The Effect of Short-Term Leases on Hotel’s Agglomeration and Market-Entry Strategies: A Game Theory Approach

Abstract

Hotel agglomeration has been recognized as a driving force behind the success of tourist destinations. However, little research has delved into the effect of short-term leases on the dynamics of agglomeration. Utilizing a game theory approach, this study fills this gap in the literature by investigating the influence of short-term leases on agglomeration in the hospitality sector. Employing random-effect and logit models, our research reveals significant changes in the capacity of hotels to harness the benefits of agglomeration in the presence of short-term leases. With these altered market dynamics, newly established hotels gain a competitive advantage by undercutting short-term leases in terms of price class. However, inertia or misinterpretation leads mid-price and upscale hotels to choose a different strategy. This study advances our understanding of demand-driven agglomeration and offers valuable insights into the optimal market-entry strategies for new hotels.

Keywords

agglomeration Game Theory short-term leases market-entry strategies competitive advantage

Get full access to this article

View all access options for this article.

References

Arenoe

van der Rest

J. P. I.

Kattuman

(2015). Game theoretic pricing models in hotel revenue management: An equilibrium choice-based conjoint analysis approach. Tourism Management, 51, 96–102. https://doi.org/10.1016/j.tourman.2015.04.007

Balaguer

Pernías

J. C.

(2013). Relationship between spatial agglomeration and hotel prices. Evidence from business and tourism consumers. Tourism Management, 36, 391–400. https://doi.org/10.1016/j.tourman.2012.10.004

Baum

Mezias

S. J.

(1992). Localized competition and organizational failure in the Manhattan hotel industry, 1898–1990. Administrative Science Quarterly, 37(4), 580–604. https://doi.org/10.2307/2393473

Barney

(1991). Firm resources and sustained competitive advantage. Journal of management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108

Baum

Mudambi

(1995). An empirical analysis of oligopolistic hotel pricing. Annals of Tourism Research, 22(3), 501–516. https://doi.org/10.1016/0160-7383(94)00066-2

Bayoumi

A. E. M.

Saleh

Atiya

A. F.

Aziz

H. A.

(2013). Dynamic pricing for hotel revenue management using price multipliers. Journal of Revenue and Pricing Management, 12, 271–285. https://doi.org/10.1057/rpm.2012.44

Bianco

Singal

Zach

F. J.

(2024). Leveraging knowledge via location proximity among hotels and short-term leases. International Journal of Hospitality Management, 119, Article 103719. https://doi.org/10.1016/j.ijhm.2024.103719

Bianco

Singal

Zach

F. J.

Nicolau

J. L.

(2023). Dual-branded hotels: Resource-based entry strategies in agglomerated markets. Tourism Management, 95, Article 104663. https://doi.org/10.1016/j.tourman.2022.104663

Bianco

Zach

F. J.

Singal

(2024). Disruptor recognition and market value of incumbent firms: Airbnb and the lodging industry. Journal of Hospitality and Tourism Research, 48(1), 84–104. https://doi.org/10.1177/10963480221085215

10.

Bourke

J. G.

Izadi

Olya

H. G.

(2020). Failure of play on asset disposals and share buybacks: Application of game theory in the international hotel market. Tourism Management, 77, Article 103984. https://doi.org/10.1016/j.tourman.2019.103984

11.

Breusch

T. S.

(1978). Testing for autocorrelation in dynamic linear models. Australian Economic Papers, 17(31), 334–355. https://doi.org/10.1111/j.1467-8454.1978.tb00635.x

12.

Breusch

T. S.

Pagan

A. R.

(1979). A simple test for heteroskedasticity and random coefficient variation. Econometrica, 47(5), 1287–1294. https://doi.org/10.2307/1911963

13.

Campello

R. J. G. B.

Moulavi

Sander

(2013). Density-Based Clustering Based on Hierarchical Density Estimates. In (pp. 160–172). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_14

14.

Canina

Enz

C. A.

Harrison

J. S.

(2005). Agglomeration effects and strategic orientations: Evidence from the US lodging industry. Academy of Management Journal, 48(4), 565–581. https://doi.org/10.5465/amj.2005.17843938

15.

Chang

H. H.

Sokol

D. D.

(2022). How incumbents respond to competition from innovative disruptors in the sharing economy—The impact of Airbnb on hotel performance. Strategic Management Journal, 43(3), 425–446. https://doi.org/10.1002/smj.3201

16.

Chen

M. H.

Wei

Huang

C. H. J.

(2021). Modeling a green supply chain in the hotel industry: An evolutionary game theory approach. International Journal of Hospitality Management, 92, Article 102716. https://doi.org/10.1016/j.ijhm.2020.102716

17.

Chung

Kalnins

(2001). Agglomeration effects and performance: A test of the Texas lodging industry. Strategic management journal, 22(10), 969–988. https://doi.org/10.1002/smj.178

18.

Conlin

Kadiyali

(2006). Entry-deterring capacity in the Texas lodging industry. Journal of Economics and Management Strategy, 15(1), 167–185. https://doi.org/10.1111/j.1530-9134.2006.00096.x

19.

Dogru

Mody

Line

Suess

Hanks

Bonn

(2020). Investigating the whole picture: Comparing the effects of Airbnb supply and hotel supply on hotel performance across the United States. Tourism Management, 79, Article 104094. https://doi.org/10.1016/j.tourman.2020.104094

20.

Fang

(2019). Does hotel location tell a true story? Evidence from geographically weighted regression analysis of hotels in Hong Kong. Tourism Management, 72, 78–91. https://doi.org/10.1016/j.tourman.2018.11.010

21.

Feng

(2022). Turn on instant booking or not? Decisions of rival hosts. Annals of Tourism Research, 96, Article 103458. https://doi.org/10.1016/j.annals.2022.103458

22.

Freedman

M. L.

Kosová

(2012). Agglomeration, product heterogeneity and firm entry. Journal of Economic Geography, 12(3), 601–626. https://doi.org/10.1093/jeg/lbr022

23.

Gan

Hernandez

M. A.

(2013). Making friends with your neighbors? Agglomeration and tacit collusion in the lodging industry. Review of Economics and Statistics, 95(3), 1002–1017. https://doi.org/10.1162/rest_a_00289

24.

Godfrey

L. G.

(1978). Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica, 46, 1293–1301. https://doi.org/10.2307/1913829

25.

Guttentag

(2015). Airbnb: Disruptive innovation and the rise of an informal tourism accommodation sector. Current Issues in Tourism, 18(12), 1192–1217. https://doi.org/10.1080/13683500.2013.827159

26.

Guttentag

Smith

S. L. J.

(2017). Assessing Airbnb as a disruptive innovation relative to hotels: Substitution and comparative performance expectations. International Journal of Hospitality Management, 64, 1–10. https://doi.org/10.1016/j.ijhm.2017.02.003

27.

Han

Bai

(2022). Pricing research in hospitality and tourism and marketing literature: A systematic review and research agenda. International Journal of Contemporary Hospitality Management, 34(5), 1717–1738. https://doi.org/10.1108/IJCHM-08-2021-0963

28.

Hemmington

Neill

(2022). Hospitality business longevity under COVID-19: The impact of COVID-19 on New Zealand’s hospitality industry. Tourism and Hospitality Research, 22(1), 102–114. https://doi.org/10.1177/1467358421993875

29.

Kalnins

Chung

(2004). Resource-seeking agglomeration: A study of market entry in the lodging industry. Strategic Management Journal, 25(7), 689–699. https://doi.org/10.1002/smj.403

30.

Kim

W. G.

Arbel

(1998). Predicting merger targets of hospitality firms (a Logit model). International Journal of Hospitality Management, 17(3), 303–318. https://doi.org/10.1016/S0278-4319(98)00023-1

31.

Kim

Y. R.

Williams

A. M.

Park

Chen

J. L.

(2021). Spatial spillovers of agglomeration economies and productivity in the tourism industry: The case of the UK. Tourism Management, 82, Article 104201. https://doi.org/10.1016/j.tourman.2020.104201

32.

Kumar

Gordon

B. R.

Srinivasan

(2011). Competitive strategy for open source software. Marketing Science, 30(6), 1066–1078. https://doi.org/10.1287/mksc.1110.0669

33.

Kutner

M. H.

Nachtsheim

C. J.

Neter

Wasserman

(2004). Applied linear regression models. McGraw-Hill/Irwin.

34.

Xiao

(2022). Location of Airbnb and hotels: The spatial distribution and relationships. Tourism Review, 77(1), 209–224. https://doi.org/10.1108/TR-10-2020-0476

35.

Lee

Pennington-Gray

Kim

(2019). Does location matter? Exploring the spatial patterns of food safety in a tourism destination. Tourism Management, 71, 18–33. https://doi.org/10.1016/j.tourman.2018.09.016

36.

Long

J. S.

Freese

(2006). Regression models for categorical dependent variables using Stata (Vol. 7). Stata Press.

37.

Magill

Quinzii

(2002). Capital market equilibrium with moral hazard. Journal of Mathematical Economics, 38(1–2), 149–190. https://doi.org/10.1016/S0304-4068(02)00068-X

38.

Marshall

(1920). Principles of economics (Vol. 8). Macmillan.

39.

Mazzeo

M. J.

(2002). Competitive outcomes in product-differentiated oligopoly. Review of Economics and Statistics, 84(4), 716–728. https://doi.org/10.1162/003465302760556521

40.

McCann

B. T.

Folta

T. B.

(2008). Location matters: Where we have been and where we might go in agglomeration research. Journal of Management, 34(3), 532–565. https://doi.org/10.1177/0149206308316057

41.

McCann

B. T.

Folta

T. B.

(2009). Demand-and supply-side agglomerations: Distinguishing between fundamentally different manifestations of geographic concentration. Journal of Management Studies, 46(3), 362–392. https://doi.org/10.1111/j.1467-6486.2008.00815.x

42.

McCann

B. T.

Vroom

(2010). Pricing response to entry and agglomeration effects. Strategic Management Journal, 31(3), 284–305. https://doi.org/10.1002/smj.805

43.

Mou

Yuan

Yang

Zhang

Tang

Makkonen

(2020). Exploring spatio-temporal changes of city inbound tourism flow: The case of Shanghai, China. Tourism Management, 76, 103955. https://doi.org/https://doi.org/10.1016/j.tourman.2019.103955

44.

Myerson

R. B.

(1991). Game theory: Analysis of conflict. Harvard University Press.

45.

Nash

J. F.

(1951). Non-cooperative games. Annals of Mathematics, 54(2), 286–295. https://doi.org/10.2307/1969529

46.

Nicolau

J. L.

Xiang

Wang

(2024). Daily online review sentiment and hotel performance. International Journal of Contemporary Hospitality Management, 36(3), 790–811. https://doi.org/10.1108/IJCHM-05-2022-0594

47.

Osborne

M. J.

(2004). An introduction to game theory (Vol. 3, No. 3). Oxford University Press.

48.

Pérez-Rodríguez

J. V.

Hernández

J. M.

(2023). The effect of type of lodging and professionalism on the efficiency of P2P accommodation. Tourism Economics, 29(6), 1624–1642. https://doi.org/10.1177/13548166221114228

49.

Porter

M. E.

(1980). Industry structure and competitive strategy: Keys to profitability. Financial Analysts Journal, 36(4), 30–41. https://doi.org/10.2469/faj.v36.n4.30

50.

Schwartz

(1998). The confusing side of yield management: Myths, errors, and misconceptions. Journal of Hospitality and Tourism Research, 22(4), 413–430. https://doi.org/10.1177/109634809802200406

51.

Schwartz

Webb

(2022). Resource similarity, market commonality, and spatial distribution of hotel competitive sets. Journal of Hospitality and Tourism Research, 46(4), 724–741. https://doi.org/10.1177/1096348020988579

52.

Simon

L. K.

(1987). Bertrand price competition with differentiated commodities. Journal of Economic Theory, 41(2), 304–332. https://doi.org/10.1016/0022-0531(87)90022-6

53.

Woo

Mun

S. G.

(2020). Types of agglomeration effects and location choices of international hotels in an emerging market. Tourism Management, 77, Article 104034. https://doi.org/10.1016/j.tourman.2019.104034

54.

Xie

K. L.

Kwok

(2017). The effects of Airbnb’s price positioning on hotel performance. International Journal of Hospitality Management, 67, 174–184. https://doi.org/10.1016/j.ijhm.2017.08.011

55.

Xie

K. L.

Kwok

Heo

C. Y.

(2020). Are neighbors friends or foes? Assessing Airbnb listings’ agglomeration effect in New York City. Cornell Hospitality Quarterly, 61(2), 128–141. https://doi.org/10.1177/1938965519890578

56.

Yang

(2012). Agglomeration density and tourism development in China: An empirical research based on dynamic panel data model. Tourism Management, 33(6), 1347–1359. https://doi.org/10.1016/j.tourman.2011.12.018

57.

Yang

Wong

K. K.

Wang

(2012). How do hotels choose their location? Evidence from hotels in Beijing. International journal of hospitality management, 31(3), 675–685. https://doi.org/10.1016/j.ijhm.2011.09.003

58.

Zach

F. J.

Nicolau

J. L.

Sharma

(2020). Disruptive innovation, innovation adoption and incumbent market value: The case of Airbnb. Annals of Tourism Research, 80, Article 102818. https://doi.org/10.1016/j.annals.2019.102818

59.

Zervas

Proserpio

Byers

J. W.

(2017). The rise of the sharing economy: Estimating the impact of Airbnb on the hotel industry. Journal of Marketing Research, 54(5), 687–705. https://doi.org/10.1509/jmr.15.0204

60.

Zhang

Zach

F. J.

Xiang

(2023). Design standardization by Airbnb multi-unit hosts: professionalization in the sharing economy. Annals of Tourism Research, 98, 103523. https://doi.org/10.1016/j.annals.2022.103523

61.

Zhang

Zach

F. J.

Xiang

(2024). Multi-level differentiation of short-term rental properties: A deep learning-based analysis of aesthetic design. Tourism Management, 100, Article 104832. https://doi.org/10.1016/j.tourman.2023.104832