Easing the adoption of agent-based modelling (ABM) in tourism research

Johnson, Peter A.; Nicholls, Sarah; Student, Jillian; Amelung, Bas; Baggio, Rodolfo; Balbi, Stefano; Boavida-Portugal, Inês; Jong, Eline de; Hofstede, Gert Jan; Lamers, Machiel; Pons, Marc; Steiger, Robert

doi:10.1080/13683500.2016.1209165

Cited by 27 publications

(25 citation statements)

References 34 publications

(41 reference statements)

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“…Pasquinelli (2016) serta Bellini & Pasquinelli (2016) juga berpendapat bahwa sektor pariwisata juga berperan penting bagi perekonomian dunia. Bahkan, sektor pariwisata juga berperan sebagai peningkatan devisa di Indonesia (Baggio, 2017;Johnson, et al, 2017;Maxim, 2019). Persaingan tersebut mendorong para pengelola wisata bahari untuk bersaing dalam mempertahakan atau bahkan meningkatkan kunjungan wisatawan (Fianto, 2018).…”

Section: Pendahuluanunclassified

“…City image diartikan sebagai sekumpulan asosiasi wisatawan terhadap objek wisata yang timbul akibat prasangka, pengetahuan dan rasa emosional (Carlisle, et al, 2016;Amelung, et ., 2016;Johnson, et al, 2017;Maxim, 2019). Sebagai pengelola wisata bahari, harus dapat memahami dengan baik tentang city image.…”

Section: Pendahuluanunclassified

“…City branding didefinisikan sebagai nama, simbol dan merek yang membedakan objek wisata tertentu dengan objek wisata yang lain (Pasquinelli, 2016;Bellini & Pasquinelli, 2016;Khrisna, et al, 2017). City branding juga penting untuk diperhatikan dengan baik oleh para pengelola wisata bahari (Baggio, 2017;Johnson, et al, 2017;Maxim, 2019). Dikatakan demikian karena city branding sebagai bukti nilai tambah bagi objek wisata sehingga melalui nilai tambah tersebut dapat menarik niat wisatawan untuk berkunjung (Fistola & La Rocca, 2017).…”

Section: Pendahuluanunclassified

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Pengaruh City Image Dan City Branding Terhadap Visit Intentiom Di Wisata Bahari Provinsi Jawa Timur

Riyanto

Fianto

Riqqoh

2020

JSH

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Penelitian ini dilakukan dengan tujuan untuk mengkaji pengaruh city image dan city branding terhadap visit intention di wisata bahari Provinsi Jawa Timur. Populasi yang ditentukan dalam penelitian ini yaitu seluruh wisatawan baik yang belum maupun sudah pernah berkunjung di wisata bahari Provinsi Jawa Timur, sehingga diperoleh sampel yakni sebagian wisatawan baik yang belum maupun sudah pernah berkunjung di wisata bahari Provinsi Jawa Timur. Jumlah sampel dalam penelitian ini sebanyak 210 responden. Teknik pengambilan sampel yang digunakan adalah purposive sampling melalui penyebaran angket kepada responden. Teknik analisis data yang digunakan adalah analisis regresi bergandam. Hasil penelitian memberikan temuan bahwa city image dan city branding baik secara parsial maupun simultan memiliki pengaruh signifikan dan positif terhadap visit intention.

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Section: Pendahuluanunclassified

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Pengaruh City Image Dan City Branding Terhadap Visit Intentiom Di Wisata Bahari Provinsi Jawa Timur

Riyanto

Fianto

Riqqoh

2020

JSH

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“…As pointed by Johnson et al (), “the tourism system consists of a large number and variety of actors who interact with each other within a series of natural and socioeconomic systems and at different spatial and temporal scales”, and it is only recently that complexity science concepts such as heterogeneous and adaptive behavior, nonlinearity, and emergence have been recognized in tourism studies (Baggio, ). Fodness () stated that this problem of destination choice cannot be adequately studied from a traditional approach and proposes the incorporation of complexity science to study this problem.…”

Section: Crowding Types and Agent‐based Modelsmentioning

confidence: 99%

An agent‐based model of tourism destinations choice

Álvarez

Brida

2018

Journal of Tourism Research

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This study introduces a dynamic agent-based model to represent the evolution of the choice of destinations by tourists. The model assumes that this choice depends on their personal tastes (characterized by their preferences for goods and services) and certain characteristics of other tourists (called crowding types), on the degree of inertia of the individuals and relevant information about the characteristics of visitors to other destinations. The model shows that the individual choices and the characterization of destinations are emergent properties of the system. The simulations show the formation of stable groups of individuals sharing destination, an endogenously increasing level of specialization of destinations, as well as an increasing market concentration, and that individuals of different characteristics can share destinations.

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“…Unlike macro-models that use aggregate data, our method effectively mimics visitors’ decision-making processes vis-à-vis destination choice in a multidestination travel context; therefore, this model seeks to unveil factors and decision-making rules related to spillover effects at the aggregate level. Because of several inherent advantages of ABM, such as considering systematical complexity and nonlinearity and overcoming assumptions of homogeneity, equilibrium, and rationality typical to traditional modeling techniques, the adoption of ABM within tourism research is suited to a wide variety of potential applications (Nicholls, Amelung, and Student 2016; Johnson et al. 2016).…”

Section: Introductionmentioning

confidence: 99%

Agent-Based Modeling of Spatial Spillover Effects in Visitor Flows

Yang

Zhong

et al. 2020

Journal of Travel Research

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In this study, we apply an agent-based modeling approach to understand individual visitors’ multidestination travel patterns and the spatial spillover effects in visitor flows as an aggregate outcome. Based on previous literature, we specify three hypothetic visitor categories (global optimizers, sequential optimizers, and radial optimizers) according to visitors’ attraction selection rules. We conduct an ABM simulation with a sample of 341 Chinese cities as destinations and 1,990 attractions to measure the degree of spillover between destinations by observing the frequency with which visitors traveling across destination boundaries visited attractions in other destinations. The simulation results demonstrate slightly different spillover effects based on visitor type and highlight three regions with particularly strong spillover effects: the Bohai Rim region, the Yangtze River Delta region, and the Sichuan and Chongqing region. These results appear consistent with those of exploratory spatial data analysis in a validation check. Lastly, policy implications are provided.

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Easing the adoption of agent-based modelling (ABM) in tourism research

Cited by 27 publications

References 34 publications

Pengaruh City Image Dan City Branding Terhadap Visit Intentiom Di Wisata Bahari Provinsi Jawa Timur

Pengaruh City Image Dan City Branding Terhadap Visit Intentiom Di Wisata Bahari Provinsi Jawa Timur

An agent‐based model of tourism destinations choice

Agent-Based Modeling of Spatial Spillover Effects in Visitor Flows

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