Location is one of the most influential factors of T&H firm performance as the production and consumption of T&H goods or services are spatially and temporally localised (e.g. Kilic and Okumus, 2005; McCann and Folta, 2008; Sainaghi, 2011; En Lado-Sestayo et al., 2016). Location can reflect market structure and influence competitiveness and the target market, which can affect firm performance significantly (Barros, 2005; Zhang and Enemark, 2016). Locational factors, such as local economic environment, local facilities, transportation, natural resources, size of the location, can also influence the performance and productivity of a firm (Chou, Hsu and Chen, 2008). For example, Yang, Wong and Wang (2012) found that agglomeration effects and accessibility to transportation are important regarding hotel location choice based on performance gains. Zhang and Enemark (2016) found that hotels located in urban areas perform better than in rural areas, whereas for restaurants, it is the opposite. However, Sigala (2004) found that location does not affect productivity, rather location may significantly affect demand variability. Negative implications of location on firm performance can also be found (e.g. Marco-Lajara, Claver-Cortés and Úbeda-García, 2014). Yet, relatively few of the location studies in T&H have addressed the issue of low productivity.
Agglomeration effects and spatial clustering are a central feature in the location of firms and how this relates to increasing returns (Tyrrell and Martens, 2007). The relationship between productivity and spatial clustering has been studied by various researchers across the main disciplines of economics and geography. The initial theorisation of why economic activities cluster spatially focusses on place-specific external economies of scale, i.e. agglomeration economies, which generate productivity advantages (Andersson and Loof, 2011). Agglomeration can enhance productivity via three mechanisms: sharing, matching, and learning (Duranton and Puga, 2004). It is contended that sharing of inputs and outputs is important due to larger regions having a greater variety of input suppliers and division of labour that makes workers more productive, but also due to proximity and subsequent lower transport costs (Krugman, 1991b). Matching refers to the match in labour demand and supply, and learning emphasises the accumulation and diffusion of knowledge within an cluster region (e.g. Rosenthal and Strange, 2004; Puga, 2010; Abel, Dey and Gabe, 2012; Melo and Graham, 2014). Most empirical research on productivity focusses on the learning mechanism,
highlighting the impact of technology and spillovers (Rosenthal and Strange, 2004). Urban agglomeration associated with more intense knowledge spillover effects is due to various reasons including the role of proximity in R&D and geographically localised knowledge spillovers (Jaffe, Trajtenberg and Henderson, 1993), concentration of innovation (Audretsch and Feldman, 2004) and the rapid transfer of innovation within clusters (Baptista, 2000). Ding, Guariglia and Harris (2016) showed that agglomeration spillovers are significant and positive for the manufacturing and production industries, and that diversification spillovers (referred to as Jacobian) are even stronger in terms of the significant positive effect and strength of the relationship. However, this is in the context of China and the manufacturing industry; results can vary by country and industry-type as market and industry structures differ. There are also external effects related to agglomeration that influence productivity, for example, the size of the region and employment density (Henderson, 1986; Ciccone and Hall, 1996; Li, Wang and Zhang, 2017).
However, it is also important to acknowledge the negative externalities or costs of agglomeration (agglomeration diseconomies), such as spatial inequalities of growth and wages, high competition and price wars (McCann and Folta, 2008, 2009; Campos, 2012; Marco- Lajara, Claver-Cortés, et al., 2016a). Broersma & Oosterhaven’s (2009) policy simulations have resulted in controversial conclusions compared to previous studies. Agglomeration effects will enhance the level of productivity, but effects are mitigated by the spillover effects to neighbouring regions with high job densities. Yet, in terms of productivity growth, the results have shown a negative effect of agglomeration owing to a decrease in returns to scale, thus explaining the decline in labour productivity in the Netherlands in the second half of the 1990s. Regional spillover effects are a significant part of the concept of agglomeration economies (Boschma and Ter Wal, 2007; Davì, López-Bazo and Barbaccia, 2009; Majewska, 2015; Chhetri et al., 2017), but contradictory findings on spillover effects on labour productivity can be found in past studies. Nevertheless, the role of agglomeration economies has been acknowledged to be significant for a region’s and firm’s labour productivity (Ciccone and Hall, 1996; Glaeser and Maré, 2001; Rosenthal and Strange, 2004).
Discussion of this phenomenon is commonly found in the context of manufacturing and other production industries but, in contrast, it is scarcely recognised in the context of the service industry, especially the T&H industry. Yet, in T&H, such a phenomenon is observed as different firms (e.g. hotels, restaurants, travel agents, etc.) tend to collocate together to seek
increased demand especially because tourists tend to reduce their search cost, but also as firms can gain greater externalities and benefit of access to resources (Michael, 2003; Yang, Wong and Wang, 2012; Marco-Lajara, Claver-Cortés, et al., 2016a). In the T&H literature, studies on the implication of clustering and agglomeration in the industry are evident but minimal (Michael, 2003; Saxena, 2005; Novelli, Schmitz and Spencer, 2006; Weidenfeld, Butler and Williams, 2010; Weidenfeld, Williams and Butler, 2014). Existing T&H studies on agglomeration have focussed on the effect upon general performance of hotels and other tourism regions (Kalnins and Chung, 2004; Lee and Jang, 2013, 2015; Marco-Lajara et al., 2014). Moreover, there are minimal studies of the labour market, due to the characteristics of high labour turnover, insufficiency of skilled labour, lack of motivation, etc. (People1st, 2015). These have resulted in the labour productivity gap between T&H and other industries of the economy.
With the persistent productivity gap and increasing competition in the business environment, spatial clustering and agglomeration economies can be an alternative way of narrowing the gap and generating subsequent regional influences (Kolko, 2010; Campos and Prothero, 2012; Brunow and Blien, 2014; Ding, Guariglia and Harris, 2016). Nachum (1999) argued that spillovers can be common in professional service industries as there are no patents to protect knowledge, and because labour mobility is the main mode of spillover between firms or industries. With such a proposition, the issue of measurement cannot be ignored. Identifying and measuring the spillover, but also its impact on productivity, is challenging. It is a further challenge as the service sector is an open system (Grönroos and Ojasalo, 2004). Nevertheless, spatial clustering and agglomeration effects can account for regional differences in productivity across regions via spillover effects, which can be captured using advanced econometric modelling. Despite the high association and relevance of spatial clustering and agglomeration effects to the T&H industry, there is limited research on their implications for the labour productivity of T&H; the actual effects are unknown. Given the persistent problem of low productivity in the UK T&H industry, and with traditional internal strategies to improve this having been relatively ineffective, there is a need for alternative and new perspectives on tackling the low T&H productivity. With the advancement of new statistical methods and using relevant proxies (refer to Chapter 5), it is possible to examine the relationship between productivity and agglomeration, which will be the focus of this research in the context of the UK T&H industry.