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This research presents a mobile augmented reality (MAR) travel guide, named CorfuAR, which supports personalized recommendations. We report the development process and devise a theoretical model that explores the adoption of MAR applications through their emotional impact. A field study on Corfu visitors (n=105) shows that the functional properties of CorfuAR evoke feelings of pleasure and arousal, which, in turn, influence the behavioral intention of using it. This is the first study that empirically validates the relation between functional system properties, user emotions, and adoption behavior. The paper discusses also the theoretical and managerial implications of our study.
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Tourists Responses to Mobile Augmented Reality Travel Guides: the Role
of Emotions on Adoption Behavior
Panos Kourouthanassis1, Konstantinos Boletsis2, Cleopatra Bardaki3, Dimitra Chasanidou4
1Department Informatics, Ionian University. 7 Tsirigoti Square, Corfu, Greece. Email:, Tel: +30 26610 87701, Fax: +30 26610 87766
2Faculty of Computer Science and Media Technology, Gjøvik University College. Gjøvik,
Norway. Email:, Tel: +47 61135498
3 Department Informatics, Ionian University. 7 Tsirigoti Square, Corfu, Greece. Email:, Tel: +30 26610 87701, Fax: +30 26610 87766
4SINTEF ICT, Networked Systems and Services. Forskningsveien 1, Oslo, Norway. Email:, Tel: +47 22067621
This research presents a mobile augmented reality (MAR) travel guide, named CorfuAR, which
supports personalized recommendations. We report the development process and devise a theoretical
model that explores the adoption of MAR applications through their emotional impact. A field study
on Corfu visitors (n=105) shows that the functional properties of CorfuAR evoke feelings of pleasure
and arousal, which, in turn, influence the behavioral intention of using it. This is the first study that
empirically validates the relation between functional system properties, user emotions, and adoption
behavior. The paper discusses also the theoretical and managerial implications of our study.
Keywords: Mobile augmented reality; tourist guide; personalization; adoption study; emotional
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Author-created version of:
Panos Kourouthanassis, Costas Boletsis, Cleopatra Bardaki, Dimitra Chasanidou
"Tourists responses to mobile augmented reality travel guides: The role of emotions on adoption behavior".
Pervasive and Mobile Computing 2014; Elsevier.
The final publication is available at:
Tourists Responses to Mobile Augmented Reality Travel Guides: the Role
of Emotions on Adoption Behavior
1. Introduction
Mobile and wireless technologies enable the provision of novel applications that support visitors while
on the move. Such applications include mobile travel guides [1,2] and location-based infotainment
services [e.g. GIS-based recommendations [3,1], annotation and bookmarking [4], and mobile social
networking [5] to name but a few popular application types]. In essence, these applications allow
tourists to have seamless and ubiquitous access to travel-related information during their visiting
experience, which is presented in a multimedia-rich way. At the same time, location sensing
capabilities of mobile devices facilitate filtering of the travel information in order to be tailored to the
travelers‘ needs and wants. The value of mobile travel solutions capitalizes on the properties of leisure
and travel; they both concern intangible goods that are highly experiential and might be consumed on
an ad-hoc basis. Therefore, efficient organization and travelers-tailored presentation of travel-related
information is of paramount importance for both tourists and tourism industry stakeholders.
Considering the above, it is not surprising that mobile travel-related applications have received
scholars‘ attention from both an academic and practical perspective. Topics of interest include
approaches and methods to design and implement mobile travel systems and services [1,6-8], user
adoption studies [9-11,2]; and business model formulation [12]. An underlying commonality among
the different research themes refers to the design scope of such applications. Mobile guides involve
users to be situated in the surrounding environment of a built place [13,14]. Nevertheless, the design
of mobile guides assumes that the built place will fit the mobile device; people, places, and any point
of interest (POI) are encoded in digital maps or context-aware notifications. Hence, the design focus of
mobile guides lies on one principle; developing digital metaphors of the real-world that assist travelers
in covering their information needs while on the move.
Mobile augmented reality (MAR) follows a different design paradigm. Instead of developing a virtual
incarnation of the real world, MAR augments the real world with digital information. As such, the
design canvas is expanded from the limited space of the mobile phone to also include the physical
properties of the built world. MAR is a relatively new technology that offers new affordances for
interaction. In essence, MAR promises to enhance user experience by superimposing digital objects or
content over the surroundings of the real world [15]. Whilst early research focused on resolving the
technical challenges of MAR [16-19] and demonstrating its application potential in several settings
[20-23], few studies associate the value of MAR with the domains of travel and tourism [24-27].
This study attempts to shed light on the potential of MAR for supporting mobile tourism applications.
We present CorfuAR, a mobile augmented reality tour guide, which supports personalized content
provision and navigation features to tourists on the move. We describe the development efforts of our
MAR travel guide and emphasize on building the users‘ profile for the personalized version based on
static, pre-discovered activity preferences of users and tracking of their actual behavior. Moreover, we
report evidence of an adoption study that assessed the users‘ intention to use CorfuAR not only in
accordance with their perceived performance and usability, but also the emotional impact of the MAR
prototype by employing Mehrabian and Russell‘s [28] PAD theory. The field study revealed the
design choices of MAR travel guides that lead to increased user satisfaction and usage intention. All in
all, we aspire to provide help to prospective designers and developers to engineer MAR tourism
The paper is organized as follows. Section 2 discusses the functionality of mobile travel guides, the
properties of mobile augmented reality applications and the potential for MAR in tourism. Section 3
outlines the functionality and architecture of CorfuAR. Section 4 emphasizes on the personalized
version of the mobile augmented reality tourist guide. Section 5 details the methodology and results of
the field study that we performed in order to assess the performance, usability and experiential impact
of CorfuAR. Finally, we conclude the paper with a critical discussion on the academic and practical
implications of our research pertaining the development and evaluation of mobile augmented reality
tourism applications.
2. Background
2.1. Mobile Travel Guides
Mobile travel guides have been the subject of scrutiny over the past years by academic scholars.
Emphasis has been paid primarily to the identification of their architectural, technological and
functional properties [1,29,30]. Consolidating their findings, mobile travel guides provide partially or
fully four types of functionality: navigation services, content-based services, social and
communication services, and commercial services.
The main concern of navigation services is routing users from their current location to a preferred
point of interest (POI) by usually displaying a map of the surrounding area [31]. Content-based
services refer to the provision of travel or POI related information. Specifically, these may include
personalization features that filter and adapt the visualized content according to users‘ current context
and profile [8,32]. Also, such services may incorporate search facilities to locate and receive
information regarding places, topics, or exhibits of interest [33]; and bookmarking which allows users
to add locations to an ad-hoc generated itinerary in order to better plan, manage, and share their leisure
experience [34].
Social and communication services support liaison between the travelers and the accommodation
providers, exhibition owners and other stakeholders involved in service provision [35,12]. Moreover,
they enable sharing of tourists‘ experiences through a variety of websites (Facebook, Twitter,
TripAdvisor, Blogger, and many other popular online social networks); and in different ways, ranging
from posting their stories, their comments, to even their pictures and movie clips [36,5]. It should be
noted that recently, social media have emerged as a substantial part of the online tourism domain [37].
Finally, commercial services support mobile purchases and reservations of tourism-related products
These functional properties of mobile travel guides follow a common user experience metaphor.
Instead of reinforcing the relationship of the travelers with the physical surroundings, these guides
develop a simulated environment where individuals are required to be immersed in for requesting and
receiving digital content and information. On the contrary, mobile augmented reality aims at shifting
the attention of individuals back to the real world, not its digital incarnation. The following
subsections discuss the characteristics of MAR technology that justify the growing` interest in MAR-
enhanced travel and tourist services and applications.
2.2. Mobile Augmented Reality (MAR)
The concept of MAR was developed around the mid-1990s, applying Augmented Reality (AR) in
mobile settings. Rather than trying to create an entirely simulated environment, MAR starts with
reality itself and then augments it by overlaying digital information on top of the real world. The
novelty of MAR relies on its usability aspect; it enhances the traditional user experience while
interacting with a mobile device [40].
Using a display, such as a mobile phone or a tablet, users may see a live view of the world surrounding
them, augmented with digital annotations, graphics and other information superimposed upon it. The
user points the device in the direction of an item of interest and the system augments the output with
additional information about the environment. The extra information varies from names of buildings
visible on a city skyline, or information related to the points of interest; to real-time notifications
regarding location or time dependent events (e.g. menu discounts in restaurants).
As such, the properties of MAR-enhanced systems rely on augmented reality principles: they combine
real and virtual objects in a real environment; they run interactively, and in real time; and they register
real and virtual objects with each other [41]. Likewise, MAR minimizes task-switching by promoting
continuous use and reducing distractions [42]. As such, it is not surprising that industrial scholars have
decided to capitalize on MAR experiential features and devise new mobile-based, enhanced
interaction means. For example, Google Glass [i.e. a wearable AR head-mounted display (HMD)]
augments users‘ visual perception of their world by adding layers of virtual information on top of it.
The same principles apply to audio information that complements users‘ audio perception of the
world. In the same spirit, it is widely believed that AR technologies are maturing and become well
established; this fact favors the broad implementation of AR applications within the next ten years
[43]. Respectively, the recent advances in mobile computing hardware and software, but specifically
the proliferation of smart mobile phones, seem to pave the way for mass, faster adoption of mobile
augmented reality applications [44-46]. Recent introductions of publicly available MAR development
platforms (e.g. Layar, Wikitude and Junaio) confirm the growing interest in MAR systems and
services, as well as support the implementation of such applications.
In agreement with industry, academia foresees an enormous potential for MAR technology;
researchers have acknowledged that the combination of mobile and AR features presents unique
opportunities for the deployment of novel applications in diverse contexts. In fact, MAR has been
employed to support students learning [47,48,20], university campus touring [49], library services
[50], architectural design [51], smart home environments [52]; and phobias treatment [53] to name but
a few application domains.
2.3. MAR Applications: the potential for MAR in tourism
The emergence of MAR has given the opportunity to tourism organizations and destinations to provide
a large amount of relevant tourist information in a different form than simply checking online sources
or travel guides, thus enhancing the overall tourism experience [54-56,44]. In a nutshell, from a
business standpoint, MAR can influence the marketing of travel destinations and reach more
customers by enhancing their travel experiences.
Specifically, MAR systems are ideal tools for guiding tourists through unfamiliar environments and
providing useful information about them. Navigation and way finding was one of the first application
areas for MAR and still remains the most widely used feature in prototypes and commercial tourism-
related applications [30]. But we should emphasize that augmented displays have the potential to
reduce the mental effort required for navigation, as well as provide to travellers with an opportunity to
discover unknown surroundings through visual, audio and 3D location-based information [55]. MAR
can show virtual paths and directional arrows to facilitate navigation (e.g. Nearest Tube application),
deliver augmented and interactive information regarding dining, museums, entertainment et al.
(examples of such applications include mTrip, Tuscany+, and MobiAR), as well as provide real-time
immediate translation of written text on signs, menus et al. (e.g. Word Lens) [57].
Moreover, AR systems can help tourists to re-live historic life and events by reviving ancient temples
and historic buildings as 3D objects, which are placed on the actual monument. The first cultural
heritage site that benefited from an augmented virtual reconstruction of an ancient temple was
Olympia in Greece, where researchers developed the ArcheoGuide AR system [58].
Further, in terms of motivating and engaging tourists, thus enhancing the overall tourism experience,
AR applications have the strengths of developing enjoyable holiday trips through the integration of
AR gaming (e.g. TimeWarp [59]). These applications provide opportunities for tourists to become
familiar with unknown areas in an enjoyable and educational manner.
MAR applications may also assist destination-marketing organizations to gain competitive advantage
through the use of advanced information technologies [60]. A significant characteristic of MAR,
which differentiates it from other context-aware systems and mostly contributes to enhancing the
tourist experience, is its innovative technological character, which engages and impresses the user.
This element of MAR functionality provides applications that follow MAR design principles with
advanced marketing-related capabilities, which - when applied correctly can lead to strong
destination branding and reaching more tourists. An example of a destination that aims to enhance the
overall tourist experience using AR is Dublin, with the Dublin AR project [60]. The use of AR in
Dublin originated from the idea to support Dublin‘s brand development of ―innovative city‖ in
Europe. During this project, they developed a mobile AR application for the tourism industry, which
will be applied via tourist trails in various parts of the destination by considering various tourism
Even though literature contains several frameworks and principles surrounding the design of MAR
(e.g. [61,57]), such works highlight the need of examining MAR development from a user-centered
point of view, i.e. developing sample MAR applications, evaluating their use acceptance and
experiential qualities and, finally, fleshing good design practices for further use and improvement.
Naturally, the development process should be described in full detail for research repeatability
purposes. This study presents the development of a MAR tourist guide for the principal city of Corfu
island in Greece. The ultimate purpose is to report evidence and provide the first insights regarding the
design of MAR applications for tourism and the visitors‘ intention to adopt such MAR services,
through a field study. As a research sub-question, the work goes one step beyond usability by
exploring the experiential impact and the stimulation of emotions from the use of the developed MAR
travel guide.
3. CorfuAR: a Mobile Augmented Reality Travel Guide Supporting
Personalized Recommendations
3.1. System overview
CorfuAR is a high fidelity prototype of a MAR tour guide for the principal city of Corfu island in
Greece, which is also named Corfu. The guide is available for Android devices in two versions: a
personalized and a non-personalized one. Generally, the system provides the basic functionality of a
mobile travel guide, namely displaying information about points of interest (POI), routing to selected
locations; as well as social media features (i.e. recommendation of POIs to other peers of the same
cluster). Moreover, the personalized version recommends specific points of interest to the system users
based on a combination of pre-discovered and real-time, dynamically updated preferences. User
preferences and segmentation have been extrapolated based on a technique recommended by the
World Tourism Organization.
3.2. Functionality
Initially, the application welcomes the user and presents the available options (figure 1). Users may
select the non-personalized version of CorfuAR, in which all content is available without any means of
filtering or aggregation. Alternatively, users may prefer the personalized CorfuAR, in which content is
automatically filtered based on user profile and contextual data. It should be noted that this option is
offered only the first time the user interacts with the system. In all future usage interactions, the guide
proceeds with the initial user preference; users may change their selection through a respective option
in the welcome menu.
Fig 1 The homepage of CorfuAR mobile augmented reality application
CorfuAR supports nine categories of POIs (figure 2). The personalized version of the application
contains all the content of the non-personalized version (approximately 90 POIs); however it
visualizes the filtered, recommended POIs in a different way, through colors, in order to easily notify
the user about recommended content in his/her surroundings. Thus, no content is excluded in the
personalized version; on the contrary, the relevant information is highlighted.
The size of each POI‘s icon is dynamically changing according to the distance of users from that POI.
The larger the icon, the closer the user is to the POI (figure 3). All POIs are displayed as grey 2D
icons, apart from the personalized ones that are displayed as colored 2D icons. We use three different
colors (red, green and blue) corresponding to three users‘ groups with common preferences produced
by the users‘ segmentation process described in section 4.
Naturally, marker-based and geo-based AR is prone to the ―occlusion problem‖; the real world (e.g.
the user‘s hand) or the AR contents itself (e.g. an AR object) may visually cover the AR content that is
being displayed, thus the user can lose valuable information [54,57]. The CorfuAR application is no
exception; indeed, the possibility of a close large POI icon that covers a smaller one of a distant POI
exists. However, we took any measure technologically possible to provide to the user extra options
that solve the occlusion problem. The user can see all the POIs in list view or on a map (using Google
Maps). Alternatively, the user can set a distance filter, excluding POIs that are very far away and may
cause extra ―noise‖. The default value for the distance filter is 500 meters.
Fig 2 The 9 categories of points of interest (POI) supported by CorfuAR
Fig 3 CorfuAR travel guide in action
The main window of the application supports three distinct types of functionality (figure 3). First,
users may request and receive information about a displayed POI by selecting it on the screen of their
mobile device (e.g. cultural information, visiting hours, ticket prices and so on ‗Info‘ in figure 3).
Second, users may recommend a POI to other peers in their cluster by pressing the ―Recommend‖
button. This social media feature is available only through the personalized version of CorfuAR.
Finally, users may ask for navigation directions to a specific location/ POI by pressing the ―Take me
there‖ button. Directions are displayed on a Google Maps terrain.
The supplementary information regarding each POI was provided by the Cultural Heritage website of
the Municipality of Corfu ( All content was exported to an additional database
server hosted within the Department of Informatics at the Ionian University (referred as CorfuAR
database) for redundancy purposes, in cases that the direct link with the host server was lost.
Information was adjusted to fit the mobile device presentation capabilities. As for the geo-location
information of each POI, it was obtained using the Google Maps platform.
3.3. Architecture
The CorfuAR application was implemented using the Layar platform and is available for Android
devices. Layar is an AR browser, which adds ―layers‖ of AR content on top of the real worldview.
CorfuAR is developed as a ―layer‖ of Layar, utilizing the functionality and the high quality features of
this platform.
According to the system architecture (figure 4), when the users open the CorfuAR application, they
have to choose between the two versions: personalized and non-personalized (getVersion in figure 4).
Then, in case the users have selected the personalized version, they fill the clustering questionnaire for
the personalized version, which is implemented in PHP scripting language and is hosted in the
CorfuAR server. Following the initial categorization of application users into one cluster, CorfuAR
opens one of the 4 respective versions (basic, blue, red, or green) and initiates communication with the
Layar Platform. The CorfuAR Client sends a getPOIs request to the Layar Platform, which, in turn,
forwards the request to the CorfuAR Service Provider (requestPOIs in figure 4). Then, the CorfuAR
Service Provider sends the augmented reality content back to the Layar Platform (getPOIs in figure 4).
Finally, the Layar Platform validates the getPOIs response and passes it to the CorfuAR Client
(getPOIs in figure 4), which visualizes the content to the mobile device.
A very important element of CorfuAR‘s architecture is the CorfuAR database, which consists of 4
tables corresponding to the versions of CorfuAR. Each CorfuAR database table contains amongst
the Layar-related ID information all the POIs‘ GPS coordination (longitude, latitude, altitude), the
2D icons of each cluster and a direct link to the information content providers. The cultural
information of each POI is also stored in the CorfuAR database (respecting the terms and conditions of
the source for copying and distributing the material), to ensure the availability of the information even
if the original source/webpage is down (requestEduInfo and getEduInfo in figure 4).
Finally, the personalization feature of CorfuAR is implemented following a two rounds algorithm.
During the first round, the application identifies in which cluster users belong to, based on their
responses in the clustering questionnaire. Users may save their preferences for all future usage
sessions, but they can also modify them should they desire to switch from one personalization layer to
another. Respectively, the second round of the algorithm takes into account the number of
‗Recommendations‘ of each POI and the total number of visits that each POI received from other users
of the same cluster. The first 15 POIs with the highest count (popularity) are automatically displayed
as colored 2D icons (blue, red or green according to the cluster) since they are the recommended ones,
whereas all the other POIs (90-15=75) are displayed keeping the ―basic‖ grey icon (requestIcons and
getIcons in figure 4). This algorithm that enables personalization in CorfuAR is further analyzed in
Section 4.
Fig 4 The architecture of CorfuAR
4. The Personalized Version of CorfuAR: Discovery and Evolution of
Users Preferences
Personalizing the information provision might prove to be an important element in the design of
mobile augmented reality applications in order to minimize risks of information overload [16]. In this
research, we embellished the CorfuAR system with personalization capabilities by developing a
filtering tool that automatically selects and presents to the users the content that matches their
preferences. Naturally, the discovery of the users‘ preferences and the subsequent users clustering are
prerequisite to the application of this filtering tool. The filtering tool presents the application content
that we have pre-allocated to each cluster (users‘ profile).
Actually, we employed the tourists‘ segmentation practice of the World Tourism Organization in order
to cluster the users and, thus, to provide the personalized content. In the mid-90s, the Irish National
Tourism Organization applied a tourist management plan based on categorization of tourists according
to their activities when in Ireland. The same technique of classifying the visitors was officially adopted
by the World Tourism Organization under the name ―activity segmentation‖. This technique is
implemented based on an activities-related questionnaire, where tourists choose the activities
appealing to them during their stay [62].
Activity segmentation captures the activities range of tourists while they visit a destination. The
tourism industry can take advantage of this method to discover and define new discrete market
segments corresponding to activities groups, as well as document the activities and examine the
visitors‘ level of satisfaction. Each activity is documented through qualitative and quantitative
research, so as to separate opportunistic activities from activities than define market segments. Hence,
the long-term benefit is the design and provision of products and services that really cover the tourist
needs or the evaluation and improvement of the existing ones.
We applied the ―activity segmentation‖ technique to cluster the CorfuAR users because activities,
which can define discrete market segments, are those that are supported by facilities, locations, and
services in various places. Even though activities are not the main reason for visiting a place, they can
be an important part of the overall tourist experience [63]. Therefore, the categorization and tracking
of tourist activities could be seen as an essential and investigative guide, in order to preserve, and
improve the experiential performance of destinations. The more the tourism industry knows about the
behavior of the average tourist, the more capable it is to provide a satisfactory plan to him [64].
Specifically for CorfuAR, we used nine activity categories to segment the Corfu tourists into three
clusters, namely three user‘s profiles. Three categories of activities were assigned to each cluster (see
table 1). Users are instructed to fill the questionnaire with those activities during their first interaction
with the application (see figure 5). The results of the questionnaire-based segmentation process assign
each user to one of the three clusters. We adopted this number of clusters based on extant
segmentation studies in tourism journals (e.g. [65-67]), which indicate that tourists may be classified
in three broad clusters based on their activities: thematic-based (i.e. business, religious etc.),
entertainment-based (i.e., shopping, night-life), and action-driven tourism (i.e. sports, tripping etc.). In
CorfuAR, the blue cluster represents thematic-driven tourists; the red and the green cluster represent
entertainment-driven tourists and action-driven tourists, respectively.
Users’ cluster (profile)
Business (seminars, conferences, business meetings)
Culture (monuments, sights, arts, history, museums, architecture)
Religion (churches, monastic sites, temples, holy shrines)
Shopping (clothing stores, souvenirs, hobbies, gifts)
Nightlife (bars, clubs, events, meeting people)
Gastronomy (food, restaurants, tavernas)
Nature Study (nature reserves, bird watching, wild life)
Tripping (walking, exploration, tripping, hiking)
Water sports (boating, surfing, waterskiing, sailing)
Table 1 Activities assigned to the three users‘ clusters (profiles)
Ultimately, the personalized version of CorfuAR displays the personalized POIs to the user as red,
blue or green icons according to the user‘s predefined profile. Nevertheless, we put an effort to
accomplish real-time update of the pre-discovered users‘ preferences and assignment to one of the
three clusters. In effect, we utilize a two-fold approach to explore and interpret the users‘ behavior
during their visit in Corfu. First, we apply a Google Analytics tracking code to every webpage with
POI-related content. Thus, we have the opportunity to find out those POIs that caught the users‘
attention and they wanted to take additional information about them. Likewise, by tracking the GPS
data on a user‘s mobile device, we were able to infer when a user physically visited a POI that was
included in his recommended list of POIs and, also, increase the relevance of the POI with the cluster
each user belonged to. Based on this real-time captured information about the users‘ preferences, the
users‘ questionnaire-based assignment to one of the three clusters was either corroborated or updated.
Hence, the application supports real-time switching of tourists between different clusters. In effect, if
tourists systematically express interest about POIs that do not belong to their cluster (either by
requesting information about them or by physically visiting them) the application will eventually
switch them to the cluster that better grasps their travel needs.
Fig 5 Sample of the activity-related questionnaire for discovering the user‘s profile
5. Evaluation
After its first upload on Google Play online store on May 2012, CorfuAR has been uniquely
downloaded and installed 729 times. The research team has not undergone any marketing/ promotion
activities to reinforce the usage of CorfuAR, because the application comprises an academic effort and
its respective downloading and use is free of charge. To assess the performance, usability and
experiential impact of CorfuAR prototype, we performed a field study. In particular, visitors of Corfu
city were invited to download, install, and use the application during their stay. As a final request, the
participants of the field study were asked to fill in an evaluation questionnaire.
5.1. Users sampling
We used convenience sampling methodology to invite prospective users of CorfuAR travel guide. Our
sample pool consisted of individuals who would visit Corfu city for leisure or business activities, were
owners of Android devices and had experience in using mobile applications. In order to achieve the
heterogeneity of the sample, instead of just enlisting only academia-related participants, we turned into
the general population by enlisting individuals in the proximity in order to avoid bias and ensure the
credibility of the results. Invitations to Corfu visitors were extended randomly and for a period of two
weeks. The research team approached random groups of friends and/ or family members, verified that
they were owners of Android devices, and explained to them the objectives of the study. In case those
visitors were interested in participating to the study, they were prompted to download the application
to their mobile phone.
The study was executed twice, in August 2013 and June 2014. These months exhibit high activity in
the local tourism sector; therefore we consider them as appropriate to measure the effectiveness of the
developed application. In total, 105 tourists accepted our invitation to participate in our field study (33
during August 2013 and 72 during June 2014). Table 2 includes the sample demographics. The sample
comprised of almost equally distributed men and women. Furthermore, the majority of the participants
were educated (holders of a university degree) and over 26 years old. All participants had over six
years experience of using mobile applications.
Total (N)
Table 2 Sample demographics
5.2. Methodology
Initially, the research team explained the objectives of the study to randomly approached groups of
tourists. Should the approached individuals expressed interest to participate to the study; they were
directed to the Google Play store to download the CorfuAR application to their mobile phone.
Subsequently, they were asked to use the system as a guiding tool during their visit to Corfu. The
participants used their own Android devices, in order for us to capture the effect of the hardware
heterogeneity (hardware performance and how that affects the overall experience), as well as to
exclude any ―wow effect‖ that introducing a new device to the participants could cause and,
potentially, skew the experiential results. Along this line, participants were free to use between the
personalized and non-personalized version of the application. Before ending their visit to Corfu,
participants were asked to fill in an evaluation questionnaire. Each questionnaire was associated with
the corresponding version of the application, based on the users‘ preferences in the home menu.
The study had a two-fold objective. First, we opted to evaluate the perceived adoption behavior of
individuals towards CorfuAR. To this end, we employed established factors from extant technology
adoption theories and environmental psychology to measure the performance, usability, emotional
stimulus, and usage behavior of the application. Specifically, driven by the experiential qualities of
mobile augmented reality [40], we explored the underlying process whereby the technological
attributes of CorfuAR influence the usage behavior of the application through the formulation of
different types of emotions. Second, we sought for differences between individuals using the
personalized version of the application and ones using the non-personalized version on the selected
user adoption and emotional factors.
5.3. Instrument Development
The evaluation questionnaire enclosed measurement dimensions that have been validated in past
information systems studies. To assess the adoption behavior of CorfuAR prototype users, we
employed factors from the second iteration of the Unified Theory of Acceptance and Use of
Technology, which is commonly referred to as UTAUT2 [68]. This framework has been originally
used to explain the adoption of mobile applications. Moreover, UTAUT2 has been utilized as a
guiding charter to explore the adoption of other application types, which are similar to CorfuAR, such
as virtual worlds [69] and multimedia heritage archive services [70].
Respectively, we measured emotions stimulation from the use of CorfuAR by employing Mehrabian
and Russell‘s [28] PAD theory, which has been primarily used to explain consumer behavior in
marketing studies [71,72]. According to this theory, all emotional responses to physical and social
stimuli can be captured on three affective states: pleasure, arousal, and dominance (PAD). Individual
positions against these emotional states may, in turn, express human affective reactions and,
consequently, influence behavior formulation. Recently, information systems scholars have articulated
PAD as a supportive basis to explain information technology adoption, usually in conjunction with
another established technology adoption theory [73]. In this spirit, we postulate that pinpointing the
emotional impact of the MAR application will be critical for understanding the degree of users
satisfaction, morale, or performance; and generally their adoption behavior. Finally, since MAR travel
applications are technological innovations for tourism, we measured the effect of participants‘
perceived innovativeness on the adoption of CorfuAR. Our consolidated framework, combining both
theories, is illustrated in Figure 6.
To bridge UTAUT2 and PAD, we employed the Stimulus-Organism-Response model (S-O-R) model,
which was originally developed by Mehrabian and Russell [28] and dictates that stimuli (e.g.,
performance of an information system) evoke individuals' emotional states, which in turn determine
behavioral responses. The framework has been validated in the context of high-technology products
[74], as in the case of MAR applications, therefore it constitutes a suitable core for our analysis.
Table 3 summarizes the measurement factors included in our evaluation instrument. Each factor was
captured by multiple items. We used a Likert scale anchored from 1 (completely disagree) to 7
(completely agree) to collect individual item scores. The detailed items of the questionnaire can be
found in the Appendix.
Fig 6 Research framework
# of
The degree to which using the application
will benefit users in their travel-related
Venkatesh et
al. [68]
Effort expectancy
The degree of usability associated with using
the application.
The perceived intention to continue using the
application after the initial usage.
The degree to which the application evokes a
pleasant (or unpleasant) emotion to users.
and Russell
The intensity degree of the pleasant or
unpleasant emotion.
The controlling and dominant nature of the
Individuals‘ propensity to experiment with
new information technologies.
Agarwal and
Prasad [75]
Price value
Cognitive tradeoff between the perceived
benefits of the application and the cost of
using it (e.g. network usage).
Venkatesh et
al. [68]
Table 3 Instrument dimensions and definitions
5.4. Results
5.4.1 The effect of personalization on adoption behavior and emotional responses
Table 4 illustrates the consolidated results per evaluation factor. First, we report the average scores for
the full sample of respondents (N=105). Then, we distinguish scores between the samples that used the
personalized version of the application (N=69) and the non-personalized version (N=36) respectively,
because we are interested in the differences between them.
To probe for statistical differences between both groups, we performed an independent samples t-test,
the results of which are also included in Table 4. Out of the 69 individuals that used the personalized
version of the application, 24 were allocated under the blue cluster, 25 were allocated under the red
cluster and the remaining 20 were allocated under the green cluster. To preserve user privacy, we did
not associate each individual questionnaire with its corresponding cluster. Therefore, we cannot report
the demographics information of each cluster.
Evaluation factor
Total AVG (Std)
Personalized Version
AVG (Std)
Version AVG (Std)
t-test results
5.69 (.94)
5.68 (1.01)
5.71 (.832)
-.187 (p=.852)
Effort expectancy
5.69 (1.02)
5.62 (1.09)
5.82 (.889)
-.950 (p=.344)
Price value
6.43 (.84)
6.39 (.903)
6.52 (.705)
-.789 (p=.432)
4.88 (1.23)
4.81 (1.25)
5.01 (1.21)
-.812 (p=.418)
5.28 (1.03)
5.18 (1.13)
5.48 (.799)
-1.435 (p=.154)
4.42 (.82)
4.44 (.86)
4.40 (.751)
.189 (p=.850)
4.75 (1.01)
4.77 (1.01)
4.71 (.895)
.300 (p=.765)
5.50 (1.21)
5.60 (1.20)
5.31 (1.21)
1.141 (p=.257)
Table 4 Descriptive Statistics of evaluation dimensions and comparison between personalized and
non-personalized version
Overall, participants favored the performance and usability of CorfuAR. Subjects appreciated the
usefulness of the application in terms of giving information about displayed points of interest and
providing navigation guidelines (mean score 5.69, SD .94). Likewise, they valued the ease of use that
mobile augmented reality introduces in the interaction elements of mobile guides (mean score: 5.69,
SD 1.02).
Furthermore, the study participants esteemed the application‘s value compared to its acquisition cost.
We treat these findings with caution, because we acknowledge that CorfuAR was offered free of
charge; users were only subject to indirect costs that, primarily, included 3G network usage.
Regarding usage behavior, the respondents expressed their overall willingness to use the system again
during their next visit to Corfu (mean score: 4.88, SD: 1.23).
From an emotional standpoint, the evaluation results suggest that participants were overall satisfied
with CorfuAR. Indeed, positive emotions predominated among the perceived feelings of individuals
that used the MAR application. Pleasure received the highest score among the three emotional states
(mean score 5.28, SD: 1.03) followed by dominance (mean score 4.75, SD: 1.01) and arousal (mean
score 4.42, SD: .82). Such responses usually indicate that participants exhibit feelings of happiness
and satisfaction pertaining the stimuli under investigation [76], which in this research reflects the
attitude of users towards CorfuAR.
Interestingly, the results indicate that there are no statistical differences between the two groups.
Hence, the tourists who used the personalized version of CorfuAR perceived the same degrees of
functional, emotional, and usability qualities with the sub-group that used the non-personalized
version of CorfuAR. Consequently, we conclude that the personalization feature did not affect the
adoption behavior and emotional response of the study participants.
Since there are no statistical differences between the two samples, we merged their responses in order
to proceed to our core research objective, namely to analyze how the technology properties of the
application influence the usage behavior of individuals through the formulation of different types of
5.4.2 The role of emotions on formulating usage behavior
We employed partial least squares (PLS) analysis using SmartPLS to obtain path weights for
relationships and coefficients of determination for the dependent variables that measure tourists‘
emotions and usage behavior towards CorfuAR. Significance of associations was determined by
running a bootstrapping procedure with 500 samples. Using two-tailed significance values,
significance intervals are set as p<0.05 (t ≥ 1.968), p<0.01 (t ≥ 2.592), and p<0.001 (t≥ 3.323). Before
empirically examining the model associations, we performed a set of reliability and validity tests to
assess whether the instrument items load adequately to their respective factors. The results of this
analysis are included in Table 5. All values are above the acceptable thresholds (composite reliability
> 0.7; AVE > 0.5; Cronbach‘s Alpha > 0.7).
Standardized Item
Performance Expectancy (PE)
Effort Expectancy (EE)
Price Value (PV)
Behavioral Intention (BI)
Personal Innovativeness (PI)
Pleasure (P)
Arousal (A)
Dominance (D)
Table 5 Confirmatory factor and reliability analysis results
Table 6 reflects all the correlations among constructs with diagonal elements containing the square
root of the average variance extracted (AVE). The correlation for every pair of constructs did not
exceed the square root of AVE, meaning that all constructs measure different objects and differ from
each other, indicating high discriminant validity. We also assessed multicollinearity through the
Variance Inflation Factor (VIF). For all constructs, VIF was slightly above 1 and below 3, thus
indicating an absence of collinearity between items.
Performance Expectancy
Effort Expectancy (EE)
Price Value (PV)
Behavioral Intention (BI)
Personal Innovativeness (PI)
Pleasure (P)
Arousal (A)
Dominance (D)
Table 6 Factor correlations and square root of AVE of final measurement model
The results of the PLS algorithm with significance of weights are depicted in Table 7. The model
explains 45.1% of the variance for CorfuAR behavioral intention, 29% for pleasure, 12.5% for
arousal, and 24.7% for dominance.
Path Significance
Effort Expectancy Pleasure
Significant at p<.05
Effort Expectancy Arousal
Not significant
Effort Expectancy Dominance
Significant at p<.01
Performance Expectancy Pleasure
Significant at p<.001
Performance Expectancy Arousal
Significant at p<.05
Performance Expectancy Dominance
Significant at p<.001
Pleasure Behavioral Intention
Significant at p<.05
Arousal Behavioral Intention
Significant at p<.05
Dominance Behavioral Intention
Not significant
Personal Innovativeness Behavioral Intention
Significant at p<.001
Price Value Behaviorial Intention
Not significant
Table 7 PLS results and significance levels
Our findings suggest a positive association between the technology properties of CorfuAR and the
examined emotional scales. In effect, the functional qualities of CorfuAR primarily evoke feelings of
pleasure (β=.358, p<.001), followed by feelings of control over the application (β=.301, p<.001) and
arousal (β=.296, p<.05). The enhanced usability provided by MAR interaction modalities induce
primarily emotions of control over CorfuAR (β=.269, p<.01) followed by feelings of pleasure (β=.258,
p<.05). The path analysis did not show any statistical association between effort expectancy and
Furthermore, our analysis revealed that not all of the affective elements of MAR-centric interactions
are likely to influence users‘ intention to continue using the application. Only pleasure =.257, p<.05)
and arousal (β=.223, p<.05) were found to be statistically significant predictors of usage behavior.
This outcome is consistent with past technology adoption studies, which displayed that pleasure and
arousal can adequately capture the range of appropriate emotional responses [77]. Based on the above,
we suggest that manipulating the MAR application in such a way that evokes feelings of pleasure or
excitement will likely lead to increased usage intention. In contrast, incorporating functional elements
that generate feelings of potency do not seem to positively influence usage intention. Moreover, the
path analysis indicated a positive association between personal innovativeness and usage behavior
(β=.372, p<.001); the more prone an individual is to experiment with a technology innovation, the
more likely he/she will continue using CorfuAR. Finally, we did not find any positive relationship
between price value and usage behavior. We attribute this result to the fact that CorfuAR is offered
free of charge.
6. Conclusions & Discussion
6.1 Summary and theoretical contribution
This research presented CorfuAR, a fully-functional prototype of a mobile augmented reality tour
guide, which supports tourists on the move. CorfuAR displays information about the points of interest
(POI) a user selects on the screen of its smart phone; and gives navigation directions to specific,
requested POIs. In addition, CorfuAR embeds personalization features, which recommend to the users
specific POIs (i.e. the colored icons in the mobile screen) according to their profile and offer an extra
social media feature; the users may rate places they have visited and recommend them to other peers
in the same cluster. The users‘ profile for the personalized version is built on static and dynamically
updated users‘ preferences. This is the first time the activity segmentation methodology of the World
Tourism Organization is followed for recognizing the visitors‘ activity profile in order to classify the
visitors and provide them with personalized content through a MAR-based travel guide application.
The personalized version of our MAR tourist guide updates these static, pre-discovered activity
preferences of visitors by tracking their actual behavior during their stay (e.g. if they physically visited
a recommended POI). The personalization features are optional. Tourists may opt to use the non-
personalized version, which provides the same functionalities with the personalized one apart from
the recommendation and social networking features.
Our study assessed the development efforts of our MAR travel guide and, specifically, emphasized on
the system‘s evaluation by tourists visiting Corfu, an island in Greece. Conducting a field study, we
assessed the users‘ intention to use the MAR tourist guide in accordance with their perceived
performance, usability and experiential effect of CorfuAR. Now that MAR technologies are
considered robust enough to provide valuable, effective services, it is critical for the broad social
acceptance of MAR services to investigate what potential users expect and need. Extant research on
MAR largely focused on the engineering challenges of the technology and users‘ perceptions of such
services appears to be the least explored issue [44,78]. To our knowledge, this is the first study that
provides empirical evidence regarding the performance of MAR applications and relates their adoption
potential with experiential attributes.
Indeed, this study paves the ground for developing new theories, tailored specifically to MAR, that
incorporate emotional qualities at their core. Extant research on technology adoption (e.g. [68,79])
primarily examines organizational settings, and the selected information technology products are
functional products devoid of any hedonic dimension. Researchers adopt this stance because these
theories are concerned with explaining individuals‘ usage behavior towards systems that aid them in
work-related tasks. In comparison, our study is set in a setting where users assume a role of service
consumers. In this role, technology simply intervenes to augment the user experience and supports
personal needs that are both utilitarian and hedonic. Therefore, the usage behavior of such applications
will logically be balanced around their functional and experiential qualities. Our research validates this
claim by highlighting a direct association of usage attitudes with feelings of pleasure and arousal.
Based on these findings, we posit that there is an opportunity for academic scholars to devise emotion-
centric theories that address the adoption behavior of highly experiential information technology
artifacts, such as MAR services.
Driven by studies that underline a positive effect of personalization on mobile usability (e.g. [80,61]),
we probed for differences between users of the personalized version and ones using the non-
personalized version. Nevertheless, the field study did not highlight any statistical differences between
the two versions of the application. We attribute this finding to our functional operationalization of
personalization. On the one hand, personalization in CorfuAR was not implemented as a core feature
but rather as an assistive functionality in the form of targeted recommendations. Tourists using the
personalized version of the application could distinguish POIs that suited their travel needs through a
color-coding scheme and had the opportunity to recommend POIs through a social networking feature.
Yet, all content of the non-personalized version was also available to their mobile screen making the
differences in functionality between the two versions of the application marginal. As such, we argue
that a different implementation of the personalization functionality might produce statistically
significant results between the two versions of the application. An indicative alternative
implementation would display only the relevant POIs to each cluster and completely hide the
irrelevant ones.
6.2 Design implications for MAR travel guides
This research provides useful insights to designers of MAR travel guides. First, we demonstrate that
the interaction technology that a designer selects for providing tourism and travel-related services can
strongly affect the interaction of a tourist application and the overall use experience. In our case, AR
enriched the use with data from several sensors (GPS, magnetic compass, and accelerometer),
improving the functionality and fidelity of location-based services, which in combination with the
mobile device see-through visualization of the tourism-related content provided a useful and pleasing
experience. Since in mobile tourism, there is the need of engaging the user while she is on the go, the
combination of aesthetically pleasing and reliable space-time content may lead to high degrees of
usability and overall performance [81], as well as provide a user-friendly interaction modality
compared to plain mobile computing metaphors. Based on the results of our field study, we
acknowledge that individuals‘ tendency to experiment with new information technologies (i.e.,
personal innovativeness) plays a significant part in engaging the user to initially adopt the provided
tourism services. When the novelty effect wears off, it is the usefulness and consistency of the content
that should kick in and further engage the user.
When it comes to interacting with mobile tourism applications, the minimization of cognitive overload
is a key design aspect. Naturally, when a tourist is constantly moving, the application should provide
relevant-to-the-task content and cultivate semantic associations in users‘ cognition, in order to
minimize the necessary interaction steps, thus not affecting the user‘s real world navigation and
awareness of the physical surroundings. Methods like the ones implemented in this study
(personalization based on predefined criteria, location-based filtering, theme-based filtering, use of
widely-known icons and symbols) are a few examples of how to eliminate the information ‗noise‘ and
support users‘ procedural and semantic memory. Although our study did not reveal any statically
significant differences between the personalized and non-personalized versions of the application, we
posit that the intuitive and user-friendly interaction modality supported by MAR plays the pivotal role
in enhancing tourists‘ user experience. Personalization may be perceived as an add-on that further
enhances the user experience with information that is tailored to users‘ needs and wants.
Finally, our study highlighted the importance of emotions regarding the design of MAR applications.
Emotional design is a recent stream of product design which postulates that the design outcome may
initiate the users‘ emotions and induce affective responses that may make them feel happy, annoyed,
excited, or frustrated [82]. Designers may manipulate the properties of the artifact to trigger the
desired emotional state. At the very least MAR travel guide designers should devise ways that
minimize the formulation of negative emotions. Negative emotions may be stimulated through various
means, such as lack of real-time feedback regarding user-system interaction, which may leave users in
a state of uncertainty [83] and privacy concerns stemming from collection and manipulation of
personal information [40]. In CorfuAR we addressed these challenges through infrastructural and
privacy-aware schemes, focusing on a) minimizing user frustration from system slow or unexpected
responses during interactions and b) dealing with mistrust by offering a non-personalized version of
the application and by allowing users to de-activate the personalized recommendations should they
desired. Moreover, designers should not neglect the importance of reinforcing positive emotions. Our
field study showed that behavioral intention to use the system was positively affected through feeling
of pleasure and excitement. This provides an indication to MAR application designers to carefully
select the functionality provided by the service. Functional elements that reinforce positive feelings
(e.g. social media features and content provision based on gamification principles) might constitute the
optimal design choices.
6.3 Limitations and avenues for further research
As with any empirical study, our outcomes are subject to certain limitations. First, the findings are
based on self-reported data; qualitative methods such as in-depth interviews and observations could
provide additional insights regarding specific elements of CorfuAR, which influenced the perceptions
of tourists that participated in the user study. Likewise, such methods would allow emotional
responses to be captured as soon as they are experienced, minimizing the distortion imposed by time
on the recall of feelings. Second, we followed a convenience sampling approach and we acknowledge
that our results are subject to this limitation. A longitudinal user study that includes a more stratified
sample, especially in terms of mobile experience and education, controlling also for possible novelty
effects, would significantly enhance the generalization of the findings. Nevertheless, we posit that our
research provides significant value in terms of devising a theoretically rigorous framework that
captures user adoption of MAR services. Future research could apply our theoretical framework to
explore individuals‘ adoption of other experiential information technologies, such as online social
networks and innovative technology products (e.g. tablets and wearable systems). Indeed, the value of
our research model lies in its capability of allowing the prediction and understanding of behavior in an
emotions-based context.
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Appendix: Measurement Instrument
I find CorfuAR useful when navigating through the city
et al. [68]
Using CorfuAR helps me getting information about points
of interest and better guidance in the city
Using CorfuAR increases my interest for new places
Effort expectancy
Learning how to use CorfuAR is easy for me
My interaction with CorfuAR is clear and understandable
I find CorfuAR easy to use
It is easy for me to become skillful at using CorfuAR
Behavioral Intention
I intend to continue using CorfuAR in the future
I will always try to use CorfuAR in my daily tours
I plan to continue using CorfuAR frequently
Please note the level that better represents your emotional state after using CorfuAR
Unhappy ----- Happy
Annoyed ----- Pleased
Unsatisfied ----- Satisfied
Melancholic ----- Contented
Despairing ----- Hopeful
Bored ----- Relaxed
Relaxed ----- Stimulated
Calm ----- Excited
Sluggish ----- Frenzied
Dull ----- Jittery
Sleepy ----- Wide awake
Unaroused ----- Aroused
Controlled ----- Controlling
Influenced ----- Influential
Cared for ----- In control
Awed ----- Important
Submissive ----- Dominant
Guided ----- Autonomous
I like to experiment with new technologies
and Prasad
If I heard about a new technology, I would look for ways
to experiment with it
Among my peers, I am usually the first to explore new
Price value
CorfuAR is reasonably priced.
et al. [68]
CorfuAR is a good value for the money.
Figure captions
Fig 1 The homepage of CorfuAR mobile augmented reality application
Fig 2 The 9 categories of points of interest (POI) supported by CorfuAR
Fig 3 CorfuAR travel guide in action
Fig 4 The architecture of CorfuAR
Fig 5 Sample of the activity-related questionnaire for discovering the user‘s profile
Fig 6 Research framework
... Previous study found that EE and PE significantly affected travellers' emotional states, whereas pleasure, arousal and IN significantly affected BI to use MAR by using Mehrabian and Russell's pleasure, arousal, and dominance theory (Kourouthanassisa et al., 2015). IN moderates the effects of content quality, personalised service quality and system quality on individuals to use AR technologies (Jung et al., 2015). ...
... Most smart apps require access to tourists' personal information on social media and real-time positions to improve tourist services and predict visitors' future demands for getting better insights to offer services suited to tourists' preferences (Gretzel et al., 2016). For example, a travel app's LBS capture users' locations using GPS to route them to the desired place (Kourouthanassisa et al., 2015) which has increased the risk of personal information being accessible by irresponsible personals for inappropriate purposes. As a result, PR negatively influences users' willingness to disclose their personal information to adopt SMTA. ...
... Consumers are more likely to accept mobile apps due to their simplicity and user-friendly interface according to previous empirical studies (Erwanti et al., 2018). The adoption of mobile AR travel apps has supported the idea that EE affects usage intention (Kourouthanassisa et al., 2015). SMTA with smart features in place can constantly send instant in-app notifications and push notifications to users, reducing users' effort and time to perform travel-related tasks. ...
Purpose Considering the limited understanding of determinants influencing the adoption of smart mobile tourism app (SMTA) featuring augmented reality (AR) and big data analytics (BDA), privacy concern (PC) and the risk of privacy information disclosure (PI) have threatened SMTA adoption. This study aims to propose an expanded consumer acceptance and use of information technology (UTAUT2) model by including new contextual components, integrated with privacy calculus theory (PCT) model to examine the determinants influencing behavioural intention (BI) to use SMTA. Design/methodology/approach Personal innovativeness (IN) and privacy information disclosure (PI) are incorporated in UTAUT2 model to determine its effect on SMTA featuring AR and BDA technologies from smart perspective. Both privacy concern (PC) and privacy risk (PR) derived from PCT model are also included to determine its influences on an individual's willingness to disclose privacy information for better-personalised services. We collected responses from 392 targeted participants, resulting in a strong response rate of 84.66%. These responses were analysed statistically using structural equation modeling in both SPSS 22.0 and SmartPLS 3.0. Findings Findings showed that personal innovativeness (IN), habit (HT) and performance expectancy (PE) significantly affect behavioural intention (BI) while privacy concern (PC) significantly affect privacy information disclosure (PI) to use SMTA. In contrast, effort expectancy (EE), hedonic motivation (HM) and privacy information disclosure (PI) had no significant effects on behavioural intention (BI) while privacy risk (PR) had no significant effects on privacy information disclosure (PI) to use SMTA. Originality/value The study findings help tourism practitioners in better comprehending recent trends of SMTA adoption for establishing targeted marketing strategies on apps to improve service quality. In addition, it enables app development companies acquire app users’ preferences to enhance their app development for leading app usage.
... The literature confirms that these two dimensions adequately capture the range of emotional responses (Koo and Ju, 2010). Exciting and pleasurable experiences arising from the use of a smartphone are predictive of the intention to use this technology (Kourouthanassis et al., 2015). However, more recent literature on MAS consumers has found only "pleasure" to be an antecedent of the intention to use a smartphone, not "arousal" (Alesanco-Llorente et al., 2021). ...
... The effect of the affective dimension on the intention to use MAR was not significant in the models of either group, contrary to the prior literature showing that the use of MAR at a physical store generates a range of emotions (Kourouthanassis et al., 2015). In the specific case of MAS consumers, albeit without distinguishing by gender, Alesanco-Llorente et al. (2021) found that "pleasure" is determinant in the intention to use a smartphone; García-Milon et al. (2021) likewise found it to be determinant of smartphone use during the tourist shopping journey. ...
... However, it is not always easy to offer real stimuli in educational environments when potential risks and dangers with limited resources outweigh the necessity of implementing certain education and training programs, such as learning essential fire-escape knowledge and skills without proper and adequate equipment. Migrating such training programs into immersive virtual reality (VR) systems would be one of the ideal alternatives and might open up another way to understand the S-O-R process to improve the effectiveness of VR programs (Kourouthanassis et al., 2015;Lopatina et al., 2020). Eventually, when the device becomes more affordable, VR objects have the potential to be integrated into virtual programs for users to interact and learn knowledge in a virtual world (Hwang & Chien, 2022;Jaung, 2022). ...
Most VR fire escape training programs only task learners to observe the procedure of fire escape in different simulated fire scenes. To improve the effectiveness of such training programs for everyone, we tested a "fire escape virtual reality training program" which takes advantage of the feedback on the action to help individuals to learn the necessary and correct steps of fire escape. The virtual program emulates a real fire scene by providing realistic visual and auditory stimuli. A single-group quasi-experimental study was carried out to measure the effectiveness of the program, and a total of 173 seventh-and eighth-grade students from a high school in New Taipei City participated. The results of structural equation modeling showed that 1) gameplay self-efficacy was negatively predicted by frustration, 2) fire presence positively predicted gameplay self-efficacy, and 3) gameplay self-efficacy positively predicted learning progress. The findings suggested that critical life-saving skills such as fire escape skills can be readily acquired and trained through individual virtual reality training programs.
... The domestic research on the subject teaching based on MAR technology in recent years was reviewed, and most of them were limited to the higher level of literature discourse in China. In countries and regions where research on MAR technology in education is more advanced, the combination of MAR technology with education and teaching is still at an immature stage of experimentation and application, and there is no real integration and lack of specific design solutions for teaching practice (Kourouthanassis et al., 2015). ...
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The English proficiency of Tourism Management students is underneath the average of college level. Providentially, the progression in technology has opened another possibility for researchers to explore the full potential of technology by integrating it into teaching and learning process in English. The combination of mobile learning and Augmented Reality (AR) is a creative and effective way for Tourism English learning, because AR presents virtual 3D content on the basis of real world and brings students directly to the real scene and working environment of Tourism, and mobile learning enables students to learn without time or space constraints. Although AR and Mobile Augmented Reality (MAR) are believed to benefit education, so far, there is limited published research that addresses MAR's benefits in higher vocational education, especially for Tourism English learning. Therefore, the objective of this research is to determine the need to develop a MAR application, namely Mobile Augmented Reality Tourism English Learning (MARTEL), for Tourism Management students in Higher Vocational College (HVC). This study surveyed the needs from 169 students from 1 st and 2 nd grades of Tourism Management program and data were analysed by descriptive statistics. In addition, 6 Tourism English teachers were interviewed and the text of the conversations was transcribed thematically. Findings revealed that both teachers and students expressed the lack of English proficiency, the insufficient effect of existing technological teaching tools, and feedback quite positively on MAR technology and MARTEL, indicating that the demand of MARTEL is considerable. It is foreseeable that as an advanced and interesting teaching tool, MARTEL application will be able to improve teaching and learning efficiency, make Tourism English an innovative and high-quality course, and even to a certain extent, change the teaching ecology of domestic Tourism English courses. Introduction As an international language, English plays an important role in many fields and industries, so there has been a worldwide increase in demand for English learning, especially in non-English speaking countries (Alharbi, 2019). According to IETLS official statistics report, among 40 countries, the mean overall and individual band scores achieved by 2022 Academic test takers from China were ranked 30 globally with 6.1 points. Among reading, listening, writing and speaking, Chinese students' weakest ability is speaking and writing comes second (Badger, 2012). This shows that the overall performance of Chinese students' English language skills is not outstanding, let alone those of higher vocational college students who have a weaker English foundation.
... Mobile tour guides replaced audio guides with the introduction of new mobile technologies in tourism. In general, these mobile tour guides should provide, fully or partially, four of these functionalities: navigation services, content-based services, communication/social services, and commercial services [9]. ...
Full-text available
Tourism on the island of Santa Maria, Azores, has been increasing due to its characteristics in terms of biodiversity and geodiversity. This island has several hiking trails; the available information can be consulted in pamphlets and physical placards, whose maintenance and updating is difficult and expensive. Thus, the need to improve the visitors’ experience arises, in this case, by using the technological means currently available to everyone: a smartphone. This paper describes the development and evaluation of the user experience of a mobile application for guiding visitors on said hiking trails, as well as the design principles and main issues observed during this process. The application is based on an augmented reality interaction model providing visitors with an interactive and recreational experience through Augmented Reality in outdoor environments (without additional marks in the physical space and using georeferenced information), helping in navigation during the route and providing updated information with easy maintenance. For the design and evaluation of the application, two studies were carried out with users on-site (Santa Maria, Azores). The first had 77 participants, to analyze users and define the application’s characteristics, and the second had 10 participants to evaluate the user experience. The feedback from participants was obtained through questionnaires. In these questionnaires, an average SUS (System Usability Scale) score of 83 (excellent) and positive results in the UEQ (User Experience Questionnaire) were obtained.
... DMOs can benefit from and effectively implement location-based AR in location marketing and communication campaigns (Nayyar et al., 2018). More personalized concierge services have always been within the scope of destinations as they offer long-term engagement for prospective visitors and a magnet effect for local businesses, such as smart recommendations (Kourouthanassis et al., 2014). The impact of AR experiences would help tourists to be better informed thanks to dynamically updated information on POIs (Yovcheva et al., 2012;Chung et al., 2015). ...
Purpose This paper aims to investigate investors' willingness to use robo-advisors from customers' perspectives and analyzes the factors that drive them to use robo-advisors, including perceived usefulness and emotional response. Design/methodology/approach The authors extend the Cognition-Affect-Conation (CAC) framework to the behavioral domain of robo-advisor users on financial technology platforms and conduct an empirical study based on 248 valid questionnaires. Findings The authors find two types of factors driving the willingness to use robo-advisors: perceived usefulness, trust and perceived risk as external driving forces and investor sentiment as an internal driving force. Trust has a significant positive effect on willingness to use, and arousal in emotional response plays a mediating role between perceived usefulness and willingness to use. Originality/value This research provides valuable insights for financial institutions to engage in robo-advisor innovation from customers' perspectives.
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Augmented reality (AR) can induce emotions among its users. However, emotional valence is often studied with a singular focus (e.g., enjoyment), which disregards and overlooks the multifaceted nature of emotional valence. Taking a multifaceted perspective of emotional valence, this study aims to broaden understanding of how induced emotions can drive consumers’ inclination to use AR. A multifaceted emotion measurement scale was modified and utilized (nStudy1: 224), followed by two experimental studies (nStudy2: 214; nStudy3: 200). These experiments entailed a design wherein the experimental group explored products using an AR app while the control group navigated the mobile website of the same company devoid of any AR features. Our findings indicate that the use of AR instigates expressive emotions, further eliciting emotion components spanning both affective and physiological dimensions. In instances of positive emotions, at least two out of the three elicited emotion components consistently led to a heightened desire to engage with AR. Negative emotions produce no significant effects. Taken collectively, the principal theoretical contribution of this study lies in its elucidation of the components and elicitation patterns of emotions tied to AR, whereas the practical standpoint of these findings underscores the necessity for both developers and marketers to comprehend the pivotal role that the induction of positive expressive emotions plays in designing effective AR apps. These insights should therefore pave the way for more intuitively engaging and emotionally satisfying AR experiences for consumers.
Bu araştırma ile “Türkiye’de Turizm sektöründe yapılan büyük veri araştırmaları” konusu üstünde çalışılmış bilimsel araştırmaların literatür araştırması yöntemiyle incelenmesi konu edinmiştir. Bu husus üzere Web Of Science, Scopus, Trdizin, Google Scholar, Ulusal Tez Merkezi, Ulakbim veri tabanlarında “turizm ve büyük veri” kelimeleri birlikte kullanılarak tespit edilen 56 adet araştırmanın sonuçları toplanmıştır. Derlenen bilgiler kavramsal olarak analiz ve içerik incelemesi yapılıp araştırma şekillenmiştir. Elde edilen deliller sonucunda, araştırma konusuna katkıda bulunan; yazar, çalışma türü, çalışma alanı, kullandığı yöntem ve veri toplama tekniği gibi hususlar noktasında ayrıntılı bilgi verilmiştir. Bilginin ana kaynağının insan olduğu büyük veri kavramı son yıllarda ivme kazanmıştır. Türkiye'de insanların en çok iletişim halinde olduğu sektörlerden biri turizmdir. Büyük veri konusundaki çalışma sayısının Türkiye’de gün geçtikçe artış gösterdiği görülmektedir. Fakat sadece nitel araştırmalar kapsamında kaldığı tespit edilmiştir. Sonuçlar, literatürdeki çalışmaların genel olarak turizm ve konaklama konularına odaklandığını, ağırlıklı olarak nitel yöntemleri kullandığını ve çoğunun kavramsal çalışmalardan oluştuğunu göstermektedir. Elde edilen en önemli sonuç, Türkiye'de teoriye dayalı ampirik araştırmaların bulunmamasıdır. Çalışma nihayetinde “Turizm Sektöründe Büyük Veri Araştırmaları” hususlu konunun teknolojik gelişme seviyesine paralel olarak geliştiği ve çalışmacılar tarafından göz ardı edilmeyip üzerine düşülmesi gerektiği kanaatine varılmıştır.
[Published in Korean] This study aims to explore the effects of consumers' individual characteristics on therapeutic metaverse behavior. To understand the role of individual characteristics on metaverse behaviors, this study explores how individuals' technology innovativeness and sensational seeking on therapeutic metaverse motivation and outcomes. A total of 304 responses from a popular metaverse platform, Zepeto, was collected through an online survey firm. The data were analyzed with confirmatory factor analysis and structural equation modeling analysis using AMOS 25.0. First, the effects of the individuals' technology innovativeness and sensational seeking on therapeutic metaverse outcomes mediated by therapeutic motivation were examined. The results showed that the effect of technology innovativeness on therapeutic metaverse motivation and outcomes was not significant; however, the effect of sensational seeking on therapeutic metaverse outcomes was mediated by therapeutic metaverse motivation. In addition, the moderating effect of self-avatar identification on the relationship between individuals' characteristics and therapeutic metaverse motivation was tested. The result revealed that the interplay effect of technology innovativeness and self-reflection on therapeutic metaverse motivation was significant; however, the interplay effect of sensational seeking and self-avatar identification was not significant. Interestingly, technology innovativeness influenced therapeutic metaverse outcomes, mediated by therapeutic metaverse motivation, when self-avatar identification was high. This study contributes theoretical knowledge to consumer behaviors in a social metaverse. Specifically, this study expands the metaverse literature by testing the effects of individuals' characteristics on therapeutic metaverse behaviors.
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We are on the verge of ubiquitously adopting Augmented Reality (AR) technologies to enhance our perception and help us see, hear, and feel our environments in new and enriched ways. AR will support us in fields such as education, maintenance, design and reconnaissance, to name but a few. This paper describes the field of AR, including a brief definition and development history, the enabling technologies and their characteristics. It surveys the state of the art by reviewing some recent applications of AR technology as well as some known limitations regarding human factors in the use of AR systems that developers will need to overcome.
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This paper discusses the use of Augmented Reality (AR) applications for the needs of tourism. It describes the technology’s evolution from pilot applications into commercial mobile applications. We address the technical aspects of mobile AR application development, emphasizing the technologies that render the delivery of augmented reality content possible and experientially superior. We examine the state of the art, providing an analysis concerning the development and the objectives of each application. Acknowledging the various technological limitations hindering AR’s substantial end‐ user adoption, the paper proposes a model for developing AR mobile applications for the field of tourism, aiming to release AR’s full potential within the field.
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The use of modern technology is becoming a necessity of many destinations to stay competitive and attractive to the modern tourist. A new form of technology that is being used increasingly in the public space is virtual- and Augmented Reality (AR). The aim of this paper is to investigate tourists’ requirements for the development of a mobile AR tourism application in urban heritage. In-depth interviews with 26 international and domestic tourists visiting Dublin city were conducted and thematic analysis was used to analyze the findings of the interviews. The findings suggest that although Augmented Reality has passed the hype stage, the technology is just on the verge of being implemented in a meaningful way in the tourism industry. Furthermore, they reveal that it needs to be designed to serve a specific purpose for the user, while multi-language functionality, ease of use and the capability to personalize the application are among the main requirements that need to be considered in order to attract tourists and encourage regular use. This paper discusses several significant implications for AR Tourism research and practice. Limitations of the study which should be addressed in future research are discussed and recommendations for further research are provided.
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Through the rapid spread of smartphones, users have access to many types of applications similar to those on desktop computer systems. Smartphone applications using augmented reality (AR) technology make use of users' location information. As AR applications will require new evaluation methods, improved usability and user convenience should be developed. The purpose of the current study is to develop usability principles for the development and evaluation of smartphone applications using AR technology. We develop usability principles for smartphone AR applications by analyzing existing research about heuristic evaluation methods, design principles for AR systems, guidelines for handheld mobile device interfaces, and usability principles for the tangible user interface. We conducted a heuristic evaluation for three popularly used smartphone AR applications to identify usability problems. We suggested new design guidelines to solve the identified problems. Then, we developed an improved AR application prototype of an Android-based smartphone, which later was conducted a usability testing to validate the effects of usability principles.
Purpose The purpose of this study is to examine customer intentions to download mobile applications in the hospitality industry. Even though major hospitality companies offer the mobile applications, many customers have not utilized them. The results showed what encouraged customers to download mobile applications in the hospitality industry. Design/methodology/approach The Technology Acceptance Model was applied for this research to explain customer intentions. College students were the target population of this study because they understand and adopt the technology well. Therefore, mobile applications will become a popular way to purchase goods and services when university students will have purchasing power. Findings Even though major hospitality companies offer mobile applications, more than a half of respondents responded that they had not used mobile applications from the hospitality firms. The results showed that promotion information was not an only reason to download mobile applications; however, the results also showed that consumers who enjoy using smartphones and who are confident in themselves are more likely to download the mobile applications. Research limitations/implications The data were collected in a university; therefore, generalizability is one of the limitations of this research. Multiple regressions only verify the relationship between dependant and independent variables. University students may not have a chance to plan their trips so they may not need the mobile applications. Originality/value This study employed the TAM to examine the reasons why customers download mobile applications offered by companies in the hospitality industry. Literature discussing mobile applications in the hospitality industry is very scarce. This research will assist managements in utilizing their mobile applications.
The goal of this chapter is to provide a high-level overview of fifteen years of augmented reality research that was sponsored by the U.S. Office of Naval Research (ONR). The research was conducted at Columbia University and the U.S. Naval Research Laboratory (NRL) between 1991 and 2005 and supported in the later years by a number of university and industrial research laboratories. It laid the groundwork for the development of many commercial mobile augmented reality (AR) applications that are currently available for smartphones. Furthermore, it has helped shape a number of ongoing research activities in mobile AR.
Many design researchers and scholars have focused on different aspects of design and emotion in recent years. Various studies, models and theories have been proposed and adopted in order to explore the relationship between design and emotion and its responses, and to explain how emotion could be applied in design effectively. Researchers have also developed different perspectives to understand what emotional design should be and the role of emotion in design. Some of them have considered emotion design as a tool that designers can use to deliver their messages and emotions, while others have believed that it is a kind of experience and response when an individual is using an object. Meanwhile, some researchers have regarded emotional design as a means to establish consumer expression, and as a representation of the users' identity or personality. The relationships between 'design and emotion' and 'users' responses' were preliminarily explored. Another similar term, emotionalize design, has been used to explain how emotions play an explicit role in design reflection, rationality and feeling. Few studies, however, have been carried out to explore the relationship between these terms, what they actually mean in their own role and how they interact with each other in the big picture of design and emotion. This paper aims to explore and illustrate the basic concepts and definitions of emotion design, emotional design and emotionalize design to help us further understand how these are closely related to human-oriented design activities. It will introduce and review these concepts, and explore their relationships from a new perspective. Based on these insights and analysis, a new model concept will be described to identify their differences by defining their meanings, in which both designers and users play an important role.
Purpose – The purpose of this paper is to introduce mobile augmented reality applications for library uses and next generation library services. Design/methodology/approach – Examples are drawn from museum and archives informatics, computer science applied research, and computer vision research as well as original research and development work from the Undergraduate Library at the University of Illinois. Findings – Mobile augmented reality uses include augmenting physical book stacks browsing, library navigation, optical character recognition, facial recognition, and building identification mobile software for compelling library experiences. Originality/value – The paper suggests uses of mobile augmented reality applications in library settings and models a demonstration prototype interface.