Description
– Destination recommender systems need to become truly human-centric in their design and
functionality. This requires a profound understanding of human interactions with technology as well as
human behavior related to information search and decision-making in the context of travel and tourism.
This paper seeks to review relevant theories that can support the development and evaluation of
destination recommender systems and to discuss how quantitative research can inform such theory
building and testing.
International Journal of Culture, Tourism and Hospitality Research
Informing destination recommender systems design and evaluation through quantitative research
Ulrike Gretzel Yeong-Hyeon Hwang Daniel R. Fesenmaier
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To cite this document:
Ulrike Gretzel Yeong-Hyeon Hwang Daniel R. Fesenmaier, (2012),"Informing destination recommender systems design and evaluation
through quantitative research", International J ournal of Culture, Tourism and Hospitality Research, Vol. 6 Iss 4 pp. 297 - 315
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Informing destination recommender
systems design and evaluation through
quantitative research
Ulrike Gretzel, Yeong-Hyeon Hwang and Daniel R. Fesenmaier
Abstract
Purpose – Destination recommender systems need to become truly human-centric in their design and
functionality. This requires a profound understanding of human interactions with technology as well as
human behavior related to information search and decision-making in the context of travel and tourism.
This paper seeks to review relevant theories that can support the development and evaluation of
destination recommender systems and to discuss how quantitative research can inform such theory
building and testing.
Design/methodology/approach – Based on a review of information search and decision-making
literatures, a framework for the development of destination recommender systems is proposed and the
implications for the design and evaluation of human-centric recommender systems are discussed.
Findings – A variety of factors that in?uence the information search and processing strategies that
in?uence interactions with a destination recommender system are identi?ed. This reveals a great need
for data-driven models to inform recommender system processes.
Originality/value – The proposed framework provides a basis for future research and development in
the area of destination recommender systems. The paper concludes that the success of a speci?c
destination recommender systemwill depend largely on its ability to anticipate and respond creatively to
transformations in the personal and situational needs of its users. Such system intelligence needs to be
based on empirical data analyzed with sophisticated quantitative methods. The importance of
recommender systems in tourism marketing is also discussed.
Keywords Destination recommender system, Destination choice, Information search,
Human-centric computing, Travel planning, Destination marketing, Tourism management, Data analysis
Paper type Conceptual paper
Introduction
The emergence of information technology and its broad adoption within the tourism industry
has led to an explosion in the availability of destination-related information, which greatly
helps travelers in planning trips and/or formulating expectations about tourism experiences
information (Buhalis and Law, 2008). At the same time, increased availability of
destination-related information can lead to information overload, creating dif?culty for
information seekers wanting to ?nd relevant information (Pan and Fesenmaier, 2002).
Further, this information is often presented in a way that does not match the way consumers
search for information (Pan and Fesenmaier, 2006). However, consumers have increasingly
come to expect truly personalized information and offers (Simonson, 2005). Thus, travel and
tourism marketers ?nd an ongoing challenge to deliver tailored information to micro-markets
(Anderson, 2006).
Fortunately, destination recommender systems have been developed that can simplify the
decision making process by identifying alternatives that meet speci?c needs/desires and by
providing this information in a highly personalized way (Fesenmaier et al., 2006). These
systems vary in sophistication, ranging from simple retrieval or ?ltering applications to
comprehensive recommender systems (Spiekermann and Paraschiv, 2002; Burke, 2002;
DOI 10.1108/17506181211265040 VOL. 6 NO. 4 2012, pp. 297-315, Q Emerald Group Publishing Limited, ISSN 1750-6182
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INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
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PAGE 297
Ulrike Gretzel is an
Associate Professor at the
Laboratory for Intelligent
Systems in Tourism,
University of Wollongong,
Wollongong, Australia.
Yeong-Hyeon Hwang is an
Associate Professor in the
Department of Tourism
Management, Dong-A
University, Busan, Korea.
Daniel R. Fesenmaier is a
Professor in the School of
Tourism and Hospitality
Management, Temple
University, Philadelphia,
Pennsylvania, USA.
Received April 2011
Revised June 2011
Accepted September 2011
This work has been partially
funded by the European
Union’s Fifth RTD Framework
Programme (under contract
DIETORECS IST-2000-29474).
The authors would like to thank
all other colleagues of the
DieToRecs (see http://
dietorecs.itc.it/) team for their
valuable contributions to this
paper.
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Schafer et al., 2001). Although sophisticated recommender systems have been
implemented for some product categories (movies, books, etc.) they are still lacking vital
elements before they can match or even exceed the quality of human recommendations
(Ha¨ ubl and Trifts, 2000).
In order to develop into more helpful and successful decision-making support tools, that is,
tools that are able to direct potential travelers to destinations they will ?nd most suitable for
ful?lling their travel-related needs, recommender systems have to become truly human-centric
in their design and functionality. Further, quantitative research based on large-scale behavioral
data is needed to inform such human-centric design. Following Mazanec (2006), advanced
recommender systems can be described as those systems with increased adaptivity based
on extensive knowledge about the user and the capability to provide real-time personalization.
Thus, these systems incorporate retrieval components that seek to identify products and
services that match user speci?cations. Users are not always able to directly specify their
preferences, however, and systems need to engage users in a dialogue similar to the
interaction with a human travel counselor (Hruschka and Mazanec, 1990). In order to achieve
this purpose, they argue that systems need to become sensitive to:
B the degree of precision gained of the user’s consumption goals during the individual
counseling interaction;
B the ful?llment of the user’s aspiration level regarding the volume of information needed;
B the ability to articulate owing to the user’s active or passive response style; and
B the situation-speci?c importance rank order of the bene?ts and product attributes sought
(Mazanec, 2006).
As a consequence of this higher adaptivity, advanced recommender systems should be
able to substantially reduce a user’s effort, which in turn, increases the enjoyment in the
process of identifying potential destination recommendations (Mazanec, 2006).
A rich literature has emerged over the past three decades in the ?elds of consumer behavior,
information search and processing, and human computer interaction that provides a
substantial foundation for the development of human-centric recommender systems. Travel
and tourism related systems, however, face an additional challenge in that they have to take
into account the idiosyncratic nature of travel behavior (Loban, 1997; Vanhof and Molderez,
1994; Ricci, 2002). Tourism research indicates, for example, that travelers often actively seek
information as part of their travel planning effort and consider information seeking an important
component of the overall travel experience (Vogt and Fesenmaier, 1998). These studies also
suggest that the information search process involves different hierarchical steps depending
upon a number of personal and situational factors (Jeng and Fesenmaier, 2002). In addition,
variety seeking and involvement are generally believed to be more pronounced in tourism
(Bigne´ et al., 2009). Based upon this literature, as well as Mazanec’s (2006) conceptualization
of intelligent travel recommender systems, this paper proposes a framework of travelers’
interactions with destination recommender systems (DRSs) that takes into account the
speci?c characteristics of travel information search and decision-making. The paper then
elaborates on implications for the design of DRSs as well as their evaluation and the need for
quantitative research to build the models that can makes these systems adaptive and
responsive to personal needs. Last, this paper discusses the important implications for
tourism marketing that arise from the potential of truly human-centric DRSs.
Factors in?uencing travel information search and processing
Destination recommender systems can only be successful if their design builds on a
comprehensive understanding of travel decision making and, speci?cally, of travel
information search (Gretzel et al., 2006). This paper extends the Gretzel et al. (2006) original
behavioral framework for destination recommender system design by positing additional
factors that should be included to make the model even more comprehensive and, thus,
more responsive to travelers’ needs. In particular, three essential factors are in?uencing
travelers’ information search and processing pattern including:
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1. personal characteristics of the traveler (e.g. socioeconomic status);
2. situational needs and constraints (e.g. trip length); and
3. aspects of the decision-making process (e.g. the speci?city of the choice task and
decision frames used).
The following provides a brief overview of these factors and their effects on information
search and processing behavior in the context of destination choice.
Personal characteristics of the traveler
The literature elaborates on a number of personal characteristics that potentially in?uence
travel information search and decision-making; the following nine characteristics have
emerged as particularly important in the context of travel planning:
1. socio-demographics;
2. knowledge;
3. personality;
4. involvement;
5. values;
6. attitudes;
7. cognitive style;
8. decision-making style; and
9. vacation style.
Socio-demographics. Socio-demographic characteristics have been extensively studied as
explanatory variables for evoked set formation, categorization of alternative destinations,
and antecedents of information processing (see, for example, Mayo and Jarvis, 1981;
Woodside and Lysonski, 1989; Um and Crompton, 1991; Woodside and MacDonald, 1994).
Part of the reason why such an extensive number of studies exist that provide information on
the in?uence of socio-demographic characteristics on travel information search,
decision-making, and behavior, is that they can be fairly easily observed or elicited from
respondents. They are also relatively stable. Both aspects provide advantages in the context
of recommender systems. Also, characteristics such as age, education, income, and marital
status are often employed as surrogates for determining the travel decision-maker’s
resources and constraints. In terms of age, existing research indicates that older travelers
tend to rely more on family and past experience as information sources (Capella and Greco,
1987) and are more interested in satisfying hedonic, aesthetic, and sign needs in the
information search process (Vogt and Fesenmaier, 1998). Also, more educated travelers
with higher levels of income tend to search for more information (Gitelson and Crompton,
1983; Etzel and Wahlers, 1985). Women are more likely to consider functional aspects in
their information search than men (Vogt and Fesenmaier, 1998); in general, females are more
comprehensive information processors who consider both subjective and objective
attributes, and are more likely to respond to subtle cues than males (Darley and Smith,
1995). Income in?uences the constraints within which trips have to be planned and also the
extent to which a trip has to be planned to avoid additional cost.
Knowledge. Travelers’ knowledge is an important cognitive domain that in?uences information
search and processing behavior as well travel decision-making (Park et al., 1988; Brucks,
1985). Knowledge, often obtained through direct experience, can be represented either as
travel knowledge in general or as knowledge of alternative destination(s), or both. In either
case, knowledge in?uences the range of alternatives considered (Snepenger et al., 1990).
Further, previous experience with a destination plays an important role in terms of how a
destination is categorized during decision-making processes with respect to how well the
location could perform when selected as a travel destination (Woodside and Lysonski, 1989).
Also, differences in the choice of destinations/attractions between ?rst-time visitors and repeat
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visitors, that is, travelers that have prior experience with the destination, are prevalent.
First-time visitors tend to choose destinations that are easily accessible while experienced
visitors are more likely to consider destinations with low accessibility (McKercher, 1998). In
addition, more experienced visitors may want to visit novel destinations since they have
already visited well-known destinations within a region or attractions within a speci?c
destination. In this sense, repeat visitors are more selective and less prone to visit multiple
destinations (Oppermann, 1992; Decrop, 1999; Hwang et al., 2002).
Interestingly, a number of different perspectives have been suggested regarding the
relationship between knowledge and information search behavior. A negative relationship
would imply that the more knowledge a traveler can draw on, the less information seeking will
occur. In contrast, a positive relationship suggests that as people acquire more knowledge
they will be more actively involved in the information search process because they can
better/more easily interpret information and, thus, derive more bene?ts from information than
people with limited knowledge. Studies also suggest an inverted U-shape function where a
positive relationship exists up to moderate levels of knowledge, and a negative relationship at
moderate to high levels of experience/knowledge (Punj and Staelin, 1983; Alba and
Hutchinson, 1987; Moorthy et al., 1997). Knowledge and previous experience have been
included in several studies within the context of travel information search (Manfredo, 1989;
Snepenger et al., 1990; Perdue, 1993). Although the results vary from study to study, two
?ndings regarding the in?uence of travel/destination knowledge and/or experience on
information source use are especially interesting and relevant for the context of designing
recommender systems. A study conducted by Kerstetter and Cho (2004) demonstrated that
prior knowledge encompasses two dimensions – i.e. past experience and
familiarity/expertise – which independently in?uence individuals’ search for vacation
information. Inexperienced travelers to a destination are likely to search for more
information than repeat visitors to minimize the risk involved in visiting an unknown
destination (Van Raaij, 1986). In contrast, experienced travelers are known to use information
sources different from those used by na? ¨ve travelers. Also, inexperienced tourists appear to
rely more on professional sources than experienced tourists (Snepenger et al., 1990;
Woodside and Ronkainen, 1980). In addition, Vogt and Fesenmaier (1998) ?nd that
experienced tourists tend to have higher innovation needs than inexperienced tourists. This
can be interpreted as resulting from a greater tendency of experienced travelers to seek
variety and, thus, more novel information.
Involvement. Travel information search and processing also depend to a great extent on
individuals’ level of involvement (Finn, 1983; Celsi and Olsen, 1988; Jamrozy et al., 1996). For
example, as the perceived risk involved in the decision task increases, situational involvement
rises accordingly, and individuals tend to invest more resources in external information search
(Murray, 1991). That is, highly involved travelers are likely to use more criteria, search for more
information, use more information sources, process relevant information in detail, make more
inferences, and will form attitudes that are less likely to change (Celsi and Olsen, 1988;
Fesenmaier and Johnson, 1989). In a complex decision and choice situation developing
commitment and stronger attitudes is of greater need in order to accomplish the task. On the
other hand, simple and routine decisions require relatively low consumer involvement (Reid
and Crompton, 1993). Fesenmaier and Johnson (1989) use the individual’s trip planning
involvement as the basis for segmenting the Texas travel market. They ?nd that
low-involvement travelers tend to have a shorter planning horizon, while the medium-high
involvement travel group shows a longer trip planning horizon. Importantly, the longer the
planning horizon, the more destination alternatives can be considered and the more extensive
their evaluation can be. In addition, the results of their study indicate that low-involvement
tourists take shorter getaway trips that involve less resource constraints and less risk factors,
whereas highly involved tourists tend to take longer vacations which require extensive
cognitive efforts, advance planning, and entail more resource constraints and risk factors.
Personality. Personality, which can be de?ned as ‘‘the re?ection of a person’s enduring and
unique characteristics that urge one to respond in persistent ways to recurring
environmental stimuli’’ (Decrop, 1999, p. 106), is a ‘‘complex outcome of a person’s
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learning, perceptions, motivations, emotions, and roles’’ (Mayo and Jarvis, 1981, p. 109).
Plog (1994) suggest two fundamental personality dimensions that are of importance within
the context of tourism: allocentricism and psychocentricism. Allocentric travelers, who
exhibit a self-assured and venturesome personality, are more likely to choose exotic
destinations while psychocentric travelers, whose center of attention is focused on
self-doubts and anxieties, are thought to prefer familiar destinations (Plog, 1994; Ross,
1994). Grif?th and Albanese (1996) have shown that Plog’s model can be used to
characterize travelers in terms of their psychographics and suggested practical use of these
traits to make destination recommendations.
Further, personality traits related to locus of control and risk avoidance, which in?uence an
individual’s decision-making style, play an important role in any decision-making process
but are of particular importance for destination choice processes because of the high levels
of uncertainty involved (Roehl and Fesenmaier, 1992). Variety-seeking is another personality
trait that is of great importance for tourism decisions but existing recommender systems
typically fail to take variety-seeking into account (Dholakia and Bagozzi, 2001). Personality
has also been identi?ed as a factor with considerable in?uence on information search and
processing strategies. For example, individuals’ differences in the complexity of the causal
explanations they reach to make sense of their environments suggest that personality
in?uences the extent and nature of information search and integration patterns (Murphy,
1994). Also, individuals with a tendency to postpone decisions when faced with dif?cult
choices or con?icts have been found to engage in search patterns that are different from
those used by individuals who are not indecisive (Ferrari and Dovidio, 2001). Recent
recommender system research also suggests that personality is an important factor to
consider when providing recommendations (Gretzel et al., 2004; Moon, 2002).
Values. Madrigal and Kahle (1994) de?ne personal values as representing central beliefs
about desirable states or behaviors. Thus, the structure of an individual’s value system
provides the basis for deriving intentions and directing human behavior. Woodside and
Lysonski (1989), for example, argue that personal value systems in?uence travelers’
destination awareness. In contrast, Um and Crompton (1991) describe personal values as an
internal input that initiates the formation of an evoked set from an awareness set. In tourism
research, studies by Madrigal (1995) indicate that personal values are a better predictor of
choice between group tours and individual tours than personality, and Zins (1998) suggests
that personal values are an important antecedent variable for hotel choice. Examples of values
are self-respect, sense of accomplishment and being well respected by others. While many
individual values exist, the literature has identi?ed four broad dimensions of values, namely
enjoyment, achievement, egocentrism, and external orientation (Madrigal and Kahle, 1994).
Attitudes. The destination images created through prior experience or exposure to
advertising and marketing efforts, and the ?t between conceptions of the destinations with
personal values and beliefs result in particular attitudes toward destinations. These attitudes
are signi?cant determinants of whether or not a destination is considered as an alternative
and how the destination is evaluated in later stages of the destination choice process.
Research by Fishbein and Ajzen (1975), among others, relates personal attitudes to
subsequent behavior, arguing that they play an important role in understanding destination
choice. The attitude-behavior model provides explanations for human behavior based on
individual attitudes and the behavioral intentions that can be derived from them (Ajzen and
Fishbein, 1980; Ajzen, 1991). Within the context of destination choice, Um and Crompton
(1990) operationalize attitude toward alternative destinations as the difference between the
magnitude of the perceived facilitators and the magnitude of the perceived inhibitors, and
argue that destinations with higher attitude scores are more likely to be included in the
evoked set and, ultimately, are more likely to be selected as the ?nal destination.
Cognitive style. Travelers differ in their perceptions and preferences for types of information.
The preferred ways in which individuals process information are referred to as cognitive style
(Biocca et al., 2001). Cognitive styles affect information gathering, evaluation, and selection
processes in the context of vacation trip planning (Grabler and Zins, 2002). Rumetshofer
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et al. (2003), and Rosen and Purinton (2004), demonstrate that information presentation
needs to match the cognitive style of the traveler in order to be processed effectively.
Decision-making style. Decision-making styles are mainly viewed as a mental, cognitive
orientation towards shopping and purchasing (Sproles and Kendall, 1986) or a learned
habitual pattern (Scott and Bruce, 1995), which dominates the consumer’s choice and
constitutes a relatively enduring consumer personality. Decision-making styles basically
describe howindividuals shop. Sproles and Kendall (1986) combine related traits described
in the literature to develop a consumer decision-making styles list, the so-called consumer
styles inventory (CSI), consisting of the following eight dimensions:
1. perfectionism;
2. brand consciousness;
3. novelty/fashion consciousness;
4. price/value consciousness;
5. recreational shopping;
6. impulsive/careless shopping;
7. confusion by over-choice; and
8. habitual/brand loyal shopping.
The CSI has been tested in the context of online shopping (Yang and Wu, 2006; Cowart and
Goldsmith, 2007; speci?cally, Park, 2007) and the results indicate that decision-making
styles substantially in?uence the online purchase of travel products and loyalty toward online
travel agencies.
Vacation style. Vacation styles combine psychographic characteristics such as travel
motives with behavioral patterns (Zins, 1999). They have emerged from earlier tourist type
research seeking to identify traveler segments that fundamentally differ in terms of the
bene?ts sought from vacations (Dolnicar and Mazanec, 2000). Vacation styles have been
found to provide a rather stable criterion for marketing segmentation (Dolnicar and Leisch,
2003) and can be seen as strong determinants of trip preferences. Not all destinations cater
equally well to the different vacation style types due to differences in offerings. Thus,
identifying someone’s vacation style seems to be very bene?cial in the context of making a
destination recommendation.
Situational needs and constraints
Destination-related decisions are highly sensitive to the situation in which they occur. The
travel literature indicates that trip characteristics are, as one would expect, the most
important determinants and include travel purpose, length of travel, distance between origin
and destination, travel group composition, as well as travel mobility. The following provides a
brief discussion of each as they relate to travel information search and decision-making.
Travel purpose. Travel purpose can be generally de?ned as one’s stated needs or motives
for travel. Travel purpose is, often times, closely connected to activities and settings (e.g. golf
vacation or visit to a cultural heritage site) and therefore, signi?cantly constrains/de?nes the
range of alternative destinations considered. Travel purpose also in?uences information
search strategies. Fodness and Murray (1998) ?nd that those traveling for vacation
purposes are the most likely to rely on their personal experience to plan their trips.
Trip length. The time available for a trip constrains the geographical range of the trip. Thus,
travelers with limited amounts of time available tend to prefer nearby destinations. In
contrast, travelers with more time tend to prefer more distant destinations (McKercher,
1998). In this sense, length of trip constrains the range of alternatives that will be considered.
Length of travel has also been identi?ed as a factor that in?uences the use of particular
information sources (Snepenger et al., 1990).
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Travel distance. Whether a destination will be considered as an alternative is also a function
of the distance from home to a destination, a factor which has been included as a key
variable in aggregated destination choice models (Kim and Fesenmaier, 1990; Lo, 1992). In
disaggregated models, cognitive distance instead of physical distance has been
emphasized to account for circumstances in which individuals use mentally measured
proximity or distance to evaluate alternatives. Empirical evidence suggests that a
relationship between travel distance and information search strategies exists. For
example, Pennington-Gray and Vogt (2003), among others, ?nd that out-of-state visitors
are more likely to obtain travel information at welcome centers than in-state residents.
Travel party. Alternative destinations considered by a person who plans to go on a family
vacation, for example, are probably different from those considered for a trip with friends.
The characteristics of the travel party also impact the geographical range of alternative
destinations in respect to the mobility of the travel group. A family with children tends to take
short vacations at easily accessible destinations. In contrast, couples without children are
more likely to choose destinations with modest accessibility (McKercher, 1998). Additionally,
the nature of the travel party de?nes the degree of heterogeneity in the group with respect to
interests. That is, as the travel party size increases, the number of needs to gratify is likely to
increase accordingly and thus, multi-destination travel is more likely to occur (Fesenmaier
and Lieber, 1985, 1988; Lue et al., 1993). In addition, travel group composition has been
found to in?uence the information search strategy selected (Fodness and Murray, 1997).
Family groups tend to use media as information sources more than other types of travel
parties, and are more likely to be involved in extensive search processes in order to assure
satisfaction of all the members (Gitelson and Crompton, 1983).
Travel mobility. Mobility is not only a function of the nature of the travel group but also
depends on the transportation mode a traveler uses during a trip (Tideswell and Faulkner,
1999). Alternative destinations, which a traveler with a rental car or personal car can think of,
might be unavailable to travelers who use, for instance, only public transportation. Travel
mobility has an impact on the ?exibility of the travel itinerary and is positively related to not
only the number of destinations but also the number of attractions and activities that can be
integrated into the trip. Transportation mode used can also explain certain tendencies
toward multi-destination travel, as travelers with greater mobility are better equipped for
visits to more than one destination (Cooper, 1981). Further, Fodness and Murray (1999) ?nd
evidence for a relationship between mode of transportation and types of travel information
sources used. Thus, a DRS needs to gauge the level of travel mobility a user has during a
speci?c trip in order to make reasonable recommendations.
Decision frames
Destination decisions can be framed in various ways depending on personal preferences for
certain decision-making strategies and the needs or constraints derived from the speci?c
trip planning situation. Speci?cally, the number and type of decision criteria taken into
account will vary based on the nature of the trip to be planned. For instance, trips de?ned
around a speci?c activity such as gol?ng will strongly in?uence the frame in which the
decision has to be made. For such a trip, beach access at the destination might be desired
but might not be perceived as being as important as in the case of a typical summer, sun,
and beach vacation. Also, personal characteristics can be assumed to in?uence one’s need,
ability, and/or willingness to take certain criteria into consideration. A low annual household
income, for instance, will probably encourage the adoption of a decision frame that
incorporates price as a main criterion. In addition, personal cognitive styles can greatly
in?uence the amount of information sought to support the decision-making process and
especially the number of alternatives considered by the individual decision-maker (Hunt
et al., 1989; Driver et al., 1990). Similarly, decision-making styles will in?uence the timing of
the decision, the extent of planning and speci?c criteria taken into account. For instance, an
impulsive style will lead to very little planning and a small number of decision criteria while
brand consciousness results in a focus on well established travel product and services
brands (Sproles and Kendall, 1986).
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Further, destination decisions can be taken at different levels in the travel planning hierarchy,
that is, one can select a main destination, a secondary destination, or places within a
destination such as attractions and restaurants (Jeng and Fesenmaier, 2002). Given the
impact of choosing a main destination on decisions with respect to lower-level facets of a trip,
being in the process of selecting the main destination of a trip implies that many
characteristics of this trip are still undetermined. In contrast, if the main destination has been
chosen and the decision-making process refers to ?nding one or more secondary
destinations, one can assume that many important characteristics of the trip have already
been outlined and that the range of destination alternatives in the consideration set will be
rather limited. At the most speci?c level, destination decisions involve choosing places to visit
at a destination. This latter formof destination decision can be characterized by a high level of
constraint and, consequently, a relatively small number of alternatives to be considered.
Depending on the speci?city of the destination decision, the amount and type of information
taken into account in the decision-making process will vary (Bloch et al., 1986). More
speci?c destination decisions require more speci?c information. If no destination decision
has been made, the information sought will be in the general form of destination alternatives
and will often be more image-based than functional. If a main destination has been selected,
the destination decision will focus on secondary destinations in proximity to the main
destination. Such a decision requires image-related information but also more speci?c
details about distances and activity/attraction portfolios to evaluate destination
complementarities. Finally, those decisions that involve selecting places/attractions at a
speci?c destination will to a large extent include detailed and more functional information in
the formof opening hours, prices, admission restrictions, etc. Therefore, knowing the level of
speci?city of a user’s decision-making process is a critical success factor for a
human-centric DRS (Mazanec, 2002; Hwang et al., 2009).
A framework for human-centric destination recommender systems
Based on the review of the travel destination choice and information search and processing
literatures, a framework can be conceptualized which integrates various factors that shape
an individual’s interaction with a destination recommender system (see Figure 1). The
framework assumes that individuals access a DRS to learn about alternative destinations
and that the nature of the information needed by a user will depend on two main factors:
1. the decision task(s) to be accomplished; and
2. the nature of the trip – that is, the context in which this trip decision will be taken.
Further, the decision task(s) depends on the decision frame that guides the decision-making
process. The nature of the trip, on the other hand, will depend on the situational needs to be
satis?ed by the trip and the constraints that have to be considered. Although destination
decisions are often high-level decisions and are typically made when most other aspects of the
trip are still unde?ned, individuals who use a DRS are expected to have at least some idea of
when they would like to travel (e.g. winter versus summer vacation), how long they would like to
stay (e.g. week-long vacation or getaway trip), who they would like to take along (e.g. spouse or
entire family), what the purpose of the tripis (e.g. relaxation versus adventure), what main activity
they will engage in (e.g. beach vacation versus skiingtrip), what the main mode of transportation
will be (e.g. car versus airplane), and from which point of origin the trip will start. If the main
destination has been selected and the search effort focuses on secondary destinations or
attractions within destinations, the situational needs and constraints are assumed to have been
established in greater detail. Thus, the speci?c decision task is shaped by the decision frame
selected, which is, of course, adjusted a priori to accommodate the speci?c aspects of a trip.
Furthermore, the needs and constraints that drive the nature of the trip are important indicators
of the particular decision task to be accomplished as they directly in?uence the nature of the trip,
but also affect the way the destination decision is framed and executed.
Information search, processing, and evaluation in the context of travel planning are complex
and iterative (Pan and Fesenmaier, 2006). A truly adaptive system as described by Mazanec
(2006) engages the user in a dialogue and allows for re-speci?cations of needs by the user
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and adjustments in recommendations by the DRS. The proposed framework is dynamic in the
sense that the framework recognizes the importance of feedback resulting from a user’s
interaction with the system. Based on the processing and evaluation of the recommendations
obtained, the user might decide that more/better information is needed and therefore might
engage in additional information search processes until a satisfactory level is reached. In a
different case, the information obtained from the system could expose additional situational
constraints and make changes in the decision frame and/or the nature of the trip necessary.
For instance, destinations could be recommended and perceived as being optimal in terms of
the activities they provide, the way in which they cater to the needs of the members of the
travel party, etc. However, they could be seen as offering too many interesting things for just a
day trip and lead to a revision of the trip length constraint. Similarly, a user could be given the
options of loosely specifying trip characteristics in the beginning of the search process and
would subsequently be encouraged to re?ne them as more information is being taken into
account. Ideally, the process ends when all necessary information has been collected and
processed and an informed destination decision is made. The time and number of iterations
necessary to reach this point will vary depending on the number of potential alternatives under
consideration, the quality of the recommendations and the changes in the decision frame as
set by the user. The worst-case scenario in terms of behavioral outcomes is, of course, a
situation whereby the user terminates the process without having reached a decision.
Alternatively, use of the system could lead to a postponing of the decision, but at least with a
narrowed-down set of alternatives.
System-user interaction
The nature and degree of interaction with the system is driven by personal characteristics,
situational factors and the resulting nature of the trip to be planned, the decision frame
Figure 1 Framework for destination recommender systems
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applied and the speci?c decision(s) to be taken, which all result in particular information
needs and search strategies. However, interaction can also be directly in?uenced by
personal characteristics. An individual’s skills, involvement, personality, etc., appear to have
direct impacts on the individual’s interaction with an intelligent information environment such
as a DRS (Hoffman and Novak, 1996). Further, numerous studies on interactions with
technologies and speci?cally with recommender systems, point out that trust is an important
characteristic in the interaction with a system (Komiak and Benbasat, 2006; Swearingen and
Sinha, 2001). Whether trust can be established depends on factors such as personality
(e.g. neuroticism negatively in?uences trust), knowledge of recommender systems,
perceived credibility of the system (Yoo and Gretzel, 2011), attitudes toward technology
in general and especially the Internet, gender (e.g. Gefen and Straub, 1997) and age (Fox
and Boehm-Davis, 1998). Innovativeness refers to a user’s desire to be among the ?rst to
adopt a product or a technology (Goldsmith and Hofacker, 1991) and is also an important
construct that has been studied in the context of technology adoption and use. The more
innovative a user, the more open he or she is to novel forms of interactions.
System characteristics
In addition to user characteristics, a user’s interaction with a DRS is shaped by the
characteristics of the recommender system itself. The design elements of a DRS play a
crucial role in shaping the user-system interaction process. Speci?cally, the amount and
presentation of the DRS’s content and the structure of its interface are key aspects
determining the nature of the interaction (Spiekermann and Paraschiv, 2002; Dholakia et al.,
2000). Zins (2003) concludes that adaptation of information provided by a DRS and
adjustment of the recommender system interface to ?t a user’s cognitive style are crucial for
improving the quality of the human-computer interaction. Further, the intelligence built into
the system through data storage and mining capabilities in?uences the level of interactivity
and personalization that can be provided. Systemintelligence, therefore, is a core element in
de?ning user interactions with a DRS. Thus, the framework clearly supports the idea that
DRSs should be highly interactive and adaptive in order to provide appropriate guidance in
the travel planning process. Another important capacity of a DRS, which is rooted in its
design, is its ability to provide users with enjoyment and excitement as well as types of
information exchanges that can convey the experiential aspects of travel and tourism
products/services. Figure 2 summarizes these core DRS design components. Each of these
design components has to be informed by the theoretical foundations outlined above to truly
support destination decision-related human behavior.
Implications for recommender system design
Although some of the relationships in the proposed theoretical framework appear to be
obvious, they are often not implemented because more emphasis is placed on technical
considerations than user interaction requirements, and system designers typically lack an
understanding of the foundations of travel behavior. Such an understanding is critical in
designing systems that can support different stages in the travel planning process and can
provide the adaptivity that is usually offered by human travel counselors. Most importantly,
the interaction with the system should feel natural and provide enjoyable experiences. Three
propositions can be derived from the theoretical framework and should guide future DRS
development:
Figure 2 Design components of destination recommender systems
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P1. Truly human-centric destination recommender systems need to be able to take into
account situational needs/constraints, decision frames, as well as personal
characteristics.
P2. Truly human-centric destination recommender systems need to support reiterative
planning and provide opportunities for feedback and modi?cation.
P3. System intelligence is crucial in providing persuasive recommendations but
destination recommender systems also need to offer experiential and enjoyable
use experiences as travel planning is an important component of the pre-trip
experience. Such high quality interactions are not only dependent on system design
characteristics but also on the system’s ability to adapt to information search and
processing strategies as well as personal characteristics and styles of the user.
Implications for research
The proposed framework illustrates the myriad of factors that can in?uence successful
interactions with a DRS and the great number of attributes that could potentially be taken into
account when the system seeks to provide a suitable destination recommendation. In
practice, a system is computationally unable to take into account all possible personal
characteristics, decision frames and trip characteristics that in?uence the destination choice
process. Adaptive systems have the advantage that they can learn from the interaction with
the user and dynamically adjust the criteria taken into account. However for such a systemto
be designed, detailed information on the relative importance of criteria and their
interrelationships is needed. Research has yet to provide the necessary insights to
determine which aspects of the framework are more important than others. Also,
determining more general user pro?les based on the elements of the framework requires
more empirical evidence. Data-mining of existing online recommendation systems will be
instrumental in providing the information needed to successfully adapt recommender
systems to the travel and tourism context as well as to speci?c customer needs (Markellou
et al., 2005). Thus, collaborative research that involves tourism academics, system
designers and tourism organizations that have implemented systems will be critical. In
addition, classifying destinations so that they can be successfully matched to particular
traveler preferences and needs might be necessary to overcome some of the limitations
recommender algorithms have. This requires a thorough understanding of destination
attributes and constraints (distance, opening hours, etc.).
While qualitative research can provide important insights as to how users engage with
information and systems as well as with other members of their travel party when planning
vacations, quantitative research is needed to develop the weights, cases, matching
algorithms, learning strategies, and interaction protocols that combine into system
intelligence. For an overview regarding web usage mining research see Pierrakos et al.
(2003). Especially data extraction and preparation are critical issues for web mining but
have not been extensively discussed in the literature. One speci?c method that can help with
deriving information from weblog data about travel planning processes is the sequence
alignment method. Currently underused in tourism, this method allows for the recognition of
patterns in the behavioral sequences of travelers’ interactions with online systems (Liu,
2007). Navigational patterns are behavioral data that can provide critical insights as to how
consumers search for information. The ultimate goal is to be able to successfully predict the
next user action or information need based on the user’s previous sur?ng behavior (Hay
et al., 2003).
Another critical area of quantitative research needed to inform DRS design is cluster
analysis, which has a long tradition in tourism research (Mazanec, 2000; Dolnicar, 2002;
Zins, 2008). However, intelligent systems such as DRSs need sophisticated clustering
approaches. Such segmentation analyses have to be based on a thorough understanding of
the underlying data structures (Dolnicar and Leisch, 2010). Neural network approaches, for
instance, have been proven to outperform other types of cluster analyses (Buchta et al.,
1997) but are still not widely used in tourism research.
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The framework outlines the scope of research that has to be conducted. While some of the
?ndings from general recommender systems research can be used for DRS design, others
have to be speci?cally established in the context of tourism. Mazanec (2006) also points to
the necessity of employing and developing new research methodologies in the context of
establishing the theoretical basis for DRS design and evaluating existing prototypes.
Travel and tourism marketers will also have to establish performance measures to
benchmark and assess the return on investment a DRS provides. Currently used web
metrics such as unique visitors and number of bookings are of little relevance for systems
that are usually only used for decision support rather than execution and whose goal is to
expose consumers to highly tailored information rather than maximizing impressions. Henry
(2005) suggests that consumers judge a DRS against the notion of a ‘‘live, adaptable
expert’’ and that this measure, although maybe not immediately available, provides a more
realistic and useful evaluation of a system’s worth because the system centers on the
consumer’s perspective.
Given this discussion, the following guidelines for future research in the area of destination
recommender systems are proposed:
P4. The identi?cation of a hierarchy of factors ranging from most critical/discriminating
to supplementary needs to be established so that DRS design can be informed.
P5. Measures of success that take the human-centeredness and adaptivity of a DRS as
well as concrete bene?ts for tourism marketers into account need to be established.
P6. New research methodologies need to be developed to better capture insights from
behavioral data and to better and more ef?ciently classify destinations and segment
travelers.
Conclusion
The rich information search and decision-making literatures offer a tourism-speci?c
theoretical framework that can be used as a basis for the design of human-centric
destination recommender systems. The outcome of this effort is a framework which should
guide system development and which emphasizes the diversity of factors that drive
destination decisions. Importantly, the framework and the guidelines derived from it
simultaneously represent the starting point in the development of an effective travel
recommender system and a road map for future research. That online recommender
systems can effectively guide consumer decision-making has been evidenced manifold.
Amazon.com is one of the most popular examples of an effective online recommender
system, as the website offers a variety of entry points, multiple formats with which to evaluate
potential products, and intelligent mining approaches which help to track consumer
purchasing behaviors and interests.
Although the framework has been established in the context of a pre-travel DRS, its overall
structure can also be applied to the design of context-aware mobile systems that typically
cater to lower-level decisions when the user is already at the destination. Various elements that
the framework stresses have been implemented in the design of such systems. Kramer et al.
(2006), for example, demonstrate the importance of exploring different preference elicitation
strategies in the case of a mobile dynamic tour guide. Adaptive interfaces have been
discussed in the context of PALIO, a location-aware information system for tourists (Zarikas
et al., 2001). Nguyen et al. (2004) emphasize the importance of integrating user feedback into
an on-the-move restaurant recommender system. Recently, system design efforts have also
discovered the importance of travel party composition and the need to integrate group
decision-makingsupport into travel recommender systems (see, for example, Ardissono et al.,
2003). Decision frames might also be in?uenced by roaming costs or lack of ubiquitous
high-speed wi-? access. Yet, no system currently offers comprehensive adaptation that
re?ects all areas put forward by the theoretical framework. Consequently, the framework
provides an important way to inform the development of newly emerging travel recommender
systems, whether they focus on pre-travel or en-route recommendations.
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An interesting and important issue is the impact of adaptive systems on consumer behavior
and the evolution of these systems as a form of persuasive technology (Fogg, 2003).
Recently, a number of scholars have begun to consider the potential impact of
recommender systems, providing considerable insight into current and potential
relationship(s) between computers and users (Nass and Moon, 2000; Ha¨ ubl and Murray,
2003; Cosley et al., 2003). A main assumption of this research is that recommender systems
are quasi-social actors (Nass et al., 1994). Dholakia and Bagozzi (2001) provide an excellent
discussion of the various roles of online technologies and consumer behavior where they
argue that web-based systems can effectively reduce cognitive effort, transfer control from
self to the system, and positively affect the quality of actual decisions. However, a number of
concerns exist regarding the use of these systems including the ease with which one can
mask the true intent of the system, the degree to which systems can manipulate the set of
alternatives under consideration, as well as the ability of the these systems to affect
emotions. Clearly, the nature and extent to which such technologies can be used to manage
consumer behavior should be discussed and guidelines need to be established. Another
important issue focuses on the emergence of the ‘‘new consumer’’ and related implications
concerning the next generation of online destination recommender systems. Given the
changing nature of the traveler and the use of internet-based systems (Cho and Jang, 2008;
Poon, 1993; Kramer et al., 2007), the success of a speci?c DRS will largely depend on its
ability to anticipate and creatively respond to transformations in the personal and situational
needs of its users.
Destination recommender systems are important tools for online travel and tourism
marketing. They not only provide cross-selling and up-selling opportunities but by
addressing individual customer needs they also have the potential to greatly increase
satisfaction, promote loyalty and establish one-to-one relationships (Markellou et al., 2005).
Recommender systems increase the relevance of information provided to the consumer,
which increases the likelihood that the information is actually processed (Shavitt and Brock,
1994). In addition, human-centric recommender systems promise marketers the ability to
reach consumers with very speci?c needs that are typically excluded in mainstream
marketing campaigns. The marketing literature suggests that the number of consumers with
obscure preferences is growing and marketing strategies should be tailored to reach these
long tails of the consumer preference distribution curve (Anderson, 2006). Recommender
systems provide a potential solution to reaching these ‘‘markets of one’’ (McKenna, 2000).
As such, they challenge traditional assumptions regarding market segmentation and target
market selection where the goal is no longer to invest only in large groups (segments) of
travelers that can be addressed in a uniform way based on common demographics or trip
motivations. Indeed, Werthner and Ricci (2004) anticipate that recommender applications
will have a great impact on travel information distribution and consumers’ travel planning
behavior. Therefore, in?uencing the design of such systems will be crucial for travel and
tourism marketers and to making the best use of their capabilities. This will not only require a
better understanding of the increasingly diverse needs and expectations of consumers, but
also calls for substantially new marketing models.
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About the authors
Ulrike Gretzel is an Associate Professor in the Institute for Innovation in Business and Social
Research and Director of the Laboratory for Intelligent Systems in Tourism. Her research
deals with the human-centric design, adoption and use of information technologies in
tourism. Ulrike Gretzel is the corresponding author and can be contacted at:
[email protected]
Yeong-Hyeon Hwang is an Assistant Professor in the Department of Tourism Management at
Dong-A University. His research focuses on information technology in tourism, travel
information search, decision-making, and spatial travel behaviors.
Daniel R. Fesenmaier is a Professor in the School of Tourism and Hospitality Management at
Temple University and Director of the National Laboratory for Tourism & eCommerce at
Temple University. He is also a Visiting Principal Research Fellow at the University of
Wollongong, Australia. His main research and teaching interests focus on the use of
information and the internet in travel decisions, the use of information technology for tourism
marketing and the development of knowledge-based systems for tourism marketing
organizations.
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This article has been cited by:
1. Heejun Kim, Zheng Xiang, Daniel R. Fesenmaier. 2015. Use of The Internet for Trip Planning: A Generational Analysis.
Journal of Travel & Tourism Marketing 32, 276-289. [CrossRef]
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doc_186716952.pdf
– Destination recommender systems need to become truly human-centric in their design and
functionality. This requires a profound understanding of human interactions with technology as well as
human behavior related to information search and decision-making in the context of travel and tourism.
This paper seeks to review relevant theories that can support the development and evaluation of
destination recommender systems and to discuss how quantitative research can inform such theory
building and testing.
International Journal of Culture, Tourism and Hospitality Research
Informing destination recommender systems design and evaluation through quantitative research
Ulrike Gretzel Yeong-Hyeon Hwang Daniel R. Fesenmaier
Article information:
To cite this document:
Ulrike Gretzel Yeong-Hyeon Hwang Daniel R. Fesenmaier, (2012),"Informing destination recommender systems design and evaluation
through quantitative research", International J ournal of Culture, Tourism and Hospitality Research, Vol. 6 Iss 4 pp. 297 - 315
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Informing destination recommender
systems design and evaluation through
quantitative research
Ulrike Gretzel, Yeong-Hyeon Hwang and Daniel R. Fesenmaier
Abstract
Purpose – Destination recommender systems need to become truly human-centric in their design and
functionality. This requires a profound understanding of human interactions with technology as well as
human behavior related to information search and decision-making in the context of travel and tourism.
This paper seeks to review relevant theories that can support the development and evaluation of
destination recommender systems and to discuss how quantitative research can inform such theory
building and testing.
Design/methodology/approach – Based on a review of information search and decision-making
literatures, a framework for the development of destination recommender systems is proposed and the
implications for the design and evaluation of human-centric recommender systems are discussed.
Findings – A variety of factors that in?uence the information search and processing strategies that
in?uence interactions with a destination recommender system are identi?ed. This reveals a great need
for data-driven models to inform recommender system processes.
Originality/value – The proposed framework provides a basis for future research and development in
the area of destination recommender systems. The paper concludes that the success of a speci?c
destination recommender systemwill depend largely on its ability to anticipate and respond creatively to
transformations in the personal and situational needs of its users. Such system intelligence needs to be
based on empirical data analyzed with sophisticated quantitative methods. The importance of
recommender systems in tourism marketing is also discussed.
Keywords Destination recommender system, Destination choice, Information search,
Human-centric computing, Travel planning, Destination marketing, Tourism management, Data analysis
Paper type Conceptual paper
Introduction
The emergence of information technology and its broad adoption within the tourism industry
has led to an explosion in the availability of destination-related information, which greatly
helps travelers in planning trips and/or formulating expectations about tourism experiences
information (Buhalis and Law, 2008). At the same time, increased availability of
destination-related information can lead to information overload, creating dif?culty for
information seekers wanting to ?nd relevant information (Pan and Fesenmaier, 2002).
Further, this information is often presented in a way that does not match the way consumers
search for information (Pan and Fesenmaier, 2006). However, consumers have increasingly
come to expect truly personalized information and offers (Simonson, 2005). Thus, travel and
tourism marketers ?nd an ongoing challenge to deliver tailored information to micro-markets
(Anderson, 2006).
Fortunately, destination recommender systems have been developed that can simplify the
decision making process by identifying alternatives that meet speci?c needs/desires and by
providing this information in a highly personalized way (Fesenmaier et al., 2006). These
systems vary in sophistication, ranging from simple retrieval or ?ltering applications to
comprehensive recommender systems (Spiekermann and Paraschiv, 2002; Burke, 2002;
DOI 10.1108/17506181211265040 VOL. 6 NO. 4 2012, pp. 297-315, Q Emerald Group Publishing Limited, ISSN 1750-6182
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
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PAGE 297
Ulrike Gretzel is an
Associate Professor at the
Laboratory for Intelligent
Systems in Tourism,
University of Wollongong,
Wollongong, Australia.
Yeong-Hyeon Hwang is an
Associate Professor in the
Department of Tourism
Management, Dong-A
University, Busan, Korea.
Daniel R. Fesenmaier is a
Professor in the School of
Tourism and Hospitality
Management, Temple
University, Philadelphia,
Pennsylvania, USA.
Received April 2011
Revised June 2011
Accepted September 2011
This work has been partially
funded by the European
Union’s Fifth RTD Framework
Programme (under contract
DIETORECS IST-2000-29474).
The authors would like to thank
all other colleagues of the
DieToRecs (see http://
dietorecs.itc.it/) team for their
valuable contributions to this
paper.
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Schafer et al., 2001). Although sophisticated recommender systems have been
implemented for some product categories (movies, books, etc.) they are still lacking vital
elements before they can match or even exceed the quality of human recommendations
(Ha¨ ubl and Trifts, 2000).
In order to develop into more helpful and successful decision-making support tools, that is,
tools that are able to direct potential travelers to destinations they will ?nd most suitable for
ful?lling their travel-related needs, recommender systems have to become truly human-centric
in their design and functionality. Further, quantitative research based on large-scale behavioral
data is needed to inform such human-centric design. Following Mazanec (2006), advanced
recommender systems can be described as those systems with increased adaptivity based
on extensive knowledge about the user and the capability to provide real-time personalization.
Thus, these systems incorporate retrieval components that seek to identify products and
services that match user speci?cations. Users are not always able to directly specify their
preferences, however, and systems need to engage users in a dialogue similar to the
interaction with a human travel counselor (Hruschka and Mazanec, 1990). In order to achieve
this purpose, they argue that systems need to become sensitive to:
B the degree of precision gained of the user’s consumption goals during the individual
counseling interaction;
B the ful?llment of the user’s aspiration level regarding the volume of information needed;
B the ability to articulate owing to the user’s active or passive response style; and
B the situation-speci?c importance rank order of the bene?ts and product attributes sought
(Mazanec, 2006).
As a consequence of this higher adaptivity, advanced recommender systems should be
able to substantially reduce a user’s effort, which in turn, increases the enjoyment in the
process of identifying potential destination recommendations (Mazanec, 2006).
A rich literature has emerged over the past three decades in the ?elds of consumer behavior,
information search and processing, and human computer interaction that provides a
substantial foundation for the development of human-centric recommender systems. Travel
and tourism related systems, however, face an additional challenge in that they have to take
into account the idiosyncratic nature of travel behavior (Loban, 1997; Vanhof and Molderez,
1994; Ricci, 2002). Tourism research indicates, for example, that travelers often actively seek
information as part of their travel planning effort and consider information seeking an important
component of the overall travel experience (Vogt and Fesenmaier, 1998). These studies also
suggest that the information search process involves different hierarchical steps depending
upon a number of personal and situational factors (Jeng and Fesenmaier, 2002). In addition,
variety seeking and involvement are generally believed to be more pronounced in tourism
(Bigne´ et al., 2009). Based upon this literature, as well as Mazanec’s (2006) conceptualization
of intelligent travel recommender systems, this paper proposes a framework of travelers’
interactions with destination recommender systems (DRSs) that takes into account the
speci?c characteristics of travel information search and decision-making. The paper then
elaborates on implications for the design of DRSs as well as their evaluation and the need for
quantitative research to build the models that can makes these systems adaptive and
responsive to personal needs. Last, this paper discusses the important implications for
tourism marketing that arise from the potential of truly human-centric DRSs.
Factors in?uencing travel information search and processing
Destination recommender systems can only be successful if their design builds on a
comprehensive understanding of travel decision making and, speci?cally, of travel
information search (Gretzel et al., 2006). This paper extends the Gretzel et al. (2006) original
behavioral framework for destination recommender system design by positing additional
factors that should be included to make the model even more comprehensive and, thus,
more responsive to travelers’ needs. In particular, three essential factors are in?uencing
travelers’ information search and processing pattern including:
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1. personal characteristics of the traveler (e.g. socioeconomic status);
2. situational needs and constraints (e.g. trip length); and
3. aspects of the decision-making process (e.g. the speci?city of the choice task and
decision frames used).
The following provides a brief overview of these factors and their effects on information
search and processing behavior in the context of destination choice.
Personal characteristics of the traveler
The literature elaborates on a number of personal characteristics that potentially in?uence
travel information search and decision-making; the following nine characteristics have
emerged as particularly important in the context of travel planning:
1. socio-demographics;
2. knowledge;
3. personality;
4. involvement;
5. values;
6. attitudes;
7. cognitive style;
8. decision-making style; and
9. vacation style.
Socio-demographics. Socio-demographic characteristics have been extensively studied as
explanatory variables for evoked set formation, categorization of alternative destinations,
and antecedents of information processing (see, for example, Mayo and Jarvis, 1981;
Woodside and Lysonski, 1989; Um and Crompton, 1991; Woodside and MacDonald, 1994).
Part of the reason why such an extensive number of studies exist that provide information on
the in?uence of socio-demographic characteristics on travel information search,
decision-making, and behavior, is that they can be fairly easily observed or elicited from
respondents. They are also relatively stable. Both aspects provide advantages in the context
of recommender systems. Also, characteristics such as age, education, income, and marital
status are often employed as surrogates for determining the travel decision-maker’s
resources and constraints. In terms of age, existing research indicates that older travelers
tend to rely more on family and past experience as information sources (Capella and Greco,
1987) and are more interested in satisfying hedonic, aesthetic, and sign needs in the
information search process (Vogt and Fesenmaier, 1998). Also, more educated travelers
with higher levels of income tend to search for more information (Gitelson and Crompton,
1983; Etzel and Wahlers, 1985). Women are more likely to consider functional aspects in
their information search than men (Vogt and Fesenmaier, 1998); in general, females are more
comprehensive information processors who consider both subjective and objective
attributes, and are more likely to respond to subtle cues than males (Darley and Smith,
1995). Income in?uences the constraints within which trips have to be planned and also the
extent to which a trip has to be planned to avoid additional cost.
Knowledge. Travelers’ knowledge is an important cognitive domain that in?uences information
search and processing behavior as well travel decision-making (Park et al., 1988; Brucks,
1985). Knowledge, often obtained through direct experience, can be represented either as
travel knowledge in general or as knowledge of alternative destination(s), or both. In either
case, knowledge in?uences the range of alternatives considered (Snepenger et al., 1990).
Further, previous experience with a destination plays an important role in terms of how a
destination is categorized during decision-making processes with respect to how well the
location could perform when selected as a travel destination (Woodside and Lysonski, 1989).
Also, differences in the choice of destinations/attractions between ?rst-time visitors and repeat
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visitors, that is, travelers that have prior experience with the destination, are prevalent.
First-time visitors tend to choose destinations that are easily accessible while experienced
visitors are more likely to consider destinations with low accessibility (McKercher, 1998). In
addition, more experienced visitors may want to visit novel destinations since they have
already visited well-known destinations within a region or attractions within a speci?c
destination. In this sense, repeat visitors are more selective and less prone to visit multiple
destinations (Oppermann, 1992; Decrop, 1999; Hwang et al., 2002).
Interestingly, a number of different perspectives have been suggested regarding the
relationship between knowledge and information search behavior. A negative relationship
would imply that the more knowledge a traveler can draw on, the less information seeking will
occur. In contrast, a positive relationship suggests that as people acquire more knowledge
they will be more actively involved in the information search process because they can
better/more easily interpret information and, thus, derive more bene?ts from information than
people with limited knowledge. Studies also suggest an inverted U-shape function where a
positive relationship exists up to moderate levels of knowledge, and a negative relationship at
moderate to high levels of experience/knowledge (Punj and Staelin, 1983; Alba and
Hutchinson, 1987; Moorthy et al., 1997). Knowledge and previous experience have been
included in several studies within the context of travel information search (Manfredo, 1989;
Snepenger et al., 1990; Perdue, 1993). Although the results vary from study to study, two
?ndings regarding the in?uence of travel/destination knowledge and/or experience on
information source use are especially interesting and relevant for the context of designing
recommender systems. A study conducted by Kerstetter and Cho (2004) demonstrated that
prior knowledge encompasses two dimensions – i.e. past experience and
familiarity/expertise – which independently in?uence individuals’ search for vacation
information. Inexperienced travelers to a destination are likely to search for more
information than repeat visitors to minimize the risk involved in visiting an unknown
destination (Van Raaij, 1986). In contrast, experienced travelers are known to use information
sources different from those used by na? ¨ve travelers. Also, inexperienced tourists appear to
rely more on professional sources than experienced tourists (Snepenger et al., 1990;
Woodside and Ronkainen, 1980). In addition, Vogt and Fesenmaier (1998) ?nd that
experienced tourists tend to have higher innovation needs than inexperienced tourists. This
can be interpreted as resulting from a greater tendency of experienced travelers to seek
variety and, thus, more novel information.
Involvement. Travel information search and processing also depend to a great extent on
individuals’ level of involvement (Finn, 1983; Celsi and Olsen, 1988; Jamrozy et al., 1996). For
example, as the perceived risk involved in the decision task increases, situational involvement
rises accordingly, and individuals tend to invest more resources in external information search
(Murray, 1991). That is, highly involved travelers are likely to use more criteria, search for more
information, use more information sources, process relevant information in detail, make more
inferences, and will form attitudes that are less likely to change (Celsi and Olsen, 1988;
Fesenmaier and Johnson, 1989). In a complex decision and choice situation developing
commitment and stronger attitudes is of greater need in order to accomplish the task. On the
other hand, simple and routine decisions require relatively low consumer involvement (Reid
and Crompton, 1993). Fesenmaier and Johnson (1989) use the individual’s trip planning
involvement as the basis for segmenting the Texas travel market. They ?nd that
low-involvement travelers tend to have a shorter planning horizon, while the medium-high
involvement travel group shows a longer trip planning horizon. Importantly, the longer the
planning horizon, the more destination alternatives can be considered and the more extensive
their evaluation can be. In addition, the results of their study indicate that low-involvement
tourists take shorter getaway trips that involve less resource constraints and less risk factors,
whereas highly involved tourists tend to take longer vacations which require extensive
cognitive efforts, advance planning, and entail more resource constraints and risk factors.
Personality. Personality, which can be de?ned as ‘‘the re?ection of a person’s enduring and
unique characteristics that urge one to respond in persistent ways to recurring
environmental stimuli’’ (Decrop, 1999, p. 106), is a ‘‘complex outcome of a person’s
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learning, perceptions, motivations, emotions, and roles’’ (Mayo and Jarvis, 1981, p. 109).
Plog (1994) suggest two fundamental personality dimensions that are of importance within
the context of tourism: allocentricism and psychocentricism. Allocentric travelers, who
exhibit a self-assured and venturesome personality, are more likely to choose exotic
destinations while psychocentric travelers, whose center of attention is focused on
self-doubts and anxieties, are thought to prefer familiar destinations (Plog, 1994; Ross,
1994). Grif?th and Albanese (1996) have shown that Plog’s model can be used to
characterize travelers in terms of their psychographics and suggested practical use of these
traits to make destination recommendations.
Further, personality traits related to locus of control and risk avoidance, which in?uence an
individual’s decision-making style, play an important role in any decision-making process
but are of particular importance for destination choice processes because of the high levels
of uncertainty involved (Roehl and Fesenmaier, 1992). Variety-seeking is another personality
trait that is of great importance for tourism decisions but existing recommender systems
typically fail to take variety-seeking into account (Dholakia and Bagozzi, 2001). Personality
has also been identi?ed as a factor with considerable in?uence on information search and
processing strategies. For example, individuals’ differences in the complexity of the causal
explanations they reach to make sense of their environments suggest that personality
in?uences the extent and nature of information search and integration patterns (Murphy,
1994). Also, individuals with a tendency to postpone decisions when faced with dif?cult
choices or con?icts have been found to engage in search patterns that are different from
those used by individuals who are not indecisive (Ferrari and Dovidio, 2001). Recent
recommender system research also suggests that personality is an important factor to
consider when providing recommendations (Gretzel et al., 2004; Moon, 2002).
Values. Madrigal and Kahle (1994) de?ne personal values as representing central beliefs
about desirable states or behaviors. Thus, the structure of an individual’s value system
provides the basis for deriving intentions and directing human behavior. Woodside and
Lysonski (1989), for example, argue that personal value systems in?uence travelers’
destination awareness. In contrast, Um and Crompton (1991) describe personal values as an
internal input that initiates the formation of an evoked set from an awareness set. In tourism
research, studies by Madrigal (1995) indicate that personal values are a better predictor of
choice between group tours and individual tours than personality, and Zins (1998) suggests
that personal values are an important antecedent variable for hotel choice. Examples of values
are self-respect, sense of accomplishment and being well respected by others. While many
individual values exist, the literature has identi?ed four broad dimensions of values, namely
enjoyment, achievement, egocentrism, and external orientation (Madrigal and Kahle, 1994).
Attitudes. The destination images created through prior experience or exposure to
advertising and marketing efforts, and the ?t between conceptions of the destinations with
personal values and beliefs result in particular attitudes toward destinations. These attitudes
are signi?cant determinants of whether or not a destination is considered as an alternative
and how the destination is evaluated in later stages of the destination choice process.
Research by Fishbein and Ajzen (1975), among others, relates personal attitudes to
subsequent behavior, arguing that they play an important role in understanding destination
choice. The attitude-behavior model provides explanations for human behavior based on
individual attitudes and the behavioral intentions that can be derived from them (Ajzen and
Fishbein, 1980; Ajzen, 1991). Within the context of destination choice, Um and Crompton
(1990) operationalize attitude toward alternative destinations as the difference between the
magnitude of the perceived facilitators and the magnitude of the perceived inhibitors, and
argue that destinations with higher attitude scores are more likely to be included in the
evoked set and, ultimately, are more likely to be selected as the ?nal destination.
Cognitive style. Travelers differ in their perceptions and preferences for types of information.
The preferred ways in which individuals process information are referred to as cognitive style
(Biocca et al., 2001). Cognitive styles affect information gathering, evaluation, and selection
processes in the context of vacation trip planning (Grabler and Zins, 2002). Rumetshofer
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et al. (2003), and Rosen and Purinton (2004), demonstrate that information presentation
needs to match the cognitive style of the traveler in order to be processed effectively.
Decision-making style. Decision-making styles are mainly viewed as a mental, cognitive
orientation towards shopping and purchasing (Sproles and Kendall, 1986) or a learned
habitual pattern (Scott and Bruce, 1995), which dominates the consumer’s choice and
constitutes a relatively enduring consumer personality. Decision-making styles basically
describe howindividuals shop. Sproles and Kendall (1986) combine related traits described
in the literature to develop a consumer decision-making styles list, the so-called consumer
styles inventory (CSI), consisting of the following eight dimensions:
1. perfectionism;
2. brand consciousness;
3. novelty/fashion consciousness;
4. price/value consciousness;
5. recreational shopping;
6. impulsive/careless shopping;
7. confusion by over-choice; and
8. habitual/brand loyal shopping.
The CSI has been tested in the context of online shopping (Yang and Wu, 2006; Cowart and
Goldsmith, 2007; speci?cally, Park, 2007) and the results indicate that decision-making
styles substantially in?uence the online purchase of travel products and loyalty toward online
travel agencies.
Vacation style. Vacation styles combine psychographic characteristics such as travel
motives with behavioral patterns (Zins, 1999). They have emerged from earlier tourist type
research seeking to identify traveler segments that fundamentally differ in terms of the
bene?ts sought from vacations (Dolnicar and Mazanec, 2000). Vacation styles have been
found to provide a rather stable criterion for marketing segmentation (Dolnicar and Leisch,
2003) and can be seen as strong determinants of trip preferences. Not all destinations cater
equally well to the different vacation style types due to differences in offerings. Thus,
identifying someone’s vacation style seems to be very bene?cial in the context of making a
destination recommendation.
Situational needs and constraints
Destination-related decisions are highly sensitive to the situation in which they occur. The
travel literature indicates that trip characteristics are, as one would expect, the most
important determinants and include travel purpose, length of travel, distance between origin
and destination, travel group composition, as well as travel mobility. The following provides a
brief discussion of each as they relate to travel information search and decision-making.
Travel purpose. Travel purpose can be generally de?ned as one’s stated needs or motives
for travel. Travel purpose is, often times, closely connected to activities and settings (e.g. golf
vacation or visit to a cultural heritage site) and therefore, signi?cantly constrains/de?nes the
range of alternative destinations considered. Travel purpose also in?uences information
search strategies. Fodness and Murray (1998) ?nd that those traveling for vacation
purposes are the most likely to rely on their personal experience to plan their trips.
Trip length. The time available for a trip constrains the geographical range of the trip. Thus,
travelers with limited amounts of time available tend to prefer nearby destinations. In
contrast, travelers with more time tend to prefer more distant destinations (McKercher,
1998). In this sense, length of trip constrains the range of alternatives that will be considered.
Length of travel has also been identi?ed as a factor that in?uences the use of particular
information sources (Snepenger et al., 1990).
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Travel distance. Whether a destination will be considered as an alternative is also a function
of the distance from home to a destination, a factor which has been included as a key
variable in aggregated destination choice models (Kim and Fesenmaier, 1990; Lo, 1992). In
disaggregated models, cognitive distance instead of physical distance has been
emphasized to account for circumstances in which individuals use mentally measured
proximity or distance to evaluate alternatives. Empirical evidence suggests that a
relationship between travel distance and information search strategies exists. For
example, Pennington-Gray and Vogt (2003), among others, ?nd that out-of-state visitors
are more likely to obtain travel information at welcome centers than in-state residents.
Travel party. Alternative destinations considered by a person who plans to go on a family
vacation, for example, are probably different from those considered for a trip with friends.
The characteristics of the travel party also impact the geographical range of alternative
destinations in respect to the mobility of the travel group. A family with children tends to take
short vacations at easily accessible destinations. In contrast, couples without children are
more likely to choose destinations with modest accessibility (McKercher, 1998). Additionally,
the nature of the travel party de?nes the degree of heterogeneity in the group with respect to
interests. That is, as the travel party size increases, the number of needs to gratify is likely to
increase accordingly and thus, multi-destination travel is more likely to occur (Fesenmaier
and Lieber, 1985, 1988; Lue et al., 1993). In addition, travel group composition has been
found to in?uence the information search strategy selected (Fodness and Murray, 1997).
Family groups tend to use media as information sources more than other types of travel
parties, and are more likely to be involved in extensive search processes in order to assure
satisfaction of all the members (Gitelson and Crompton, 1983).
Travel mobility. Mobility is not only a function of the nature of the travel group but also
depends on the transportation mode a traveler uses during a trip (Tideswell and Faulkner,
1999). Alternative destinations, which a traveler with a rental car or personal car can think of,
might be unavailable to travelers who use, for instance, only public transportation. Travel
mobility has an impact on the ?exibility of the travel itinerary and is positively related to not
only the number of destinations but also the number of attractions and activities that can be
integrated into the trip. Transportation mode used can also explain certain tendencies
toward multi-destination travel, as travelers with greater mobility are better equipped for
visits to more than one destination (Cooper, 1981). Further, Fodness and Murray (1999) ?nd
evidence for a relationship between mode of transportation and types of travel information
sources used. Thus, a DRS needs to gauge the level of travel mobility a user has during a
speci?c trip in order to make reasonable recommendations.
Decision frames
Destination decisions can be framed in various ways depending on personal preferences for
certain decision-making strategies and the needs or constraints derived from the speci?c
trip planning situation. Speci?cally, the number and type of decision criteria taken into
account will vary based on the nature of the trip to be planned. For instance, trips de?ned
around a speci?c activity such as gol?ng will strongly in?uence the frame in which the
decision has to be made. For such a trip, beach access at the destination might be desired
but might not be perceived as being as important as in the case of a typical summer, sun,
and beach vacation. Also, personal characteristics can be assumed to in?uence one’s need,
ability, and/or willingness to take certain criteria into consideration. A low annual household
income, for instance, will probably encourage the adoption of a decision frame that
incorporates price as a main criterion. In addition, personal cognitive styles can greatly
in?uence the amount of information sought to support the decision-making process and
especially the number of alternatives considered by the individual decision-maker (Hunt
et al., 1989; Driver et al., 1990). Similarly, decision-making styles will in?uence the timing of
the decision, the extent of planning and speci?c criteria taken into account. For instance, an
impulsive style will lead to very little planning and a small number of decision criteria while
brand consciousness results in a focus on well established travel product and services
brands (Sproles and Kendall, 1986).
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Further, destination decisions can be taken at different levels in the travel planning hierarchy,
that is, one can select a main destination, a secondary destination, or places within a
destination such as attractions and restaurants (Jeng and Fesenmaier, 2002). Given the
impact of choosing a main destination on decisions with respect to lower-level facets of a trip,
being in the process of selecting the main destination of a trip implies that many
characteristics of this trip are still undetermined. In contrast, if the main destination has been
chosen and the decision-making process refers to ?nding one or more secondary
destinations, one can assume that many important characteristics of the trip have already
been outlined and that the range of destination alternatives in the consideration set will be
rather limited. At the most speci?c level, destination decisions involve choosing places to visit
at a destination. This latter formof destination decision can be characterized by a high level of
constraint and, consequently, a relatively small number of alternatives to be considered.
Depending on the speci?city of the destination decision, the amount and type of information
taken into account in the decision-making process will vary (Bloch et al., 1986). More
speci?c destination decisions require more speci?c information. If no destination decision
has been made, the information sought will be in the general form of destination alternatives
and will often be more image-based than functional. If a main destination has been selected,
the destination decision will focus on secondary destinations in proximity to the main
destination. Such a decision requires image-related information but also more speci?c
details about distances and activity/attraction portfolios to evaluate destination
complementarities. Finally, those decisions that involve selecting places/attractions at a
speci?c destination will to a large extent include detailed and more functional information in
the formof opening hours, prices, admission restrictions, etc. Therefore, knowing the level of
speci?city of a user’s decision-making process is a critical success factor for a
human-centric DRS (Mazanec, 2002; Hwang et al., 2009).
A framework for human-centric destination recommender systems
Based on the review of the travel destination choice and information search and processing
literatures, a framework can be conceptualized which integrates various factors that shape
an individual’s interaction with a destination recommender system (see Figure 1). The
framework assumes that individuals access a DRS to learn about alternative destinations
and that the nature of the information needed by a user will depend on two main factors:
1. the decision task(s) to be accomplished; and
2. the nature of the trip – that is, the context in which this trip decision will be taken.
Further, the decision task(s) depends on the decision frame that guides the decision-making
process. The nature of the trip, on the other hand, will depend on the situational needs to be
satis?ed by the trip and the constraints that have to be considered. Although destination
decisions are often high-level decisions and are typically made when most other aspects of the
trip are still unde?ned, individuals who use a DRS are expected to have at least some idea of
when they would like to travel (e.g. winter versus summer vacation), how long they would like to
stay (e.g. week-long vacation or getaway trip), who they would like to take along (e.g. spouse or
entire family), what the purpose of the tripis (e.g. relaxation versus adventure), what main activity
they will engage in (e.g. beach vacation versus skiingtrip), what the main mode of transportation
will be (e.g. car versus airplane), and from which point of origin the trip will start. If the main
destination has been selected and the search effort focuses on secondary destinations or
attractions within destinations, the situational needs and constraints are assumed to have been
established in greater detail. Thus, the speci?c decision task is shaped by the decision frame
selected, which is, of course, adjusted a priori to accommodate the speci?c aspects of a trip.
Furthermore, the needs and constraints that drive the nature of the trip are important indicators
of the particular decision task to be accomplished as they directly in?uence the nature of the trip,
but also affect the way the destination decision is framed and executed.
Information search, processing, and evaluation in the context of travel planning are complex
and iterative (Pan and Fesenmaier, 2006). A truly adaptive system as described by Mazanec
(2006) engages the user in a dialogue and allows for re-speci?cations of needs by the user
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and adjustments in recommendations by the DRS. The proposed framework is dynamic in the
sense that the framework recognizes the importance of feedback resulting from a user’s
interaction with the system. Based on the processing and evaluation of the recommendations
obtained, the user might decide that more/better information is needed and therefore might
engage in additional information search processes until a satisfactory level is reached. In a
different case, the information obtained from the system could expose additional situational
constraints and make changes in the decision frame and/or the nature of the trip necessary.
For instance, destinations could be recommended and perceived as being optimal in terms of
the activities they provide, the way in which they cater to the needs of the members of the
travel party, etc. However, they could be seen as offering too many interesting things for just a
day trip and lead to a revision of the trip length constraint. Similarly, a user could be given the
options of loosely specifying trip characteristics in the beginning of the search process and
would subsequently be encouraged to re?ne them as more information is being taken into
account. Ideally, the process ends when all necessary information has been collected and
processed and an informed destination decision is made. The time and number of iterations
necessary to reach this point will vary depending on the number of potential alternatives under
consideration, the quality of the recommendations and the changes in the decision frame as
set by the user. The worst-case scenario in terms of behavioral outcomes is, of course, a
situation whereby the user terminates the process without having reached a decision.
Alternatively, use of the system could lead to a postponing of the decision, but at least with a
narrowed-down set of alternatives.
System-user interaction
The nature and degree of interaction with the system is driven by personal characteristics,
situational factors and the resulting nature of the trip to be planned, the decision frame
Figure 1 Framework for destination recommender systems
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applied and the speci?c decision(s) to be taken, which all result in particular information
needs and search strategies. However, interaction can also be directly in?uenced by
personal characteristics. An individual’s skills, involvement, personality, etc., appear to have
direct impacts on the individual’s interaction with an intelligent information environment such
as a DRS (Hoffman and Novak, 1996). Further, numerous studies on interactions with
technologies and speci?cally with recommender systems, point out that trust is an important
characteristic in the interaction with a system (Komiak and Benbasat, 2006; Swearingen and
Sinha, 2001). Whether trust can be established depends on factors such as personality
(e.g. neuroticism negatively in?uences trust), knowledge of recommender systems,
perceived credibility of the system (Yoo and Gretzel, 2011), attitudes toward technology
in general and especially the Internet, gender (e.g. Gefen and Straub, 1997) and age (Fox
and Boehm-Davis, 1998). Innovativeness refers to a user’s desire to be among the ?rst to
adopt a product or a technology (Goldsmith and Hofacker, 1991) and is also an important
construct that has been studied in the context of technology adoption and use. The more
innovative a user, the more open he or she is to novel forms of interactions.
System characteristics
In addition to user characteristics, a user’s interaction with a DRS is shaped by the
characteristics of the recommender system itself. The design elements of a DRS play a
crucial role in shaping the user-system interaction process. Speci?cally, the amount and
presentation of the DRS’s content and the structure of its interface are key aspects
determining the nature of the interaction (Spiekermann and Paraschiv, 2002; Dholakia et al.,
2000). Zins (2003) concludes that adaptation of information provided by a DRS and
adjustment of the recommender system interface to ?t a user’s cognitive style are crucial for
improving the quality of the human-computer interaction. Further, the intelligence built into
the system through data storage and mining capabilities in?uences the level of interactivity
and personalization that can be provided. Systemintelligence, therefore, is a core element in
de?ning user interactions with a DRS. Thus, the framework clearly supports the idea that
DRSs should be highly interactive and adaptive in order to provide appropriate guidance in
the travel planning process. Another important capacity of a DRS, which is rooted in its
design, is its ability to provide users with enjoyment and excitement as well as types of
information exchanges that can convey the experiential aspects of travel and tourism
products/services. Figure 2 summarizes these core DRS design components. Each of these
design components has to be informed by the theoretical foundations outlined above to truly
support destination decision-related human behavior.
Implications for recommender system design
Although some of the relationships in the proposed theoretical framework appear to be
obvious, they are often not implemented because more emphasis is placed on technical
considerations than user interaction requirements, and system designers typically lack an
understanding of the foundations of travel behavior. Such an understanding is critical in
designing systems that can support different stages in the travel planning process and can
provide the adaptivity that is usually offered by human travel counselors. Most importantly,
the interaction with the system should feel natural and provide enjoyable experiences. Three
propositions can be derived from the theoretical framework and should guide future DRS
development:
Figure 2 Design components of destination recommender systems
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P1. Truly human-centric destination recommender systems need to be able to take into
account situational needs/constraints, decision frames, as well as personal
characteristics.
P2. Truly human-centric destination recommender systems need to support reiterative
planning and provide opportunities for feedback and modi?cation.
P3. System intelligence is crucial in providing persuasive recommendations but
destination recommender systems also need to offer experiential and enjoyable
use experiences as travel planning is an important component of the pre-trip
experience. Such high quality interactions are not only dependent on system design
characteristics but also on the system’s ability to adapt to information search and
processing strategies as well as personal characteristics and styles of the user.
Implications for research
The proposed framework illustrates the myriad of factors that can in?uence successful
interactions with a DRS and the great number of attributes that could potentially be taken into
account when the system seeks to provide a suitable destination recommendation. In
practice, a system is computationally unable to take into account all possible personal
characteristics, decision frames and trip characteristics that in?uence the destination choice
process. Adaptive systems have the advantage that they can learn from the interaction with
the user and dynamically adjust the criteria taken into account. However for such a systemto
be designed, detailed information on the relative importance of criteria and their
interrelationships is needed. Research has yet to provide the necessary insights to
determine which aspects of the framework are more important than others. Also,
determining more general user pro?les based on the elements of the framework requires
more empirical evidence. Data-mining of existing online recommendation systems will be
instrumental in providing the information needed to successfully adapt recommender
systems to the travel and tourism context as well as to speci?c customer needs (Markellou
et al., 2005). Thus, collaborative research that involves tourism academics, system
designers and tourism organizations that have implemented systems will be critical. In
addition, classifying destinations so that they can be successfully matched to particular
traveler preferences and needs might be necessary to overcome some of the limitations
recommender algorithms have. This requires a thorough understanding of destination
attributes and constraints (distance, opening hours, etc.).
While qualitative research can provide important insights as to how users engage with
information and systems as well as with other members of their travel party when planning
vacations, quantitative research is needed to develop the weights, cases, matching
algorithms, learning strategies, and interaction protocols that combine into system
intelligence. For an overview regarding web usage mining research see Pierrakos et al.
(2003). Especially data extraction and preparation are critical issues for web mining but
have not been extensively discussed in the literature. One speci?c method that can help with
deriving information from weblog data about travel planning processes is the sequence
alignment method. Currently underused in tourism, this method allows for the recognition of
patterns in the behavioral sequences of travelers’ interactions with online systems (Liu,
2007). Navigational patterns are behavioral data that can provide critical insights as to how
consumers search for information. The ultimate goal is to be able to successfully predict the
next user action or information need based on the user’s previous sur?ng behavior (Hay
et al., 2003).
Another critical area of quantitative research needed to inform DRS design is cluster
analysis, which has a long tradition in tourism research (Mazanec, 2000; Dolnicar, 2002;
Zins, 2008). However, intelligent systems such as DRSs need sophisticated clustering
approaches. Such segmentation analyses have to be based on a thorough understanding of
the underlying data structures (Dolnicar and Leisch, 2010). Neural network approaches, for
instance, have been proven to outperform other types of cluster analyses (Buchta et al.,
1997) but are still not widely used in tourism research.
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The framework outlines the scope of research that has to be conducted. While some of the
?ndings from general recommender systems research can be used for DRS design, others
have to be speci?cally established in the context of tourism. Mazanec (2006) also points to
the necessity of employing and developing new research methodologies in the context of
establishing the theoretical basis for DRS design and evaluating existing prototypes.
Travel and tourism marketers will also have to establish performance measures to
benchmark and assess the return on investment a DRS provides. Currently used web
metrics such as unique visitors and number of bookings are of little relevance for systems
that are usually only used for decision support rather than execution and whose goal is to
expose consumers to highly tailored information rather than maximizing impressions. Henry
(2005) suggests that consumers judge a DRS against the notion of a ‘‘live, adaptable
expert’’ and that this measure, although maybe not immediately available, provides a more
realistic and useful evaluation of a system’s worth because the system centers on the
consumer’s perspective.
Given this discussion, the following guidelines for future research in the area of destination
recommender systems are proposed:
P4. The identi?cation of a hierarchy of factors ranging from most critical/discriminating
to supplementary needs to be established so that DRS design can be informed.
P5. Measures of success that take the human-centeredness and adaptivity of a DRS as
well as concrete bene?ts for tourism marketers into account need to be established.
P6. New research methodologies need to be developed to better capture insights from
behavioral data and to better and more ef?ciently classify destinations and segment
travelers.
Conclusion
The rich information search and decision-making literatures offer a tourism-speci?c
theoretical framework that can be used as a basis for the design of human-centric
destination recommender systems. The outcome of this effort is a framework which should
guide system development and which emphasizes the diversity of factors that drive
destination decisions. Importantly, the framework and the guidelines derived from it
simultaneously represent the starting point in the development of an effective travel
recommender system and a road map for future research. That online recommender
systems can effectively guide consumer decision-making has been evidenced manifold.
Amazon.com is one of the most popular examples of an effective online recommender
system, as the website offers a variety of entry points, multiple formats with which to evaluate
potential products, and intelligent mining approaches which help to track consumer
purchasing behaviors and interests.
Although the framework has been established in the context of a pre-travel DRS, its overall
structure can also be applied to the design of context-aware mobile systems that typically
cater to lower-level decisions when the user is already at the destination. Various elements that
the framework stresses have been implemented in the design of such systems. Kramer et al.
(2006), for example, demonstrate the importance of exploring different preference elicitation
strategies in the case of a mobile dynamic tour guide. Adaptive interfaces have been
discussed in the context of PALIO, a location-aware information system for tourists (Zarikas
et al., 2001). Nguyen et al. (2004) emphasize the importance of integrating user feedback into
an on-the-move restaurant recommender system. Recently, system design efforts have also
discovered the importance of travel party composition and the need to integrate group
decision-makingsupport into travel recommender systems (see, for example, Ardissono et al.,
2003). Decision frames might also be in?uenced by roaming costs or lack of ubiquitous
high-speed wi-? access. Yet, no system currently offers comprehensive adaptation that
re?ects all areas put forward by the theoretical framework. Consequently, the framework
provides an important way to inform the development of newly emerging travel recommender
systems, whether they focus on pre-travel or en-route recommendations.
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An interesting and important issue is the impact of adaptive systems on consumer behavior
and the evolution of these systems as a form of persuasive technology (Fogg, 2003).
Recently, a number of scholars have begun to consider the potential impact of
recommender systems, providing considerable insight into current and potential
relationship(s) between computers and users (Nass and Moon, 2000; Ha¨ ubl and Murray,
2003; Cosley et al., 2003). A main assumption of this research is that recommender systems
are quasi-social actors (Nass et al., 1994). Dholakia and Bagozzi (2001) provide an excellent
discussion of the various roles of online technologies and consumer behavior where they
argue that web-based systems can effectively reduce cognitive effort, transfer control from
self to the system, and positively affect the quality of actual decisions. However, a number of
concerns exist regarding the use of these systems including the ease with which one can
mask the true intent of the system, the degree to which systems can manipulate the set of
alternatives under consideration, as well as the ability of the these systems to affect
emotions. Clearly, the nature and extent to which such technologies can be used to manage
consumer behavior should be discussed and guidelines need to be established. Another
important issue focuses on the emergence of the ‘‘new consumer’’ and related implications
concerning the next generation of online destination recommender systems. Given the
changing nature of the traveler and the use of internet-based systems (Cho and Jang, 2008;
Poon, 1993; Kramer et al., 2007), the success of a speci?c DRS will largely depend on its
ability to anticipate and creatively respond to transformations in the personal and situational
needs of its users.
Destination recommender systems are important tools for online travel and tourism
marketing. They not only provide cross-selling and up-selling opportunities but by
addressing individual customer needs they also have the potential to greatly increase
satisfaction, promote loyalty and establish one-to-one relationships (Markellou et al., 2005).
Recommender systems increase the relevance of information provided to the consumer,
which increases the likelihood that the information is actually processed (Shavitt and Brock,
1994). In addition, human-centric recommender systems promise marketers the ability to
reach consumers with very speci?c needs that are typically excluded in mainstream
marketing campaigns. The marketing literature suggests that the number of consumers with
obscure preferences is growing and marketing strategies should be tailored to reach these
long tails of the consumer preference distribution curve (Anderson, 2006). Recommender
systems provide a potential solution to reaching these ‘‘markets of one’’ (McKenna, 2000).
As such, they challenge traditional assumptions regarding market segmentation and target
market selection where the goal is no longer to invest only in large groups (segments) of
travelers that can be addressed in a uniform way based on common demographics or trip
motivations. Indeed, Werthner and Ricci (2004) anticipate that recommender applications
will have a great impact on travel information distribution and consumers’ travel planning
behavior. Therefore, in?uencing the design of such systems will be crucial for travel and
tourism marketers and to making the best use of their capabilities. This will not only require a
better understanding of the increasingly diverse needs and expectations of consumers, but
also calls for substantially new marketing models.
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About the authors
Ulrike Gretzel is an Associate Professor in the Institute for Innovation in Business and Social
Research and Director of the Laboratory for Intelligent Systems in Tourism. Her research
deals with the human-centric design, adoption and use of information technologies in
tourism. Ulrike Gretzel is the corresponding author and can be contacted at:
[email protected]
Yeong-Hyeon Hwang is an Assistant Professor in the Department of Tourism Management at
Dong-A University. His research focuses on information technology in tourism, travel
information search, decision-making, and spatial travel behaviors.
Daniel R. Fesenmaier is a Professor in the School of Tourism and Hospitality Management at
Temple University and Director of the National Laboratory for Tourism & eCommerce at
Temple University. He is also a Visiting Principal Research Fellow at the University of
Wollongong, Australia. His main research and teaching interests focus on the use of
information and the internet in travel decisions, the use of information technology for tourism
marketing and the development of knowledge-based systems for tourism marketing
organizations.
VOL. 6 NO. 4 2012
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INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
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PAGE 315
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This article has been cited by:
1. Heejun Kim, Zheng Xiang, Daniel R. Fesenmaier. 2015. Use of The Internet for Trip Planning: A Generational Analysis.
Journal of Travel & Tourism Marketing 32, 276-289. [CrossRef]
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