Smart tourists as a profiling market segment: Implications for DMOs

Abstract

The concept of smart tourism has gained a significant attention in the last years, leading to fruitful discussions among scientists and practitioners; however, there has been lack of attention paid to smart tourists so far. Although this emerging type of tourist has been conceptualized, it is now important to find whether it can be considered as a market segment. The article fills the gap in the smart tourism research by using two-step cluster analysis to classify 5975 tourists, finding a smart tourism market segment and analysing the characteristics and travel behaviour of this segment. This segment is worth focusing on and differentiates in all trip experience phases. The rethinking of business models of today’s destination management organizations towards creating value proposition during all trip experience phases based on personalization and experience enrichment is needed in order to reach the smart tourist market segment.

Keywords

destination management organization smart tourism smart tourist tourism market segmentation tourist experience

Introduction

Development of information and communication technologies (ICTs) has changed how tourism businesses, destination management organizations and consumers behave (Benckendorff et al., 2014). While the importance of ICTs has been recognized since 1960s with the rise of central reservation systems and later with global distribution systems (O’Connor, 1999; Sheldon, 1997), it was the commercialization of the Internet in 1990s that has reshaped the tourism market (Werthner and Klein, 1999) and has resulted in the digitalization of all processes and value chains in tourism (Buhalis, 2003; Buhalis and Law, 2008), and thus creating the baseline for e-tourism. Leaders in the tourism industry began to realize that the Internet enabled them to communicate easily and effectively with their existing and potential customers (Xiang and Fesenmaier, 2017b). With the rise of the experience economy (Pine and Gilmore, 1999), and the ability of ICTs to become a mediator or core experience itself (McCarthy and Wright, 2004), the role of ICTs in the tourism industry has shifted more towards personalization. Web 2.0, online travel agencies (OTAs) and meta search emerged in order to serve ‘new global tourists’ demand (Sigala and Gretzel, 2017; Sigala et al., 2012; Xiang et al., 2015). However, it was the advent and widespread use of a smartphone (Wang et al., 2014) as well as recent advancements in sensor technologies (Shoval and Ahas, 2016), cloud computing and Internet of things (IoT) (Li et al., 2017) that have pushed the emergence of smart tourism conception (Xiang and Fesenmaier, 2017a; Xiang et al., 2015) as a tool for responding these structural changes.

Gretzel et al. (2015a) define smart tourism as a direct extension of e-tourism in connecting the physical world with the digital. It uses advanced technologies to transform the data into experiences and business value propositions leading to efficiency, sustainability and better experiences of tourists (Gretzel et al., 2015c; Ivars-Baidal et al., 2017). Based on the concept of smart tourism ecosystem (Gretzel et al., 2015c; Perfetto and Vargas-Sánchez, 2018; Zhang et al., 2012; Zhu et al., 2014), smart tourism is built on technologies used by consumers (tourists, residents), businesses (stakeholders) and destination (space governed by destination management organization (DMO), government). So far the analysis of the state-of-the-art technologies (e.g. Bibri and Krogstie, 2017; Huang et al., 2017; Sigala, 2018; Wang et al., 2016b; Yoo et al., 2017) as well as the smart tourism destinations and the role of stakeholders in them (e.g. Baggio and Scaglione, 2018; Boes et al., 2016; Buhalis and Amaranggana, 2015; Gretzel et al., 2015b; Ivars-Baidal et al., 2017) have made a significant contribution to the tourism theory and praxis. There has been lack of attention paid to smart tourists so far (Femenia-Serra et al., 2018c), although it is more important than ever to better comprehend how tourists perceive and use ICT to create and shape their trips (Femenia-Serra et al., 2018a) in order to develop better destination management based on their behaviour. Moreover, Cohen et al. (2014) emphasize that further research should focus on how technology impacts the consumer behaviour as technological developments, and the ways in which consumers deploy such developments, continue to evolve. Only a few studies have addressed the influence of the strategy of a smart destination on the tourist experience so far (Buonincontri and Micera, 2016). In order to progress in research, it is necessary to know in-depth the opinion and behaviour of tourists (Liberato et al., 2018) and their needs (Zhang et al., 2018), based on empirical research.

Furthermore, Femenia-Serra et al. (2018b) call for empirical studies investigating if there is a clear smart tourist segment and if it is possible to develop a typology of smart tourists. This study answers these calls by answering the research question: ‘Are smart tourists a market segment with different characteristics and travel behaviour?’

The evolution of a smart tourist

Tourists, their behaviour and typology have gained a significant attention in research since 1970s. Cohen’s (1972, 1974) typology has laid the baseline for conceptual clarification of the term ‘tourist’, followed by MacCannell (1976) and his model of tourist as a ‘modern man’. Pearce (1982) examined the role of tourist through the lenses of social psychology, while Urry (1990) dealt with the tourist’s view out of ordinary life. The more recent research include Wickens’ (2002) tourist typology followed by the criticism of McCabe (2005, 2009) and the examination of tourist experience (Kim et al., 2012; Tussyadiah and Fesenmaier, 2009; Uriely, 2005). Nowadays, we are witnessing the reinvention of tourist behaviour both in the theory and praxis (Cohen et al., 2014) formed, inter alia, by information technology.

The development of information technologies in recent years, such as search engines, OTAs or social media, has influenced the number of travellers around the world to use these technologies across the trip experience (Buhalis and Law, 2008; Choe et al., 2017) and thus new conceptual schemes that characterize tourists in this context can be observed. Firstly, it was the informed tourist – more demanding, experienced and empowered thanks to the availability and the use of ICTs (Gretzel et al., 2006). Later, with the proliferation of smartphones in everyday life and travel (Wang et al., 2016a), it was the digital tourist – well-travelled, and thus easy blasé expecting the extraordinary, requesting personalized experience (Gelter, 2017), where technology shapes the key components of its journey (Navío-Marco et al., 2018; Pearce, 2011a). With the continual rise of the state-of-the-art technologies (IoT, cloud computing and end-user Internet services) (Huang et al., 2017), the smart tourist is being constructed (Femenia-Serra et al., 2018b; Gretzel et al., 2015a; Xiang and Fesenmaier, 2017a).

Tourists are smart in the sense that they want to have a supper-connected experience (Femenia-Serra et al., 2018c). For a smart tourist, technology represents an opportunity to actively participate in destination activities and to take part in the construction of its own experience (Prebensen et al., 2013). Information technologies should enhance experience of a smart tourist by giving all the related real-time information about the destination and its services in the planning phase, enhance access to real-time information to assist tourists in exploring the destinations during the trip and prolong the engagement to relive the experience by providing the descent feedback after the trip (Buhalis and Amaranggana, 2015). Due to this engagement, the smart tourist himself becomes a co-creator and co-promoter of the destination (Gahr et al., 2014). Buhalis (2018) indicates that a non-smart tourist is confused in an unfamiliar environment and has many barriers, such as language or mobility needs. On the other hand, there is a smart tourist, who uses personalized and contextualized services, engage and explores the destination and is in search for authentic and immersive experience during all stages of travel. From a conceptual point of view, the smart tourist can be defined by three characteristic behaviours (Femenia-Serra et al., 2018b).

Uses smart technologies for the experience

Tourism experience is undergoing constant change characterized by the growing importance of consumer involvement, co-creation and the implementation of technology (Neuhofer et al., 2014). ICTs have been recognized as a major change of tourism experience (Prebensen and Foss, 2011) and technology can function as an enabler, creator, attractor, enhancer, protector or even a destroyer of the experience (Benckendorff et al., 2014; Stipanuk, 1993). Nowadays, tourists use different technologies for the experience (Tanti and Buhalis, 2017). While latest generation websites with recommender systems and OTAs provide tourists with an easy access and booking of services, the use of augmented and virtual reality, mobile apps supported by ubiquitous connectivity through Wi-Fi or 3/4G network, destination smart cards and wearables enable to construct new personalized experience. Moreover, tourists share experience live on social media (Wang et al., 2014) and by acquiring positive emotional support and encouragements from connected family and friends, these experiences are more enjoyable and memorable (Kim and Fesenmaier, 2017; Kim et al., 2013).

Co-creates the experience through smart technologies

In recent years, technologies have enabled tourists to become more involved and more innovative in creating their own experience. These technologies have led to a significant shift in the roles of travellers from passive information recipients to active information creators (Choe et al., 2017; Wang et al., 2012). Nowadays, tourists actively contribute to the overall design and delivery of the tourism experience knowing that it is for them (Ballantyne et al., 2017). By using the mobile device and location based services, tourists can exchange information in real time, be active in conversations and personalize the findings on the Internet. Moreover, the technology enables to engage and explore more by providing useful context-based information (Neuhofer et al., 2014). Sigala (2018) adds that tourists become co-designers, co-marketers, co-advertisers, co-promoters, co-distributors of tourism experience through user-generated content, customer review platforms, blogs, wikis, participation in innovation tests and toolkits and crowdsourcing and crowdfunding practices.

Shares data with stakeholders

Tourists, during their trip experience phases, contribute to the creation of massive flow of data of personal, behavioural and geographical character. The sources of personal and behavioural data are social media, OTAs, mobile apps or destination smart cards (Gajdošík, 2019). They may contain name, age and gender of a tourist, or bounce rate, page time view, page duration view and click path, frequency. Information about tourists’ consumption in a destination can be derived from credit card payments or payments via near field communication (NFC). Visitor flows are analysed by geographic data (Baggio and Scaglione, 2018) obtained also from destination cards, mobile apps or social media (Salas-Olmedo et al., 2018) or by using passive mobile positioning (Ahas et al., 2008; Raun et al., 2016). Moreover, Bluetooth low energy used in beacons, used mainly inside the tourist attractions, can provide useful information about tourists’ flows inside a building (Yamaguchi et al., 2017). The data can be shared on voluntary or non-voluntary basis. The willingness to share data with other stakeholders dynamically has been so far limited (Femenia-Serra et al., 2018c), mainly due to the privacy reasons. However, a rising trend has been observed among consumers in general, as they are becoming more open to sharing data (Pingitore et al., 2017). About 40% of consumers from Asia and Central and Eastern Europe are willing to share data (Akselsen et al., 2015). This trend is anticipated to evolve also in tourism.

Although the conceptual view on smart tourist gives valuable information about the characteristic behaviour of the smart tourist, there has been a lack of empirical research focusing whether smart tourists are a real market segment with adequate size and different characteristics and behaviour.

Research approach

The main aim of the article is to find out whether smart tourists are a real market segment and analyse their characteristics and travel behaviour. Market segmentation in tourism is a strategic tool to account for heterogeneity among tourists by grouping them into market segments which include members similar to each other and dissimilar to members of other segments (Dolnicar, 2008). Focusing on a homogenous group in a heterogeneous tourism market makes it possible to better tailor services, provide higher satisfaction, achieve repeat visitation and more revenue for businesses, as well as to create a more dynamic and competitive destination (Hernández et al., 2018). For segmentation to be managerially useful, each segment needs to be accessible, measurable and substantial (Kotler, 1980), as well as distinct and suitable in size (Wedel and Kamakura, 2000). Apart from being sufficiently heterogeneous from each other, the segments need to be suited to the values of the destination (Neuts et al., 2016). In this rapid changing environment, tourism marketers should target potential travellers depending on experiences rather than aspects such as socio-demographic characteristics (Choe et al., 2017).

The presented paper uses primary data from the questionnaire survey conducted among tourists in Slovakia. Slovakia was chosen as a reference country, where the use of the Internet services and integration of digital technology are at the average level of the European Union countries (European Commission, 2018) and the country is recognized as a digital challenger (Novak et al., 2018). The survey was made from January to March 2018, using CAWI and CAPI method. The online questionnaires were distributed with the help of the marketing agency ZľavaDňa. The database of the agency’s contacts was used to send an e-mail with the questionnaire. Moreover, personal (face-to-face) interviews were conducted to reduce the biases of online survey using area probability sampling. Firstly, the major urban (8) and rural destinations (10) in Slovakia were identified as sample segments. Then the interviewers guided by the research assistant were instructed to start at a corner of the segment and proceed around the segment, contacting housing units until a specific number of interviews were completed in the segment. The overall quota sampling was utilized concerning the age and education of respondents, as these two variables are crucial in technology usage and acceptance (Czaja et al., 2006; Ellis and Allaire, 1999) to obtain a reasonably sound approximation of the population of Slovakia.

The questionnaire was structured into three sections. Firstly, the use of specific information technologies during the trip experience phases was examined, as the travelling is often characterized by a three-stage framework: planning and booking (pre-trip), staying in a destination (during-the-trip) and sharing the experience (post-trip). ICTs represent an important tool throughout the entire trip by fulfilling a number of specific needs of tourists at a particular moment across all three stages of the trip (Choe et al., 2017). The Likert-type scale was used in order to access the frequency of use of these technologies (0 – never, 1 – sometimes, 2 – often, 3 – regularly). The list of the technologies was elaborated based on the previous research (Benckendorff et al., 2014; Buhalis and Law, 2008; Femenia-Serra et al., 2018c; Huang et al., 2017; Ivars-Baidal et al., 2017; Navío-Marco et al., 2018), where the most frequent information technologies were picked up and if relevant, the commercial name was used.

The second part of the questionnaire was focused on the factors influencing the choice of the destination. Its aim was to find out the behaviour of tourists when choosing a destination. The respondents were provided with the list of responses, any of which they can choose as their factor of destination preference. The list of responses was elaborated based on the previous studies focused on destination choice by tourists (Pearce, 2011b; Prayag and Ryan, 2011; Won et al., 2008; Wu et al., 2011).

Finally, psychographic characteristics concerning technology acceptance and use as well as socio-demographic criteria were questioned. The technology use and acceptance was evaluated based on a category question on a 0–3 scale (0 – fear of technology, 1 – minimal acceptance and use, 2 – good acceptance and use, 3 – regular use and enjoyment of technology) based on models of Venkatesh et al. (2003) and Benckendorff et al. (2005). For the use of technologies for co-creation, the list of possible ways of co-creation (exchanging information through mobile app, enabling location-based services, active conversation with tourism businesses through social media and using personalized websites) was provided based on Neuhofer et al. (2014) and Buonincontri and Micera (2016). Data sharing was analysed on a category question on a 0–3 scale (0 – no data, 1 – basic information/age, name, nationality/, 2 – basic and more personal information/+hobbies, relationship status, social media profile, 3 – basic, personal and real-time information/+real time position, specific expenses in each place and service, smartphone history) based on the research of Femenia-Serra et al. (2018c).

Analysis and discussion of results

Together 5975 complete responses were recorded. The respondents were motivated by a reward to fill in the questionnaire. They could win a three-day holiday stay in Slovakia. The average age of respondents is 42.35 with the almost equally distribution of secondary and tertiary education. The majority of respondents were women (69.08%), as they are more likely to participate in surveys (Curtin et al., 2000). The frequencies of respondents’ age, education and salary are presented in Table 1.

Table 1.

Frequencies of respondents’ age, education and salary.

Age			Education			Salary
Range	Frequency	%	Range	Frequency	%	Range	Frequency	%
0–24	631	10.56	Primary	70	1.17	0–599 €	1015	16.99
25–34	1308	21.89	Secondary	2879	48.18	600–1200 €	2985	49.96
35–44	1582	26.48	Tertiary – bachelor	643	10.76	1300–1999 €	1439	24.08
45–54	1171	19.60	Tertiary – master	2330	39.00	2000 + €	536	8.97
55–64	834	13.96	Tertiary – PhD	45	0.75
65+	449	7.51	Other	8	0.13
∑	5975	100.00	∑	5975	100.00	∑	5975	100.00

Segmentation procedure and profiles of segments

In order to find the smart tourist segment among the analysed tourists, all three features of their characteristic behaviour (the use of smart technology, co-creation and willingness to share data) were used as input variables to two-step cluster analysis. The two-step cluster analysis was chosen as it is good for very large data sets (Chiu et al., 2001) and it automatically selects the number of clusters based on statistical criteria (Sarstedt and Mooi, 2019). Moreover, cluster analysis is frequently used to segment tourists into homogenous groups (e.g. Andreu et al., 2005; Laesser et al., 2006).

The two-step cluster analysis was performed using IBM SPSS software. The number of clusters was chosen according to Schwarz’s Bayesian Information Criterion (BIC), which indicated a six-segment solution. The silhouette measure of cohesion and separation of the two-step cluster analysis reached the value of 0.70, indicating a good cluster quality. The values of BIC, ratio of BIC changes and distance measures are presented in Table 2. As the values of BIC continue to decrease with the increase of the number of clusters, the changes in BIC and changes in distance measure determine the number of clusters. The best solution is with six clusters, as there is a reasonably large ratio of BIC changes (0.377) and the largest ratio of distance measures (1.781).

Table 2.

Criteria to choose the number of clusters.

Number of clusters	Schwarz’s Bayesian Information Criterion	BIC changes^a	Ratio of BIC changes^b	Ratio of distance measures^c
1	37,605.904
2	30,428.937	−7176.966	1.000	1.590
3	25,959.949	−4468.988	0.623	1.245
4	22,393.278	−3566.671	0.497	1.292
5	19,660.353	−2732.925	0.381	1.010
6	16,954.931	−2705.422	0.377	1.781
7	15,489.473	−1465.457	0.204	1.124
8	14,198.538	−1290.935	0.180	1.057
9	12,983.424	−1215.114	0.169	1.083
10	11,871.245	−1112.179	0.155	1.083
11	10,854.077	−1017.168	0.142	1.570
12	10,250.498	−603.579	0.084	1.014
13	9656.609	−593.889	0.083	1.189
14	9176.367	−480.242	0.067	1.132
15	8766.282	−410.086	0.057	1.134

Note: BIC: Bayesian Information Criterion.

^aThe changes are from the previous number of clusters in the table.

^bThe ratios of changes are relative to the change for the two cluster solution.

^cThe ratios of distance measures are based on the current number of clusters against the previous number of clusters.

Subsequently, the analysed tourists were divided into six segments. For better characterization, the mean values and standard deviations of psychographic characteristics concerning technology acceptance and use were calculated (Table 3) and the typology of tourists according to technology use, co-creation and sharing was elaborated and displayed in Figure 1.

Table 3.

Profile of the sample and the clusters regarding the technology use, co-creating and sharing.

		Total	1	2	3	4	5	6	Kruskal–Wallis
Cluster		N = 5975 (100%)	N = 1075 (17.99%)	N = 800 (13.39%)	N = 862 (14.43%)	N = 1178 (19.72%)	N = 1222 (20.45%)	N = 840 (14.06%)	χ ²	p Value
Technology use and acceptance	x¯	2.22	2.49	3.00	0.81	2.00	2.47	2.57	3926.46	0.000
Technology use and acceptance	s	0.31	0.50	0.00	0.39	0.00	0.49	0.49
Co-creating	x¯	1.29	3.06	0.32	0.79	0.36	0.38	3.13	4002.73	0.000
Co-creating	s	0.61	1.42	0.16	0.37	0.17	0.16	1.4
Sharing	x¯	1.02	0.72	0.74	0.58	0.54	1.70	1.83	2287.28	0.000
Sharing	s	0.45	0.38	0.45	0.25	0.22	0.68	0.74

Figure 1.

The visualization of clusters.

Segment 1: No-sharers are tourists who appreciate the use of technologies, use them also during the stay in the destination for co-creation, however, they have a fear of data sharing and prefer the privacy.

Segment 2: Compensators are keen on technologies, use them in everyday life, but try to avoid them during travelling seeking for ‘digital detox’.

Segment 3: Technophobes do not use or accept technologies in their lives and during travelling.

Segment 4: Traditionalists are good at technology use, but do not want to share data and co-create the products during the holidays. For them travelling is time for relax.

Segment 5: Recipients are very good in technology use and want to share their basic as well as more personal information. They look for standardized products, as they are passive information recipients.

Segment 6: Smart tourists are very good in information technologies, looking for personalized product resulting from co-creation. They share their basic and more personal data, some of them also the real-time position.

Smart tourists as a market segment and its travel behaviour

In order to examine deeply the smart tourists, the further analysis is focused on their characteristics and travel behaviour (Tables 4 and 5) in comparison with other identified segments. Kruskal–Wallis and χ ² tests were used to show the existence of statistically significant differences between the segments.

Table 4.

Profile of the sample and the clusters regarding socio-demographic characteristics and the use of technologies during trip experiences phases.

	Total sample	1	2	3	4	5	6	Kruskal–Wallis
	N = 5975 (100%)	N = 1075 (17.99%)	N = 800 (13.39%)	N = 862 (14.43%)	N = 1178 (19.72%)	N = 1222 (20.45%)	N = 840 (14.06%)
Cluster	Mean	Mean	Mean	Mean	Mean	Mean	Mean	χ ²	p Value
Socio-demographic characteristics
Age	42.35	41.94	36.59	50.17	44.07	40.36	40.89	494.49	0.000
Salary	1136.52	1155.63	1218.38	964.09	1120.42	1176.64	1173.87	166.20	0.000
Consumption	136.09	142.92	135.42	122.24	131.66	135.32	149.56	67.16	0.000
Planning and booking
IDS/OTA	1.56	1.67	1.73	0.95	1.28	1.76	2.02	364.404	0.000
Hotel website	1.59	1.88	1.48	1.33	1.44	1.52	1.94	231.936	0.000
Destination website	1.26	1.61	1.06	1.12	1.15	1.04	1.65	287.524	0.000
Meta search	0.61	0.88	0.47	0.5	0.49	0.5	0.85	174.710	0.000
Sharing economy platform	0.39	0.64	0.47	0.17	0.16	0.35	0.57	248.745	0.000
Staying in a destination
Digital map	1.24	1.54	1.38	0.74	1.02	1.23	1.56	691.966	0.000
Hotel website	0.86	1.19	0.68	0.56	0.68	0.78	1.27	621.879	0.000
Destination app	0.53	1.20	0.17	0.32	0.18	0.20	1.22	2217.018	0.000
Destination website	0.77	1.13	0.56	0.49	0.6	0.62	1.21	739.188	0.000
Smart cards	0.41	0.93	0.10	0.26	0.14	0.14	0.99	1864.164	0.000
Wearables	0.19	0.50	0.03	0.10	0.02	0.03	0.50	1096.523	0.000
Augmented reality	0.16	0.42	0.02	0.11	0.02	0.01	0.42	1074.237	0.000
Sharing the experiences
Social media	1.02	0.724	0.736	0.58	0.539	1.7	1.83	2287.275	0.000

Note: IDS: internet distribution system; OTA: online travel agency.

Table 5.

Profile of the sample and the clusters regarding the factors influencing the choice of a destination.

Cluster	Total	1	2	3	4	5	6	χ ² test
	N = 5975 (100%)	N = 1075 (17.99%)	N = 800 (13.39%)	N = 862 (14.43%)	N = 1178 (19.72%)	N = 1222 (20.45%)	N = 840 (14.06%)
	%	%	%	%	%	%	%	χ ²	p value
Reviews	0.81	0.78	0.83	0.66	0.76	0.84	0.88	242.984	0.000
Price	0.88	0.88	0.9	0.86	0.87	0.9	0.86	16.259	0.006
Authentic experience	0.50	0.49	0.54	0.38	0.49	0.54	0.55	70.937	0.000
Recommendations from family and friends	0.37	0.37	0.35	0.39	0.35	0.37	0.41	9.754	0.083
Official photos and videos	0.33	0.31	0.34	0.26	0.30	0.38	0.37	46.152	0.000
Customer photos and videos	0.29	0.29	0.31	0.18	0.21	0.38	0.37	166.76	0.000
Ease of reservation process	0.31	0.33	0.30	0.29	0.30	0.31	0.34	9.308	0.097
Travel distance	0.17	0.16	0.15	0.25	0.17	0.16	0.16	45.039	0.000
Customer service	0.05	0.06	0.05	0.05	0.04	0.05	0.09	28.397	0.000
Sustainable principles	0.04	0.05	0.03	0.03	0.03	0.04	0.06	19.244	0.002

The average age of a smart tourist is 40.89 which is higher than in the segment of compensators and recipients. This finding requires the reconsideration of the fact that the millennials form the majority of smart tourists (Femenia-Serra et al., 2018c), as the average age indicates also the generation X takes a significant part in the smart tourist segment. Smart tourists are on a third place in terms of the salary, after the compensators and recipients. However, they have the highest additional consumption in destination among the other segments, which makes them an interesting segment to focus on, from a DMO point of view.

In the planning and booking phase, they use quite often Internet distribution systems (IDS) and OTAs (e.g. Booking.com, Expedia), as well as hotel and destination websites. The use of sharing economy platforms (e.g. AirBnB) and meta search (e.g. Trivago) is on the second place after the no-sharers. It is in line with the research of Amaro and Duarte (2015), who state that the favourable attitude and compatibility with the lifestyle of Internet users are the key factors that influence to purchase travel online, as both smart tourists and no-sharers have positive attitude to information technologies and their lifestyle includes the online presence.

During the stay in a destination, smart tourists use often a digital map to get to know the destination better. It supports the research of Park (2014), where 36% of tourists in the United States claim that the digital maps belong to the most useful travel features. Thanks to location-based services, the actual position of a tourist is displayed on the map and tourist can easily find the best route to the desired place. Moreover, maps play a major role in spatially edging tourist experiences (Farías, 2011) and thus contributing to overall tourist experience.

Smart tourists also use quite often hotel and destination websites, through which they can consult up-to-date information and book additional services. The biggest difference among segments is in the use of destination app. The use of destination app enhance the overall experience and supports the co-creation and personalization by providing all necessary information about a destination with the opportunity to consult the information in real-time with the member of a DMO.

Smart cards are used sometimes, as not all destinations have incorporated these cards to their destination management and marketing strategy. Smart tourists together with no-sharers use seldom the wearables (e.g. smart watches or glasses), however, the other segments do not use them at all. These devices are relatively new and are predicted to have a significant effect on the interaction with the surroundings of a destination (Tussyadiah, 2014). Also the use of augmented reality as a new way how to enhance the experience (Kounavis et al., 2012) is not very common, but comparing to other segments is much higher.

Using the social media to share the experience is quite common among the smart tourists and much higher than in other segments. Sharing the experience by smart tourists can be expressed by saying ‘if it is not on social media, it did not happen’. The overall result of total sample in sharing the experience after the trip is comparable to the findings of Choe et al. (2017) stating that one-third of tourists use at least one type of social media for their trip-related behaviours after the trip.

When choosing a destination (Table 5), the most important decisive factor for smart tourists are reviews. Thanks to information technologies, reviews are up-to-date and visible to all tourists. Moreover, as Xiang and Gretzel (2010) state, tourists consider reviews as more reliable source of information than other sources. Reviews and recommendation for future intentions to travel to a destination from social media have an impact on prospective tourists (Volo, 2010), mainly smart tourists.

Following the recommendations, price is the second most important factor for smart tourists, however, not as crucial as in other segments of tourists. Surprisingly, for 90% of compensators and recipients, having the highest salaries, price is the most important criterion. The third most important factor is the offer of authentic experience in a destination. Smart tourists are, more than other segments, keen on memorable activities and experiences that influence the senses and create the relationship with destination. This finding supports the shift towards the experience economy (Pine and Gilmore, 1999), where the offering is stating or producing experience and it is in line with the research of Kumar et al. (2014), who demonstrate that people derive more happiness from the anticipation of experiential purchases and that waiting for an experience tends to be more pleasurable and exciting than waiting to receive a material good.

Recommendations from family and friends are also important, however, there is no significant statistical difference between the segments (p value 0.083). Moreover, for almost 37% of smart tourists, official and customer photos and videos are important decisive factor. Only in the segment of smart tourists and recipients, the customer photos and videos are equal to official photos. All other segments prefer the official photos. Therefore, it can be stated that the smart tourist segment challenge the marketer-generated media content and strengthen the user-generated content (UGC), which has more positive impact on destination brand (Lim et al., 2012). Smart tourists are taking, sharing and viewing visuals more than other segments, as the real-time aspect and objectivity is more appealing to them.

Ease of reservation process is important for 34% of smart tourists, with almost the same share of other segments. Travel distance, customer service and sustainable principles do not pay such an important role when choosing a destination from the smart tourists point of view, as less than 20% of members of the segment consider it as a decisive factor. In the light of this research, the interest in sustainability and responsibility of destination from the smart tourist point of view (Gahr et al., 2014) can be questioned as only 6% of analysed smart tourists pay attention to sustainable principles when choosing the destination, although it is the highest share among other analysed segments.

The analysis of smart tourists’ characteristics and their travel behaviour proved that they can be treated as a market segment. This segment accounts for more than 14% of tourists and therefore is suitable in size. Its behaviour differs from other segments and due to the large usage of information technologies, it is better measurable and accessible more efficiently. Smart tourists are mostly formed by the members of the generation X and Y and have the highest spending on additional services in a destination. Therefore, the segment is substantial and worth to focus on.

Implications for DMOs

The rapid evolution of ICTs and smart tourism has definitely changed the rules of the game. If a destination want to be competitive and differentiate itself in terms of innovation and knowledge, it should embrace the smart approach (Petrović et al., 2017), which positively influences economics of destination, as well as societies and cultures (Koo et al., 2017). This approach is being pushed forward not only from the supply side (Femenia-Serra et al., 2018c), but the rise of the smart tourist market segment adds the demand side.

Based on the empirical analysis, it can be stated that smart tourists are accustomed to use the information technologies during all the trip experience phases. The willingness to co-create and share data leads to the need of personalized solutions, while reviews, authentic experiences and UGC are more crucial than in other segments in destination selection process. In this context, experience makes a useful conceptualization to access the competitiveness and economic success of a destination (Shoval and Birenboim, 2018). Many DMOs in Europe and North America consider planning and booking stage of the trip experience phases as a top priority for their digital strategies (Trekksoft, 2017). While organizations should not ignore this stage, they have to acknowledge that they simply cannot compete with the giants who have much larger budget (e.g. Booking.com, Expedia) and established brand. From the digital point of view for smart tourists, each phase is important and should be addressed by DMOs (Figure 2). Therefore, the business models of DMOs should be changed towards creating value proposition during all trip experience phases based on personalization and experience enrichment in order to reach the smart tourist market segment.

Figure 2.

Creating value proposition during all trip experience phases for smart tourists.

In the planning and booking phase destination websites should be transformed to smart web portals. These portals should be able to filter suitable information and learn from the processes to provide users with explicit and customized information and services (Zhang et al., 2018). It can be done by using artificial intelligence (AI) tools, such as anticipatory product customization, collaborative filtering or text analysis. Based on the available data on tourists, DMO can pre-create and display the tailor-made product to each tourist or use that kind of language that visitor consumes to affect a personalized experience. The portal should be connected to the major review sites, enabling the user to browse the customer reviews. Moreover, DMOs should use user-generated visuals to attract and engage smart tourists and by the application of AI and machine learning to UGC filter and display only relevant photos and videos.

The staying phase should be built on authentic experience, which can range from ecological, adventurous or local experience to unique experience. The experience in destinations should be developed from staging experience and passive experience consumption, to co-creation or even the self-direction of experience, where DMOs provide only the tools for the active self-experience production of the tourist (Gelter, 2017). It can be done by creating destination apps incorporating the most common requirements of smart tourists and the state-of-the-art technologies, such as digital map, real-time position monitoring, listings of attractions and tourism businesses in destination, augmented reality and social media integration. With geolocation information, mobile apps can offer users timely information, offers and promotions (Qin et al., 2017). Providing the app with artificial intelligence enables to understand tourist habits and preferences in order to offer personalized experiences. The experience can be enrichment by the support of wearables, where well-informed smart tourist with extended cognitive abilities can become a smart explorer. The use of wearables will influence destination management in terms of programming, guiding and information provision to tourists and enables the space-time relevant recommendation systems (Tussyadiah, 2014).

Traditionally, the post-travelling phase has been left to the tourist with his photos, videos and souvenirs physically shared among friends. With the massive use of social media, sharing the experience in forms of status updates, comments, photos and videos is gaining the importance and significantly affects the reputation of a destination. Therefore, DMOs should embrace online reputation management, which involves interacting with tourists online, creating shareable content, monitoring what tourists are saying, keeping track of their dialogue, addressing negative content found online and following up on ideas that are shared through social media (Dijkmans et al., 2015). However, the volume of UGC on social media reached a level that makes manual processing almost impossible, creating a demand for new analytical approaches, such as sentiment analysis applied to text and visuals.

Traditionally, in order to find out whether the marketing campaign was successful, DMOs had to wait until tourists arrived at the destination. With the massive use of information technologies through the trip experience phases by smart tourists, DMOs have real-time information on searches, bookings, movements and sentiments of tourists, which provides DMO with the right information to measure the effectiveness of campaigns. Smart tourists improve the booking and communication efficiency by using destination apps and communicating through social media. Creating a personalized product increases the demand of the product and thus creating revenue opportunities. Integrating booking engine to smart portals and destination apps provides DMOs with additional income, which can be used for destination development, while intermediaries (IDS/OTA) use it for their own purposes. Smart portals, destination apps and the support of augmented reality give DMOs full control of tourist experience and thus create engagement. Together with online reputation management it strengthens the loyalty of tourists.

Conclusion

With the development and everyday use of information technologies by consumers a new market segment in tourism is being constructed – smart tourists. So far, there has been more conceptual than empirical investigation of the characteristics and behaviour of this segments. This research aimed to fill the gap in the literature by examining whether smart tourists are a real market segment and empirically analysing the differences in their travel behaviour. The findings revealed that smart tourists can be treated as a market segment, as it is suitable in size, distinct, accessible, measurable and substantial. Moreover, it is economically interesting segment.

Subsequently, the analysis of smart and non-smart tourists’ behaviour showed differences in the use of reviews, demand for personalization, authentic experiences and UGC. Besides the different behaviour, the article reveals some other interesting facts. The consumption of smart tourist in a destination is much higher than of other segments. Conversely, although it was considered that the majority of smart tourists are formed by the millennial generation, the analysis showed that also generation X takes a significant role in smart tourist market segment.

Subsequently, the research presented the implications for DMOs. Targeting the smart tourist market segment implies rethinking the business models of DMOs towards creating value proposition during all trip experience phases based on personalization and experience enrichment. In order to satisfy the needs of the ‘new’ profiling segment, DMOs should built smart web portals, destination apps and support using wearables that result in personalization and experiences enrichment. The use of e-reputation management is also welcomed, as the huge amount of UGC influences the image of a destination. Destination management and marketing is entering the new age of innovation and value creation through information technologies and data analytics, where big data analytics and the application of artificial intelligence tools are welcomed. This approach challenges the economics of DMOs. The additional costs related to the implementation of new technologies and innovations should be overwhelmed by the benefits resulting from targeting the smart tourist market segment. These benefits include marketing effectiveness, booking and communication efficiency, more revenue opportunities and increased loyalty.

The limitations of the study lie in the analysis of tourists from one country, which is considered as a digital challenger. The biases of used questionnaire survey (e.g. belief vs. behaviour, quota sampling) could also have influenced the generalizability of results. Consequently, more quantitative and qualitative studies can extend this research to test the findings. Additionally, further research on smart tourists should be potentially focused on several topics, including: (1) tourists from other countries (e.g. digital frontrunners), (2) studies focusing more on in-depth investigation of smart tourists’ behaviour using big data, (3) the readiness of smart tourism destinations for smart tourists or (4) driving forces (e.g. European union (EU) digital market strategy) and barriers (e.g. data privacy and GDPR) for smart tourists.

As smart tourism is acknowledged as a further step in tourism development and is included in national policies (e.g. China, Spain, South Korea) and supported also on supranational level (e.g. EU), both academics and practitioners need to ensure that it will create, deliver and capture value in economic, social or environmental context. For smart tourists, as a demand side of smart tourism ecosystem, it includes positive impact of economics of destination through higher spending and interacting with local residents during authentic experience.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was financially supported by the research project VEGA 1/0809/17 Reengineering of destination management organizations and good destination governance conformed to principles of sustainable development.

ORCID iD

Tomáš Gajdošík

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