, we first analyzed systematic within and between years variance in the agglomeration’s attractiveness by calculating a baseline model where the level 1 equation includes no predictors; therefore, the regression equation includes only an intercept estimate. The level 2 groups (year in our analysis) are treated as a random sample from a population of hotels. Table 2 shows that the effect of year represents an appreciable proportion of the total variance (18%). To judge significance for variances, we used a likelihood ratio test (Rasbash et al., 2019). We concluded that significant variation between foundation years can be controlled with a multilevel model as proposed (variance of likelihood ratio = 15.678; p-value = .000). We estimate an extended multilevel model including our proposed independent variables to test Hypotheses 2 and 3. As previously discussed, our model includes two levels to control the effect of the year in our analysis Level 1: (Agglomerationsattractiveness HotelSiz ’)ij ij =+ββ 01 eeChain Membership Low-category hotels Den 3 () + () + () + ij ij j β β β 2 4ssityof theagglomeration Demand-relatedservices () + () j j β5 Table 1. Means, standard deviations, and correlations.a M SD 1 2 3 4 1. Agglomeration’s attractiveness −0.970 0.958 2. Urbanization 9.960 0.951 0.162 (.000) 3. 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