Current Issues in Tourism
ISSN: 1368-3500 (Print) 1747-7603 (Online) Journal homepage: http://www.tandfonline.com/loi/rcit20
Where to vacation? An agent-based approach to
modelling tourist decision-making process
Inês Boavida-Portugal, Carlos Cardoso Ferreira & Jorge Rocha
To cite this article: Inês Boavida-Portugal, Carlos Cardoso Ferreira & Jorge Rocha (2017) Where
to vacation? An agent-based approach to modelling tourist decision-making process, Current
Issues in Tourism, 20:15, 1557-1574, DOI: 10.1080/13683500.2015.1041880
To link to this article: https://doi.org/10.1080/13683500.2015.1041880
Published online: 26 May 2015.
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Current Issues in Tourism, 2017
Vol. 20, No. 15, 1557–1574, http://dx.doi.org/10.1080/13683500.2015.1041880
CURRENT ISSUES IN METHOD AND PRACTICE
Where to vacation? An agent-based approach to modelling tourist
decision-making process
∗
Inês Boavida-Portugal , Carlos Cardoso Ferreira and Jorge Rocha
Institute of Geography and Spatial Planning, Universidade de Lisboa, Lisbon, Portugal
(Received 30 June 2014; accepted 10 April 2015)
Agent-based models (ABMs) are becoming more relevant in social simulation due to the
potential to model complex phenomena that emerge from individual interactions. In
tourism research, complexity is a subject of growing interest and researchers start to
analyse the tourism system as a complex phenomenon. However, there is little
application of ABMs as a tool to explore and predict tourism patterns. The purpose
of the paper is to develop an ABM that increases knowledge in tourism research by
(i) considering the complexity of tourism phenomenon, (ii) providing tools to explore
the complex relations between system components and (iii) giving insights on the
functioning of the system and the tourist decision-making process. A theoretical
ABM is developed to improve knowledge on tourist decision-making in the selection
of a destination to vacation. Tourists’ behaviour, such as individual motivation, and
social network influence in the vacation decision-making process are hereby discussed.
Keywords: agent-based models; complexity; tourism system; tourist behaviour;
decision-making process; simulation
Introduction
Identifying the underlying mechanisms of tourism system is a fundamental challenge for
tourism research and holds implications for planning and management (Farrell &
Twining-Ward, 2004; Mill & Morrison, 2009). In general, traditional tourism research
has focused on aspects of behaviour and development patterns exhibiting order, linearity
and equilibrium. Based on the Newtonian paradigm of scientific inquiry, systems are understood to be highly dependent on initial conditions that explain the future outcomes
(McKercher, 1999). The system is dissected into its components and behaviours are interpreted individually, assuming clock-like relationships. Tourism forecasting has been based
in techniques that compile data from past phenomenon in order to extrapolate future predictions, assuming that the dynamics of the system will be maintained (Baggio & Klobas,
2011). These kinds of techniques assume that the system is predictable and operates in a
linear way. However, systems characterized by disorder, instability and non-linearity are
predominant. In such cases possible key events can be filtered out of research due to unpredictability and noise as a way to produce more accurate predictions (Faulkner, 2000).
Most tourism models recognize the complex nature of the tourism system. Some early
studies such as Gunn (1988), Pearce (1989) and Leiper (1990) sustained that the system as a
whole could be understood by identifying and deconstructing the components and the
∗
Corresponding author. Email: iboavida-portugal@campus.ul.pt
© 2015 Informa UK Limited, trading as Taylor & Francis Group
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I. Boavida-Portugal et al.
relationships between them. By understanding how each component works, knowledge of
the whole system is believed to emerge. These models conceptualize tourism as a system
formed by interconnected and interdependent parts, characterized by discrete components
such as market, travel, destination and marketing, but failed to test the mechanisms that
drive the individual interactions. There is an implicit assumption that tourism is a linear,
deterministic activity that can be controlled and predicted by analysing the sum of its component parts (McKercher, 1999). In fact, this reductionist approach advocates that the
system is just the sum of its components.
The current work reports a complex systems approach to the comprehensive investigation of the tourist decision-making process in the selection of a destination. Where and
why tourists decide to vacation form the main objective of this agent-based model
(ABM). The work starts by exploring complexity and ABM applications in tourism
and the need to adopt such approaches. Then an ABM is developed as a proof of
concept to enhance the understanding of the tourist decision-making process. The
tourist decision ABM considers (i) individual motivations, that is, behaviour in response
to basic need or preferences; (ii) rationality based on if – then rules; (iii) human emotions
and satisfaction and (iv) the influence of the social network in which the individual is
inserted in the destinations choice (Kennedy, 2012). For this purpose, in this paper,
two scenario experiments were developed to explore how changes in awareness that a
tourist has for a destination and how tourist individual preferences change the destination
selection process.
Complexity in tourism
Recently, the understanding of the functioning of the tourism system has taken a rather
different perspective, with the application of complex system and chaos theories in
tourism research. Recognition that tourism is a complex phenomenon is currently discussed
within academic literature (Baggio, 2008; Baggio & Sainaghi, 2011; Faulkner & Russell,
1997; Faulkner, Russell, Moscardo, & Laws, 2001; Russell & Faulkner, 1999). Complexity
and chaos are acknowledgeable concepts in the tourism system and many scholars believe
these concepts are able to give a better understanding of the dynamical behaviours that
define the system.
Complexity and chaos approaches address tourism as being composed of interconnected and interdependent components which behave in a non-linear, self-organizing,
on-the-edge-of-chaos way. When incorporating features that in a reductionist approach
would be considered as noise, one begins to grasp the complexity of the interactions
between components and understand that the system is to be analysed as a whole.
The interactions are mathematically non-linear, that is, a change in one component may
not have a proportional effect in other components. In fact, a very small change can produce
large effects on the system, addressing Lorenz’s butterfly effect theory which defended the
feasibility that a wing flapping of a butterfly in Beijing could initiate a series of effects,
resulting in a cyclone in Florida (Lorenz, 2000). On the contrary, effects thought to have
great impact may actually produce hardly any changes on the overall system (Horn,
2002). Therefore, emergent properties arise in larger scales driven by changes at lower
ones (Batty, 1995; Wu & Webster, 1998). Emergent properties occur when local interactions produce phenomena that are more than a simple sum of the components of the
system (Epstein, 1999), justifying the need to analyse it as a whole. The Schelling
(1969) model is a classic example of how emergent behaviour of class segregation can
result from individual preferences (local scale) for neighbour type.
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Systems are dynamic and often change from an equilibrium state to a chaotic one.
Therefore no state of equilibrium is permanent. Tourism itself is a phenomenon characterized by a high level of dynamism. Faulkner (2000) argued that in the tourism system, equilibrium and chaotic states often shift due to changes in system components. The author
points out changes in socio-economic environment affecting tourism demand; changes in
natural environment jeopardizing sustainability of tourism development; and technological
innovations that create new market opportunities, among other initiatives that potentially
affect destination competitiveness. According to Johnson and Sieber (2010), the success
or failure of destinations can be considered as a pattern that emerges from the multiscaled interactions of tourists, destinations and communities. Thus, even shifting
between stability and instability, systems have the ability to self-organize through the adaptation of its components and their relationships to a changing environment (Bertalanffy,
1968; Lewin, 1999).
ABMs and tourism research
Over the last decade, ABMs registered increasing applications in human behaviour modelling. Despite the growing interest in complexity and chaos theory in tourism research
(Johnson & Sieber, 2009; McDonald, 2009; McKercher, 1999; Scott, Cooper, & Baggio,
2008; Zahra & Ryan, 2007), there has been little empirical research on the subject. The
relationship between ABMs and complexity is reciprocally favourable. On one hand, complexity theory provides the theoretical background and concepts for ABMs, while advances
in ABMs provide tools to explore and represent complex phenomena (Manson, Sun, &
Bonsal, 2012). ABMs allow for the representation of a system from a bottom-up perspective, designing the components at an individual level through entities known as agents.
These are modelled according to a predetermined set of rules that describe their key attributes and behaviours. By modelling at an individual level, it is possible to incorporate the
diversity of behaviours, attributes and interactions inside the system by adopting a bottomup, agent-by-agent and interaction-by-interaction approach (Macal & North, 2010).
Through this approach one can represent emergent properties that usually characterize
systems comprising components that (i) display non-linear relations, (ii) have thresholds,
(iii) possess memory, (iv) are path dependent and (v) have learning and adaptation capabilities (Bonabeau, 2002).
Agents can represent any scale, from single individuals to institutions. In the tourism
system, agents can represent a single tourist, destination, stakeholder or heterogeneous
groups of individuals with different characteristics (Johnson & Sieber, 2011). The relationships between agents are specified, linking agents to other agents and to the environment.
The environment serves as the support for the simulation and is represented by a cellular
model that can contain geographical information. An ABM develops over a simulated
time frame (or time steps) in which agents behave according to a set of defined if– then
rules (Bonabeau, 2002). This requires for an adaptation of the system through feedback
loops. As Baggio and Baggio (2009) showed in their simulation to verify the relationships
between the attractiveness of a tourist destination and tourist arrivals, agents tend to go
where their personal criteria are fulfilled. At every time step, agents act according to the
defined rules repeating the process until the selection of the best destination is true, constituting an adaptive behaviour. Usually the simulation stops when the criteria of the rules are
met.
ABMs can be thought of as a social virtual laboratory as they provide a tool to model
emergent complexity, where the key attributes of the agents and the environment can be
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I. Boavida-Portugal et al.
simulated over multiple time steps, allowing an examination of possible futures or pasts of a
system. Hence ABMs provide a tool to “think with”, assisting in the decision-making
process (Batty, Crooks, See, & Heppenstall, 2012).
When using an ABM, one has to make a commitment between the purpose of the model
and its precision, that is, the type of data and knowledge that is required. Throughout the
literature the authors point out two categories for ABM utility. Although the nomenclature
is not consentaneous in all cases, on one hand we have the theoretical or exploratory
models, and on the other hand empirical or predictive models (Batty, 2008; Couclelis,
2005; Crooks, Castle, & Batty, 2008; Parker, Manson, Janssen, Hoffmann, & Deadman,
2003).
Theoretical models enable the understanding of how relationships between components
of the system work over time. These models are hypothetical and proof of concept oriented,
providing the exploration of theories, behaviours and patterns within a simulation environment (Johnson & Sieber, 2009). Theoretical models are built for illustrative purposes, for
example, not recurring to real-world data, and are often called as toy models. These
models can generate insights about theory and thinking on complex systems. However,
their validation is difficult and therefore raises issues in perceiving if the knowledge produced is relevant (Crooks et al., 2008).
Empirical models are based on real-world data. The selection of data to incorporate in
the model is a thorough process in order to accurately describe and represent the system
components. By adding redundant information, the empirical model can be over-fitted,
that is, the calibration is constrained to existing data making the model too specific to
analyse alternative system simulations (Castle & Crooks, 2006). In tourism research, variables such as visitor counts, origin/destination, tourism establishments, accommodation
capacity and tourist choice preferences, among others, could be incorporated in an empirical model (Zellner, 2008).
By accounting for individual heterogeneity and multiple relationships between agents,
ABMs can reproduce feedback processes, providing a tool to determine how the system has
evolved to the present state and to test future simulations. Therefore, the choice of adopting
a theoretical or empirical approach in an ABM is dependent on the purpose of the model
and its precision. The availability of data and knowledge required for modelling also influences the choice of the approach. In this paper we present a theoretical proof-of-concept
ABM which main purpose is to enrich the understanding of the tourist decision-making
process, through controlled computational simulations.
Developing a tourist decision-making ABM
Developing an ABM, as in any model, is based on creating simplifications of real-world
problems. By modelling tourists’ decision-making process, the aim is to describe in
simple terms a complex phenomenon, based on incomplete knowledge and data. This
process is defined by simple if – then rules that replicate tourists’ behaviour and pattern formation. The goal of the model, the data available and the knowledge of system dynamics set
the boundaries on the ABM detail. In a theoretical model there are little limitations to what
data and parameters can be included in the model, while empirical models should be based
on reliable data and have solid theoretical understanding of the system. Either way, there is a
model-building process in ABM that can be represented in several stages: (i) the conceptualization of the system that leads to the definition of purpose of the model; (ii) the definition
of variables, parameters and data to be used; (iii) the setting of if– then rules which translate
behaviours and (iv) finally, the coding and application of the model.
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In order to understand where and why tourists decide to vacation requires a knowledge
on tourists behaviour, which is determined by factors such as motivation, perception, learning and attitudes, which are themselves influenced by personality, culture and society (Mill
& Morrison, 2009). There is the hypothesis that people travel if they learn that travel will
help satisfy various needs and wants considered important to them, within constrains such
as time and money. Dann (1977) noted two stages in a vacation decision: (a) push factors
such as motivation and desire to travel, that is, factors internal to the individual; and (b) pull
factors which are external to the individual. Tourists travel to a destination if the benefits
offered by the destination (pull factors) are perceived to satisfy their needs (Goossens,
2000). The tourists demonstrate loyalty to the destination that satisfies their needs by
coming back and recommending the destination to others (Yoon & Uysal, 2005). The
model of buyer behaviour in tourism research suggests that the stimuli internal to the individual relating to needs and wants are the beginning of the decision-making process
(Cohen, Prayag, & Moital, 2014). Then, based on external stimuli the potential traveller
is aware (or not) of possible destinations which are evaluated. The tourist weights
various alternatives against a list of criteria considered important and likely to satisfy individual needs. The alternative destination chosen will be the one that best meets the motivation. If a destination is considered as an alternative depends greatly on the satisfaction of
previous individual experiences the tourist had in that same destination. The level of satisfaction is a function of what is expected and the actual experience. As the satisfaction level
increases, the number of alternatives considered in the next vacation decreases. The more an
individual is pleased with a vacation choice, the higher that choice will be placed on the list
of alternatives and the more likely the tourist is to come. Tourists make their choice based
on previous experiences and also based on specific criteria, such as information taken from
commercial or social network (friends and relatives). Finally, the tourist travels to the destination that best meets the criteria defined. As a result of the trip, tourists learn whether or
not that experience satisfied the needs and wants previously identified, and if it did, they are
likely to return to the same destination next time (Bansal & Eiselt, 2004).
Taking this theoretical approach, the ABM developed intends to reproduce tourist
behaviour in a simple way. Several parameters are incorporated and tested in order to
model individual behaviour in the destinations choice. As mentioned earlier, the tourist
decision ABM considers (i) individual motivations; (ii) rationality based on if– then
rules; (iii) human emotions and satisfaction and (iv) the influence of the social network
(Kennedy, 2012).
Functioning of the ABM
The methodological structure of the ABM building can be seen in Figure 1. According to
Gilbert and Troitzsch (2005), an ABM is developed first by abstracting agent behaviour
from the target system under study through a parameterization process. The parameters
selected aim to represent realistically complex phenomena and can be assigned randomly,
from statistical distributions, according to heuristic decision trees, or other methods
(Epstein, 1999). The theoretical model is substantiated by the parameters that rely on theoretical and empirical assumptions of the system functioning, which require an understanding
of system components and agent behavioural rules. The ABM is then adjusted to real-world
dynamics through the process of calibration using gathered statistical data and translating it
into parameter thresholds for the business as usual scenario. The model is then run and the
resulting simulation is compared to real-world data gathered from the target system. This
process leads to the validation (or not) of the ABM. If the model is validated, it is robust
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Figure 1.
I. Boavida-Portugal et al.
Methodological structure of the ABM.
enough to support scenario testing. If not, the process of ABM building starts from the
beginning adjusting and modifying theoretical assumptions and parameters.
This ABM models the decision context of tourists coming from one origin to five destinations. Because geographical data were not incorporated, the model is aspatial and does
not incorporate variables such as distance and cost to travel from the origin to the destination. Although this ABM is a toy model, an empirical case study was selected and realworld data were incorporated to allow model calibration and validation. Data for the
total number of tourist’ arrival and specific weight for each destination were derived
from tourism data sets from the National Statistics Institute. The tourist motivation was
based on a survey to characterize tourists’ types and personal motivation to travel, produced
in 2013 by Alentejo Tourism Observatory for the Alentejo Regional Tourism Entity. The
attractiveness of the destination for Alentejo coastal area is based on empirical knowledge
of the area and on the theoretical main strategic tourism market segments pointed out by
Tourism National Strategic Plan (PENT) for the period 2013 – 2015.
To operationalize the ABM, Alentejo coastal area was selected as the case study. It is a
NUTIII located in Alentejo (south of Portugal) with approximately 5300 km2 and 98,000
inhabitants. Typifying a rural region, Alentejo coastal area has great tourism potential
and is highlighted in the PENT as a touristic development pole with several strategies outlined to increase the number of tourists and their expenditure. In 2012, the region received
140,000 tourists, mainly Portuguese and Spanish during the summer season. Alentejo
coastal area has a set of distinctive assets that provide a very attractive atmosphere, such
as climate, pristine beaches, natural and protected areas, gastronomy, golf and proximity
to Lisbon. The strategic market segments for the region are sun and sea, golf, food and
wine, integrated resorts and residential tourism, mostly vacationed for national and
Spanish tourists. However, there are some land-use restraints due to 30% of total area
being covered by natural protected areas. This fact together with the projected increase
in tourism supply raises awareness about questions such as the promotion of sustainable
tourism development or balancing tourism accommodations development with the preservation of natural landscape characteristics. The knowledge of how tourism demand operates
provides insights for a better adjustment between supply and demand, in order to avoid
some unsuccessful tourism destination’s examples. Some areas of Algarve and south of
Spain are examples of inadequate tourism planning and constitute well-known examples
of destinations that are nowadays subject of planning intervention for conversion and
restructuring of touristic spaces.
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Setting the agents: tourists and destinations
This ABM represents tourism as the relationships between the destinations and tourists
(supply and demand), and tourists among themselves. Thus, there are two types of
agents in the ABM, each endowed with different attributes: tourists and destinations. Tourists (T ) are mobile agents. They have distinctive profiles that are related to their personal
preferences. Each tourist has a personal motivation list (motT) with three key elements:
sun and sea, culture and gastronomy-related activities. These elements and the tourist profiles were set up based on the mentioned survey (ERTA, 2013). For each element on the
motivation list, the tourist has a value that corresponds to the drive for these activities in
a destination; for example, if a tourist wishes sun and sea, type of vacation will have a
high value in the sun and sea seeker parameter. The setup function generates three different
kinds of tourist agent profiles (P): sun and sea seeker, culture seeker and gastronomy seeker,
defined in Table 1.
Destinations (D) are non-mobile agents and serve as the environment for the simulation.
There are five different destinations in Alentejo coastal area: Alcácer do Sal, Grândola,
Odemira, Santiago do Cacém and Sines. Each destination has an attraction list (AD
Table 2) defining the most attracting resources of the destination: sun and sea, culture
and gastronomy attractiveness. The values for the attraction are based on the main strategic
tourism market segments pointed out by PENT 2013– 2015 and by empirical knowledge of
the region’s tourism characteristics.
Destinations have a specific tourist weight (weightD) which defines the maximum
threshold of tourists for each destination and time step in order to make the vacation satisfactory (see Table 3). It is defined by a function of the ratio between the yearly total number
of tourist for Alentejo coastal area and the yearly total number of tourists for each
destination.
ABM parameterization, calibration and validation
The outline of the tourist decision-making process developed in the ABM is shown in
Figure 2. At each time step, the tourist decision-making process in the selection of a destination depends on two main factors: the individual-level (I ) and the social influence (SOC).
The individual-level parameters refer to the personal characteristics of tourists and include
the priority (P) for a destination, the compatibility (C) between tourists’ motivation list
(motT) (Table 1) and destination attractiveness (AD) (Table 2) and the individual satisfaction
(S) of the last experience the tourist had in a specific destination.
In the ABM tourists have different priorities for each destination. The priority ranks the
destinations according to the urge tourists have to go there, that is, if tourista goes to
destinationx in time step 1, the next vacation tourista is likely to want to go to other destinations rather than x. This way it is created a hierarchy of priorities to visit each destination.
At the beginning of the simulation, each tourist agent has the same priority for all the
Table 1. Tourist motivation list.
Motivation (motT)
Sun and sea
Culture
Gastronomy
P1 Sun and sea seeker
P2 Culture
P3 Gastronomy seeker
0.8
0.1
0.1
0.1
0.8
0.1
0.1
0.2
0.7
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I. Boavida-Portugal et al.
Table 2. Destination attraction list.
Attraction (AD)
Sun and sea
Culture
Gastronomy
Alcácer Sal
Grândola
Santiago Cacém
Sines
Odemira
0.2
0.2
0.6
0.8
0.1
0.1
0.4
0.4
0.2
0
0.8
0.2
0.8
0.1
0.1
Table 3. Specific tourist weight of the destinations (2012).
Destinations
Alcácer Sal
Grândola
Odemira
Santiago Cacém
Sines
Total
Tourists/year
WeightD
19,775
48,845
17,532
23,610
27,802
137,564
0.14
0.36
0.13
0.17
0.20
1.00
destinations, equal to 0.2 for each destination. Every time a tourist visits a destination, the
list of priority is updated. The priority for the destination just visited is set to zero because
the tourist is not likely to visit the same destination in the next vacation, and the value of the
priority before the update is split among the other destinations in equal proportion. The
update equation is the following:
⎧
⎨ Plast (t + 1) = 0
⎩ Pother (t + 1) = Pother (t) +
⎫
⎬
Plast (t)
Plast (t) ,
= Pother (t) +
⎭
( # destinations − 1)
4
(1)
where Plast is the priority value for the destination visited at the last step and Pother is the
priority value for all the other destinations (i.e. different from the one visited at the last step).
Figure 2.
Tourist decision-making process.
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To match tourists’ motivation with destination attractiveness, the model incorporates a
compatibility parameter (C), computed in the individual level. Tourists with different profiles want to vacation to destinations that match their motivations. For instance, a tourist
seeking sun and sea-related activities is more likely to want to vacation in a sun and sea
type of destination. Thus the compatibility between the tourists’ motivation list (motT)
and the characteristics of the destination depicted in the attraction list (AD) is fundamental
in the ABM. The compatibility parameter is computed as
CD =
n
motT · AD .
(2)
i=1
At the beginning of the simulation, each agent has the same satisfaction score (S) for all
the destinations (equal to 1). When a tourist visits a destination, the satisfaction score is
updated based on how many tourists there are in the same destination at the same time,
designated as occupancy rate. This parameter is based on the assumption that if the destination is close to the maximum number of tourists it usually receives (based on the specific
tourist weight, see Table 3), the destination is crowded and the tourist is less likely to
enjoy the vacation experience as much. Thus, the level of satisfaction decreases and tourists
are less likely to revisit. The occupancy threshold varies between 0 and 1, computing a ratio
between actual tourists and maximum occupancy for each destination; for example, if the
value is 1, there is enough space for all tourists (100%), but if the value is 0.2, only 20% of
tourists will have space and thus a satisfactory experience. There is also a satisfaction
weighing parameter (weightD) with a threshold between 0 and 1, which defines the
weight of the last and current vacation; for example, if the value is 0.5, the satisfaction
of the last visit and the satisfaction of the current have the same weight in the computation
for the satisfaction parameter.
At each time step, the satisfaction score is updated:
⎫
⎧
SD (0) = 1
⎪
⎪
⎪
⎪
⎬
⎨
# touristsD
sD (t) = 1 −
.
⎪
⎪
weightD
⎪
⎪
⎭
⎩
SD (t + 1) = w · SD (t) + (1 − w) · SD (t − 1)
(3)
To make this parameter more realistic, because the satisfaction of the experience
depends on other variables rather than the crowdedness of a destination, some randomness
was introduced in this parameter. Therefore, the score of the satisfaction is a function of SD
and a random-float value between 0 and 2.
The social influence is the second factor in the ABM and was incorporated in order to
test the formation of networks that result in the “small world” phenomenon. The small
world theory, or the concept of six degrees of separation (Milgram, 1967), is based on
the idea that a person is only a couple of connections away from any other person in the
world. Similarly, tourists are inserted in their own social network (family, friends or
co-workers) with whom they share and discuss previous vacation experiences, usually
giving an overall score to the destinations (i.e. individual satisfaction). To replicate the
small world phenomenon in the ABM was used a network as substrate with an average
degree of four, where tourists are connected with their four closest neighbours. Tourists
can also be randomly connected with other non-neighbour tourists through an adjustable
rewiring probability incorporated in the model. The rewiring probability is a parameter
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I. Boavida-Portugal et al.
Figure 3.
Substrate social network.
of the model with a threshold between 0 and 1; that is, if the value is 0.2, there is a 20%
probability to be connected to another tourist who is not one of the four closest neighbours.
This parameter aims to represent social interactions in a more realistic way, in the sense that
tourists can share information by other means of communication, such as in an online social
network. Thus, a network is created (as shown in Figure 3) incorporating information
sharing among tourists about previous vacation experiences.
Tourists share and inform connected neighbours about their individual satisfaction score
(SD) for each destination they visited at previous time steps. The social influence factor is
calculated as a mean of connected neighbours’ satisfaction score for previous vacations at a
destination; that is, if my four connected neighbours share values for satisfaction score for
destinationx of {1; 0.2; 0.5; 0.1}, the satisfaction score is computed as 0.45. The influence
that the social network has on tourist destination choice is a model parameter with a
threshold between 0 and 1.
SOCD =
SA /# neighbours.
(4)
A[{neighbours}
There is also a social network weighing parameter that allows the modeller to weight the
influence the social network has on the final score FD for the choice of the destination.
By incorporating the social influence factor the ABM replicates the relationships that
exist in the real world, for example, if one wishes to go on vacation to destinationx is
likely to ask the social network their global appreciation of past experiences in that destination and to take it into account in its choice. The social influence is updated every time the
individual satisfaction score is updated, that is, at each time step.
The modeller can change parameter thresholds and run different simulations. At each
time step the tourist agent computes a final score FD for each destination using
Current Issues in Tourism
FD = SOCD · ID .
1567
(5)
This way the agent ranks the destinations according to the FD value and chooses to
vacation in the one with the highest score.
To test the robustness of the model, the parameter values were calibrated for one destination until the results of the simulation significantly reproduce the real total number of
tourists. Afterwards the process of validation was carried out in order to verify if the parameter thresholds defined in the calibration reproduced similar results to the other four destinations of the ABM. The simulation parameter thresholds found to best mirror the real
total number of tourists/yearly for the period 2002 – 2012 were (all parameters on) satisfaction weight 0.60; social network rewiring probability 0.50 and social network weight 0.40,
and the occupancy rate was set at 0.55.
The same thresholds were applied to the simulation of all the destinations and a
regression analysis was performed to verify the significance of the results. The results
from the validation process show that there is a significant positive correlation between
the real total number of tourists in the period 2002 – 2012 and the simulation results for
the same variable, as can be seen by the R2 for each destination in Figure 4.
Figure 4. Regression analysis for the real total number of tourists and simulated results for the period
2002–2012.
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I. Boavida-Portugal et al.
Scenario testing
Different simulations were performed in order to explore the tourist decision-making
process. The setup process assigns the values for the tourist motivation and destination
attractiveness. Then the user defines the ABM parameter thresholds and the simulation
runs 365 time steps (one year period). The ABM was used to generate two different scenarios of tourist visitation. The first scenario investigates the effect of changing tourist awareness of a destination. By giving a wider knowledge to tourist agents about prior experiences
in a destination, we increase their awareness. This way we can compare the behaviour of a
more aware tourist versus a tourist with less information. The second scenario explores how
the individual level drives tourist destination choice, testing how the previous vacation
experience and priority for a destination affect the behaviour pattern. These two scenarios
are compared to the business as usual scenario (BAU) to evaluate possible effects of awareness increase and individual-level changes.
BAU consists on the simulation carried out in the validation process using the parameter
thresholds found to best mirror the real total number of tourists/yearly for the period 2002 –
2012. This is the starting point to compare the scenario simulation results. At each time step
430 tourist agents enter the simulation. This value reflects the linear tendency registered for
tourist number increase for 2017. The profile for tourists was based on strategic tourism
market segments pointed out by PENT 2013 – 2015 for the region: food and wine, cultural
touring and sun and sea. Thus, the tourist agents profile was set as 50% sun and sea seekers,
20% culture seekers and 30% gastronomy seekers. Assuming that BAU illustrates, in
simple terms, the reality registered in tourist destination decision-making process, possible
scenario simulations are hereby discussed.
Awareness scenario
The discussed scenario aims to test different levels of awareness that a tourist has for each
destination. We assume that the information given by the social network may increase the
awareness of a destination. The model is programmed so that when a tourist goes on
vacation, he or she shares information with its linked neighbours, exchanging knowledge
about previous vacation experiences. This process leads to a more informed choice. Therefore tourists will be more aware if the destination they are considering is likely to meet their
satisfaction criteria. The weight the social network has on the tourists’ destination choice is
a threshold between 0 and 1 (maximum awareness).
The first simulation (Sim1) tested a full awareness scenario in which 1 is the value for
social weight. When tourists are well informed they choose to go to the destinations where
their neighbours had satisfactory experiences, that is, 34% of tourists went to Grândola (as
shown in Figure 5). This value reflects the tourist decision-making process using BAUdefined parameter thresholds with a maximum social weight. Grândola is the most
popular destination due to its high occupancy (0.36), parameter which was defined as
accountable for the tourist satisfaction.
However, in the real world we are not always connected to our neighbours. A second
simulation was tested (Sim2) in which 0.1 is the value for social weight. This simulation
results in a more even distribution through the destinations because there is little influence
by the social network in choosing a destination.
When comparing BAU with Sim1 and Sim2, we verify significant changes (Table 4).
In Sim1 Odemira registers the highest decrease in tourist total number because tourist
neighbours share a less positive experience due to low occupancy weight, which
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Figure 5.
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Total number of tourists in the awareness scenario.
means a more crowded experience. Santiago Cacém (26%) and Sines (37%) have an
increase in tourist numbers because they have a diversified supply base for different
tourist profiles and high occupancy weight with a relative low total number of tourists
compared to the competitor destinations. Sim2 produces rather different results. Overall,
the resulting distribution is more even through the destinations. Grândola registers a
38% decrease compared to BAU. This fact has to do with the lack of information
sharing with neighbours about positive experiences in the destination, since it is the
biggest receiving destination in the Alentejo coastal area with the highest occupancy
rate. This decrease in total number of tourists in Grândola is accompanied by an increase
in all the other destinations. Therefore, in Sim2 the social network is not strong enough
to influence significantly tourists’ decision-making process. One can speculate that the
influence of the social network produces rather different patterns of tourist distribution
throughout the destinations.
Individual-level scenario
In the second ABM scenario, the aim is to explore how the tourist individual-level parameters influence the destination’s choice behaviour pattern. The first simulation (Sim3)
tested the individual satisfaction impact in the selection of a destination. To do so, the
satisfaction parameter is off. This way one can access comparing to BAU the differences
the individual satisfaction introduces in the model. There is a quite even distribution of
tourists through the destinations (see Figure 6), which means that once the satisfaction of
the experience is not a factor of decision, tourists go to the destination that best meets
their motivations and priority. Sim3 can be related to Sim2 for producing similar
results due to the fact that the social influence is closely related to the satisfaction of
the experience.
When comparing BAU to Sim3 (as given in Table 5), it is noticeable a significant
decrease in total tourist number in Grândola because the overall experience is the most
Table 4. Change in tourist visitation (%) between BAU –Sim1 and BAU–Sim2.
Awareness scenario
Sim1
Sim2
Alcácer Sal
Grândola
Santiago Cacém
Sines
Odemira
9%
33%
9%
238%
26%
45%
37%
17%
236%
13%
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I. Boavida-Portugal et al.
Figure 6.
Total number of tourists in the individual motivation scenario.
satisfying there (reflecting its high occupancy rate), but when the satisfaction is not a criteria in the choice of the destination, tourists go to other destinations where there is
supply for the type of activities they wish to pursue and according to their priority.
For instance, if a sun and sea seeker goes to Grândola in the first time step because it
is the best destination for sun and sea activities, when taking into account the priority
for a destination in the next time step the same tourist will go to another destination
with sun and sea activities, and so on, constituting a loop and ensuring the tourist
agent goes through all the destinations during the simulation. All other destinations,
which registered lower number of tourist than Grândola, verified an increase in tourist
number, reflecting the priority and compatibility parameter influence regardless of occupancy and experience satisfaction.
The second simulation (Sim4) of the individual-level scenario explores the possible outcomes that the priority parameter may input in the ABM. We assume that the priority for a
destination is a changing parameter that at each time step is updated; for example, when a
tourist agent goes to a certain destination, in the next time step the priority for the destination will decrease. This generalization aims to investigate tourist behaviour at its simplest
level assuming that this is the most basic way in which an agent can operate. The priority
parameter was switch off to test the changes in distribution patterns produced. In Sim4, 39%
tourists decide to go to Grândola, 26% to Sines, 13% to Santiago Cacém and 11% to
Odemira and Alcácer do Sal (see Figure 6). Tourist concentration is dispersed throughout
the destinations; still the majority go to Grândola because of the motivation compatibility
and satisfaction related to highest occupancy.
Relating BAU to Sim4, the destinations that lost more tourists (Alcácer Sal and
Odemira) are the ones that have lower occupancy rates; therefore, tourists also have a
less satisfactory vacation and, since there is no priority for visiting the destinations, stop
going there. All other destinations gain tourists without priority for visitation because of
compatibility between supplied activities and tourist demand, occupancy rates and satisfaction of the experience.
Table 5. Change in tourist visitation (%) between BAU –Sim3 and BAU–Sim4.
Individual motivation scenario
Alcácer Sal
Grândola
Santiago Cacém
Sines
Odemira
Sim3
Sim4
30%
216%
248%
20%
39%
17%
32%
42%
14%
260%
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Discussion
The ABM developed is a simplistic representation of the tourist decision-making process.
Several assumptions made comprehend a great abstraction of the reality and require both
theoretical and methodological deepening. The aim was to develop a theoretical proofof-concept ABM to explore, even with all the simplifications introduced, how tourists
make decisions when selecting a destination to vacation. The ABM operates taking into
account the social network and individual level, in order to represent and explore the
basic theoretical principles of tourist decision-making behaviour (Bansal & Eiselt, 2004;
Cohen, Prayag, & Moital, 2014; Kennedy, 2012). Tourist agents are endowed with rationality and their choices follow if– then rules imitating real-life behaviours; for example, the
compatibility parameter is based on a mathematical match between tourists’ motivation list
and destinations’ attraction list. They also have more abstract characteristics such as their
social network influence and satisfaction of the vacation.
Two experiments were designed to explore how changes in the awareness for a destination and in tourist individual preferences change the destination selection. The scenarios tested point out that some relevant aspects such as the increase in awareness level of
a destination make tourists’ decisions more informed, so they are more likely to be satisfied with the vacation experience. The results also provide information about the individual preferences of the tourists, namely that priority, compatibility and satisfaction level
from previous vacation experiences introduce different behaviour patterns. These behaviour patterns have been discussed by authors, such as Baggio (2008) and Mill and Morrison (2009), and are tested in the developed toy model. In fact, the model shows that
tourists are pushed by their own internal forces such as motivation, priority of returning
and satisfaction. There is indication that the number of previous satisfactory trips to a
destination increases the probability of returning and fulfilling personal motivations.
Also with the increase of awareness, or connectedness with the social network, tourists
will take more informed decisions and they are more likely to choose a destination
that meets the expectations, which leads to a satisfactory vacation. That is to say that
the individual satisfaction and social network influence are intertwined in the way they
influence the destination choice. Thus, we observe the way in which changes in the
model parameters generate different patterns of visitation. These emergent patterns are
driven by the interactions between tourists and with destinations, and by the feedback
processes produced among them, which also infer with the destination decision-making
process.
In future studies the aim is to increase the level of detail in the model. This toy model
leads us to think that some parameters need refinement. For instance, in the individual preferences the motivation parameter will be divided by the type of activity and destination
sought, not only because tourists sometimes choose destinations based on the type of
activity but also because they really want to visit a specific place caring only afterwards
about what activities they might engage in. The individual satisfaction will depend not
only on the available accommodation space, and on some randomness that reflect other subjective and personal factors that influence tourists’ satisfaction, but also on if what is
expected is fulfilled.
In the social influence parameter, online networks with tourism information will be
tested and eventually incorporated in the model, for example, tripadvisor and/or booking,
in order to better represent how tourists’ base their choices. We know online research is
one of the main components in destination choice nowadays (Miguéns, Baggio, &
Costa, 2008).
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Other parameters, with geographical imprint, outside the scope of individual preferences and social network will be included in the model, for example, cost of the travel, seasonality and political strategies. The introduction and refinement of the model parameters,
rules and information intend to represent the tourist decision-making process in a more realistic way.
Concluding remarks
In this paper, we explore the tourist individual decision-making process in selecting a
vacation destination. A theoretical ABM was developed to try and replicate, in a simplified
level, the tourist behaviour when evaluating and choosing destinations. A series of experiments were performed by building scenarios and changing model parameters to present
results that reasonably reproduce real-world tourism system patterns. Even though the
model presented is a basic representation with strongly simplified assumptions of the
system processes, at the same time it contributes to the understanding of how tourism
demand patterns are formed. An ABM is a tool that represents individual behaviour and
interaction between agents, with simple if– then rules, producing simulations that resemble
real-world patterns. With ABM it is possible to go beyond more traditional approaches to
tourism research, identifying and addressing complex dynamics and processes that underlie
the tourism system.
However, there are many challenges regarding the development of a tourism applied
ABM, namely, deepening the understanding of the system dynamics, its components
(specifically their motivations and behaviours) and how the network of interactions
among them is placed. Also, with a deeper knowledge of the system comes the challenge
of adapting more and more complex data on tourist characteristics and processes into model
parameters and rules. The absence of standard validation procedures for ABM building
raises issues regarding the level of reliability that researchers and stakeholders have in
the model results. Nevertheless, once these challenges are overcome, ABM can be a
useful tool for researchers in advancing tourism understanding and producing new insights
about system dynamics. Equally for stakeholders these models can provide a toolbox for
decision-making support, because they are a platform to explore from simple theoretical
models that investigate hypothetical relationships, to empirical models that can replicate
real-life problems. This way they are better equipped in their effort to understand the
complex phenomenon in tourism destinations.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was supported by Fundação para a Ciência e a Tecnologia [grant number SFRH/BD/75984/
2011].
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