عملکرد تجاری فرودگاه ها: درآمد غیر هواپیمایی و عوامل تعیین کننده آن  Business performance of airports: Non-aviation revenues and their determinants

1. Introduction Changes in the modern air transport business have increasingly transformed the role of airports and their perception by travellers and consumers. The joint interaction of factors such as the expansion of low-cost carriers (LCC), raising competition between airlines, increasing ease in purchasing tickets, changes in travelling habits, privatization of infrastructure, have modified the business worldwide (Papatheodorou and Lei, 2006; Graham, 2009; CastilloManzano, 2010). As a consequence, the search for revenues maximisation has gradually shifted its main focus from traditional core aeronautical service to non-aviation or commercial sources (Edwards, 2005; Morrison, 2009). In fact, the strong interrelationships between tourism and shopping have convinced airport managers to expand their view of airports from serving the sole transportation of passengers to leisure attraction (Freathy & O’Connell, 1999; Geuens et al., 2004). Today airports provide a wide variety of entertaining services to travellers, besides having expanded traditional shopping-related ones. Such revolution has been relatively recent. Indeed, airport managers have dealt with non-aviation activities as important assets for their decisions for six decades (Castillo-Manzano, 2010). However, only since 1980s airports began to transform from central or local government organizations to enterprises capable of generating substantial profits (Kim and Shin, 2001). Starting from about a decade later, non-aviation sources of revenues have considerably grown (Francis et al., 2004; Graham, 2009; Morrison, 2009; Fasone and Maggiore, 2012), to the point that such parallel business has become crucial for many airports, sometimes showing a more rapid rise than passengers traffic (Doganis, 2006; Brechin, 1999; Kim and Shin, 2001; Torres et al., 2005; Fasone and Scuderi, 2012). Such timing goes parallel with the evolution of tourism since the Eighties, from mass phenomenon to larger and highly segmented market (Aguilo Perez and Juaneda Sampol, 2000;
Brida and Scuderi, 2013), whose growth and economic effect has put pressure to policymakers in building infrastructures such as roads, airports and harbours (Mak, 2004; Van Vijk and Persoon, 2006). All this justifies the growing interest towards the assessment of the elements that likely influence commercial or non-aviation revenues (NAR), although the topic has still remained under investigated (Geuens et al., 2004; Castillo-Manzano, 2010). Consistently, different contributors have tried to explain the main factors influencing these important sources for profits. The topic iscomplex, inasmuch as various factors such as passengers characteristics, structure of airport, supply of retail shops and their positioning at the airport, contingent factors as flight delays may cause travellers to spend (Graham, 2008; Castillo-Manzano, 2010). Within the literature, only a limited number of works applied regression models in order to assess the determinants of spending. A subset of them used a demand-based approach and gathered data from direct interviews to passengers. Others adopted a supply-side perspective through airport-level information. Of course, both have provided useful though different indications to managers. The former approach has been usually limited to a single structure e with the exception of Castillo-Manzano (2010) e and it can potentially survey a considerable number of variables that provide highly detailed information on passengers. The latter exploits data from different structures and can be suitable to find significant regularities in the way structures are managed. The present contribution adopts the second approach. We try to learn lessons for management practice from data coming from a set of airports. In a sense, we follow Graham’s (2009) and Papatheodorou and Lei’s (2006) invitation to test new models for explaining the determinants of airport revenues. It is based on a longitudinal dataset of German airports. However, unlike similar previous studies we extend the set of control variables in order to test the simultaneous significance of different regressors and provide a more complete overview of the topic.