Keeping the competition close: The impact of competitor distance in the lodging industry

Abstract

This study applies a distance-based measurement outlining market boundaries for competition in the lodging industry and investigates the effects of competition across a range of distances. Predetermined administrative boundaries have been the conventional metric used when estimating competition effects in a given geographical area in the lodging industry. For this study, distances are calculated using the location information of 45,623 hotels in the United States extracted from the Smith Travel Research Hotel Census. Our findings suggest a U-shaped relationship: hotel room prices fall as relative distance increases, due to increasing negative price pressure, until a threshold distance is reached (a radius of 20 km), after which price pressure falls and prices begin to increase. These research findings have implications for hotel managers with respect to competitor management and for hotel investors interested in locating and developing future accommodation establishments.

Dynamics of agglomeration and competition in the hotel industry: A geographically weighted regression analysis based on an analytical hierarchy process and geographic information systems (GIS) data

“…Following previous studies on confrontation agglomeration vs. competition that consider hotel price as the dependent variable (Becerra et al, 2013;Lee, 2015;Park et al, 2022;Zhang et al, 2011a), we accounted for the yearly average room rate for a standard double room in euros during the year 2017 as the dependent variable for each hotel included in the sample, due to it being invariant to seasonal effects and special events (Lee, 2015). Following Rosen (1974) and several previous studies in hotel price determinants (see a review of previous studies that considered this model in Zhang et al, 2011b for more details), this variable was log-transformed to facilitate the effect interpretation.…”

“…This confrontation of two opposing effects has been extensively analyzed by previous hospitality research, highlighting two research streams. One stream, based on agglomeration models (Yang et al, 2014), has focused on analysing whether the hotel industry tends towards agglomeration and the factors that favour this trend (Adam & Mensah, 2014;Baum & Haveman, 1997;Cró & Martins, 2018;Fang et al, 2019;Fang et al, 2021;Freedman & Kosová, 2012;Kalnins & Chung, 2004;Lee et al, 2018;Li et al, 2015;Luo & Yang, 2016;Qin et al, 2021;Urtasun & Gutiérrez, 2006;Yang et al, 2014) while another stream has analyzed the impact of hotel agglomeration on hotel economic results and the factors that unbalance the confrontation between agglomeration and competition (Balaguer & Pernías, 2013;Becerra et al, 2013;Canina et al, 2005;Chung & Kalnins, 2001;Enz et al, 2008;Kalnins, 2016;Kim et al, 2020;Lee, 2015;Lee & Jang, 2013;Lee & Jang, 2015;Li & Du, 2018;Marco-Lajara et al, 2014;Marco-Lajara et al, 2016;McCann & Vroom, 2010;Park et al, 2022;Rezvani & Rojas, 2020;Silva 2016;Tsang & Yip, 2009;Urtasun & Gutiérrez, 2017).…”

“…Given the existence of customers with low price sensitivity, hotels can compete vertically to attract those customers who are willing to pay a premium price for higher-quality services (Liu et al, 2020). Previous literature has highlighted the essential role played by vertical differentiation as a factor that influences both the formation of hotel agglomerations (Adam & Mensah, 2014;Freedman & Kosová, 2012;Kalnins & Chung, 2004) as well as the moderating role in the influence of hotel geographic concentration or agglomeration on economic results (Lee & Jang, 2015;Park et al, 2022;Rezvani & Rojas, 2020). Thus, in addition to the results already commented on asymmetric agglomeration benefits based on vertical differentiation, there is also contradictory evidence both in favour of differentiated agglomerations (Freedman & Kosová, 2012;Kalnins & Chung, 2004;Park et al, 2022;Rezvani & Rojas, 2020) and vertically undifferentiated agglomerations (Adam & Mensah, 2014;Lee, 2015;Lee & Jang, 2015;Tsang & Yip, 2009) based on the premise that similar hotels can cooperate when they are located in the same area (Lee, 2015;Silva 2016).…”

“…Nonetheless, previous agglomeration research has neglected online reputation as a factor of vertical differentiation in the assessment of the competitive environment, requiring an alternative approach that accounts for the quality perceived by consumers, thus responding to a challenge in agglomeration research regarding the classification into good and bad hotels to measure the asymmetric effect of agglomeration benefits (McCann & Folta, 2008). Given the relevance of geographical distance and online reputation in price competition (Rezvani & Rojas, 2020), our approach combines both factors using GIS, a tool which is widely considered in agglomeration studies (Park et al, 2022;Rezvani & Rojas, 2020;Urtasun & Gutiérrez, 2017) for competitor assessment.…”

“…Second, the measurement of hotel agglomeration has been carried out in an inaccurate way, mainly through the number of establishments or available hotel rooms (Becerra et al, 2013;Canina et al, 2005;Freedman & Kosová, 2012;Lee & Jang, 2015;Park et al, 2022), considering the geographical distance between the nearest competitors (Baum & Haveman, 1997;Park et al, 2022;Silva, 2016) or the set of the geographically nearest competitors (Becerra et al, 2013;Lee, 2015;Park et al, 2022), but hotels can compete beyond geographical barriers with hotels that are less differentiated in quality (Lee, 2015) and the set of competitors can be identified through the quality perceived by consumers (Ye et al, 2022) making alternative approaches necessary in the agglomeration measurement (McCann & Folta, 2008).…”