The role of internal knowledge generation and external knowledge acquisition in tourist districts

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The role of internal knowledge generation and external knowledge acquisition in tourist districts ⁎

Bartolomé Marco-Lajara , Enrique Claver-Cortés, Mercedes Úbeda-García, Francisco García-Lillo, Patrocinio Carmen Zaragoza-Sáez Department of Management, University of Alicante, P.O. Box 99, E-03080 Alicante, Spain

A R T I C LE I N FO

A B S T R A C T

Keywords: Agglomeration Internal knowledge generation External knowledge acquisition Hotel performance Tourist districts

This paper analyzes the role played by internal knowledge generation and external knowledge acquisition in the impact of agglomeration on the profitability of Spanish hotels. The results are of interest not only because they demonstrate that knowledge management strategies have a partial mediation effect in the relationship between agglomeration and profitability, but also because they find a substitution effect between internal knowledge generation and external knowledge acquisition. The analysis uses a sample of 2003 Spanish hotels and helps us to understand the real reasons behind the district effect.

1. Introduction This research links two important fields in the area of management, knowledge management theory and the district effect, applying them to the tourism sector. Knowledge is a basic factor and the most important strategic resource (Grant, 1996; Spender, 1996; Zack, 1999). Cooper (2006) and Hjalager (2002) point out that the generation and use of new knowledge to feed innovation and product development is critical for the competitiveness of tourism firms. A firm has two main options to obtain new knowledge: internal generation and external acquisition (Doloreux, 2015; Grimpe & Kaiser, 2010; Junfeng & Wei-ping, 2017). Several studies into the district effect show that externalities generated by agglomeration in an industrial district positively affect profitability and competitiveness (Camisón & Forés, 2015; Lazzeretti, Boix, & Sánchez, 2018). Part of this positive effect comes from externalities created by specialized knowledge from universities, vocational training centers and technological organizations (Todling, Lehner, & Kaufmann, 2009; Knudsen, 2007; Sanna-Randaccio & Veugelers, 2007). Most agglomeration studies focus on high-technology industrial sectors or on manufacturing sectors but researchers are increasingly analyzing and demonstrating the effect of agglomeration on service companies, including hotels (Canina, Enz, & Harrison, 2005; Chung & Kalnins, 2001; Enz, Canina, & Liu, 2008; Marco-Lajara, Claver-Cortés, Úbeda-García, & Zaragoza-Sáez, 2016; Peiró-Signes, Miret-Pastor, & Verma, 2015). This is of great importance for the competitiveness of tourist companies as they are often dependent on externally generated knowledge (King, Breen, & Whitelaw, 2014; Williams & Shaw, 2011).

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From another point of view, some researchers claim that if companies have the opportunity to acquire external knowledge, they will not be motivated to generate knowledge internally (Caloghirou, Kastelli, & Tsakanikas, 2004; Al Ansari, 2013). Given the impact of tourism on Spanish GDP, this paper aims to add knowledge about the relationship between the district effect and knowledge strategies in the tourism sector. Several research questions are considered: (1) How do the externalities of tourist districts affect hotel profitability? (2) In what way does agglomeration affect the knowledge strategies of hotels? (3) Do these knowledge strategies influence hotel profitability? (4) Is the relationship between agglomeration and hotel profitability mediated by knowledge strategies? and (5) Do hotel size and chain membership moderate the relationship between agglomeration and profitability? Focusing on the hotel industry, the paper has a twofold aim. First we aim to confirm the existence of externalities in tourist districts which positively affect hotel profitability. Second we will study the possible mediation effect of hotel knowledge strategies in the relationship between agglomeration and hotel performance. The main results show first that agglomeration reduces internal knowledge generation and second that the impact of agglomeration on performance is positive but lower than expected. The second result is due to hotels' substituting part of their internal knowledge generation for external acquisition. These results are in accordance with part of the literature. The paper has theoretical and practical implications. One of the most important is that hotels should choose a good location to ensure

Corresponding author. E-mail address: [email protected] (B. Marco-Lajara).

https://doi.org/10.1016/j.jbusres.2018.12.045 Received 10 June 2018; Received in revised form 11 December 2018; Accepted 15 December 2018 0148-2963/ © 2018 Elsevier Inc. All rights reserved.

Please cite this article as: Marco-Lajara, B., Journal of Business Research, https://doi.org/10.1016/j.jbusres.2018.12.045

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H1. Agglomeration in a tourist district has a positive impact on the profitability of hotels located there.

performance and competitiveness. From a theoretical point of view, our paper is linked to the knowledge-based view of the firm and industrial district theory and highlights the importance of location in the development of the knowledge required to increase competitiveness. From an empirical point of view, this study focuses on hotels due to the labor that they require and to the development in the areas where they are located. The results of this analysis help demonstrate the existence of a substitution effect between knowledge generated internally by hotels and knowledge acquired externally. The findings of this research can be of great importance for the competitiveness of hotel companies and should encourage public institutions to foster the sharing of specialized knowledge. This is a crucial point considering the weight of the tourism industry in the economy of many countries. The paper is structured as follows. After the introduction, we present a review of the literature about the link between knowledge and agglomeration in tourist districts and propose a number of hypotheses. This is followed by an explanation of the methodology and variables, complemented with a description and discussion of the findings. The paper finishes with the main conclusions and implications.

2.2. Agglomeration and knowledge One source of externalities in a tourist district is external acquisition of knowledge. The literature establishes that it is far easier for firms to create and accumulate knowledge in tourism districts due to the constant interactions with other agents such as similar companies, training and research centers and destination management organizations (Audretsch & Feldman, 1996; Jaffe & Trajtenberg, 2002; Malmberg & Maskell, 2002; Maskell, 2001; Rosell, Lakemond, & Melander, 2017; Tho, 2017). Geographical and cultural proximity facilitate interactive learning and a propensity to share knowledge and establish collaboration relationships because common rules and shared values prevent opportunistic behaviors (Boschma & Ter Wal, 2007). This knowledge provides useful intelligence and contacts which can increase the level of knowledge and experience (Hayer & Ibeh, 2006) by allowing the development of close links between individuals and firms. The success of a firm will depend on the mechanisms used to acquire external knowledge, which are related to its absorptive capacity (Cohen & Levinthal, 1990). New knowledge can be internally generated or it can be acquired from the external environment. In principle, internal and external mechanisms can be considered as complementary (Doloreux, 2015; Grimpe & Kaiser, 2010; Junfeng & Wei-ping, 2017). However, the various strands of extant empirical research are inconclusive about the complementariness or substitutability of different innovation mechanisms or knowledge sources. Many studies obtain a substitution effect (Caloghirou et al., 2004; Al Ansari, 2013). This effect primarily takes place when internal efforts in R&D are scarce (Hagedoorn & Wang, 2012), as is the normal situation of hotels, which often rely more on external sources of knowledge (King et al., 2014; Williams & Shaw, 2011). This substitution effect could be due to production costs incurred when developing internal knowledge and transaction costs incurred when obtaining knowledge through the market. The relative size of the transaction and production costs depends on industry structure and the type of knowledge needed, and it should guide firms in their choice of internal versus external sourcing of knowledge (Falkenberg, Woiceshyn, & Karagianis, 2003). As tourist district transaction costs tend to be quite low, firms will be inclined toward the acquisition of external knowledge. To understand this substitution effect, it is necessary to know the sources of internal knowledge generation. Usually, internal knowledge is generated through investments in human capital as well as in development, research and innovation (Cohen & Levinthal, 1990; Dyer & Singh, 1998; Lane & Lubatkin, 1998). The human element in tourism firms is critical for service quality, customer satisfaction and loyalty, competitive advantage, and organizational performance (Kusluvan, Kusluvan, Ilhan, & Buyruk, 2010), so human capital is a valuable asset for knowledge creation and acquisition (Chen, Shih, & Yang, 2009). In other words, human resource policies and practices can generate internal knowledge (Nieves & Quintana, 2016). Another way to obtain new knowledge is through innovation and Beesley and Cooper (2008) indicate that innovation is fundamentally linked to new knowledge. Substitution is produced because external knowledge helps to boost internal knowledge (Chatterji, 1996). For instance, knowledge can be obtained by hiring experts from other companies or institutions (Song, Almeida, & Wu, 2003). This employee mobility is one of the main drivers of externalities (Almeida, 2003; Malecki, 1997). Tourism districts, characterized by the existence of universities and training centers specialized in tourism, also allow small- and medium-sized firms to acquire trained staff who otherwise would be impossible to obtain through internal training programs. From another point of view, innovation is related to investment in R&D but tourism is dominated by

2. Review of the literature and HYPHOTESES 2.1. Tourist districts, agglomeration and profitability Tourism involves a network of organizations interacting to produce a service, usually within destinations. Several authors apply social network analysis in tourism-related contexts (Baggio, 2011; Prats, Guía, & Molina, 2008; Scott, Cooper, & Baggio, 2008; Casanueva, Gallego, & García-Sánchez, 2016). More specifically, authors apply industrial district theory to the tourism sector, although its application is relatively recent. A destination can only be considered a tourism district if tourist companies constitute the main economic activity and the resident population is an integral part of this activity (Marco-Lajara et al., 2016; Sainaghi, 2006). The second of these requirements distinguishes the present study focused on tourism districts from others where industrial districts also play an important role. Moreover, “a tourism district presents some peculiar characteristics (with respect to an industrial district) which can, for the most part, be traced to the horizontal production model and the importance of the role played by metamanagement” (Sainaghi, 2006, p. 1054). According to the industrial district theory, initiated by Marshall (1890/1920) and later developed by Becattini (1979), small- and medium-sized enterprises (SMEs) in an industrial district obtain better results than those outside the district due to certain externalities linked to geographical concentration. The main sources of externalities generated by the concentration of firms are (1) the exploitation of common resources and infrastructures including higher accessibility to suppliers and distributors; (2) a large labor market, with a specialized and efficient workforce; and (3) knowledge transfer between agents based in the territory. Becattini (1990, p. 39) defines the industrial district as “a socio-territorial entity characterized by the active presence of both a human community and a group of firms within a naturally and historically delimited area”. Several studies show that externalities generated in a tourist district positively affect profitability and competitiveness, and a number of them are based on the hotel sector (Camisón & Forés, 2015; Lazzeretti et al., 2018). Many authors go further and relate externalities—that is, the possibility of obtaining higher profitability—with the degree of agglomeration (Canina et al., 2005; Chung & Kalnins, 2001; Enz et al., 2008; Marco-Lajara, Claver-Cortés, & Úbeda-García, 2014; Peiró-Signes et al., 2015). Looking deeper into the impact of externalities generated by agglomeration on hotel profitability, Marco-Lajara et al. (2016) reveal that they can both increase revenues and reduce costs. Accordingly, the first hypothesis is as follows: 2

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SMEs, which generally own fewer financial, material, and human resources (King et al., 2014). Therefore, small firms can see the knowledge-related benefits from being located in a specific place as a great opportunity (Acs, Audretsch, & Feldman, 1994; Tang, 2016). As Rodríguez-Pose and Refolo (2003) point out, university research expenses are an essential input to small firms. The second hypothesis is based on the ideas above and can be divided into a number of sub hypotheses:

H3b. A hotel's external knowledge acquisition strategies positively influence its profitability. 2.4. The mediating effect: agglomeration, knowledge and profitability Firms located in a cluster are more profitable because they have a greater chance of acquiring knowledge, which is a source of innovation and business competitiveness (Expósito-Langa, Molina-Morales, & Capó-Vicedo, 2010; Hervas-Oliver, Gonzalez, Caja, & Sempere-Ripoll, 2015; Kim, Lee, Paek, & Lee, 2013). As several studies point out (Casanueva et al., 2016; Hjalager, 2010; Novelli, Schmitz, & Spencer, 2006), firms belonging to a cluster are more likely to capture market changes and they do so quicker than their non-clustered counterparts. Due to the triangular relationship between agglomeration, knowledge management strategies and hotel profitability, one aim of our research is to determine whether agglomeration exerts a direct effect on a firm's profitability (as predicted by H1) or whether knowledge management strategies act as a mediator between agglomeration and profitability. So, the following hypotheses are proposed:

H2. Agglomeration has an impact on knowledge management strategies. H2a. Agglomeration has a negative impact on internal knowledge generation and on strategies that hotels use to generate it. H2b. Agglomeration has a positive impact on external knowledge acquisition and on strategies that hotels use to absorb it.

2.3. Knowledge and profitability Knowledge is a basic factor and the most important strategic resource (Grant, 1996; Spender, 1996; Zack, 1999). The hospitality industry is knowledge-intensive as a result of the nature of the service product. Service delivery occurs as a result of interaction between customers and employees and employees are required to be knowledgeable of customers' needs (Chalkiti, 2012; Hallin & Marnburg, 2008; Khale, 2002). Therefore, firms must constantly generate new knowledge to stay competitive. Knowledge is the basis of competitive advantage because it feeds innovation and product development, both critical for the competitiveness of tourism destinations and enterprises (Cooper, 2006; Hjalager, 2002). Tourism is increasingly characterized by changes in markets, consumer preferences, technology and the organization of production (Hall & Williams, 2008). Innovation is considered a major driver of competitiveness in tourism. This implies that innovation should be seen as a persistent but shifting goal. A positive link between innovation and competitiveness in tourism firms has been found. For instance, Campo, Díaz and Yaguee (2014) found that innovation leads to increased competitiveness in their review of ten studies in the tourism industry. Other studies such as Nordin (2003), Tseng, Kuo, and Chou (2008), Carvalho and Sarkar (2014) and Pereira-Moliner et al. (2015) found a positive impact of innovation on hotel performance. From the point of view of literature review studies, Hjalager (2010) and Gomezelj (2016) also found a positive relationship between innovation and performance in the tourism industry. To obtain knowledge and to have a competitive advantage, knowledge management strategies are needed (Danskin, Englis, Solomon, Goldsmith, & Davey, 2005; Rahimli, 2012; Sharkie, 2003). So, it is possible to formulate the following hypothesis:

H4. The relationship between agglomeration and hotel profitability is mediated by knowledge management strategies. H4a. The relationship between agglomeration and hotel profitability is mediated by internal knowledge generation strategies. H4b. The relationship between agglomeration and hotel profitability is mediated by external knowledge acquisition strategies. 2.5. The moderation effect of size and chain membership Despite being in a tourism district, firms can find difficulties in acquiring knowledge. For individuals and firms to understand new knowledge they need absorptive capacity. Cohen and Levinthal (1990) suggest a direct connection between absorptive capacity and extant knowledge stock, in such a way that the greater the knowledge base, the higher the expected absorptive capacity. Additionally, the myopia of learning (Levinthal & March, 1993) and the not-invented-here syndrome (Gupta & Govindarajan, 2000) can also impede the acquisition of new knowledge. The above difficulties in acquiring external knowledge can be due to several features of hotels. For instance, the size of an establishment is directly related to its absorptive capacity. As Cooper (2006) points out, the dominance of SMEs in the tourism sector can act as a barrier to knowledge creation and sharing due to activity fragmentation and poor human resource practices. Some studies specifically analyze absorptive capacity and knowledge management in small and medium enterprises (Fernandes, Sartorello, Bansi, Galli, & Vasconcelos, 2016; Grandinetti, 2016; Muscio, 2007). Thus, it can be expected that size of establishment moderates the impact of clustering on hotel profitability, leading to the following moderation hypothesis:

H3. A hotel's knowledge management strategies positively influence its profitability.

H5a. Hotel size moderates the impact of agglomeration on profitability.

Innovation and competitiveness can be achieved with internal knowledge generation and external knowledge acquisition and some studies jointly analyze their effects on performance (Pedersen, Soo, & Devinney, 2002; Svetina & Prodac, 2008). Other studies separately analyze their effects on performance. Maâlej, Zaied, Louati, and Affes (2015) focus on the relationship between internal sources of knowledge and performance, while Segarra, Palomero, and Roca (2012), VegaJurado, Gutiérrez-Gracia, and Fernández-de-Lucio (2009), Kang and Kang (2009), Simao and Franco (2018) and Segarra-Ciprés and BouLlusar (2018) analyze external sources of knowledge and their impact on innovation and competitiveness. So, the previous hypothesis can be divided into two sub hypotheses:

Finally, affiliation to a hotel chain and the establishment of strategic alliances also contribute to the supply of new knowledge. Ingram and Baum (1997) investigate the implications for hotel failure in terms of their inter-organizational relationships with their chain affiliations and knowledge transfer. Later, developing their 1997 study, Ingram and Baum (2001) found that when hotels have either low or high levels of experience, they are more likely to join a chain (Hallin & Marnburg, 2008). We can expect that tourist district externalities will have a lesser effect on a hotel's performance when it is affiliated to a chain, because it obtains knowledge from the chain. So, another moderating hypothesis is proposed: H5b. Chain membership moderates the impact of agglomeration on hotel profitability.

H3a. A hotel's internal knowledge generation strategies positively influence its profitability. 3

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To identify tourist districts we follow the methodology developed in Italy by the Instituto Nazionale di Statistica. First, identify local labor systems (LLSs) in the Spanish coastal area. Second, categorize tourism districts as LSSs with above average employment concentrations in small and medium sized tourist enterprises. The result of the following equation has to be > 1:

Z

Tourism employment in destination i Total employment in destination i Tourism employment in Spain ÷ >1 Total employment in Spain

Spanish LLSs were already identified by Boix and Galletto (2005), whose work serves as a basis for our study. Our task was limited to finding which LLSs corresponded to each of the tourist municipalities (as identified by the Ministry of Agriculture and Environment) on Spain's Mediterranean coastline. This led us to identify 231 towns pertaining to 113 LLSs. 3.3.1.1. Main industry firms' agglomeration (MIA). Degree of agglomeration is found with the result obtained with the previous eq. Z, which determines if an LLS constitutes a tourist district. The data used to estimate the equation corresponding to each LLS were obtained from the firm database of the Spanish Chambers of Commerce (Camerdata), updated to January 2015. Our search focused on tourism firms with fewer than 250 employees belonging to codes 5510, 5610 and 5630 of the Clasificación Nacional de Actividades Económicas (National Classification of Economic Activities-CNAE), CNAE2009—corresponding to hotels, restaurants and cafés.

Fig. 1. Model with hypotheses.

Fig. 1 summarizes the proposed hypotheses. 3. Methodology 3.1. Population and sample The population under study includes all the Spanish hotels located in Mediterranean coastal towns—both in the Iberian Peninsula and in the Balearic Islands. We originally intended to include all the coastal hotels listed on SABI. However, it was impossible to obtain information for some hotels about number of rooms, establishment category, or affiliation to a chain so the study sample was reduced to a total of 2003 establishments.

3.3.1.2. Number of firms belonging to related and complementary industries (RCIA). This was estimated with the number of firms obtained from sections 47.6 and 47.7 (retail trade of cultural, recreational, and other items), as well as 90, 91, 92, and 93 (artistic activities, shows, libraries, museums, games of chance, sports activities, recreational activities, entertainment) of CNAE2009. The data stemmed from the firm database of the Spanish Chambers of Commerce (Camerdata), updated to January 2015.

3.2. Statistical procedure A structural equation model is used to test the proposed hypotheses (Fig. 2). The data analysis method used is partial least squares (PLS)—specifically Smart PLS 3.2. The use of PLS is appropriate when the research features one or more of the following circumstances (Henseler, Ringle, & Sinkovics, 2009; Sosik, Kahai & Piovoso, 2009; Leyva-Cordero and Olague, 2014): some observable variables are categorical, the observable variables have some degree of non-reliability, there are formative indicators, the data come from unknown or not normal distributions, secondary data are used, the sample is very long. Prior to estimating the models, we examined common method variance. According to Harman's single factor test (Podsakoff, MacKenzie, & Podsakoff, 2012), if common method variance exists, a factor would emerge from a factor analysis with all research indicators. This test must be preceded by a confirmatory factor analysis (CFA) estimate that includes all the indicators from every scale, with a view to determining the extent to which most of the variance in this model is explained by a general factor (Podsakoff et al., 2012). Twelve factors were identified, the main factor accounting for 19.8% of variance. None of the factors explain > 50% of variance, which suggests no common method variance.

3.3.1.3. Institutional agglomeration. Based on previous works (Todling, Lehner, & Kaufmann, 2009; Knudsen, 2007; Sanna-Randaccio & Veugelers, 2007; Jaffe & Trajtenberg, 2002; Feldman & Audretsch, 1999; Audretsch & Feldman, 1996), institutional agglomeration is a formative first order construct made up of universities and vocational training centers in the area of tourism, as well as tourism research centers in the geographical area to which the tourism district belongs. This information came directly from the Internet and the resources were measured as follows: Universities (U): number of universities which offer tourism degrees at the provincial level relativized by the number of inhabitants in the province. Higher-Level VT (HLVT): number of higher-level vocational training centers with tourism programs at the tourist district level, relativized by the number of inhabitants in the area. Medium-Level VT (MLVT): number of medium-level vocational training centers with tourism programs at the tourist district level, relativized by the number of inhabitants in the area. Technological Research Centers (TRC): number of public and/or private technological institutes, including university institutes focused on tourism research as well as tourist observatories at the autonomous region level.

3.3. Measurement 3.3.1. Agglomeration This is a second-order formative construct shaped by three firstorder formative constructs which are related to the agents of a tourist district: agglomeration of tourism firms, agglomeration of related and complementary industry firms, and institutional agglomeration.

3.3.2. Knowledge internal generation strategies (KIG) Based on several authors (Acs et al., 1994; Beesley & Cooper, 2008; Chen et al., 2009; Kusluvan et al., 2010; Nieves & Quintana, 2016; Rodríguez-Pose & Refolo, 2003; Song et al., 2003; Tang, 2016), KIG has 4

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a

b

H1 = Agglomeration Profitability = c’ = 0.42** H2a = Agglomeration KIG = a1 = - 0.48* H2b = Agglomeration KEA = a2 = 0.18ns H3a = KIG Profitability = b1 = 0.735*** H3b = KEA Profitability = b2 = - 0.11ns H4a = Agglomeration KIG Profitability = a1b1 = - 0.35** H4b = Agglomeration KEA Profitability = a2b2 = - 0.025 ns Fig. 2. a. Model with total effect. b. Model with two-path mediated effects.

size and innovation in tourism firms and hotels (López-Fernández, Serrano-Bedia, & Gómez-López, 2011; Vila, Enz, & Costa, 2012). The service sector and, more precisely, the hotel sector often dedicate this investment not only to developing new working processes but also to creating registered trademarks to pursue establishment differentiation. The second reason is that being service enterprises, it is very difficult for hotels to patent their technology. Hence why knowledge was only estimated with registered trademarks, a type of information which can be directly collected from SABI. A dummy variable with a value of 1 if the hotel has registered trademarks and 0 otherwise was used for this purpose.

been built as a formative first order construct, which considers several factors related to the generation of internal knowledge, such as knowledge coming from employees, knowledge generated by R&D and knowledge registered in trademarks. 3.3.2.1. Knowledge coming from employee. Two measurements can be used to assess this knowledge, namely: number of employees per room (ER) and personnel expenses per room (PER). In both cases, the information was directly collected from SABI, taking the mean of the last five years available. 3.3.2.2. Knowledge generated by R&D. This was estimated by the average value of intangible assets per room (IAR) for the last five years available, obtained from SABI.

3.3.3. Knowledge external acquisition strategies (KEA) External sources include a broad range of mechanisms, such as interaction with other actors and establishing strategic alliances (ParraRequena, Molina-Morales, & García-Villaverde, 2010). Based on other works (Lane, Salk, & Lyles, 2001; Ireland, Hitt, & Vaidyanath, 2002; Morrison & Rabellotti, 2009; Schreiner, Kale, & Corsten, 2009; Schilke & Goerzen, 2010; Niesten & Jolink, 2015; Claver, Marco, & Manresa, 2015), this first order construct was estimated with two variables, depending on whether knowledge is provided by capital alliances or participated firms.

3.3.2.3. Knowledge registered in trademarks. There are several reasons why registered trademarks (RT) can be used as an additional innovation indicator for tourism and knowledge-intensive services (Gotsch & Hipp, 2012). The first reason is that tourism is dominated by SMEs, which generally own fewer financial, material, and human resources to undertake R&D; innovation being related to investment in R&D. The sector's share in the official R&D statistics is small (Miles, 2000), with empirical studies showing a positive relationship between large firm 5

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3.3.3.1. Knowledge provided by capital alliances. Knowledge obtained from hotel alliances was estimated with a quantitative variable that measures the number of firms belonging to the same group (GF). This value is supplied by SABI.

agglomeration on profitability, and the second (Fig. 2b) where the mediated effects of KIG and KEA are included.

3.3.3.2. Knowledge provided by participated firms. An additional variable was considered to assess knowledge obtained through equity participated firms (EPF).

Because the formative indicators do not need to be correlated and it is assumed that they are free from error, the traditional assessment of reliability and validity is not applicable (Bagozzi, 1994). Thus, the assessment of the measurement model for formative indicators in PLS can be based on an assessment at the construct level and at the indicator level (Chin, 2010). The first was discarded, since to perform it we need a previously validated reflective construct in order to compare it with the formative construct. Therefore, we rely exclusively on evaluation at the indicator level. The variance inflation factor analysis (VIF) rules out high potential multicollinearity between indicators. The evaluation of the weights of the indicators shows that they are all statistically significant (see Fig. 2). Even if an item contributes little to the explained variance in a formative construct, it should be included in the measurement model (Roberts & Thatcher, 2009, p. 30), because dropping a formative indicator implies dropping part of the composite latent construct.

4.1. Stage 1. Measurement model evaluation

3.3.4. Hotel profitability (P) The many ways to measure a hotel's profitability (Sainaghi, 2010) include RevPar (revenue per available room), GopPar (gross operating profit per available room) and average occupancy. An increasingly high number of studies work with GopPar (Higgins, 2006). Because GopPar is directly estimated by hotel establishments and would need their cooperation we decided to use the gross operating profit per room as an approximate measure of GopPar, since it can be obtained from the SABI (Sistema de Análisis de Balances Ibéricos—Iberian Balance Analysis System) database. The search criterion consisted in identifying all hotels belonging to headings 5510 (hotels and similar accommodation establishments) and 5520 (tourist accommodation establishments and other short-stay accommodation establishments) of CNAE2009, located in the 231 coastal towns of the Spanish Mediterranean. Both headings were used because many establishments are not classified within the right category—but it must be stressed that only hotels were considered. The information used is the mean of the last five years available, from 2011 to 2015, inclusive. This is due to the fact that both revenues and profit in any given year may have been affected by a large number of external factors. The problem with SABI is that it does not supply data on number of rooms. We used the Internet to find the data from the web sites of hotels or booking agents. In cases where it was impossible to obtain the data, the hotels concerned were discarded. The consultation of this information was made over several years to consider possible variation in the number of rooms of each establishment.

4.2. Stage 2. Structural model analysis The algebraic sign, magnitude, and significance of the structural path coefficients, the R2 values and the Q2 test for predictive relevance permit an evaluation of the structural model. Bootstrapping (5000 resamples) was used to generate standard error and t-statistics. This makes it possible to assess the significance of path coefficients (Hair et al., 2017). The confidence intervals of standardized regression coefficients were also calculated. According to Henseler et al. (2009), if a confidence interval for an estimated path coefficient w does not include zero, the hypothesis that w equals zero is rejected. More specifically, it was decided to use a percentile approach—which has the advantage of completely free distribution (Chin, 2010). Three of the five direct effects described in Fig. 2 are significant because they exceed the minimum level by a Student's t-distribution with one tail and n-1 (n = number of resamples) degrees of freedom (Table 1). The same result is also obtained with a 95% percentile bootstrap confidence interval. In other words, H1, H2a and H3a are supported. These findings (H1) are consistent with previous findings that agglomeration has an impact on the profitability of hotels (Canina et al., 2005; Chung & Kalnins, 2001; Enz et al., 2008; Marco-Lajara et al., 2016; Peiró-Signes et al., 2015). This positive effect is partly due to externalities generated by agglomeration in the form of greater knowledge which hotels can access. This is of great importance for the competitiveness of tourism firms as they are often characterized by dependence on externally generated knowledge (King et al., 2014; Williams & Shaw, 2011). From another point of view, some researchers claim that if companies have the opportunity to acquire external knowledge, they are not going to generate it internally (Caloghirou et al., 2004; Al Ansari, 2013). It is understood that knowledge generated externally replaces internal generation (H2a). Findings related to H3a are also in accordance with Maâlej et al. (2015), who find a positive relationship between internal sources of knowledge and performance. H2b and H3b are rejected and several reasons could explain these results. First, we have assumed that location in a highly agglomerated tourist district implies belonging to a great number of firm groups and participation in numerous firms, something that can be uncertain. Second, alliances and participation in other firms facilitate access to external knowledge, but it is the development of dynamic capabilities such as absorption capability and alliance management capability that really allows its acquisition (Niesten & Jolink, 2015; Rothaermel & Deeds, 2006; Schreiner et al., 2009). Finally, external knowledge is not only generated by other firms, but also by academic and research

3.3.5. Size Hotel size was determined by the number of employees, which was also easy for us to collect from SABI. 3.3.6. Chain Affiliation to a hotel chain was estimated as a dummy variable which takes a 0 value when the hotel does not belong to a chain and 1 otherwise. The qualification for a chain was 3 or more affiliated establishments with different addresses, whether on an ownership, management, rental, or franchise basis. The consideration was under no circumstances given to associations and/or federations of hotel firms. 4. Analysis and results Because PLS does not allow us to represent second-order factors directly, we first calculate the factor scores of first-order constructs (latent variable scores), which were considered as the indicators of second-order factors (Bock, Zmud, Kim, & Lee, 2005; Chin, Marcolin, & Newsted, 2003). In the first stage, the first-order factors that agglomeration represents were separately included in the model with their respective indicators. In the second stage, a model was estimated that used the latent variable factor scores calculated in the first stage for each of the first-order components. After building the second-order variables, the model was assessed on the basis of the stages proposed by Hair, Hult, Ringle, and Sarstedt (2017): measurement model evaluation and structural model analysis. Tables 1 and 2 summarize the main results, which are shown in Fig. 2 with the weights of formative indicators used to estimate the latent variables, and the path coefficients for each hypothesis. The figure is split in two parts, the first (Fig. 2a) with the total effect of 6

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Table 1 Effects on endogenous variables. Effects on endogenous variables

Direct effect

t-Value (bootstrap)

Percentile 95% confidence intervals

Explained variance (%)

Knowledge internal generation R2 = 0.1275/Q2 = 0.05 Agglomeration

−0.48⁎

1.343

[−0.156; −0.038] Sig.

12.75%

Knowledge external acquisition R2 = 0.001/Q2 = 0.05 Agglomeration

0.18

0.646

[−0.073; 0.109] Ns.

0.1%

Profitability R2 = 0.2625/Q2 = 0.175 Knowledge internal generation Knowledge external acquisition Agglomeration Size Size moderation Chain Chain moderation

0.735⁎⁎⁎ −0.11 0.42⁎⁎ −0.15⁎⁎ 0.14 0.000 0.305⁎⁎

2.853 0.762 1.677 1.495 1.221 0.017 1.881

[0.018; 0.2002] Sig. [−0.072; 0.036] Ns. [0.005; 0.111] Sig. [−0.057; −0.005] Sig. [−0.029; 0.067] Ns. [−0.047; 0.056] Ns. [0.001; 0.068] Sig.

11.8% 1.4% 6.0% 1.9% 1.7% 0.0% 3.5%

⁎

p < 0.01. p < 0.005. ⁎⁎⁎ p < 0.001. ⁎⁎

confidence interval (CI) for mediators: KIG (H4a) and KEG (H4b). If the interval for a mediation hypothesis does not contain the zero value, it means that the indirect effect significantly differs from zero at a 95% confidence level. Taking into account Fig. 1a, Table 2 reveals that agglomeration has a significant total effect on performance (c). When the mediating variables are introduced (Fig. 1b), both the direct impact of agglomeration (c’) and the indirect impact of KIG (a1b1) on performance are also significant. This means that KIG partially mediates the influence of agglomeration on performance of hotels, and that H4a is accepted. The results also show that KEA is not a mediating variable between agglomeration and performance (a2b2), that is, that H4b is not supported.

institutions (Molina-Morales & Martínez-Fernández, 2008). Their presence in the tourist district is relevant, but hotels have to use suitable strategies to absorb this knowledge. The research model proposed has a predictive value for two of the three dependent variables (Table 1). Performance presents the highest explained variance (R2 = 0.2625). The structural model was also evaluated using the cross-validated redundancy index (Q2) for endogenous reflective constructs (Chin, 2010). This measure examines the predictive relevance of the theoretical/structural model. A Q2 greater than zero implies that the model has predictive relevance. The findings shown in Table 1 confirm that the model suggested has a satisfactory predictive relevance for all three dependent variables—Knowledge internal generation (KIG), Knowledge external acquisition (KEA) and performance. Related to the moderation effects proposed by Hypothesis H5, only the chain moderation effect is supported (H5b). The moderation effect of size (H5a) is rejected, although size has a significant impact on performance (see Table 1). Testing the mediation hypotheses (i.e., H4a and H4b) was possible thanks to the analytical approach of Hayes and Scharkow (2013). The indirect effects are specified and tested with the mediators (KIG and KEA) (Table 2). We also studied the total effect (c) and the direct effect (H1: c’) of the independent variable (agglomeration) on the dependent variable (profitability). Chin (2010) suggests a two-stage process to test mediation in PLS: 1) using the specific model—including both direct and indirect effects—and performing N bootstrap resampling and explicitly calculating the product of the direct paths that form the indirect path under assessment; and 2) Estimating significance by means of percentile bootstrap (Williams & MacKinnon, 2008). This generates a 95%

5. Conclusions This paper aims to add knowledge about the relationship between district effect-tourism sector-knowledge strategies. More specifically, the paper has a twofold aim. First, we aim to confirm the existence of externalities in tourist districts which positively affect the profitability of hotels. Second, we will study the possible mediation effect of the strategies used by hotels to gain knowledge (either through internal generation or external acquisition) in the relationship between agglomeration of firms and the performance of hotels. To achieve these aims, we proposed five research questions. The results of our model confirm that agglomeration has a positive impact on the profitability of Spanish hotels, thus achieving the first part of our aim. The three following research questions (second, third and fourth) are related to the second part of the aim. The results show that agglomeration causes a reduction of internal generation of knowledge by hotels, with a positive impact of internal knowledge

Table 2 Summary of mediating effect test. Total effect of agglomeration on P (c) Coefficient

0.045

⁎⁎

Direct effect of agglomeration on P

t Value

1.509

Coefficient

H1 = c′

0.42

⁎⁎

Indirect effects of agglomeration on P

t Value

1.677

Point estimate

Total H4a = a1b1 (via KIG) H4b = a2b2 (via KEA)

KIG, Knowledge Internal Generation; KEA, Knowledge External Acquisition; P, profitability. ⁎⁎ p < 0.005. 7

⁎⁎

0.42 −0.35⁎⁎ −0.025

Percentile bootstrap 95% confidence interval Lower

Upper

0.005 −0.022 −0.004

0.111 −0.003 0.002

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generation strategies on profitability. The study was unable to demonstrate an effect of agglomeration on external knowledge acquisition strategies, and of these strategies on profitability. It seems clear though that internal knowledge generation strategies partially mediate the effect of agglomeration on performance. The results also indicate that the total impact of agglomeration on performance is lower than expected—although it continues to be positive—because hotels substitute part of their internal knowledge generation for external acquisition. In other words, most probably hotels prefer to acquire external knowledge generated in their location. These results are in accordance with part of the literature. We have not been able to demonstrate that hotels must use specific strategies to absorb external knowledge. This result can be due to different reasons. For instance, participation in alliances and in other firms does not guarantee the absorption of their knowledge, which requires the presence of dynamic capabilities such as alliance management capability and absorption capability. So, a future line of research is to assess these dynamic capabilities and to determine what mechanisms should be applied to increase the possibilities of absorbing knowledge. The paper has theoretical as well as practical implications. It is very important for hotels to choose a good location, since it can be crucial for their performance and competitiveness. From a theoretical point of view, two scientific areas of great interest are currently linked—the knowledge-based view of the firm and industrial district theory—highlighting the importance of the firm's location in the development of the knowledge required to increase its competitiveness. This empirical study focuses on the hotel sector because of its prominence within the service sector. The hotel sector is important because it requires a large amount of labor and drives development in areas where hotels are located. The results of this analysis can be helpful to demonstrate the existence of a substitution effect between the knowledge generated internally by hotels and the knowledge they acquire from the outside. Moreover, the findings obtained from this research can be of great importance for the competitiveness of hotel companies, encouraging public institutions to foster the sharing and transfer of specialized knowledge within a location.

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