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Why Understanding Your Preferences Is Crucial in Travel Planning

Traveling is an exhilarating and enriching experience that allows people to explore new places, cultures, and cuisines. Whether you’re planning a weekend getaway or a month-long adventure, every journey begins with a choice – where to go. However, while the destination is undoubtedly important, it’s equally crucial to understand your preferences when planning your trip. Your travel preferences can significantly impact the overall experience, from the activities you engage in to the memories you create. This article explores why understanding your choices is crucial in travel planning and how it can elevate your adventures.

The Importance of Personalization

Minimizing stress.

Travel can be stressful, especially when faced with unfamiliar environments, languages, and customs. However, knowing your preferences can serve as a powerful stress-reduction tool. For instance, if you’re an introvert, choosing a more secluded destination or accommodations can provide much-needed solitude and tranquility. On the other hand, extroverts may thrive in bustling urban areas with plenty of social interactions. Theme parks like Universal Studios and tourist attractions may delight some travelers, while others might find them overwhelming. For the former, get tickets for Universal Studios early and plan your visit during off-peak hours to enjoy shorter lines and a more relaxed experience. You can minimize stress and enhance your overall well-being by aligning your preferences with your travel choices.

Maximizing Enjoyment

Imagine embarking on a hiking expedition when you despise the outdoors or planning a culinary tour when you’re a picky eater. In both scenarios, the likelihood of enjoying your trip decreases significantly. Understanding your preferences allows you to avoid pitfalls and select activities that genuinely pique your interest. This ensures a higher level of enjoyment and minimizes the risk of disappointment or frustration.

The Impact on Accommodation

Budget vs. luxury.

Budget constraints often play a significant role in travel decisions. Understanding your financial preferences is essential to strike the right balance between affordability and comfort. If you’re a budget-conscious traveler, you may opt for hostels, guesthouses, or Airbnb rentals . Conversely, if you prefer luxury and pampering, boutique hotels or upscale resorts might be more your style. Acknowledging your financial preferences allows you to find accommodations that fit your budget without compromising quality.

Location Matters

The location of your accommodation can shape your entire trip. Do you prefer staying in the heart of the action, within walking distance of popular attractions, or in a quieter, residential neighborhood? Your choice can impact your daily routine and overall experience. If you value convenience and accessibility, a central location might be ideal. However, a less touristy area could be more appealing if you seek tranquility and authenticity.

Planning for Travel Companions

Communication and compromise.

Effective communication and compromise are essential when traveling with others. Understanding their preferences and finding common ground can prevent conflicts and enhance the overall experience. Discuss your travel goals, interests, and expectations beforehand to create a well-rounded itinerary that accommodates everyone’s preferences.

Balance in Activities

Each member likely has different preferences and interests if you’re traveling with a group. Strive for balance in your activities and create an itinerary that includes a variety of experiences. This way, everyone can indulge in their favorite activities while exploring new interests.

Flexibility

Flexibility is crucial when traveling with companions. Sometimes, you may need to adjust your plans or make spontaneous decisions to accommodate the preferences of others. You can make the most of your collective travel experience by embracing flexibility and a spirit of adventure.

Enhancing Your Travel Memories

Authentic Experiences

Choosing activities and destinations that resonate with your preferences makes you more likely to have authentic and meaningful experiences. These experiences include interacting with locals, immersing yourself in the culture, and discovering hidden gems that align with your interests.

Personal Growth

Traveling in line with your preferences can also foster personal growth. It encourages you to step out of your comfort zone , try new things, and embrace different perspectives. Whether conquering fear, learning a new skill, or gaining a deeper appreciation for a specific culture, travel can transform and enrich your life.

Lasting Impressions

The memories you create during your travels are the souvenirs that stay with you long after you return home. By understanding your preferences and planning a trip that caters to them, you’re more likely to create lasting impressions you’ll cherish forever.

In travel planning, understanding your preferences is not a luxury but a necessity. It’s the compass that guides you to destinations and experiences that resonate with your individuality. By aligning your travel choices with your preferences, you maximize enjoyment, minimize stress, and create memories that are uniquely yours. So, the next time you embark on a journey, take the time to reflect on your preferences and let them lead you on a path of discovery and fulfillment. Travel is not just about the destination; it’s about the journey, and understanding your preferences ensures that every step of that journey is tailored to your desires and dreams.

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Travel Preferences: What’s Your Style?

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From Nigeria to Boston

When you first meet Oluwagbeminiyi Osidipe, you encounter a very vibrant, friendly, and unique personality. Oluwagbeminiyi or Niyi – as she shortened her name for simplicity – was named by her mother, who had a “very personal experience” when she had her, Niyi explained. Niyi is a Yoruba Nigerian transplant who arrived in the U.S. in 2006. As one of the most densely populated (West) African countries, Nigeria derives its name from the river that spans its land. To the South, it borders the Gulf of Guinea to the Atlantic Ocean. Originally colonized by the British, Nigeria gained independence in 1960. Its main ethnic groups are the Hausa, Igbo and Yoruba, who speak English and their own respective languages, while major religions include Islam, Christianity and indigenous beliefs. Niyi shares her story, her views on politics, cultural differences she’s embraced with humor, and what we can learn from each other by expressing curiosity. Her message is simple: travel enriches us through its exposure to new cultures, and enables us to grow.

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Mark Twain on Travel

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• Local Culture

Whether you’re a seasoned traveler, a part-time traveler (like I am), or beginning to explore, travel has something for everyone.

• Maybe you prefer guided tours where you stick with a group and explore historic sites and learn about a country’s culture together.
• Perhaps you can’t wait to travel   solo , where you make your own schedule and meet new people along the way.
• Or maybe, you love the idea of an adventure where you push your physical boundaries through hiking, biking and other activities.
• And, then there’s luxury travel with all-inclusive resorts where you get pampered and you can kick back!
• What’s your preferred travel style? (for example: luxury or adventure)? Why?
• Describe your ideal trip and how your travel style fits into that. Got a pic?
• What advice would you share with someone who might be hesitant to explore different travel styles? #CultureTravChat
• Tell us about a time you really pampered yourself during travel. Got a pic? #CultureTravChat
• Have you ever strayed from your favorite travel style to explore a new one? What did you learn? #CultureTravChat
• Ever travel with limited access to electricity or clean water? What’d you enjoy about that trip? Got a pic? #CultureTravChat
• What do different travel styles teach us about ourselves? Do they influence our experiences?

JOIN THE CHAT:

1. Follow @nicolette_o  on Twitter for chat announcements/questions. 2. Use #CultureTravChat in each tweet – so others can always read your tips! 3. Invite friends to join, and RT anything you personally find interesting.

• #CultureTravChat
• guided tour
• solo travel
• travel preferences
• travel style

Comments (33)

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[…] Alternative Modes of Transportation: A Comparative Glimpse: While the T Line presents an enticing proposition, other modes of transportation offer their own distinct advantages that cater to different travel preferences. […]

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[…] the Portofino. We’d like to assist you in determining which direction to take based on your travel preferences. Explore breathtaking landscapes, cultural treasures, and hidden gems that await you if you choose […]

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[…] into the rich cultural tapestries, we unveil a treasure trove of experiences that cater to diverse travel preferences. Prepare to be captivated by the spellbinding geographical beauty and intricate cultural heritage […]

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[…] situations. Our team can customize trips to the group’s interests based on the group’s travel preferences and expectations of an active holiday because we understand how important it is for us to enjoy our […]

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Oooh I’m really looking forward to this one! See you there!

Thanks for sharing! Excited to have you and hear about your experiences! 🙂

I love making my own itineraries so I will never do a full guided tour of a country! Solo (or with a companion) traveler all the way!

Thanks for sharing! I’m with you on that, but curious to hear more from fellow travelers. Will you be joining tomorrow? 🙂

Let us know what you think Cancel reply

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What is Travel Preferences

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ORIGINAL RESEARCH article

Complexity and simplification in understanding travel preferences among tourists.

• 1 Norwegian School of Hotel Management, Faculty of Social Sciences, University of Stavanger, Stavanger, Norway
• 2 Department of Psychosocial Science, Faculty of Psychology, University of Bergen, Bergen, Norway

Travel preferences are complex phenomena, and thus cumbersome to deal with in full width in diagnostic and strategic planning processes. The aim of the present investigation was to explore to what extent individual preferences can be simplified into structures, and if tourists can be grouped into preference clusters that are viable and practically applicable for tourism planning. Building on prior studies that have validated survey instruments designed to measure different tourist role orientations, we used a factor analytical approach to develop a simplified structure of individual preferences, and a standard clustering technique for grouping tourists into preference clusters. Further analyses indicated that preference clusters based on reduced factor preference-data are to some extent related to context-specific valuations, perceptions, and revisit intentions; however, the magnitude of differences between groups was rather small. Overall findings provide reason to suggest that the identified preference clusters are insufficient when it comes to explaining variability in which aspects tourists emphasize as part of their vacation. Possible managerial implications and methodological limitations of the present investigation are noted.

Introduction

A fundamental need for any business or public planning in the tourism sector is to map heterogeneity among people ( Dolnicar, 2008 ) and to understand the psychological processes involved in the construction of the tourist experience ( Larsen, 2007 ). Products and services offered within the sector are complex and individual preferences may comprise one or more of the following elements: nature, culture, food, activities, social interaction etc. The experiences of a destination will comprise an even more complicated process involving both elements that a person has preferences for, and numerous additional expected and unexpected exposures to elements that may be important for the total experience. Planners and managers in the tourism industry who are eager to make evidence-based decisions may therefore benefit from searching, surveying, incorporating, synthesizing, and presenting such information; for a more broad discussion on opportunities for tourism marketing research, see Dolnicar and Ring (2014) .

While information about preferences can be available from public source materials, such as the number of tourists visiting a specific destination at a given moment in time, further information that is more detailed can be collected and then be evaluated by the tourism industry itself. For this purpose, there have been numerous market segmentation studies to suggest a wide range of objective and subjective measures aimed at categorizing individuals into groups ( Díaz-Martín et al., 2000 ; Frochot and Morrison, 2000 ). The groups developed in these studies more often than not were data-driven, and in spite of them producing adequate segmentation of specific groups in particular contexts and at a given point in time, the resulting segments were less comparable across studies; thus, limiting cumulative knowledge development. And even though these studies quite often were able to simplify the data-structure, the obtained structure was typically not standard and transferable across studies, and the stability of the segments was not a major concern ( Dolnicar, 2008 ).

An inspection of the existing literature suggests that efforts to simplify and structure data addressing preference heterogeneity in tourism settings have progressed along different lines. Some studies have focused on simplifying the structure of the data by reducing its complexity through collapsing the number of independent elements into broader categories (e.g., Mo et al., 1993 ; Jiang et al., 2000 ; Gnoth and Zins, 2010 ), whereas other studies have attempted to group together travelers based on homogeneity alongside individual preferences (e.g., Yiannakis and Gibson, 1992 ; Gibson and Yiannakis, 2002 ). The empirical investigation reported as part of this paper sequentially addresses both of the aforementioned approaches. We first simplify the data structure of the preference-data through a factor analytic approach, and then group the tourists into clusters based on the simplified preference-data. These procedures are followed by a validation of the clusters by evaluating if they are useful for explaining context-related valuations, perceptions, and revisit intentions; for reviews on psychological and sociological approaches to understanding travel motivations, see Heitmann (2011) and Dann (2018) .

One of the first attempts to describe and categorize tourists stems from Cohen (e.g., 1972) who distinguished four types based on their relationship with the tourism industry and the host country. 1 On one hand, there are individuals who engage in institutionalized arrangements that have a strong need for familiarity, low interest for adventures, and little contact with local culture or hosts (described as the “organized mass tourist”), whereas others in this category are less rigid insofar that they allow some greater room for personal choice (described as the “individual mass tourist”). On the other hand, there are individuals traveling independently that restrict any meeting with the industry unless unavoidable (described as the “explorer”), and those who refuse contact with the industry altogether, put as much distance between themselves and their familiar home environment as possible, and become part of the host culture (described as the “drifter”). It is the extent by which individuals seek to move away from their familiar (social and cultural) environment that shapes their relationship with the tourism industry ( Cohen, 1972 ) and their respective mode of experience ( Cohen, 1979 ). While there is empirical evidence to support the view that tourists can be clustered according to the aforementioned types ( Snepenger, 1987 ), some scholars argued that these are merely examples of an even larger number of possible roles individuals can take on when traveling for leisure purposes ( Yiannakis and Gibson, 1992 ).

The International Tourist Role Scale (ITR; Mo et al., 1993 ) operationalizes the abovementioned taxonomy by distinguishing individuals based on their preferences on a novelty-familiarity continuum. The scale comprises of 20 items that aim at measuring one of three distinct dimensions used to differentiate tourists in an international context. One so-called “destination-oriented” dimension that measures the degree to which tourists prefer novel versus familiar holiday destinations. Another so-called “travel services” dimension that attempts to measure the degree to which tourists prefer institutionalized arrangements at their holiday destination. As well as an additional so-called “social contact” dimension, that seeks to assess the degree to which tourists prefer contact with local residents at their holiday destination. Previous research has reported empirical evidence in favor of a three-dimensional structure proposed by the original 20-item version ( Mo et al., 1994 ) as well as by a revised 16-item version ( Jiang et al., 2000 ).

With the above studies having focused upon confirming the psychometric properties of the ITR scale, others have demonstrated its predictive validity in explaining aspects of the tourist experience. For example, Wolff and Larsen (2019) showed that people who prefer traveling to familiar rather than unfamiliar destinations tend to be less likely to show an interest in trying out new food. The same study found that people who like better to avoid institutionalized forms of tourism were on average more interested in trying out new food than those favoring the opposite; the same applied to those who prefer seeking out social encounters with local people and culture. Others studies indicate that individual differences on one or more of the dimensions outlined by the ITR scale can be associated with social interactions experienced by guests ( Basala and Klenosky, 2001 ), risk perceptions about international tourism ( Lepp and Gibson, 2003 ), importance ascribed to vacation activities ( Keng and Cheng, 1999 ), and preferred social contacts with hosts ( Fan et al., 2017 ). Further research suggests that the degree to which individuals emphasize either one of the proposed dimensions can vary as a function of cultural values ( Gnoth and Zins, 2010 ).

Research Aims

Previous studies attempting to validate theoretically developed tourist typologies have employed different methods including quantitative approaches (e.g., Mo et al., 1993 ) as well as qualitative approaches (e.g., Uriely et al., 2002 ). The present paper draws upon the idea that tourists differ in their preferences, and that considering these differences can yield useful information for planners and managers in the tourism industry. Consequently, the following section reports on an empirical investigation that set out to explore the relationship between travel preferences, destination valuations, destination perceptions, and revisit intentions. Research on these aspects can provide useful insights for at least two reasons: to gain some better understanding about what tourists search for in their vacation and to provide some guidance for those interested in developing products and services that address these preferences at a given destination.

Materials and Methods

Participants.

The analyzed data comprises the responses from N = 2041 individuals who were visiting Western Norway during the summer months. Norway as a holiday destination tends to attract visitors from abroad who seek to experience both nature and culture, preferably in combination ( Innovation Norway, 2018 ). Participants were between 18 and 82 years ( M age = 37.06, SD age = 14.88), were on trips that lasted between 1 day and more than 2 years ( Mdn days = 12), and the most prevalent day for filling in the questionnaire was at day five during their current trip ( Mdn days = 5). They differed in a variety of different ways including their gender (47.8% men, 52.0% female), accommodation (camping facility 15.0%, private pension 12.7%, HI hostel 10.8%, hotel 28.3%, cruise ship 8.7%, not specified 22.6%), continent (Europe 67.6%, North America 12.8%, South America 2.3%, Oceania 5.0%, Asia 7.5%, Africa 0.7%), and tourism type (international 94.9%, domestic 3.8%). 2

To recruit participants, research assistants first approached potential participants at well-known sightseeing spots, either in person or in groups, accompanied by the question of whether they are on vacation. In a second step, research assistants invited them to participate in a study on tourist experiences granted that they affirmed positive to the prior questions. A written statement on the questionnaire informed participants that there are no right (or wrong) answers, and that each of the answers would be handled confidentially. Informed consent was implied by the completion of the questionnaire.

Each participant responded to a survey distributed as part of a larger research project investigating various aspects of the tourist experience (paper questionnaire, four pages long, English language). Items included in the survey focused upon a broad range of topics relevant to illuminate experiences, perceptions, and behaviors in tourism settings; yet, this paper focuses exclusively on the aforementioned socio-demographic characteristics (see above), as well as on measures pertaining to travel preferences, destination valuations, destination perceptions, and revisit intentions (see below).

Travel preferences were measured with a revised 16-item version ( Jiang et al., 2000 ) of the ITR scale ( Mo et al., 1993 , 1994 ). The revised scale comprises three subscales that measure reported preferences with regard to the destination itself, travel arrangements, and socio-cultural aspects linked to traveling. Respondents were asked to indicate their agreement to each of the item statements presented on a 7-point Likert-type scale (1 = Strongly disagree, 7 = Strongly agree; see Table 1 ).

Table 1. Measurement items for travel preferences.

Destination valuations were measured with 13 item statements asking participants about how important various aspects relating to their current trip were when they initially bought the trip. For each statement, respondents were asked to rate the importance of the aspect when buying the current trip using a 7-point Likert-type scale (1 = Not important, 7 = Very important; see Table 2 ).

Table 2. Measurement items for destination valuations a and destination perceptions b .

Destination perceptions were assessed using a similar approach as outlined above; that is, the same selection of items were rated on the extent to which the present trip to the country offered each respective aspect, which was again made based on a 7-point Likert-type scale (1 = Not at all, 7 = Very much; see Table 2 ).

All surveys contained the measures described above, yet one share of the distributed surveys ( n = 1162) further included three items to measure revisit intentions. Respondents indicated the likelihood by which they – within the next 3 years – would repeat the current trip, revisit the same city, and/or revisit the same country. Higher scores on a 7-point Likert-type scale were taken as greater revisit intentions respectively (1 = Very unlikely, 7 = Very likely; see Table 3 ).

Table 3. Measurement items for revisit intentions.

The statistical analyses were performed with IBM SPSS Statistics v. 25 in the following order. First, we re-validated the factor structure of a revised version of the ITR scale. Second, we used the developed factor structure for grouping tourists into preference segments (clusters) and then we evaluated the concurrent validity of the groups by analyzing demographic differences between these segments. Third, we analyzed if (and how) the valuation of destination aspects varies between groups; the same analysis was run with destination perceptions and revisit intentions as dependent variables.

Factor Analyses

Jiang et al. (2000) suggested three substantial factors addressing destination-oriented, socio-cultural, and travel arrangement aspects when people choose to go on vacation abroad. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was well above 0.5 and Bartlett’s test of sphericity was significant at p < 0.001, which indicates that the current data was suitable for factor analysis (see Field, 2018 ). Table 4 contains results from an exploratory factor analysis (principal axis analysis with direct oblimin rotation). The analyses produced a slightly diverging pattern with four factors having an eigenvalue greater than one. The fourth factor (eigenvalue 1.23), containing items O5, O12, and O15, split the travel arrangement factor in two. A parallel analysis ( Horn, 1965 ) performed on 1000 random datasets with 16 variables resulted in an average eigenvalue of the fourth factor of 1.09, indicating that the eigenvalue of the fourth factor of the present set is marginally above a random value.

Table 4. Pattern matrix, exploratory factor analysis, principal axis, oblimin rotation, 16 items.

Table 4 shows that most items were well accounted for by the factor structure, with factor loadings above 0.40 on the main factor and comparatively low cross loadings. Item O13 was excluded from further analyses because only items with factor loadings greater than 0.40 were retained for more exploration. Item O15 showed a modest loading on the travel arrangement-two dimension, but also a non-negligible cross loading on the destination-oriented dimension. This item was retained for further analyses after an inspection of its corresponding Marker Index, which was above the recommended 0.40 threshold ( Gallucci and Perugini, 2007 ).

Rather than unequivocally reproducing the factor structure reported by Jiang et al. (2000) , the current data suggests that the scale could be developed further to improve fit to the data, particularly by splitting travel arrangement aspects in two factors. Including an extra factor would nevertheless only account for a marginal part of the total variance, whereas some of the common variance of this tentative factor may further be due to sample- or context-specific features. We therefore chose to keep the three-factor structure to allow direct comparisons with other studies that have utilized the scale. This means that we calculated average scores for each individual respondent on each of the three dimensions displayed in Table 5 . Each item was weight equally (unit weights) and cross loadings of items were not included ( Steenkamp and Baumgartner, 1998 ). An overview of descriptive statistics, Cronbach’s alpha, and bivariate correlations of the three dimensions is reported in Table 6 .

Table 5. Pattern matrix, exploratory factor analysis, principal axis, oblimin rotation, 15 items.

Table 6. Descriptive statistics, Cronbach’s alpha, and bivariate correlations.

Cluster Analyses

Distinct clusters were uncovered using a multistep cluster analysis with individual scores on the three dimensions as inputs. We used a simplified version of the procedure recommended by Dolnicar and Leisch (2003) . The respondents were randomly split into an analysis sample and a validation sample. We used the non-hierarchical K-means method on the analysis sample to identify an initial optimal solution that was next used as cluster seeds for a restricted analysis in the validation sample. The constrained solution was then compared to an unconstrained solution in the same sample; the fit was evaluated with the validity coefficient Kappa. This procedure was repeated for solutions with two to six clusters. Kappa values for the two-, three-, four-, five-, and six-cluster solutions were 0.57, 0.81, 0.74, 0.99, and 0.62 respectively. The high value for the five-cluster solution suggested that these clusters were homogenous and distinct from each other. A discriminant analysis with the five clusters as the dependent variable and dimensions as the independent variables correctly classified 98% of the cases. Thus, individual preferences on the three dimensions yield a high degree of convergence and cohesion along the five preference clusters (see Table 7 ).

Table 7. Mean differences in travel preferences by cluster.

Table 7 also reveals that the five clusters differ significantly along each dimension. Cluster 1 is high on the destination-oriented, the socio-cultural, and the travel arrangement dimension, corresponding to the “individual mass tourist”. Cluster 2 is high on the destination-oriented dimension, and low on the socio-cultural and travel arrangement dimensions, which corresponds to the preferences of the “organized mass tourist”. Cluster 3 is low on the destination-oriented dimension but high on the socio-cultural and travel arrangement dimensions, which is similar to the “drifter”. Cluster 4 is similar to the aforementioned in the sense that it is low on the travel-oriented dimension, which makes it correspond to the “explorer”. Cluster 5 scores fairly low on all three dimensions; this type appears to be similar to the previous, albeit with no strong preferences for social contact with locals and cultural immersion. We suggest labeling this additional group as the “lone explorer”, noting that the viability and structure of the group still has to be validated. Each one of the five emerging preference clusters reflect shared perceptions of 14, 28, 23, 16, and 20 percent of the respondents respectively; and even though some clusters are slightly larger than the “organized mass tourist” configuration, each one reflects the shared perceptions of a significant number of tourist.

To test whether the clusters revealed in the analysis can be associated with individual factors, we ran a cross-table with gender as dependent variable and the five clusters as independent. There were no significant gender differences χ 2 (4) = 2.12, p = 0.71. We also checked for age differences between the clusters. An analysis of variance suggested that the identified clusters were not closely related to age, F (4, 1954) = 9.28, p < 0.001, except for the “organized mass tourist” segment being slightly older ( M = 39.01) and the “explorer” segment slightly younger ( M = 33.55) than the total average ( M = 37.92). Table 8 reports the results on whether the continent participants originated from related to the clusters. Though the number of respondents was quite low in some continents, and that not all differences were significant, the table indicates that “explorer” (Cluster 4) and “lone explorer” (Cluster 5) originate in somewhat different proportions from the continents.

Table 8. Cluster members by continent.

Further Cluster Validations

If we assume that the clusters based on travel preferences are meaningful, they could relate to valuations of a destination before the trip as well as to perceptions of a destination during the visit. To simplify further analyses, we performed an exploratory factor analysis (principal axis analysis with oblique rotation) on items measuring the initial buying valuations. Item TA1 (affordable price) did not load on any factors, which is why it was decided to remove TA1 from the further analyses. Item TA4 (opportunity to go fishing) formed a single factor, and since this could be a context-specific valuation, we decided to also exclude this item. The remaining items formed three substantial factors that could be labeled “sustainable” (i.e., TA11, TA12, TA13), “nature” (i.e., TA2, TA3, TA6), and “active” (i.e., TA5, TA7, TA8, TA9, TA10). For each factor, average scores were computed with unit weights and no cross loadings; the same factor structure was thereafter used to compute three corresponding average scores for items measuring destination perceptions.

Relationships between the clusters and destination valuations were evaluated with an ANOVA with the valuation of elements of the buying process as dependent variables and the five clusters as the independent variable. The expectation is that preference clusters, because they have different preferences, will value these elements quite differently. A similar set of analysis was run with destination perceptions as dependant variables and the five clusters as the independent variable.

Table 9 shows that although not all differences are significant, destination valuations and destination perceptions tend to vary between preference clusters; however, these differences are comparatively small considering the available response scale options. The same table reports results on associations between the preference clusters and revisit intentions, showing that some clusters differed in their intention to repeat the trip, as well as in their intention to visit the city or the country again.

Table 9. Mean differences in destination valuations, destination perceptions, and revisit intentions by cluster.

The analyzed data failed to invariantly replicate the factor scores from earlier applications of the revised ITR scale ( Jiang et al., 2000 ), which is in line with other studies ( Gnoth and Zins, 2010 ). These small irregularities may signal that the factor structure of the scale is not yet fully developed, or they could be due to contextual or sample-specific processes in the current data. Because the aim was to further opportunities for comparative analyses, and since the analyzed data only weakly suggested a four-factor solution, we chose to stay with a three-factor structure (see Table 5 ).

Our analyses indicated that based on the three dimensions tapped into by the revised ITR scale, individuals can be meaningfully grouped into clusters that mimic the four types by Cohen (1972) . A closer look at the results from the cluster analyses suggested to furthermore distinguishing the “explorer” type into two sub-forms. Both prefer to visit new places, yet one group prefers cultural immersion and to meet and blend with local people, whereas the other group puts much less emphasis on cultural immersion and social contact. This supports previous literature suggesting that dividing the proposed four types further into sub-forms could prove useful to elicit information about more homogenous groups ( Uriely, 2009 ). A next step could be to test if the same clustering pattern emerges among individuals who visit destinations other than currently investigated. Further applications of the revised framework will open for much needed comparable studies, which have the potential to benefit the communication between cooperating stakeholders.

The clustering had a high discriminant validity in that most of the participants clearly belonged to one, and only one of the preference clusters. Nonetheless, these different clusters did not report very different valuations of destination aspects when they initially bought the trip, and only moderate differences in their intentions to return. This seems to suggest that although the tested framework has been proliferating in research, clusters derived from individual preferences for novelty and familiarity are not complete for explaining individual destination choices. An interpretation of these pattern results is that in spite of its intuitive appeal, the present study only establishes weak evidence that the suggested taxonomy has nomological validity at the individual level ( Churchill, 1979 ).

Results from the present study suggested preferential differences between tourists from different continents, in particular between the two segments in our suggested expansion of Cohen’s explorer segment. For example, the proportion of those classified as a “lone explorer” was greater for individuals from the Oceania region than for any other subsample. If it should be the case that the identified preference clusters reflect stable patterns, these might as well relate to many aspects of the tourist experience, of which we have explored only a small fraction. Research in this vein may consider including measures of cultural values to increase the robustness of its findings. There is some indication in the literature that individual scores on dimensions included in the ITR scale can be explained partly through values that are shared by individuals visiting a given destination ( Gnoth and Zins, 2010 ).

Regarding the extent to which broader travel preferences are attached to what aspects tourists emphasize in their vacation, as can be indicated through valuations and perceptions, there was only low predictive validity. This fits to an earlier investigation by Larsen et al. (2011) who found that budget travelers were more likely to agree with statements that ascribe themselves to the “drifter” than mainstream tourists. Although the former were on average more likely to see themselves as more individualistic compared with their mainstream counterpart, both groups were quite similar when it comes to other psychological characteristics. Larsen and colleagues speculated that this might be rooted in the self-perception of backpackers and their socially constructed views of themselves as a group with distinguishing characteristics. Our current findings support this interpretation in the sense that revisit intentions were only to a limited extent (if at all) associated with broader travel preferences, at least when considering the three dimensions included in the revised ITR scale. This ties in well with literature suggesting that having a sole focus on the extent to which individuals seek novelty versus familiarity provides an incomplete picture with regard to understanding the complexity of tourist behavior ( Chen et al., 2011 ).

The revised ITR scale has a potential to reproduce stable clustering for practitioners across time, travelers and contexts, and theoretically meaningful and comparable clusters, but much research remains before unequivocal management recommendations can be made. 3 In our analyses of the preference-data, we applied the simple K-means clustering technique that is sensitive to sample specific variance. By our rigorous use of estimation samples and validation samples for establishing the number of clusters, as well as the cluster structure, we lowered the risk of sample specific findings. An important fact is that the sample was rather heterogeneous despite that all parts of the data collection took place at the same destination. It contains individuals with different socio-demographic characteristics, that varied in the duration of their traveling, and that were approached at different points during their trip. All this together lowers the probability of sample specific findings, but still, the study will have to be replicated with samples from other destinations to support our present findings.

Forthcoming studies that follow up on our suggestion to replicate the current findings in other destinations could address some limitations of the present investigation. First, given that all constructs were assessed in the same questionnaire, the predictive ability of the clusters could have been inflated by common method variance. We tried to design the questionnaire to limit common method variance by keeping measures of the different constructs well separated in the questionnaire, by wording them differently, by applying different response scales, and to lower method-dependent social desirability issues by letting the participants complete the questionnaire by themselves. Still, these remedies cannot exclude the presence of common method variance in the data ( Podsakoff et al., 2012 ). Future studies are well advised to use different methods for assessing behavior associated with each cluster to get reliable and valid estimates of the associations ( Podsakoff et al., 2003 ). Second, it is because of the cross-sectional design of the current study that we cannot make any conclusion about whether the clustering based on individual scores of the revised ITR scale remains stable across time. If such stability can be demonstrated for at least certain time periods, an interesting future development would be to predict future buying behavior within these periods. Third, forthcoming studies would benefit from broadening the scope toward measures of actual buying behavior since intentions to return are an imperfect predictor for future destination choices ( McKercher and Tse, 2012 ). Fourth, Cronbach’s alpha was acceptable for the destination-oriented (α = 0.77) and socio-cultural dimension (α = 0.73) but poor for the travel arrangement dimension (α = 0.50), based on conventional guidelines (see Streiner, 2003 ). This represents a noteworthy limitation to the present investigation as clustering that relies upon aggregated measures on each of the three dimensions may lead to biased and less precise clusters, which in succession, may pose a threat to the validity of the reported findings.

This paper shows that findings from earlier applications of the revised ITR scale replicate to a certain extent in the current sample; the three main dimensions of the scale are with a few modifications reproduced. Further analyses show that the tourists can be grouped (clustered) into preference segments based on the main dimensions included in the scale, which in turn appear to be quite similar to the suggested tourist types of Cohen (1972) . When comparing data on the importance assigned to specific aspects when buying the trip, or on the extent to which these aspects were offered at the destination, the various preference segments were dissimilar. Since the magnitude of these differences was rather small, preference-based clusters of tourists have nonetheless only to a limited extent different valuations and perceptions; the same seems to be the case when comparing different groups on their revisit intentions.

It is important to note that belonging to one particular preference cluster may not be unequivocally stable, just as tourist roles might develop and change over time ( Gibson and Yiannakis, 2002 ). To get a deeper understanding of the psychological processes that take part in shaping tourist experiences, future research could extend the scope beyond theoretically developed taxonomies as employed in the present case. It seems plausible that the extent by which initial valuations correspond with perceptions during the trip constitutes another aspect that feeds into how a destination is experienced, and related decisions, potentially more so than whether individuals report to prefer more (or less) novelty (or familiarity) if traveling away from home. A more detailed discussion on how a psychological approach can be a useful starting point for social scientific enquiries on tourist experiences is provided by Larsen (2007) as well as Larsen et al. (2017) .

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

This work complied with the general guidelines for research ethics by the Norwegian National Committees for Research Ethics in the Social Sciences and the Humanities (NESH). Formal approval by an ethics committee was not required as per applicable institutional guidelines and regulations.

Author Contributions

RD, SL, and KW contributed to the conception, design, and data collection of the study. TØ performed the statistical analysis. TØ and RD wrote the first draft of the manuscript. All authors contributed to the manuscript revision, read, and approved the submitted version.

Data collection was funded by the Department of Psychosocial Science (Småforsk).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

• ^ The assumption that novelty seeking is an important factor underlying individual travel decisions can also be found in the social psychological model of tourism motivation ( Iso-Ahola, 1982 ), the psychographic approach ( Plog, 2001 ), and the travel career pattern approach ( Pearce and Lee, 2005 ) amongst others.
• ^ The reported number only refers to respondents who were 18 years and over at the time of the data collection. Total percentages are not 100 for each of the listed categories due to some missing values.
• ^ Note that our empirical development of the preference clusters was based on the revised ITR scale, which is but one of a number of theoretically founded alternatives (e.g., Yiannakis and Gibson, 1992 ; Keng and Cheng, 1999 ).

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Keywords : tourist role orientation, destination valuations, destination perceptions, revisit intentions, novelty, familiarity

Citation: Øgaard T, Doran R, Larsen S and Wolff K (2019) Complexity and Simplification in Understanding Travel Preferences Among Tourists. Front. Psychol. 10:2302. doi: 10.3389/fpsyg.2019.02302

Received: 01 May 2019; Accepted: 26 September 2019; Published: 17 October 2019.

Reviewed by:

Copyright © 2019 Øgaard, Doran, Larsen and Wolff. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Torvald Øgaard, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Change in travel accommodation preferences continues

Despite successful vaccination programs in many countries around the world, the continued challenges around COVID-19, like the new Omicron variant, have brought back travel restrictions and social distancing requirements across the globe. STR’s Tourism Consumer Insights team continues to keep a close eye on tourism trends as well as travel accommodation preferences because of the pandemic.

In November 2021, shortly before news of Omicron had surfaced, STR undertook a new online survey using its Traveler Panel –an engaged audience of travel consumers–to examine sentiment and how it might affect industry fortunes at this uncertain time. The research gathered the views of nearly 1,500 global travelers.

Leisure preferences

A new standard of preferences has been set since the beginning of COVID-19, and accommodation in the current situation continues to be less desirable than in pre-pandemic times.

Travelers continue to prefer short-term rentals and smaller size hotels (properties with less than 50 rooms) due to lingering concerns about the virus. In November 2021, traveler interest in short-term rentals was 12% above the pre-pandemic level of interest. In the context in which consumers continue to seek space and wish to minimize their contact with others, it is perhaps not surprising that self-catering and smaller sized accommodation continue to standout.

On the flipside, due to the room share factor and communal facilities, hostels are significantly less attractive compared with pre-pandemic times (-61% net interest in November 2021). At the same time, there’s a sense that economy and budget operators are less well perceived in the current situation as well. 

Reflecting these accommodation preferences, travelers continue to seek out more rural experiences to escape the crowds and engage more with the outdoors. More than 30% of respondents agreed that they preferred these types of trips in the current situation compared with before the pandemic.

Many have been forced to holiday at home in the last year or so and there is a sense now that domestic tourism is more appealing (as well as being more common) than international tourism (domestic trip +11% net interest versus international trip -12% net interest). This is perhaps due to a combination of recent positive staycation experiences and concerns around the potential hassle and inconveniences of international travel, such as testing protocols, potential quarantine, and infection risk.

Another type of trip which is less appealing in the current environment is event-specific trips (-48% net interest). This is likely linked to heightened concerns of infection by attending a mass event.

Traditional determinants of choice

The importance of reviews and recommendations has increased in recent waves. More than 20% who had booked accommodation recently stated these were a key factor which had influenced their choice where to stay. Meanwhile, the importance of cancellation policies continues to diminish (18% in November 2021 vs. 23% in July 2021 vs. 29% in February 2021).

Interestingly, while consideration of hotel social distancing policies appears to be less significant now with fewer personal health concerns due to vaccination, there has been no decline in the importance of cleanliness. This finding suggests that consumers may have new perspectives and expectations regarding cleanliness post-pandemic.

The latest research shows that accommodation preferences and behaviors are continuing to shift and evolve in response to the current COVID-19 situation. Aspects which were important in the past, like the location and perceived value for money offering of accommodation, continue to be relevant and important. However, new preferences and behaviors have led to new opportunities and challenges for the industry. Efforts to capitalize on consumers’ new-kindled interest in the outdoors and a “sense of space,” and enabling flexible bookings are a few relevant strategies for remaining competitive.

NOTE: This research was undertaken in early November 2021 before the Omicron variant emerged. As a result, respondents’ views do not represent the newest situation around COVID-19.

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Aerial views: How brands can cater to a new breed of traveler as borders reopen

The travel industry is kicking into gear. Border and quarantine restrictions are easing up with increasing vaccination rates, and people are ready to travel even as the pandemic lingers. 1 Those traveling at this time, however, will no longer behave like pre-pandemic travelers.

Indeed, new research on APAC’s four biggest travel markets: Australia, India, Indonesia, and Japan, reveals that among travelers now, there is a 3X increase in intent to travel internationally. Sixty-one percent of travelers have also indicated a preference toward international travel for future leisure vacations, and the majority intend to travel for longer periods, and plan to visit only one or two countries per trip. 2

With this shift in travel trends from “when” to “how,” brands will have to adapt to the needs, preferences, and expectations of this new breed of traveler, and find ways to reach and excite them to go on trips.

Here’s what we’ve learned about this new breed of traveler that can help your brands prepare for the future of travel.

The traveler we’ve not met before

Given the complex nature of traveling during a pandemic, travelers will need to spend more time researching and planning, and they will want to get the most out of their trips. Across the four markets, we saw a 17% increase in the average booking time. In particular, travelers spent an average of 56 days planning for international travel, which is 30% longer than the time taken to plan domestic travel. 3

The effort that goes into planning international leisure trips means that for the new breed of traveler, such trips are likely to be longer and more focused milestone events than was the case pre-pandemic. Our research shows that travelers are twice as likely to make fewer trips than before, and they are also 3X more likely to cover only one or two countries per trip. 4

When they travel, they’ll make time to do, see, and spend more: 25% say they will travel for more than two weeks, and around 87% of travelers will organize international trips that last five days or longer. 5 This is an increase from 2019, when tourist stays at international accommodations averaged three to four days. 6

The preferences of this new breed of traveler mean it’s even more critical for brands to engage them throughout the path to purchase, from research and discovery to bookings and activities.

The new breed of traveler

They also have a strong preference for luxury and convenience, and they are willing to spend more to pamper themselves. For one, we’ve seen a growth in clicks for accommodations that are more than $300 per night. 7 Additionally 78% of travelers say they would be interested in luxury stays and experiences, with 77% interested in package holiday tours. 8

When these travelers have to quarantine as part of their trip, they prefer to spend their time meaningfully. Our research shows that they are twice as likely to opt for entertainment-related amenities in their quarantine accommodation, including streaming services and fitness equipment, over and above options such as upgraded meals, bigger rooms, and balcony views. The only exception was with travelers from Japan, for whom the option to have a balcony and fresh air appealed the most. 9 For hotel, lifestyle, and entertainment brands, this means an opportunity to get creative and offer services that will appeal to this new breed of traveler.

Wooing the new traveler as borders reopen

With the industry seeing a fundamental shift to a less-frequent and high-ticket-size travel model, marketers in the know have been adjusting their business models accordingly. For example, Rakuten Travel has been catering to this new breed of traveler by promoting its luxury hotel inventory.

Sustain engagement over diverse marketing channels

As a niche holiday destination, Tourism New Zealand knew it had to get a head start on engaging travelers, so it launched a multimarket campaign in key international markets, including Australia, telling travelers to “stop dreaming about New Zealand and go.”

The campaign ran across all major channels, including cinema, TV, on-demand, social, and digital to reach as wide an audience as possible. PR and trade activity also supported the campaign.

René de Monchy, chief executive of Tourism New Zealand, says: “We found we had to keep engaging with consumers to get them to dream about New Zealand. We also really accelerated our digital channels by enabling them to convert business for New Zealand.”

Use digital to reach and inspire travelers

To stay top-of-mind among travelers, travel-booking company Klook experimented with live events on its mobile app, where it could reach a wide audience with content geared toward their various interests.

Some live events were sales-driven, whereas others invited people to share travel ideas and trends. The live sessions enabled audiences to interact with the hosts and to connect with others on the livestream. One session hosted by a celebrity, for example, received over 11,000 comments from participants within the first hour of streaming, and many of the comments were from people sharing travel ideas and suggestions.

By offering entertaining and educational content around travel, Klook gave people reasons to open its travel app and start thinking about and planning for future travels.

Indeed, brands that understand and meet the needs and expectations of this new breed of traveler are well-poised to capture travel demand as it rebounds. To do this, brands should keep up-to-date with changing traveler preferences and adapt quickly to shifts in demand. Investing in a strong digital presence will also help brands reach APAC’s growing online population and be ready for the future of travel.

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1,2,3,4,5,8,9 Google-commissioned Kantar Travel Trends research, AU, ID, IN, JP, n=3999, 2021.

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Check Out the Factors Influencing Airline Preferences and Booking Behavior

When it comes to choosing an airline for their travel needs, travelers consider various factors that influence their preferences, loyalty, and booking behavior. From pricing and convenience to service quality and loyalty programs, these factors play a crucial role in shaping travelers’ decisions. Let’s delve into the key factors influencing airline preferences and booking behavior. And explore how airlines can cater to these preferences to attract and retain customers.

Pricing and Value for Money

Price is often a key consideration for travelers. They compare ticket prices across different airlines to find the best deal that offers value for money. While budget-conscious travelers may prioritize lower fares, others may be willing to pay a premium for added benefits such as flexibility in ticket changes or more inclusive fares. Airlines that offer competitive pricing and transparent fare structures are more likely to capture the attention of cost-conscious travelers.

Flight Schedule and Convenience

The convenience of flight schedules plays a crucial role in travelers’ decisions. Airlines that offer a wide range of departure and arrival times, as well as multiple route options, give travelers the flexibility to plan their trips according to their preferences. Additionally, airlines that operate from well-connected airports and provide efficient ground services contribute to a seamless travel experience.

Service Quality and Reputation

Service quality and reputation greatly impact travelers’ choices. It is one of the key factors influencing airline preferences and booking behavior . Positive experiences, such as friendly and attentive staff, comfortable seating, on-time departures, and efficient baggage handling, enhance overall customer satisfaction. Airlines that consistently deliver exceptional service and prioritize customer needs are more likely to gain loyal customers and positive word-of-mouth recommendations.

Safety and Reliability

Safety and reliability are paramount for travelers when choosing an airline. Airlines that have a strong safety record, adhere to strict maintenance and operational standards, and invest in modern aircraft instill confidence in passengers. Regularly updated safety protocols, transparent communication about safety measures, and efficient crisis management procedures create trust and positively influence travelers’ preferences.

Loyalty Programs and Rewards

Loyalty programs play a significant role in travelers’ booking behavior. Airlines with robust loyalty programs that offer attractive rewards, such as free flights, upgrades, priority boarding, and access to exclusive lounges, can significantly influence travelers’ choices. Providing personalized offers and incentives to loyal customers further strengthens their allegiance to the airline.

Ancillary Services and In-Flight Amenities

Travelers appreciate airlines that offer a range of ancillary services and in-flight amenities to enhance their travel experience. These may include Wi-Fi connectivity, in-flight entertainment, comfortable seating options, quality meals, and extra legroom. Airlines that go the extra mile to provide these additional services create a positive impression and differentiate themselves from competitors.

Brand Reputation and Image

The brand reputation and image of an airline significantly impact travelers’ perceptions and preferences. Airlines that have a strong brand presence, positive customer reviews, and a distinct identity are more likely to attract travelers. Establishing a clear brand message that resonates with the target audience helps create a strong emotional connection and fosters loyalty.

Also Read : Indian Eagle Flight Booking

Environmental Sustainability

In recent years, travelers have shown an increased interest in airlines that prioritize environmental sustainability. Airlines that demonstrate commitment to reducing their carbon footprint through initiatives such as fuel-efficient aircraft, waste reduction, and carbon offset programs appeal to environmentally conscious travelers. Incorporating sustainable practices and communicating these efforts can positively influence travelers’ preferences.

These are several factors influencing airline preferences and booking behavior. Airlines that understand these factors and tailor their services accordingly have a better chance of attracting and retaining loyal customers. By focusing on pricing, convenience, service quality, loyalty programs, safety, in-flight amenities, brand reputation, and environmental sustainability, airlines can create a competitive edge in the industry and provide a positive travel experience for their customers.

Book cheap flights from USA to India through Indian Eagle and experience excellent customer support during the booking process. You can also make use of the ongoing offers to save further. If you are booking a flight before 30th June, get $25 off using the special Father’s Day discount . 

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Interview Questions

Comprehensive Interview Guide: 60+ Professions Explored in Detail

How to Answer “Are You Willing to Travel?” (Interview Question)

By Biron Clark

Published: December 5, 2023

If a job involves any travel, you’re likely to hear interview questions like, “Are you willing to travel?” “How much are you willing to travel?” etc.

So in this article, I’m going to walk you through how to answer all of these interview questions. And we’ll look at how to understand the meaning of “travel percentage,” so you’ll know what the job is really going to require before you say “yes” or “no.”

And finally, I’m going to share multiple word-for-word example answers to help you get confident and comfortable with this type of question.  So make sure you read until the end. 

Let’s get started…

Answers to “How Much Are You Willing to Travel?”

If they ask an open-ended interview question like this about your willingness to travel, you should state your answer as a percentage.

For example, you could say:

“I’m willing to travel up to 30% of the time. That’s what I did in my last job, and I know I’m comfortable with that amount.”

They may ask you directly for a percentage, with a question like, “what percentage are you willing to travel?” and you’d answer that in the same way. What does travel percentage mean, though? If you’re not sure, it’s essential to understand. So let’s discuss the meaning of “travel percentage.”

Travel percentage meaning: What is travel percentage?

So what does 70 percent travel mean? It means that the employer expects you to be traveling or in cities other than your home city for 70 percent of your working days. So you would expect to spend seven days traveling or away from home for every three days in your home town/office.

This is a very high amount of travel. In my experience working as a recruiter , most travel jobs are 50% or below, because this is less stressful and more sustainable for the worker. So, this is something to keep in mind when deciding how much you’re willing to travel, and whether you’ll take or decline the job offer . 

How to Answer, “Are You Willing to Travel X Amount?” – Examples

The hiring manager may also come out and tell you how much travel is involved, and then ask an interview question to determine if this is an acceptable travel amount. In this case, if it’s acceptable to you, then you can indicate that you are on-board with what they’re proposing. For example, you could say:

“That amount of travel will work for me. In my last company, I traveled that same amount, and it worked out fine.”

(It’s always good to show you’ve done something successfully in the past. This is the best way to improve to a new employer that you’ll be successful with them, too!)

No worries if you haven’t traveled for a job before, though…

Here’s an example of how you could still answer this question:

“That amount of travel sounds acceptable to me. I have no problem doing that for this role.”

Here’s another example:

“That sounds acceptable to me. I’d love to hear more about the role, and if it’s a good fit, then I am able to travel.”

Make Sure You Know What You’re Agreeing To

Another thing to keep in mind is the actual travel schedule. Two jobs could both have the same travel percentage – let’s say 50%. But one could have you spending two weeks away and then two weeks at home, while the other could have you traveling for 2-3 days at a time, returning, and doing it all again a few days later.

Depending on your family, children, etc., you may be able to handle one of these travel requirements but not the other. So the travel duration and schedule are two factors you should clarify before answering. You can say, “I would like to understand the company travel schedule a bit better. Can you give me an example of how long each trip would be, or what a typical month looks like?” This will help you get a clear picture of what your work schedule would look like before you answer the interview question. So don’t be afraid to ask questions of your own. You can’t answer interview questions like, “Are you willing to travel for this job?” without knowing what the company expects! For example, if they ask, “Can you travel if the job requires it?” you’d want to respond by saying, “How much travel is expected in the role?” You can’t give a good answer without knowing what they’re proposing or asking, so clarify that first. Once you know what the company expects, then it’s time to directly answer their question and indicate whether you can travel the amount they require.

You Can Also Try to Negotiate Your Travel Percentage/Willingness to Travel

If you’re interested in the job but can’t travel quite as much as they’re proposing, you can say:

“I don’t think I can travel quite that amount. The job and work sound interesting, and I’d love to consider the position if the travel requirements can be reduced to 30%”.

This may work, or it may not (depending on the role and company’s flexibility), but it’s worth asking! This way, you’ll find out the best they can do! You never know if they’re asking, “How much are you willing to travel?” because it’s a hard requirement, or if they’re just wondering how much you’re willing to do So give an honest answer and don’t be afraid to make a counter-proposal.

A lot of job seekers are afraid to set limits or “push back” in a job interview, but this can actually make you more attractive to the company. It shows confidence! However, you also don’t want to rule yourself out in an interview. So if you’re not quite sure, but think it’s possible to travel the amount that the company would like, just say “yes” for now. You’re not accepting the job or signing a contract. You’re just indicating whether this might be possible for you. And your goal in any interview is to get invited to the next step in the process… or get a job offer. So if you think it’s even remotely possible to travel the amount they want, then yes “Yes” and keep interviewing!

You can always go home and talk to friends and family and make a better decision about whether this is right for you! You do NOT need to decide this in the interview!

How to Answer, “Are You Willing to Travel or Relocate?” – Examples

This is a slightly different question. But just like with the questions and sample answers above, you should give an honest, upfront answer. There’s no sense in wasting their time if you absolutely cannot relocate. But if it’s even slightly possible, say “Yes” when an employer asks if you’re willing to relocate. Don’t rule yourself out. 

Remember: Your goal in the interview is to impress them and get invited back to the next round – so keep going with the job interview, and ask questions to learn more as you go! You’re NOT wasting the recruiter’s or hiring manager’s time by exploring the opportunity, as long as there’s a tiny chance you’d be willing to travel or relocate for the job. They want the opportunity to sell you on their position! I can’t stress this enough: You’re not wasting their time. I hear a lot of job seekers bring up concerns about this, so I just wanted to set the record straight!

You should now know what travel percentage is, and how to answer any time an employer asks about what percentage you’re willing to travel.

Remember – you’re not signing a contract or agreeing to anything in writing; you’re merely indicating whether this could potentially work (for the right opportunity). So stay calm, use the sample answers above, and be direct/concise when responding in a job interview.

This isn’t one of those interview questions where the hiring manager needs to hear a long-winded answer. So once you’ve answered the question, stop and let the interviewer move on!

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Posted by Guest Post on Feb 15th, 2023 at 13:11

Guest Post: The traveller preferences that will shape 2023 and beyond

Cheryl Miller, senior vice president and chief marketing officer of Expedia for Business, says points to the key findings in the OTA's  Traveler Value Index 2023

After several years of lockdowns and restricted travel the appetite for travel has never been bigger. 

According to Expedia Group’s recent Traveler Value Index 2023 study , nearly half of consumers globally (46%) feel that travel is more important to them now than it was before the pandemic, and almost as many (43%) are planning to increase their travel budgets in 2023. 

The number of people who say they are planning to take a leisure trip within the next year is also on the rise, with more than two-thirds (79%) planning to do so. 

As the travel industry continued to rebound last year, there is no question that it also experienced unprecedented, and likely permanent, changes. 

Arguably the most significant change is the higher expectations travellers now have for their entire travel experience. 

Expedia Group’s Traveller Value Index examined what travellers truly want, and how travel professionals can tap into that demand to provide the best end-to-end experience possible.

New Motivators for Travel

As a starting point, travel professionals should be aware of why people are travelling. 

According to the study, nearly half (49%) of consumers cited mental and physical well-being and change of scenery as their top reasons for travel in 2022, and self-care will continue to remain a priority in 2023. 

Many people also view travel as a way to gain new experiences (43%) and get out of their comfort zones (22%). 

Loyalty programs are also increasing in importance. More than half of consumers (53%) say that it is more important to travel with loyalty providers than before the pandemic, with 52% valuing discounted pricing from those programs the most. 

The good news is that 75% of travel providers are operating loyalty programs to encourage repeat bookings. 

It is important to make processes for redeeming these points and vouchers as easy as possible to capitalise on this demand and ensure travellers start off their trips right.

Flexibility is Still In Demand

The travel industry is far less tumultuous today than it was during the height of the pandemic. 

However, travel preference has permanently shifted to favour flexible reservation policies in case the need to change or cancel a trip should arise. 

In fact, nearly half of the respondents would not book non-refundable lodging or transportation domestically, and more than half say they wouldn’t book non-refundable lodging or transportation for an international trip. 

An overwhelming number of travel businesses adapted to this change in consumer behaviour. 

Ninety-six per cent say they offer refundable services or credits, and most of them (77%) introduced some of their refundable offerings because of the pandemic. 

Travel providers who offer flexible policies will be the ones best positioned to give travellers peace of mind in times of continued uncertainty.

Travellers Will Vote with Their Values

Travellers are increasingly choosing to book with providers that align with their personal values. In fact, more than two-thirds (70%) say they are more likely to choose more inclusive travel options, even if it means paying more. 

A staggering eight in ten (78%) of consumers say that they’ve made a travel purchase based on promotions or ads they feel represent them through messaging or visuals. 

The travel industry is quickly adapting to this continuing trend: three in five organizations (60%) made changes in the last year to ensure their services are inclusive and accessible, and an additional 21% have plans to do so.

UK travellers searching for last minute getaways 

Consumers in the UK are the most eager to set sail overseas, with forty-three per cent of UK consumers revealing they are very likely to book or have already booked an international trip in the next 12 months, ranking higher than the global average (30%) internationally. 

This eagerness has transpired into last-minute bookings, with almost half (49%) of UK consumers comfortable booking trips less than a month in advance. 

Therefore, it is unsurprising that flexibility is the most valuable policy for UK travellers when choosing booking providers. 

As air travel returns to popularity, UK travel professionals and consumers are aligned on ranking full refunds as the top priority when travelling via plane.

Travellers are rapidly becoming more savvy as they place greater importance on travel as a way to heal and reconnect with the world. 

It is vital for the industry that travel organisations continue to adapt with flexible, sustainable, and value-driven options to not only attract consumers, but to follow through on the promise to delight travellers from the booking experience to the taxi ride home. 

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Study of travellers’ preferences towards travel offer categories and incentives in the journey planning context

Eva malichová.

1 Faculty of Management Science and Informatics, University of Žilina, Žilina, Slovakia

Milan Straka

Ľuboš buzna, damiano scandolari.

2 Cefriel, Milano, Italy

Mario Scrocca

Marco comerio, associated data.

The dataset generated by the survey research and analysed during the current study is available in the Zenodo repository ( https://doi.org/10.5281/zenodo.4593471 ).

Nowadays, efforts to encourage changes in travel behaviour towards eco-friendly and active modes of transport are intensifying. A promising solution is to increase the use of sustainable public transport modes. Currently, a significant challenge related to this solution is the implementation of journey planners that will inform travellers about available travel solutions and facilitate decision-making by using personalisation techniques. This paper provides some valuable hints to journey planner developers on how to define and prioritise the travel offer categories and incentives to meet the travellers’ expectations. The analysed data were obtained from a survey conducted in several European countries as part of the H2020 RIDE2RAIL project. The results confirm that travellers prefer to minimise travel time and stay on time. Also, incentives such as price discounts or class upgrades may play a crucial role in influencing the choices among travel solutions. By applying the regression analysis, it was found that preferences of travel offer categories and incentives are correlated with some demographic or travel-related factors. The results also show that subsets of significant factors strongly differ for particular travel offer categories and incentives, what underlines the importance of personalised recommendations in journey planners.

1 Introduction

Efficient public transport is one of the promising solutions for sustainable mobility [ 1 ]. Together with other sustainable modes of transportation (walking, cycling, micro-mobility options, shared transport services), it encourages multimodality and brings several positive effects such as congestion reduction, decarbonisation, physical health improvement, but also societal impacts such as increasing access to life opportunities, easing integration into society and many others [ 2 , 3 ]. Although a big emphasis is currently placed on using these forms of transport, Europeans still prefer to travel mainly by private motorised vehicles [ 4 ]. It is evident that, in addition to promoting sustainable transport, it is necessary to focus on creating solutions that will facilitate people’s transition from private motorised vehicles to sustainable transport. One such solution is a multimodal journey planner.

Multimodal transport is recognised as a key element of sustainable transport, as it takes advantage of combining modes of transport [ 5 ]. Although several journey planners across Europe have been developed, many have focused only on one mode of transport or have been able to plan trips only within a particular geographical area [ 6 ]. Thus, if travellers want to plan a trip across several regions or countries, they must combine multiple journey planners, which makes trip planning difficult. Modern journey planners should provide travellers with relevant available travel solutions combining different modes of transport while considering their preferences, needs, and other factors. However, due to the consideration of multiple possible transport modes and other criteria, journey planners can overwhelm the traveller with a large number of suitable travel solutions. Hence, journey planning might be a complex decision-making situation with a plethora of influence factors and relevant criteria.

One of the ways to address this problem and provide a comprehensible and straightforward way of presenting travel solutions is the usage of the categorisation technique, often applied in recommender systems. Categorisation, in this case, is understood as assigning a specific label (e.g., cheap) to travel offers based on their characteristic properties (e.g., low price). When presented to the travellers, this travel offer label should facilitate their decision-making process, as they will be able to recognise faster travel offers that match their needs. Furthermore, categories can be used as features describing travel offers in various machine learning tasks (e.g., building a filter that will rank travel offers based on previous choices of a given or similar traveller). Incentivisation is another tool used in the recommender system that a service provider can use to affect decisions taken by travellers. It is possible to use various incentives to motivate travellers to modify their original travel decisions for some reward, whether financial or non-financial. In the context of journey planning, incentives can represent an important mechanism for changing initial travel decisions into more sustainable ones. Here, we are concerned with the question: How should we define categories and incentives and which of them could be the most suitable for these tasks? Therefore, this paper defines travel categories and incentives and focuses on analysing travellers’ preferences towards them.

2 Literature review

In an effort to improve the quality of sustainable transport and facilitate its usage, many initiatives are being taken across Europe. An important role among them is the development of multimodal cross-border journey planners [ 7 – 10 ]. Currently, unimodal, urban and regional journey planners dominate [ 6 , 11 ], as the development of large-scale European multimodal journey planners is hampered by insufficient travel data integration, a large number of travel service providers, and the requirement of significant investments to maintain it.

Typical travel planner offers information on the route, distance, schedule, timing, and additional options such as schedule comparisons, fare calculations, integrated ticket purchase and information about points of interest. Nevertheless, a journey planner could foster sustainability, flexibility and resilience [ 11 , 12 ]. It could combine a wide range of transport modes (public, private), be capable to react to real-time events that impact the transport network and be able to re-plan travellers’ trips automatically [ 13 , 14 ]. Furthermore, it could be considered as a key to the future of seamless door-to-door travel experience by nudging traveller behaviour towards active modes through incentives and travel information [ 15 , 16 ]. In order to ensure that journey planners will reach the desired impact on society, they should facilitate the traveller’s decision-making or even guide travellers to more sustainable travel solutions. Tools that can be used to achieve these goals are travel recommendations based on travellers’ preferences or previous journeys and the use of incentives.

2.1 Travel solutions recommendation

The task of journey planners is to recommend a travel offer (single travel connection described by attributes such as time, location, and transport mode) or travel solution (single or multiple travel offers, representing a possible realisation of a trip) that ensure travel from the origin point A to the destination point B while possibly combining several modes of transport. After presenting travel solutions to a traveller, a selection of one of the solutions should follow. Choosing a travel solution is a demanding process, and it might be impacted by many factors. To simplify this task, traditional journey planners implement filters to rank travel solutions by a criterion selected by the traveller. Consequently, this provides rather unilateral view on travel solutions and does not facilitate their comparison.

To reduce people’s cognitive effort and to reduce the need to search for additional information, the utilisation of recommendation systems is a possible solution. The recommendation systems are indispensable decision support tools in different application areas such as restaurant services [ 17 ], tourism [ 18 ], energy efficiency [ 19 ], social networks [ 20 – 22 ], teammate networks [ 23 ] and in numerous others. They were designed to provide meaningful recommendations based on users’ preferences and collaborative filtering of information. To take the needs of travellers into account, journey planners may consider traveller profiles and past interactions with the system [ 14 , 24 ]. Such an approach should increase the diversity of choices and prevent recommending the most popular options [ 25 ]. This can be achieved by evaluating candidate journey plans, e.g., considering the price, emissions, or traveller convenience [ 26 ].

Although by using the recommender system, the journey planner can present travel solutions based on their previous trips, it is still up to the travellers to recognise whether the given solution fulfils their requirements. For this purpose, the concept of categorisation can be used, i.e., assigning the label (category) to a travel solution can help travellers efficiently sort out a large number of travel options in multimodal networks. Categories may help to choose between different travel solutions by grouping multimodal journey offers based on pre-defined factors [ 27 ]. Although there are many studies in this area, we find only a few examples addressing the categorisation of travel solutions. In 2016, Barsky and Galtzur [ 28 ] proposed the promotion of sustainable transport modes through a trip planning application, which categorised travel solutions based on departure and arrival times, price of tickets, sustainability (CO 2 emissions), and calorie consumption. In the project SMaRTE [ 29 ], 14 categories for travelling by metro, tram, taxi, car, bus and train (e.g., cost, traffic, speed, reliability, possibility to socialise, etc.) and 13 categories for travelling by train, coach, car, plane (e.g., cost, time, flexibility, comfort, safety, accessibility, etc.) were defined to compare travel solutions. Such well-defined categories can streamline the travellers’ decision-making process and also provide an opportunity to promote selected travel solutions.

The existing research applying travel offer categories in a journey planning context can provide valuable inspirations on how to define categories, however, it does not provide a comprehensive set of categories to be implemented by a journey planner. Some useful travel offer categories can also be derived from the knowledge of factors influencing travel behaviour. Hamidi and Zhao [ 3 ] examined influence of attitudes, access possibilities and skills and competencies on mobility and travel behaviour. By applying multinomial logistic regression, they found that in addition to the price of tickets and the departure and arrival times, which are the essential factors influencing travel choices, travellers make decisions based on their attitudes, opinions, and habits. Several studies have focused on identifying these factors. Clauss and Doppe [ 30 ] summarised the factors influencing travel solution choice and grouped them into instrumental (general practical aspects of travel choice), affective (aspects linked to individual preferences), and symbolic (social expression and social identity) groups. They used a repertory grid methodology to obtain data and then aggregated it by bootstrapping to groups. Their research confirmed the impact of 28 factors on travel mode choice that can be organised into six perceptual dimensions (affection, convenience, stress, individuality, cost, and flexibility). They also analysed factors’ importance, finding privacy, autonomy, stresslessness, flexible route choice and sustainability to be the most impactful. Golightly et al. [ 31 ] considered the number of interchanges and the possibility of travel from door to door as essential factors influencing travel choices. For some travellers, minimising the distance between the exact start/end location and the start/end point of the travel (travel offers proximity) is crucial when choosing a travel solution. The importance of travel offers proximity was also confirmed by Lem [ 32 ] in the context of carpooling. Results of the multinomial logit model confirmed that the main factors influencing the choice of a travel solution are travel time, costs and distance from the carpool meeting point. The service quality attributes are also crucial for the selection of travel solutions. Hansson et al. [ 33 ], by analysing literature using PRISMA method, identified four attributes that influence modal choice and customer satisfaction: costs, comfort, punctuality and availability (frequency). Even though these studies differ in their data collection and methodology approach, they provide a set of factors that influence travel choices and, therefore, could be used to categorise travel offers.

2.2 Incentives

In addition to recommendations, the impact on travel decisions can also be achieved by incentives [ 34 , 35 ]. There are two basic types of incentives: financial and non-financial. When choosing a travel solution, travellers focus primarily on reducing travel costs in terms of travel time or financial resources spent on travel. Therefore, financial incentives have been the most often used tools to promote sustainable modes of transport [ 36 ]. Although such a form of incentives brings a noticeable change in people’s travel behaviour [ 36 , 37 ], it also undermines the intrinsic motivation to engage voluntarily, which leads to reducing interest in the promoted transport mode in the event of the removal of the incentives, especially in the case of free use or price discounts [ 38 ]. On the contrary, intrinsic motivation leads to greater engagement and better results over a longer period but needs to be backed up by extrinsic motivators to incentivise people to do tasks that do not appear inherently interesting or enjoyable to them, thus expanding its reach and efficiency [ 39 , 40 ]. Therefore, the use of non-financial incentives is increasingly coming to the fore. Non-financial incentives target psychological, social, and emotional needs. These incentives enhance travellers’ self-image (e.g., by promoting their best behaviours concerning social, health or environmental matters as well as their ability to save time and money) and to avoid the negative impacts of their actions (sometimes it might be more effective to highlight the bad aspects of a given behaviour instead of emphasising the positive ones, like showing the increase of CO 2 consumption of a specific transport solution instead of the amount a different option would save) [ 41 ]. Thus, this type of incentive is characterised by the provision of information (e.g., about travel time, calorie consumption), points for a choice of more sustainable transport modes or features such as the possibility of sharing trips with other people, e.g., through social media or use of gamification (sharing information about achievements with other travellers to spark the contest) to increase traveller’s engagement and interest [ 28 , 42 , 43 ].

Incentives, whether financial or non-financial, have so far been applied primarily territorially, in a certain area in which a change in users’ travel behaviour was expected and regardless of individual traveller preferences. As in the case of travel offer categories, due to the use of smartphone applications in travel, taking into account the subjective needs and preferences of travellers, i.e., personalisation, has become also demanded in the case of incentives [ 44 – 46 ]. This way it is possible to recommend appropriate incentives to travellers based on individual characteristics and travel preferences, e.g., to high-income travellers information about travel time and its changes and to young people who prefer entertainment while travelling various gamification activities [ 44 ]. Another example is a system that personalises the reward in the form of points to influence travellers’ decisions [ 45 ]. However, the efforts to incorporate such a system into a journey planner are limited. Mobility management tools such as Metropia or IncenTrip already contain some incentive instruments. As incentives, they use credit points, which can be exchanged for monetary rewards, transit passes, rideshare, or cash [ 44 ]. Personalisation, in this case, consists in designing a travel plan based on personal needs, preferences and previous experiences, while providing information about points allowance and expected time savings, but it does not include customised recommendations of incentives.

2.3 Contribution and structure of the paper

The review of existing studies shows that although the use of categorisation and incentivisation has its justification, categories of travel solutions and incentives are not commonly defined and used in journey planners. Therefore, the paper addresses this gap and delivers the following contributions:

• By summarising state of the art, we proposed candidate sets of travel offer categories and incentives, respectively.
• We designed and conducted a survey. By analysing the collected data, we identified the declared priorities of respondents towards travel offer categories and incentives.
• To gain a deeper understanding of results and to interpret them, we employed regression analyses to explore the correlations between declared priorities of respondents and demographic or travel-related factors.

Our findings provide valuable guidelines to developers when designing a journey planner employing personalisation techniques and recommender systems. The remainder of the paper is structured in the following fashion. In Section 3, we introduce the conceptualisations of initial sets of categories and incentives, respectively, together with survey design, data collection and data analysis methods. Section 4 describes the results of the data analysis. Section 5 concludes the paper by summarising the main findings and suggesting pathways for future research.

3 Materials and methods

The research presented in this paper is an integral part of the H2020 European project RIDE2RAIL [ 7 ]. The RIDE2RAIL falls under the fourth Innovation Programme (IP4) [ 47 ] from Shift2Rail [ 48 ], which addresses the subject of IT Solutions for attractive railway services. IP4 efforts focus on providing a journey planner application that should enable a seamless passenger experience. The RIDE2RAIL project aims to develop solutions and tools that will facilitate the efficient combination of ride-sharing and scheduled transport services such as bus and rail, extending the number of available travel modes. A ranking algorithm has been implemented to facilitate comparison and selection among multiple transport options. The algorithm considers passenger profiles and previous choices. Travel solutions are characterised by categories that describe travel offers. Categories are used as predictors by the ranker. Moreover, in the form of visually attractive icons, they are also displayed in the journey planner together with transport solutions. Another set of icons informs travellers about the availability of incentives that travel service operators offer to promote selected travel solutions. In this paper, we present the research that has been conducted to support decisions on categories and incentives to be implemented in the RIDE2RAIL project. However, the findings can be utilised in the design of any other journey planner.

3.1 Terminology

To clarify the terminology used in the paper and in the survey, we provide a brief description of the main terms utilised:

• A travel offer is a single travel connection (i.e., a product that can be purchased by the traveller) described by a set of characteristics expressed as travel offer features (e.g., start and end locations, start and end time, mode of transport, etc.), i.e., a set of variable-value pairs.
• A travel solution is constituted by single or multiple travel offers, representing a possible realisation of a trip . A traveller mobility request for journey planning typically results in a set of travel solutions.
• A traveller preference represents the subjective desirability (i.e., a quantifiable preference weight) of a specific characteristic of an offer for a traveller. The preference weight of a traveller can change considering context-awareness, i.e., under different conditions, the traveller may have different preferences. Traveller preference can be used to filter or rank the different travel solutions for a traveller.
• A travel offer category can be seen as a label attached to offers having particular objective characteristics. A travel offer category is computed taking into account a set of offer features.
• An incentive is a technique to influence the behaviour of a traveller towards a specific travel solution. The allocation of incentives can be done by evaluating rules (incentive conditions), determining the applicability of the incentive to a given travel solution for a given traveller, and the incentive mechanisms specifying the benefit proposed to the traveller if the specific travel solution is selected.

3.2 Methodology

To provide an advice to journey planner developers on how to implement efficient decision support to travellers when selecting a multimodal travel solution and gives to travel service providers suitable tools to incentivise travel offers, we followed the workflow composed of the steps:

• Step 1: Conceptualisation of travel offer categories.
• Step 2: Conceptualisation of incentives.
• Step 3: Design of the questionnaire.
• Step 4: Execution of the survey.
• Step 5: Analysis of the survey results.

By collecting data at a European scale, the aim is to investigate traveller preferences with respect to travel offer categories and incentives that can influence the behaviour in the journey planning context. Sections 3.3 and 3.4 describe, respectively, the conceptualizations of travel offer categories and incentives adopted and validated through the performed survey. The proposed conceptualisation, reported in the Ride2Rail deliverable D2.1 [ 49 ], resulted from a detailed analysis of state of the art and an alignment with past and ongoing European research projects.

3.3 Conceptualisation of travel offer categories

From the analysis of the state of the art, a set of patterns were identified to provide a conceptualisation of the term travel offer category and to formulate candidate categories [ 27 , 49 ]. Each travel offer category is defined considering a set of variables to be evaluated as factors to determine the membership of an offer to a given travel offer category.

First, we considered contributions discussing the different types of variables that can be used to characterise a multimodal travel offer. Integrating the models proposed by Clauss and Doppe [ 30 ] and Zhao [ 50 ], it is possible to identify the following macro-areas:

• Instrumental : variables related to the measurable characteristics of the travel solution (cost, time, etc.);
• Perception : variables related to the users’ perception while travelling (comfort, safety, etc.);
• Symbolic : variables related to the personal value attributed by a user to a specific travel solution (prestige, status, etc.).

Considering these types of variables, we analysed the state of the art to identify the set of actual variables that can be used as determinant factors to formulate travel offer categories [ 31 , 33 , 51 ]. It is important to highlight that we considered for our selection process only objective variables describing travel offers. Indeed, the process of associating offers to travel offer categories should be objective to minimise the risk of assigning a label that may be misleading because interpreted differently by different users. We introduce this distinction to clarify the difference between the process of travel offer categorisation and the filtering and ranking based on the subjective preferences of a traveller. As a relevant example, it is not possible to univocally define an Accessible offer category. The accessibility of a travel offer for a traveller strictly depends on her/his needs and cannot be generically assessed. This very important topic should be considered in defining a proper set of travellers’ preferences to enable filtering of the accessible travel solutions. As an additional remark, the categorisation process should not be confused with the process of recommending travel offers labelled with certain travel offer categories to specific users. Indeed, any traveller may have different subjective preferences in the selection of travel offers belonging to one or more travel offer categories.

Following the introduced conceptualisation, we mainly focused on instrumental variables for the selection of variables to be considered in the categorisation process since they are objective and easily measurable. We decided to take into account also perception variables since an objective quantification could be evaluated through feedback collected from an adequate statistical sample of users, e.g., measuring the feeling of personal safety or the level of comfort. The same does not hold for symbolic variables that are more subjective, which is related to problems of cognitive effects, social desirability and unstable attitudes [ 52 ]. For this reason, they cannot be considered to characterise offers for a generic user.

For each instrumental and perception variable extracted from the literature analysis, it is possible to define a low-level offer category, i.e., a class that can be associated with an offer relying on a single determinant factor. For example, the total travel time variable is the determinant factor for a low-level offer category that minimizes the said travel time identifying the quickest solution. The determinant factors extracted from the literature are: total travel time, frequency of the service, stops required, total travel distance, variability of travel time, waiting/idle times, traffic congestion likelihood, accident or breakdown likelihood, influence of weather on travel time, total cost of the trip, integrated fare, polluting emissions, charity/volunteering activities, people sharing the travel, transfers required, different means of transport, distance on foot, distance to drive, distance from start/stop location, protection from bad weather, personal safety feeling, level of privacy, overcrowding likelihood, cleanliness of vehicle, internet access, and space available.

Given the large number of variables identified, we decided to define ten macro-categories clustering the identified determinant factors. The goal of the final list of travel offer categories is not to be exhaustive, but to elicit the most common ones that can be relevant for travellers. The catalogue of travel offer categories defines the following instances:

• QUICK category measures how convenient and efficient the solution is in terms of time-related issues, considering the total travel time, the frequency of service, the waiting time between legs and the number of stops required. Real-time data on traffic congestion can also be taken into account if the solution includes a segment on-road (e.g., bus/car).
• SHORT category focuses on minimising the distance covered.
• RELIABLE category concerns the likelihood of delays, traffic congestion, breakdowns or last-minute changes that could affect the travel time and comfort of the trip. Some solutions are inherently variable (e.g., traffic delays when crossing a city at rush hour), while other solutions might offer a small window to change the mode of transport that could cause massive idle times. Lastly, the influence of the weather on the trip is taken into account.
• CHEAP category concerns the total price of a trip, the possibility of sharing part of it with others and the ease of payment, giving additional value to solutions that offer an integrated fare system and do not require the traveller to purchase different tickets from different platforms.
• DOOR-TO-DOOR category covers the distance of the traveller’s start and endpoint from the beginning and destination locations of the solution provided. It is measured by the amount of walking or driving distance the traveller has to cover.
• COMFORTABLE category, concerns objective factors such as weather protection, the number of transfers required, and the number of different means of transport used, it also covers a set of other elements about the quality of the trip that has to be evaluated through travellers’ feedback. This category should consider also the likelihood of overcrowded vehicles, the feeling of personal safety, the level of privacy and the cleanliness of the stations and vehicles used.
• SOCIAL category concerns the identification of offers that, based on the context and means of transport used, facilitate the sharing of the trip with other passengers and the possibility to network and socialize.
• MULTITASKING category concerns the extent to which the traveller can perform other tasks while travelling. These activities can regard productivity (private or work), fitness, or enjoyment. This category considers the amount of space available, as well as, whether the internet connection is provided. Lastly, the level of privacy might also influence the extent to which a person can work and could be considered as a determinant factor for this category.
• ENVIRONMENTALLY FRIENDLY category covers the green aspects of the trip, taking into account at least the amount of CO 2 emissions measured per kilometre/traveller for each mean of transport included in the offer and considering the distance covered and the number of passengers. If available, additional determinant factors can be considered as energy consumption, NOx emissions (nitrogen oxides) and the carbon footprint.
• PHILANTROPIC category relates to the willingness of the traveller to choose a solution that contributes to social causes or charity activities (e.g., donations included in the offer price).

3.4 Conceptualisation of incentive categories

The analysis of the state of the art discussed in Section 2.2 resulted in the identification of the following patterns to be considered for the conceptualisation of incentives that could influence the behaviour of a traveller:

• Extrinsic motivators;
• Increase of awareness on specific choices;
• Gamification strategies;
• Personalized incentives tailored to the specific user.

The identified patterns were used to identify a catalogue of candidate incentives that could be used to analyse the potential impact of different strategies on travellers. The incentives are divided between: (i) tangible incentives, i.e., providing a practical benefit to the traveller such as a gift or a discount (incentives 1—6), and (ii) intangible incentives, i.e., not employing practical benefits (incentives 7—10). The list of incentives is composed as follows:

• IMMEDIATE DISCOUNT —immediate price discount on a given travel offer,
• FUTURE DISCOUNT —discounts on the following purchases if a given travel offer is chosen,
• LOYALTY PROGRAM —earning points associated with travel offers, while collected points can be converted to prizes,
• DISCOUNT FOR SERVICES —ancillary services for free or discounted (e.g., meal),
• ADDITIONAL SERVICES —discounts on complementary services (e.g,. hotel),
• CLASS UPGRADE —discounted or free upgrade of the travel class.
• ENVIRONMENTAL INFORMATION —provide to the traveller information that can increase her/his awareness of the environmental sustainability of a travel offer (e.g., displaying the CO 2 emissions),
• ADDITIONAL INFORMATION —provide to the traveller additional material promoting the offer, e.g., include in an offer involving a bus the images of city monuments that can be spotted during the travel,
• GOAL —adopt a gamification strategy assigning badges to award the achievement of pre-defined goals (e.g., trying a ride-sharing solution for the first time, or choosing the solution with the lowest environmental impact),
• COMPETITION —assign points to the travellers for virtuous choices in travel offers and set up a daily/weekly/monthly shared leaderboard (e.g., among friends).

3.5 Survey design

Considering the defined conceptualisation, the survey would like to validate the proposed catalogue of travel offer categories and the preference model to obtain insights on what choice criteria are more relevant for the traveller. Moreover, we would like to assess the completeness of the identified catalogues by asking the traveller to propose additional entries. This second aspect can provide valuable information also to understand if the proposed definition of the concept has been understood by the traveller. Considering incentives, similarly, we would like to investigate, through a set of examples, which of the approaches that emerged from state of the art could be more attractive for a traveller. Moreover, we would like to obtain additional suggestions on incentives that can influence the behaviour of a traveller.

The survey focused on gathering information on a choice of travel scenario considering a traveller’s perspective with their specific mobility needs. Since the choice criteria and incentives influencing the traveller behaviour depend on the specific traveller but also on the specific context describing the type of trip to be performed, we designed the initial part of the survey to let the traveller focus on a specific trip. Since the travel contexts can vary, we decided not to propose predefined contexts to choose from. Instead, we decided to ask the traveller to focus on their last trip and then describe it considering a list of context dimensions. All questions Q1-Q18 in the questionnaire are presented in Section Appendix C in S1 File .

The defined set of travel context dimensions and potential values to let the traveller describe their last trip were “reason of the trip” (Q1), “accompanying persons” (Q2), “length of trip” (Q3), “trip origin” (Q4), “trip destination” (Q5), and “means of transport used” (Q6). The central part of the survey has been designed to obtain useful insights to validate and finalise the conceptualisation of choice criteria and incentives. To do this, the travellers were asked to imagine using a travel app to plan/optimise a trip similar to the one described at the beginning by comparing different journey solutions. With this journey in mind, travellers were asked information on choice criteria (Q7-Q11) and incentives (Q12 and Q13) with a set of questions following the usual interaction order in a typical journey planning application: definition of preferences with reference to travel solutions (traveller preferences), visualisation of travel solutions (travel offer categories) and proposal of incentives for selecting different travel solutions or additional services (incentives).

We decided to place focus on the most common variables through which a traveller can set some travel preferences in Q7. We aimed to provide the respondent with a general set of characteristics applicable to different types of trips and travellers to obtain comparable answers. We selected the following: “transportation company”, “time interval for the departure and arrival times”, “number of transport changes”, “travel class”, “seat type”, “meal inclusion”, “refundability”, “live notifications on trip status updates”, and “on-board connectivity”. To investigate also additional and more specific offer features on which a traveller may be interested in expressing preferences we decided to adopt a different strategy, starting from the traveller needs. We first provided the respondents with a set of potential additional needs to choose from, and we then asked to specify, through an open-ended question, which traveller preferences related to the indicated needs that they would like to specify. The additional needs considered in Q8 have been: “large/multiple baggage/s”, “special baggage (sports equipment, instruments, etc.)”, “animal allowance”, “help needed because of reduced mobility”, “health-related needs”, “travel with an infant”, “other needs”. To investigate travel offer categories, in Q9 we asked travellers to indicate which categories they consider more relevant to discriminate among different travel solutions. This was achieved by a set of questions collecting a 1 to 5 relevance score related to each of the ten proposed travel offer categories, namely: “quick”, “short”, “reliable”, “cheap”, “door-to-door”, “social”, “multitasking”, “environmentally-friendly”, “philanthropic”, “comfortable”. The last block of journey-related questions addressed incentives. We identified different approaches in the state-of-the-art 3.4, and we proposed a distinction between tangible and intangible incentives. We included question Q12 for each one of the tangible and intangible incentives. More in detail, the tangible incentives tackled were described as: “immediate price discount”, “price discount on future purchases”, “loyalty program with points collection to unlock different rewards”, “being offered additional services”, “discounts on complementary services (e.g., hotel, restaurants, etc.)”, “free (or discounted) class upgrade”. Concerning the intangible incentives, the questions addressed the following items: “provide more information about the positive aspects of a solution”, “provide information on the solution’s environmental impact”, “challenge you to achieve a specific goal”, “competition with friends and a shared leader-board with points assigned based on your travel choices”.

To conclude the design of the survey, we selected a set of socio-demographic dimensions to be asked about in Q14-Q18. This set of variables allowed us to identify the characteristics of the population answering the survey and to check if the sample was well distributed or unbalanced towards specific values. The socio-demographic dimensions selected were: “age”, “gender”, “country of residence”, “education level” and “employment status”.

3.6 Data collection

The survey was implemented and administered via Coney [ 53 ], an innovative toolkit designed and developed by Cefriel to administer surveys. Coney uses a conversational approach, disguising a quantitative data collection process as a qualitative interview by administering the survey in a chat-like interface that resembles an actual conversation with the goal of enhancing the traveller experience and engagement. The toolkit offers different web applications that cover all the stages of survey design and delivery processes, from the survey creation to its administration and the subsequent data analysis.

Once the survey was implemented, both the chat interface and the survey content were translated in twelve different languages, namely: English, Italian, Greek, Finnish, Slovak, Czech, Spanish, French, German, Ukrainian, Portuguese, and Croatian. Once finalised, the questionnaire was administered via the Coney Chat web application, offering the respondents an easy-to-use chat-like interface to fill up the survey. Coney’s live dashboard, Coney Inspect, was used to keep track of the completion progress. The data collection process started on the 2nd of July 2020 and lasted around two months. The request to fill in the survey was distributed by partners of the Ride2Rail project and shared through several dissemination channels like mailing lists, social media, or websites together with an URL that opened the chat application and started the survey. The identity of survey respondents was kept anonymous. The data collection process was finalised on the 7th of September 2020.

3.7 Dataset

Once the data collection process ended, the gathered data was exported in CSV format and analysed. While more than 787 participants started the survey, the total number of respondents that completed the survey is 609, signalling a drop-out rate of around 22%. The average time taken to complete the survey is 8 minutes and 58 seconds and all the twelve available languages were used. To obtain a consistent sample of data a pre-processing procedure was applied. First, the data was cleaned by filtering out questionable and incomplete data (e.g., data collected during the survey preview or questionnaires with less than 80% of provided replies). We selected only the countries from which more than 80 respondents participated in the survey, while in those that were not included it was less than 30. For the analysis only the respondents’ data from Slovakia, Czechia, Italy, Finland, and Greece were used. These countries are involved in the pilots developed by the Ride2Rail project, and thus the dissemination of the information about the survey was there the most intensive. This way we obtained a dataset with 502 observations each corresponding to a respondent.

The survey featured several socio-demographic questions examined in Fig 1 . The participants included in the analysed sample mostly identify as males or females, with a good balance between the two (53.59% males and 45.62% females). Most of the respondents were between 18 and 50 years old, with a good representation recorded for the 51–65 group and very few answers collected from people below 18 or older than 65. While most of the respondents were full-time workers (54.01%), a significant amount of students (33.33%) were also recorded. With regards to education level, almost all the participants achieved at least a higher education diploma, with the majority of participants having obtained a Master’s Degree or more. Only three persons selected the basic education and so we merged this category with higher education to the category without university degree. Further, we merged five occupation categories (Unemployed and looking for a job, Unemployed and not looking for a job, Unable to work, Prefer not to say) into one category Other, as they had only 23 occurrences altogether. Similarly, three categories Employed full time, Employed part time and Self-employed were merged into the Employed category. The representation of the countries in the sample is not balanced but given that the goal is to create one system for all countries participating in the demonstrations and propose one list of travel offer categories and incentives, it is not considered crucial. Nevertheless, considering the total population of these countries, with a 5% margin of error, a 95% confidence interval, the sample can be considered representative [ 54 ].

A Gender distribution. B Country of residence. C Age distribution. D Employment status. E Education level.

3.8 Data representation

To apply the regression analysis, the data was described by variables introduced in Appendices A and B in S1 File . The variables are identified by variable names that are further used in the presentation of results of the regression analysis. The provided description indicates the meaning of variables and the way how they encode the data. Table in Appendix A in S1 File presents the response (dependent) variables, each corresponding to either one travel offer category or incentive type. The preferences of survey respondents expressed on the 5-star scale are represented by ordinal variables taking integer values ranging from 1 to 5. In Appendix B in S1 File we present the explanatory (independent) variables that describe the answers of respondents to questions related to the last trip, traveller preferences and their basic socio-demographic characteristics.

3.9 Ordinal response regression

The 5-star rating was used in the survey to quantify the level of preference towards travel offer categories and incentives and thus it is necessary to use a method that is able to handle ordinary response variables. In addition, we wanted to provide a picture of the basic data dependencies and identify the most vital factors influencing travellers’ offer and incentive choices. Therefore, we apply ordinal response regression [ 55 ], commonly used in research related to travel behaviour [ 56 – 59 ], to model the dependency of ratings on the participants’ current travel behaviour, travel preferences and socio-demographic characteristics. The response variable Y represents the number of stars assigned by participants to a given offer category. The symbol π j denotes the probability that Y takes the value j for j = 1, …, 5, i.e., π j = P ( Y = j ). Hence, the cumulative probability for the assigned number of stars j of Y is P ( Y ≤ j ) = π 1 + …+ π j for j = 1, …, 5. The regression model examines the effects of explanatory variables x 1 , …, x p on the cumulative logits,

The model assumes that the logit of cumulative probabilities changes linearly with the explanatory variables, x 1 , …, x p , i.e.,

Consequently, the model for the cumulative probability takes the form

where P ( Y ≤ 0) = 0 and P ( Y ≤ 5) = 1. Values of regression coefficients were found by using the porl() function from the MAAS library available in the CRAN repository of R language. The significance of regression coefficients is evaluated based on p-values that were calculated from t-values provided by the porl() function. The p-value is calculated by considering the normal distribution N (0, 1) as probability of observing a value that is more distant from zero than the t-value. Hence, small probabilities indicate that the t-value is reliably distinguishable from zero. If a calculated p-value is less than 0.05, we consider the corresponding regression coefficient as significant. For the presentation purposes, in Figs 5 and 6 we report values of exp( β i ) for i = 1, …, p , hence, value larger (smaller) than 1 (0) indicates positive (negative) impact of an explanatory on a response variable.

First, we present the exploratory analysis of ratings assigned by respondents to individual travel offer categories and incentives. Second, we present the findings regarding the relationship between ratings and explanatory variables obtained by the ordinal response regression.

4.1 Exploratory data analyses

The first impression about perceptions of travellers regarding travel offer categories provides the average ratings presented in Fig 2A . The ratings go from 1 (not important) to 5 (very important), and they were provided as a reply to question Q9. On average, most of the travel offer categories were ranked higher than 3 out of 5, hence in the upper part of the range. The “reliable” and “quick” were highly preferred among the respondents. More than 60% indicated these categories as the most relevant. The distribution of ratings, presented in Fig 3 confirms that only a few respondents assigned to these categories rates 1 and 2. It means that most people would like to be informed whether a travel solution is among those that demand as little time as possible or those where the probability of a change in travel time due to delays, traffic jams or breakdowns is not high. The second group of categories, “comfortable”, “cheap”, “door-to-door”, “short”, “environmentally friendly” and “multitasking” reached average ratings between 3 and 4 and featured very similar rating distributions. The lowest interest was found in the case of “philanthropic” and “social” travel offer categories, where the majority of respondents assigned the rating 1.

A Average ratings of travel offer categories. B Average ratings of incentives.

After rating travel offer categories, respondents were asked to select the three most important ones. The purpose was to validate and identify priorities since previously the ratings were collected individually. The top five most frequently selected travel offer categories match those presented in Fig 2 . The category “quick” was selected by 81.1%, “reliable” by 74.5%, “cheap” by 46.7%, comfortable by 27.4% and door-to-door by 24.9% of respondents.

We applied a similar approach to the incentives by asking the respondents in question Q11 to rate them based on how effective they could be. The average ratings are presented in Fig 2B . The average ratings are closer to the value of 3, which is the midpoint of the rating interval. The respondents would be willing to change their travel choices mainly because of “immediate price discount” and “discounted class upgrades”. Thus, the money-related tangible incentives are among the highest-rated. The distributions of ratings for these two incentive types (see Fig 4 ) are very similar, reaching the maximum at the value of 4. The average value close to 3 we find for “price discounts on future purchases”, “discounts on complementary services”, “loyalty program”, “information on travel solution’s environmental impact”, “information about positive aspects of a travel solution”, and “discount on complementary services”. All these incentives have relatively uniform rating distributions with a slight preference towards the mid values. On the contrary, the incentives that are the most unlikely to change the respondents’ choices are the “challenge of achieving a specific goal” and “competition with friends”, which are intangible incentives.

Respondents were also asked to list any additional incentives that could influence their choice of the means of transport. Several of them stated that the main incentive would be to get further discounts, especially those targeting specific groups like students, youngsters, or older people. Another suggested incentive is to offer travel insurance (covering health issues during the trip or delays and cancellations) included in the ticket price. Several respondents mentioned a free cancellation policy or a possibility to change the ticket booking freely, free food during the trip, food with gluten-free or vegan choices, sharing of the information or the proposition of other activities (e.g., a possibility to visit a museum or a possibility to take some historic or newly adopted means of transport).

4.2 Results of the ordinal response regression

The purpose of the categorical regression analysis is to tackle the following questions:

• Who and in what context is interested in presenting travel offers together with examined categories and incentives?
• How is it possible to tailor the presentation of travel offer categories and incentives to a specific group of travellers?

Travel offer categories are in the Ride2Rail project used together with other traveller and trip-related information as features to build a prediction model of purchase decisions. Such a model can be used to rank the offers, and this way simplifies the selection of the travel offer for a traveller. Hence, the analyses could give us insights on quantities represented by categories in the prediction of purchase decisions. Except for travel offer categories quick and reliable, we created ordinal regression models for each offer category and incentive. Models for these two categories could not be created because the distribution of respondents’ answers (see Fig 3 ) means that the choice of these categories is not dependent on specific factors, but it is a general interest of all participants to prefer travel and reliable travel options.

Tables ​ Tables1 1 and ​ and2 2 show McFadden’s and Nagelkerke’s R 2 of the created models. In general, realistic values of the proportion of variability in response variables explained by explanatory variables might be in domains like psychology and marketing well below 0.1 [ 60 ]. Although the achieved values of McFadden’s R 2 are relatively low (from 0.045 to 0.097) it must also be taken into account that explanatory and response variables are categorical, for which R 2 type of measures are typically lower than for ordinary least squares. The author of the McFadden’s R 2 coefficient stated that values between 0.2 and 0.4 of this coefficient represent an excellent fit [ 61 ]. To provide additional information about the fit, we evaluated the Nagelkerke’s R 2 [ 59 ], which extends commonly used Cox-Snell R 2 [ 58 ], by scaling it between 0 and 1.Nagelkerke’s R 2 reaches higher values than McFadden’s R 2 and it does not fall below 0.1. As both coefficients are computed from the model likelihood, their values are correlated. Models having best fit are OC[cheap], OC[comf], OC[phil], IN[class.up], IN[ad.serv]. To check the overall significance of models, i.e., if the coefficients are different from zero, we performed the likelihood ratio test by ANOVA [ 55 ]. All the models had p-value below 0.05 and hence we consider them as significant.

The statistically significant explanatory variables with values of exp( β ) resulting from these models are presented in Figs ​ Figs5 5 and ​ and6. 6 . Values of regression coefficients together with p-values are presented in Appendices D and E in S1 File . Since regression analysis results strongly depend on categories and incentives, we describe separately the findings for each travel offer category and incentive. Both are sorted based on their ratings resulting from the exploratory data analyses. In addition, we also focus on the implications of our findings for the possible utilisation of travel offer categories and incentives in recommender systems.

Only values of exp( β ) corresponding to statistically significant predictors are shown. By the horizontal line we indicated the value 1.0, which is the borderline between predictors with a positive and negative impact on the response variable.

4.2.1 Results: Travel offer categories

Among those travel offer categories for which a model has been created, the most relevant is “comfortable”. The results show that this category could be interesting for travellers who used ride-sharing, carpooling or shared taxi services on their last trip. Other factors that positively affect the choice of this category are factors defining a comfortable solution: “a minimum number of interchanges”, “feeling of personal safety”, and “having a comfortable seat”. These comfort factors could be important elements when developing an algorithm to recommend a travel offer because they are clearly linked to the “comfortable” travel offer category, and at the same time, they are consistent with the identified significant explanatory variable: transport mode “carpooling/ride-sharing/shared taxi”.

The significant factors positively influencing interest in the “cheap” travel offer category are the “reason of a trip (business)”, traveller preferences “meal inclusion” and “refundability”, and comfort factor “weather protection”. Contrariwise, males, people from Czechia and Slovakia, and those who prefer privacy during travel are less interested in inexpensive travel options. It is likely that people who desire to seclude themselves from other passengers are willing to pay extra and therefore do not seek cheap travel solutions. We also found that participants from Czechia and Slovakia where the average monthly income is less than in other analysed countries are not interested in spending less money on travelling. There are several possible reasons which contribute to this situation, e.g., different transport costs across countries, price discounts for some specific groups, quality of the service, culture, etc. Identified influential preferences are closely related to the reason of the trip. People travelling for work-related purposes often prefer to eat while travelling or have a possibility to refund costs in case of cancellation.

As expected, people who prefer a minimum number of interchanges during their journeys are interested in the “door-to-door” travel offer category. The results also show that this category is relevant for people typically travelling with large/multiple baggages or who do not have additional needs. Interestingly, people with a university degree are more likely to be interested in the “door-to-door” category.

It can be seen that the “short” travel offer category tends to be preferred by people whose last trip was performed by metro, and they did not travel alone but with colleagues. The interest in this offer category decreases when travellers are men, travel from a suburban area with respect to the reference category (rural area), or the distance of a trip is longer. Also, people who want to receive updates about trip status are less interested in “short” travel options, as they are probably more interested in other factors than distance (e.g., travel time). These results suggest that people travelling over shorter distances try to minimise them even more, especially within the city using fast public transport.

Naturally, people who walked on their last trip are more likely to be interested in the “environmentally friendly” travel offer category. In addition, a significant positive impact was also identified for people who considered personal safety and cleanliness of stations and vehicles as a comfortable solution. Thus, if implemented into a recommender system, this travel offer category, is likely to be appreciated by travellers interested in active modes of transport and travellers sensitive to the safety and cleanliness of public spaces.

Interest in the “multitasking” travel offer category depends on a transport mode, traveller preferences and origin of a trip. People who travel by shared services (carpooling/ride-sharing/shared taxi) or train are more willing to appreciate this category than those travelling by other transport modes. This also applies to people whose preferences are “meal inclusion” and “seat type”. In addition, people travelling from urban and suburban areas are less interested in multitasking travel solutions than people travelling from rural zones. Hence, people who typically travel from rural to urban areas might be more used to working, studying or doing other activities during their trips because of longer travel time and therefore be more interested in the multitasking category.

The least interest of respondents was identified for the “social” and “philanthropic” travel offer categories. In “social” category are interested people who used a shared service (carpooling/ride-sharing/shared taxi), people who defined as a comfortable factor “feeling of personal safety” and people whose preference is “meal inclusion”. Since 12 significant explanatory variables have been identified for the “philanthropic” travel offer category, it is difficult to derive a recommendation regarding an interested group of travellers.

4.2.2 Results: Incentives

Respondents indicated that they would be willing to change their travel choices mainly due to financial incentives. The results of the ordinal regression in Fig 6 show that the respondents who travelled by bus on their last trips and respondents who selected onboard connectivity as travel preference are more likely to choose “immediate price discount” to change their travel choice. Interestingly, respondents from Czechia and Slovakia are less interested in price discounts than respondents from other countries. This finding is well aligned with the findings about the “cheap” travel offer category. On the contrary, these respondents are more likely to select the “free(or discounted) class upgrade” incentive. In addition, a significant positive impact on this incentive was also identified for a bus as a transport mode, comfort factor seat availability and travel preference meal inclusion. Thus, the relevance of incentives “immediate price discount” and “free(or discounted) class upgrade” is predetermined by the used transport mode, country of residence and travel preferences.

“Price discount on future purchases” was the third highest-rated incentive. The regression analysis identified only one explanatory variable, “transport mode (carpooling/ride-sharing/shared taxi)” positively affecting interest in this incentive. The interest in this incentive drops with the increasing age and trip distance. Therefore, this type of incentive can be recommended especially to young people travelling by shared transport.

People with a master’s degree with respect to people without a university degree and those whose travel preference is meal inclusion and refundability are more likely to choose the incentive “being offered additional services”. The analysis indicate also higher interest in this incentive by people considering a low number of interchanges as a comfort solution. Four other explanatory variables with negative influence were identified as significant, two of which were travel preferences. Therefore, considering also positive regression coefficients, we can conclude that the relevance of this incentive can be to some extend revealed based on travellers’ preferences.

The following incentives were of moderate interest to respondents, as evidenced by the achieved average values lower than the value 3. The “Loyalty program with points collection to unlock different rewards” would be interesting for people who travel by shared services or walk. Likely, people using ride-sharing/carpooling/shared taxis already have experience with the loyalty program of the providers of these services, and therefore, they selected it. Results also show that people from the Czechia and Slovakia and those who need to carry special bags are interested in this stimulus.

The “provision of information on the solution’s environmental impact” was the best rated among the non-financial incentives. The occupation has a significant impact on selecting this incentive. Employed people or students are more likely to choose it. Contrarily, people living in Czechia and Slovakia are less likely to select this incentive than people from other analysed countries.

People who walk, travel for leisure, consider as comfortable feeling of personal safety while travelling and their preference is meal inclusion would be willing to change their travel decision due to “information about the positive aspects of a solution”.

The incentive “discounts on complementary services” could be interesting for people travelling to work or school, who prefer to have a meal included during travel or appreciate the cleanliness of stations and vehicles. Model results for the incentive “challenge you to achieve a specific goal” indicate that based on it people who travelled by bicycle on their last trip, employed people and students are more likely to change their travel choice. The incentive “competition with friends” could be relevant only for people travelling with their partners.

5 Discussion and conclusions

The aim of this article was to identify travellers’ perceptions towards travel offer categories and incentives in the journey planning context and the factors influencing them. We proposed a catalogue of travel offer categories and incentives, respectively. To find out which of the catalogue items are preferred by travellers, we designed and conducted a survey primarily in RIDE2RAIL demonstration countries. In order to determine who and in what context is interested in proposed travel offer categories and incentives, we built a data model for every offer category and incentive. Based on the results of ordinal response regression models, we identified a variety of factors influencing the selection of individual travel offer categories and incentives. These factors include trip characteristics as the mode of transport, trip origin and distance, perceptions of the comfort, and socio-demographic characteristics.

In general, travel offer categories received high ratings. Among the top categories, we find “reliable” and “quick”, which means that people prefer to spend as little time travelling as possible and be on time. These two factors are often considered critical quality attributes in public transport [ 33 , 62 , 63 ] and also significantly influence choice of transport mode [ 64 , 65 ]. All other categories received a similar ratings with an average situated around the value 3 out of 5 except for the categories “philanthropic” and “social”. These results are in line with the studies of Friman et al. [ 66 ], Olsson et al. [ 67 ] and Sarriera et al. [ 68 ] who found out, that the possibility of socialisation is not a factor based on which travellers would choose a specific travel solution. Regarding results of the philanthropic category, Verplanken et al. [ 69 ] state that individual interest strongly impacts travel mode choice, while pro-social motives always stay behind. This may be the reason why interest in this category is so low. Although several categories achieved similar values, they can be ranked based on this evaluation and prioritised in order to gain clarity when displaying travel offers to travellers.

Compared to the travel offer categories, the incentives achieved a lower rating. Among the most rated are mainly financial incentives. According to our results, the preferred ways how to get incentivised to amend travel decisions are “immediate discount” and “class upgrade”. These findings support the current trend in journey planners’ design to modify travellers’ decisions by using credit systems with the possibility to change credits for financial incentives [ 44 , 45 ]. Although our results indicate that financial incentives are preferred by travellers more than non-financial ones, literature shows that they are not very sustainable and do not have a lasting impact on changing travel behaviour [ 36 , 38 ]. It is therefore important to look for forms of incentives that would be interesting for travellers on the one hand and sustainable on the other. In our research of the non-financial incentives, the respondents were most interested in the “loyalty program” with points collection to unlock different rewards and “environmental information”. The least interesting incentive for travellers was “a competition with friends”.

By using ordinal regression, for every travel offer category and incentive, several factors were found significant. The most recurring significant factor in the case of travel offer categories was gender and, in the case of incentives, age. These socio-demographic characteristics still play a significant role in planning and travel behaviour [ 70 , 71 ], and therefore, they should be considered as strong criteria for recommendations in journey planners. The results also show that travellers’ choice of travel offer categories and incentives depends on different factors. The great variety of other identified factors underlines the importance of focusing on the personalisation of recommendations for travellers.

To implement travel offer categories into a journey planner, we need to identify and quantify contributing factors. In a case, if there is more than one relevant factor, they can be prioritised and weighted [ 72 ] to obtain for each category a single numeric value. To put numeric values on the same footing and make them more easily comparable, data transformations, such as z-score or min-max normalisation [ 73 , p.114], can be applied. Final numerical values of travel offer categories can be presented, using visually attractive labels, together with travel offers and be further used as features describing travel offers in various machine learning tasks to extend journey planning functionalities. Implementation of incentives is methodologically simpler. For example, it is sufficient to define conditions which, if satisfied, the traveller is entitled to receive given incentives from the transport operator. In practice, the significant challenge might be a technical one, residing in ensuring application programming interfaces between transport operators and journey planning services to support the exchange of required information.

It is worth to reiterate that the use of travel offers categories can be justified by journey planners that offer to travellers many transport connections. Typically, such journey planners cover a large geographical area and connect several means of transport (e.g. a Europe-wide journey planner). Offer categories can help to travellers to identify faster which traveller offers correspond to they needs or be used as features characterising travel offers in machine learning tasks.

The research conducted in this paper has several limitations. The sample of respondents consisted mainly of people from Slovakia and Czechia, aged 18 to 50, with a university degree. Therefore, the interpretation of research results should be perceived primarily from the perspective of this group of people. Another limitation is that incentives were studied without considering a broader context (e.g., travel purpose, travel frequency, travellers’ motivations, etc.). This could contribute to low ratings received by non-financial incentives, which could become more attractive for travellers if combined with some externalities. For example, “provisions of information that can increase traveller’s awareness on the environmental sustainability of a travel offer” can be more relevant for a traveller if their employer monitors the environmental impact of business trips. In addition, only a few features characterising the demographic profile of travellers and travel behaviour are entering the analysis. Consequently, R 2 values of the categorical regression model are relatively low. Nevertheless, according to conducted statistical tests, the results are significant.

Although the research contains the mentioned limitations, we consider the results to be valuable and relevant inputs in the initial stages of the design of journey planners. An ideal use case for our results would be a multimodal journey planner enabling searching for travel offers on a large geographical scale (e.g., across country borders or even Europe-wide). In such a situation, we can expect that travellers would appreciate a guidance on which travel solution to choose. However, such journey planners are currently rare, mainly due to difficulties with funding their development and operation. Perhaps, this could be resolved by an EU-wide policy prioritising support to the operation of such a journey planner or finding a way how to operate it via open collaboration like OpenStreetMap. For future work, we envisage further validation of the paper results by evaluating the level of satisfaction and usefulness of travel offer categories based on data collected during Ride2Rail demonstrations (i.e., the comparison of stated preferences with revealed preferences). It is also necessary to verify the ability of travel offer categories to be used as features in prediction models of travel offer choices made by travellers and identify traveller’s segments to enhance these prediction models.

Supporting information

Funding statement.

This work was supported in part by Ride2Rail project financed from the Shift2Rail Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 881825. L.B. and M.S. were in part supported by project VEGA 1/0077/22 - Innovative prediction methods for optimisation of public service systems, APVV-19-0441 Allocation of limited resources to public service systems with conflicting quality criteria and in part by the Operational Program Integrated Infrastructure 2014-2020 Innovative Solutions for Propulsion, Power, and Safety Components of Transport Vehicles" through the European Regional Development Fund under grant ITMS313011V334. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Travel Advisers: When to Let a Professional Plan Your Trip

By Julie Weed

Decades ago, your vacation most likely began with a visit to a travel agent, who relied on a combination of expertise and connections to find the best deals on plane tickets, hotels, tours and more. Since then, the internet has turned most of us into our own travel agents, and artificial intelligence software is making research and self-booking even easier. But for some trips, that special insider knowledge can still make a big difference.

So when should you hire a professional, and how does it all work? Here are some tips.

Why should I consider a travel adviser?

It’s easy for a traveler to do the research for a standard trip, said Chris Anderson, a professor at the Cornell University School of Hotel Administration, “so they should look for a specialist for the type of tour they are looking for, say a bike trip in Ireland, who can really add value.”

The insider knowledge offered by a travel adviser can add the most value to trips that have multicity itineraries, involve a wide age range of travelers , are very significant (like an anniversary vacation) or are to destinations you are unfamiliar with, said Gary R. Johnson, who has run the travel agency Woodside Travel in Seattle for nearly 30 years. An adviser could help you decide, for instance, in which order to visit European cities based on local events and transportation options.

What can an adviser give me that a booking site can’t?

Travel advisers can help you research the best destinations, lodging, or activities for your particular group and travel goals, offering up specific advice that might be hard or time-consuming to find yourself. Those specializing in cruises might know which cabin to choose if you are prone to seasickness, while a safari planner could help you decide which park would be best for bird-watching or seeing specific animals, like rhinos.

Travel advisers typically have relationships with tour companies, hotels and cruise lines, sometimes through networks. Those connections can allow advisers to offer extra perks such as late checkout, free breakfast, airport transfers, a welcome basket or a credit to spend on a cruise ship.

“A good travel agent will be a better steward of your travel budget than you are,” said Guy Rubin, managing director of Imperial Tours , which arranges travel in China.

When bad weather or other circumstances disrupt your itinerary, travel advisers often have direct lines of communication with providers and can do the work of rebooking and changing plans, saving you time and stress.

OK, let’s say I need help. How do I find an adviser?

Networks like the American Society of Travel Advisors and Travel Leaders have websites that can help you start your search for a travel adviser by answering a few questions about your desired trip. Once you have a handful to choose from, get on the phone with them to talk about what they might do for you, how they charge and the level of service you can expect. Special trips can cost thousands of dollars, so it’s worth investing time up front, Mr. Rubin said.

Make sure to read over the travel agent’s reviews and any user-generated social content that mentions them, Dr. Anderson said. “If there is no external validation, that’s a red flag.”

How do advisers get paid, and how much will it cost me?

Advisers receive commission from suppliers, typically 10 to 15 percent of the price, when selling cruises, lodging and tours. They also sometimes charge travelers a planning fee, from a few hundred dollars, which may be credited to the final bill if the booking is completed, all the way up to tens of thousands of dollars annually for a luxury concierge travel planner they can call on all year. Mr. Johnson said that he charges a planning fee the first time he works with customers. If they return for other trips, he waives the fee.

Advisers may be tempted to sell you something that will earn them a higher commission, Dr. Anderson said. But, he points out, the same is true for the large online services, which promote hotels that pay them larger commissions. Travelers can ask advisers about specific commissions they receive or how they are affiliated with the products they are recommending, he said.

Sometimes a local tour company will package transportation, lodging and experiences for an adviser, who tacks on a percentage before passing it along to a client. But a bill that is not itemized can make it harder to make trade-offs — between a more expensive hotel and a special experience, for example. If pricing transparency is important to you, discuss it with the adviser up front.

How are A.I. and other technologies affecting travel advisers?

While new technologies are allowing do-it-yourselfers to create their own itineraries online based on individual preferences, and to type questions directly into travel websites, advisers are also taking advantage of those technologies to improve their services. Joan Roca, chief executive of the upscale travel planning company Essentialist said his team “uses technology to enhance the human touch,” employing artificial intelligence to choose options from a database of travel offerings selected by a human team. If a couple wants to take an after-dinner stroll, for example, Essentialist’s app will offer up ideas of where to go, based on what part of the city the travelers are in and conversations they’ve had with their travel adviser.

Open Up Your World

Considering a trip, or just some armchair traveling here are some ideas..

Italy :  Spend 36 hours in Florence , seeking out its lesser-known pockets.

Southern California :  Skip the freeways to explore the back roads between Los Angeles and Los Olivos , a 100-mile route that meanders through mountains, canyons and star-studded enclaves.

Mongolia : Some young people, searching for less curated travel experiences, are flocking to the open spaces of this East Asian nation .

Romania :  Timisoara  may be the most noteworthy city you’ve probably never heard of , offering just enough for visitors to fill two or three days.

India: A writer fulfilled a lifelong dream of visiting Darjeeling, in the Himalayan foothills , taking in the tea gardens and riding a train through the hills.

52 Places:  Why do we travel? For food, culture, adventure, natural beauty? Our 2024 list has all those elements, and more .

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