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TILES: classifying contextual information for mobile tourism applications Esther Meng-Yoke Tan, Schubert Foo, Dion Hoe-Lian Goh and Yin-Leng Theng Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore

Classifying contextual information 565 Received 12 April 2009 Revised 19 June 2009 Accepted 18 August 2009

Abstract Purpose – The design of context-aware mobile applications can be improved through a clear and in-depth understanding of context and how it can be used to meet users’ requirements. Using tourism as a case application, this paper aims to address the lack of understanding of context and tourists’ goals. Design/methodology/approach – This is achieved through a literature review of existing research and focus groups to gather information needs for tasks commonly executed by tourists. Findings – This paper proposes the TILES (temporal, identity, location, environmental and social) model to define and classify five main contextual types, and properties associated with each type for tourism-related applications. The TILES model (with 32 factors) derived from the analysis of the literature review is refined through inputs from two focus groups to incorporate an additional ten factors. Research implications/limitations – The TILES model can be generalised to support domains other than tourism, such as medical and edutainment. Originality/value of paper – The model will help to achieve a better understanding of context, users’ information needs and their goals. In addition, this work extends findings in the field of context-aware computing and information retrieval on mobile devices. Solution providers will also be able to adopt TILES as a framework for guiding the design of their context-aware mobile applications. Keywords Mobile communication systems, Information retrieval, Tourism Paper type Research paper

1. Introduction In the past few years, the adoption of mobile devices has grown tremendously (Kawash et al., 2007), and their characteristics of mobility and connectivity support on-demand services that are tailored to users and their specific situations, any time, anywhere. CRUMPET (Poslad et al., 2001) and GUIDE (Cheverst et al., 2002), for example, are mobile tourism applications designed to be aware of the tourist’s location and interests. They are described as context-aware applications because they are sensitive to a user’s context. Saracevic (1996a, b) argued that context should be used to consider the relevance of information. Setten et al. (2004) have demonstrated that context can be used to measure the relevance of information such that only appropriate information is presented. They are supported by Wilson (1973) and Mizzaro (1997) as well as Borlund and Ingwersen (1997). This is also confirmed by Albers and Kim (2002), who have also highlighted that “delivering the right content for the right context is crucial” (p. 194). In addition,

Aslib Proceedings: New Information Perspectives Vol. 61 No. 6, 2009 pp. 565-586 q Emerald Group Publishing Limited 0001-253X DOI 10.1108/00012530911005526

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Spink et al. (1998) have cautioned that the usefulness of the content is largely dependent on the users’ judgment. For these reasons, many mobile applications in different fields are designed with context-awareness features. These include those in the medical field (Kjeldskov and Skov, 2004), office automation (Schilit et al., 1994) and tourism (Setten et al., 2004). As will be discussed, while context-awareness has advantages, there are many challenges in incorporating such techniques into mobile applications. This research aims to analyse limitations in context-aware application design using mobile tourism as a basis. This is because there are many good examples of how the context can be used in context-aware applications for tourists. Existing research has also explored and identified some outstanding problems faced by tourists. These findings form the foundation of this research when analysing the weakness of such applications as discussed in the following section. 1.1 Challenges in context-awareness in mobile tourism Contextual information is important when adapting information to meet tourists’ needs. A review of mobile tourism applications has uncovered major issues such as a lack of consensus in the definition of context, and a lack of understanding of tourists’ goals. 1.1.1 Lack of consensus in the definition of context. Researchers have proposed different context types in their definitions of context. For example, Dey and Abowd (2000) defined context to include location, temporal elements, identity and activity. Schilit et al. (1994) also included descriptions on identity and environment. Korkea-aho (2000) extended the scope to include the social aspect, proximity, device and physiology. Context-aware mobile tourism applications are also designed to support different context types. GUIDE supports identity, social and environmental contexts (Cheverst et al., 2002). COMPASS supports location, temporal, identity and environmental contexts (Setten et al., 2004), and CRUMPET supports location, identity, network and device contexts (Poslad et al., 2001). Designers of context-aware applications have also classified contextual properties differently. For example, GUIDE supports current time and date under the environmental context (Cheverst et al., 2002), but m-ToGuide (Kamar, 2003) and CATIS (Pashtan et al., 2003) include time and date under the temporal context. Some context types, such as the identity context, are supported by rich contextual properties. They include the user’s name, age, preference in food, lodgings, price range, information-seeking trend, shopping lists and travel agenda (Pashtan et al., 2003; Poslad et al., 2001; Setten et al., 2004). On the other hand, the social context is less well-defined in general, and merely includes information on tour companions, people nearby and others’ comments (Schilit et al., 1994; Cheverst et al., 2002). With different definitions of context, mobile tourism applications have proposed different repository designs for storing and indexing of the contextual information (Feng et al., 2004; Hinze and Buchanan, 2005). Therefore, information providers, such as places of interest and restaurants, will have to structure and format their information differently to suit these different applications. The user interface designs of these mobile tourism applications are also tightly associated with their definition of context. When using different mobile tourism applications during their tours, tourists

will have to adapt to different definitions of context and properties, making it difficult for them to learn and use the applications. 1.1.2 Lack of understanding of tourists’ goals. Current work in context awareness typically employs logic that determines tourists’ contexts, such as interest and current location. They then adapt their information automatically based on these contexts. However, as Cheverst et al. (2002) have highlighted, there is a concern that there could be a mismatch between tourists’ goals and the adaptation due to a lack of understanding fo tourists’ goals with respect to context. In the case of GUIDE, the locations of attractions pertaining to tourists’ interests will not be revealed if they are closed, but their study has revealed that tourists may still want to visit a place despite it being unavailable. For example, tourists could still visit and view the Eiffel Tower outside its operating hours. Hinze and Buchanan (2005) have also highlighted that users change their roles and situations very frequently. For instance, while walking on the street towards their destination, they may meet with a friend who has access to a car, hence having a new set of preferences. The former planned route, estimated travelling time and destination may no longer be valid and there would be a need for re-adaptation. This observation shows that despite the intelligence embedded in the information adaptation process, such information may not meet tourists’ requirements, especially when situations change frequently. Thus, there is a need to investigate how contextual information should be gathered and used to meet tourists’ changing goals. 1.2 Research objectives Using mobile tourism applications as a basis, this paper proposes and evaluates the collection of contextual information relevant to this domain with sub-objectives that include: . reviewing the contextual information that applies to mobile tourism with the aim of developing a comprehensive typology of information attributes that relate to the context (herein referred to as typology of contextual information); and . evaluating the comprehensiveness and usefulness of the typology of contextual information. Section 2 describes existing contextual information models supporting context-aware mobile tourism application, while Section 3 gives a brief description of the research methodologies used to triangulate the results. Section 4 reports the findings of the research, while Section 5 presents our proposed contextual information model. 2. Related work A study of the related literature has shown that contextual information is organised differently to support each context-aware mobile application. The following sections include reviews of organisational methods used for contextual designs in existing work. The goal is to understand possible ways of organising and presenting contextual information. These context categorisation frameworks unanimously present contextual information in a hierarchical method. Feng et al. (2004) organised contextual information into two major groups: (1) user-centric; and (2) environmental.

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The user-centric context refers to any contextual information related to the user, such as behaviour, and physiological and emotional state. The environmental context refers to contextual information related to the physical, social and computational environment. The physical environment refers to elements such as time and location. The social environment refers to people nearby, and the computational environment refers to devices nearby. Hinze and Buchanan (2005), however, have grouped contextual information into three major categories: (1) network context; (2) device context; and (3) application context. The application context is then further categorised into user and site context. The user context can be described as user static information such as user interests and background and user-fluent information such as time, location and direction of the user. This grouping of user context into static and fluent is supported by O’Grady et al. (2007). They also assumed that the user’s profile will remain the same for a certain length of time and that the user’s current location and activity will change dynamically. Hinze and Buchanan (2005) also adopted site context as site general, such as the location and operating hours of places of interest, and site current, which ties in with the user context such as the popularity of a site adapted by the user’s interests. It is also important to observe that only Hinze and Buchanan (2005) indicated the interaction between the categories. They proposed that these contexts are inter-dependent when used for information adaptation. As opposed to Hinze and Buchanan (2005) and Feng et al. (2004), Korpipaa et al. (2003) levelled out the categories and sub-categories. They placed location, time, environment, user and device at the same level. Any newly discovered contexts could be added by expanding the architecture breadth-wise. They proposed that each context type can be further refined into sub-context types and properties at the lowest level: . environment:sound:intensity, with a value of {silent, moderate, loud}; . environment:light:intensity, with a value of {dark, normal, bright}; . environment:light:type, with a value of {artificial, natural}; and . environment:temperature, with a value of {cold, normal, hot}. In this case, the environment is a context type. Sound and light are sub-context types of the environment. Intensity, types and temperature are context properties in their own group of context and sub-context types. Finally, Wang et al. (2004) proposed an ontology to model context information. In their context ontology (CONON), four main context types are identified: (1) location; (2) user; (3) activity; and (4) computational entity.

Each context type was further classified into sub-context types such as service, application, device, network and agent for computational entity. It is interesting to observe that they suggested that this set of general contexts with specific features could be applied in different domains. For example, the location context has sub-categories of indoor and outdoor space. For the home domain, indoor space includes building, room, corridor and entry, while outdoor space includes garden and dooryard. All four of the organisation methods reviewed adopt the hierarchical tree as the common approach for organisation. From the analysis of techniques, it is observed that modelling of contextual information using the hierarchical tree allows expansion and easy addition of new context types and properties. This is consistent with the findings of Peng and Choi (2002), who suggested that a tree structure allows easy addition of new categories and facilitates searching for information. Fan et al. (2006) also suggested that hierarchical classification can be used to index information for easy retrieval. They adopted it to index images differently, depending on the context of use. In the hierarchical tree, items can be indexed in a nested manner and can be used to support techniques to bridge the gap between users’ understanding and the actual classification of media clips for easy retrieval. These organisation methods also focused on classifying contextual information to better support the design of the mobile tourism application. However, to the best of our knowledge, there is no work on validating whether this information fulfils tourists’ needs. 3. Research methodology The present research employs two research methodologies. First, a review of context-aware mobile tourism applications was used to collate the existing list of contextual information adopted by these applications. Next, two focus groups were conducted to gather tourists’ information needs so as to validate and improve on the existing list. 3.1 Review of existing context-aware mobile tourism applications The objective was to collate and synthesise the context types and contextual properties adopted by existing context-aware mobile tourism applications. This review included observations of the use of contextual information in these applications. This was done through reading and extracting context-related information from journal articles published on these applications. The context types were extracted from 25 articles as referenced in Table I. These articles have frequently been referred to in context-related research. The contextual information related to mobile tourism applications was extracted from 12 journal articles as referenced in Table II. These 12 articles gave comprehensive descriptions of the contextual information used by well-recognised mobile tourism applications. The review also included a sampling of printed materials and websites to obtain a general understanding of how these traditional media relate to the various contexts. These included four official tourism websites from countries popular among tourists and three printed tourist brochures commonly used by them (listed in Table II). The classification of context type and properties was done through a preliminary assessment and will be confirmed in future research through inter-coder agreement to


Table I. Categories of context-aware applications and their context

Context-aware mobile tourism applications Cheverst et al. Outdoor (2000a, b, c, 2002); guide GUIDE

General purpose context-aware applications Ghosh-Dastidar et al. Medical (2007); epileptic seizure detection Jung et al. (1997); detect Medical drowsiness in drivers Kjeldskov et al. (2004); Medical MobileWARD Pousman et al. (2004); Office event planner automation Fogarty et al. (2004); Office MyVine automation Schilit et al. (1994); Office PARCTAB automation Elliott and Tomlinson Edutainment (2006); Personal soundtrack Dornbush et al. (2007); Edutainment XPOD Jeong and Lee (2007); Edutainment Context-aware HCI for ubiquitous learning £

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(continued)

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Location Temporal Identity Environmental Social Proximity Network Activity Device Physiology Cognitive

570

Research findings on usage of context in context-aware applications Dey and Abowd (2000) Survey on context £ £ £ Korkea-aho (2000) Survey on context £ £ £

Domain

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Hinze and Buchanan (2005), Hinze and Voisard (2003); TIP Setten et al. (2004); COMPASS Cano et al. (2006); UbiqMuseum Roffia et al. (2005); MUSE Hsi (2002); electronic guidebook

Kamar (2003); mToGUIDE Pashtan et al. (2003); CATIS Poslad et al. (2001); CRUMPET Roth (2002); Pinpoint

Indoor guide

Indoor guide

Outdoor guide Indoor guide

Outdoor guide Outdoor guide Outdoor guide Outdoor guide Outdoor guide

Domain

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Location Temporal Identity Environmental Social Proximity Network Activity Device Physiology Cognitive


Table I.

Table II. Contextual properties and their classification in mobile tourism applications, websites and printed media

22

21

20

19

17 18

16

15

12 13 14

7 8 9 10 11

4 5 6

2 3

1

Users’ interests (e.g. history, architecture) Current location Time of the day, current date Nearby attractions Weather Types of device, screen resolution, colour Duration of stay Name Preferred language Types of network Latest happening events Travelling speed Country of origin Purpose of trip (leisure, LOHAS, MICE) Attractions visited by tourists Last visited date of a place Favourite cuisine Refreshment preferences Dietary preferences Preferred information level of detail Elapsed time since last measured location (e.g. three hours ago) Events round the year I

I

I

I

I

I

L

T

L

CATISc

I

I

N

I

T

T L

I

I L

mToGUIDEb

I L

GUIDEa

N

L

I

N

D

I

I

I

D

I

D

L

D

T

I L

UbiqMuseumg

L

T

L

TIPf

L

I L

PinPoint e

Mobile tourism applications CRUMPETd

I

L

L E

I L

COMPASSh

I

L

MUSEi

I

L

Electronic Guidebookj

T

Californiak

I

I

T

I

I

E

I

I

I

I

E

I

Official tourism websites New Zealandm Australian

Singaporel

E

I

Torontoo

T

I

I

YES Guidep

572

Properties

I

Frommer’sq

(continued )

I

DK Eyewitnessr

Print material

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Distance between location and services Travelling direction Current mode of transport Informationseeking trend Groups’ interests Group name Age Preferred price range Number of repeated visits People nearby Recommendations and reviews by other tourists Travelling companions – who are they? Agenda Shopping list User activity (e.g. walking, driving) User status (e.g. free or busy) Traffic/road condition Storage, download and display capability

S

S

I S

S I

GUIDEa

mToGUIDEb

I

L

L

CATISc

I S

PinPoint e

D

L

TIPf

UbiqMuseumg

Mobile tourism applications CRUMPETd

E

I

I

I I

COMPASSh

MUSEi

Electronic Guidebookj Californiak

Official tourism websites New Zealandm Australian

Singaporel

Torontoo

YES Guidep

DK Eyewitnessr

Print material Frommer’sq

Notes: T, time; I, identity; L, location; E, environmental; S, social; D, device; N, network. aGUIDE (Cheverst et al., 2000a, b, 2002); bm-ToGUIDE (Kamar, 2003); cCATIS (Pashtan et al., 2003); dCRUMPET (European Media Laboratory, 2006; Poslad et al., 2001); e PinPoint (Roth, 2002); fTIP (Hinze and Buchanan, 2005; Hinze and Voisard, 2003); gUbiqMuseum (Cano et al., 2006); hCOMPASS (Setten et al., 2004); iMUSE (Roffia et al., 2005); jElectronic Guidebook (Hsi, 2002); kCalifornia (see www.visitcalifornia.com); l Singapore (see www.visitsingapore.com); mNew Zealand (see www.newzealand.com); nAustralia (see www.australia.com); oToronto (see www.torontotourism.com); pYES Guide (YES, 2007); qFrommer’s (Frommer, 2007); rDK Eyewitness (DK Eyewitness, 2003)

40

39

38

35 36 37

34

32 33

31

27 28 29 30

26

25

24

23

Properties


Table II.

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ensure that the context type and properties are analysed consistently among other analysts when given a set of criteria (Lombard et al., 2002). A total of 40 contextual properties are supported by ten mobile tourism applications, five tourism websites and three printed materials, of which each contextual property is classified under different context types including identity, location, environmental, social, network, device and temporal context. The relative importance of each context type is measured by the number of contextual properties supported by the mobile tourism applications. 3.2 Focus group findings to understand tourists’ information needs The objective of the two focus groups was to explore tourists’ information needs and understand the problems they encountered during their trips so as to validate the gaps in existing context-aware mobile tourism applications. From their information needs, contextual information can be extracted to validate and extend the list of context types and context properties derived from review conducted on the existing mobile tourism applications. Two focus groups were held, each with eight participants aged between 20 to 35 years old. Each group had an equal number of male and female participants. Participants were working professionals with both leisure and business travelling experiences. The results gathered from the first session were used to refine and enhance the effectiveness of the second session. Each focus group consisted of two parts. In the first part, participants were asked to share their information needs through scenario-based questions. In the second part, they were asked to identify contextual properties supporting their information needs based on these questions. 4. Findings and analyses This section reports the findings from the review of mobile tourism applications and the focus group. It will describe the contextual information derived from the two studies, while Section 5 presents the derived model. 4.1 Review of existing context-aware mobile tourism applications 4.1.1 Context types in context-aware mobile applications. This section discusses the contextual information used in context-aware applications. These applications were grouped by their domains and purposes, which included the medical field, office automation, edutainment, outdoor guides and indoor guides. The medical group was selected for its unique design consideration, which included users’ physiological data (Raskovic et al., 2004). The office automation group was selected because their daily activities may be similar to those activities carried out by tourists (Perttunen and Riekki, 2005). The edutainment group was selected because they might involve useful features and information relevant to tourist applications, such as users’ preferences and their location (Dornbush et al., 2007; Jeong and Lee, 2007). The context types adopted by these context-aware applications were extracted and compiled as parameters used in the evaluation as shown in Table I. The context types include: . The location (proximity) context – describes the data varying based on the user’s location. . The temporal context – describes the temporal characteristics of the application. The identity context refers to the profile of the users. Some examples include users’ interests, preferred language and gender.

. .

.

. . .

.

The environmental context – describes the surroundings of the users. The social context – refers to the social setting of users, which includes their travelling companions and people nearby. The network context – describes the available network resources for the applications. The activity context – refers to the tasks currently executed by the users. The device context – describes the capabilities and features of the device. The physiology context – refers to information about users’ physiological status such as blood pressure and heart rate. The cognitive context – describes the user’s state of mind when using context-aware applications. It includes consideration of users’ emotions, current mindset and stress level.

As expected, it was observed that context-aware applications adopt different context types largely based on the purpose of the applications and users’ needs. As stated by Kaasinen (2003), it is difficult to identify the context types necessary for context-aware applications. While there is no standard set of context types designated for applications in each field, context design can be improved by considering a suitably comprehensive set of contexts. This is supported by Christensen et al. (2006), who explained that gathering more contextual information will not necessarily help context-aware applications meet users’ needs. The key here lies in how to adopt the appropriate context types, and interpret and use them in the applications. Thus, a comprehensive typology of contextual information proposed in this paper will serve as a useful guide for developers deciding which contextual information is to be incorporated in the design of their context-aware applications. 4.1.2 Contextual properties supported by mobile tourism applications. This section reports on the context types and their respective properties commonly adopted by the mobile tourism applications as shown in Table II. From the analysis of literature, no mobile tourism application has adopted properties under the physiology context and activity context. Put differently, mobile tourism applications are not sensitive to the human physiological state and current activity, probably because this sensitivity is not required by users. 4.2 Focus group findings to understand tourists’ information needs The focus groups gathered general tourists’ information needs by walking through four scenarios which represent commonly executed tasks. The scenarios included visiting places of interest, watching cultural performances, purchasing souvenirs and selecting restaurants. The participants suggested grouping the information needs into the context types (i.e. temporal, identity, location, environmental and social). The information needs and grouping were presented in mind-maps. The mind-map shown in Figure 1 is an example of information needs when selecting restaurants. Other mind-maps have similar characteristics and are not shown here for brevity. Participants grouped related information into restaurant settings, restaurant services, restaurant requirements, when (timing), how to get there, factors affecting what to eat and search-by factors. As shown in Figure 1, they identified information related to the five context types (i.e. temporal, identity, location, environmental and social). In the


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Figure 1. Tourists’ information needs when selecting restaurants

temporal context, they included time of day and time of year. In the identity context, they included preferences such as preferred price range, preferred transport, preferred portion size, acceptable waiting time, and acceptable hygiene level. In the location context, they included nearby available food and route to the nearest restaurant. Finally, they classified what others were eating and rating by others under the social context. The focus group participants also decided that the search for restaurants should take into consideration information from all the context types (i.e. temporal, identity, location, environmental and social). 5. The proposed contextual information model – TILES From the findings presented in previous sections, we propose our contextual information model in this section. The TILES model encapsulates contextual information, which includes context types and their respective contextual properties. The acronym TILES stands for the five context types, i.e. temporal, identity, location, environmental and social. The TILES model focuses on mobile tourism guides at the application level, supported by software applications. It does not include device and network contexts because sensing these contexts involves detecting and reading the hardware’s configuration. It also does not include the physiology context because that involves sensing the human body’s condition. The device, network and physiology contexts were also not identified during the focus group, probably because participants did not see the need to filter information by these contexts. Table III reveals the widely accepted properties in each context type that are emphasised in the TILES model. Table III includes properties adopted by existing mobile tourism applications, findings

Temporal – data varying according to time Current time of day and year Latest happening events Events round the year Seasons of the year Identity – data varying according to user’s identity User interests Profile – name, birth date, country of origin and age Preferred language Duration of stay Purpose of trip Attractions already visited by tourists Last visited date Preferred types of food – refreshments, dietary, cuisine Preferred information level of detail Information-seeking trend Preferred price range Number of repeated visits Tour schedule Current activity – walking, driving, etc. User status – free or busy Tourists’ free time Cravings Acceptable waiting time Acceptable security level Preferred transport mode Preferred quality level such as service quality, meal portion size Acceptable hygiene/safety level Location – data varying according to user’s location Current location Nearby attractions Travelling speed Age of last measured location Distance between location and services Travelling direction Current mode of transport

Literature review

Focus group

U U U

U

U U U U U U U U U U U U U U U U


U U U

U

U U

U U U U U U U U U U U U U U

U U

Environmental – data varying according to user’s environment Weather – temperature, climate, humidity, air quality Traffic/road conditions Available seats

U U

U U U

Social – data varying according to user’s social setting Group’s interests Group name Nearby people – tour members, friends and like-minded strangers Recommendation, reviews by other tourists Travel companions – who are they? Pictures and video clips posted by others

U U U U U

U U U

Table III. Refined TILES model

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from existing research, as well as properties gathered from the focus group (indicated by tick marks in the columns of Table III). The following explains the context types and properties, and how they are verified and supported by our findings. 5.1 Temporal Time of day and current date are temporal context properties used to describe the current time of the day and current date. They are frequently used in information adaptation to determine relevant events or activities happening in the near future. TIP (Hinze and Voisard, 2003; Hinze and Buchanan, 2005) is a tourist guide that incorporates time of day and the location of tourists to suggest tour plans that include relevant tour activities such as conference talks, lunch venues and places of interest. It was also observed that m-ToGuide (Kamar, 2003), CATIS (Pashtan et al., 2003), GUIDE (Cheverst et al., 2000c) and Pinpoint (Roth, 2002) also support this contextual property. Some of the focus group participants suggested that their selection of places of interest such as restaurants depended on whether they were planning for breakfast, lunch, dinner or a late-night snack. Therefore, it was essential to consider the time of day when making decisions about restaurants. Tourists might also like to participate in seasonal or festive events, such as visiting a Chinese garden during the lantern festival. In this case, the event and seasons of the year in the temporal context would affect their decisions on places of interest. As one participant mentioned: “I used to schedule my Hong Kong tour trip around the month of June to catch the summer sales”. The property of current time of day and year was also included in the design of GUIDE (Cheverst et al., 2002), m-ToGuide (Kamar, 2003), CATIS (Pashtan et al., 2003), Pinpoint (European Media Laboratory, 2006; Poslad et al., 2001) and TIP (Hinze and Buchanan, 2005). 5.2 Identity The property of users’ interests refers to their interests and concerns. These might include topics such as architecture, history and shopping. In the review, all five mobile tourism applications supporting this property classified it under the identity context (Cheverst et al., 2000a, b, c; Kamar, 2003; Poslad et al., 2001; Hinze and Voisard, 2003; Hinze and Buchanan, 2005; Setten et al., 2004). It was also interesting to observe that all the official tourism websites and printed media, except the California tourism website (see: www.visitcalifornia.com), aimed to tailor their tourist information according to the interests of travellers. This is confirmed by Myrhaug et al. (2004), who have highlighted that users’ contexts should include their personal preferences. Goker and Myrhaug (2008) also represented the user’s situation from the user’s perspective. In an example, they included user’s preference as a property. The preferred language is a property that describes the language best understood by tourists. Among all the mobile tourism applications, only Ubiqmuseum (Cano et al., 2006) was designed to support different languages and allow tourists to choose their preferred one. In terms of websites, Singapore (see www.visitsingapore.com) and New Zealand (see www.newzealand.com) also supported the preferred language of tourists. The duration of trip property is supported by m-ToGuide (Kamar, 2003), the official Singapore tourism website (see www.visitsingapore.com) and the YES guide print material (YES, 2007). Depending on the tourists’ trip duration, different activities and

tour agenda are recommended so that the tourists can visit as many places of interest as possible during their stay. In COMPASS (Setten et al., 2004), third-party services were employed to consider users’ status and to provide appropriate information depending on whether the user is free or busy. They also adapted tourist information according to the user activity; for example, if the user is driving, the amount of time required to get from the current location to the destination will be shorter than if he or she is walking. Some of the focus group participants mentioned that they would consider the price of services, such as entrance fees and menu prices, and quality of services such as duration of show and portion sizes in the restaurant, ensuring that they were within their acceptable price range and quality of service. The acceptable price range property was consistent with the findings in the literature review and was supported by Pashtan et al. (2003). Participants were also concerned with the hygiene or safety conditions of the place of interest and the average waiting time. They also agreed that acceptable hygiene or safety level and waiting time duration varies according to an individual’s preferences. Many of them also indicated that their selection of places of interest sometimes depends on their cravings, such as a sudden preference for a certain type of food or to watch a performance. One participant mentioned that he would not mind travelling further to satisfy his cravings. Concern was expressed about whether the physical surroundings of places of interest and the journey there were safe – that is, whether the place is within their acceptable level of security. Another participant said: “For example, when I was visiting San Francisco, I chose to dine in my hotel cafeteria instead of a highly recommended restaurant in Chinatown . . . It was already 8.00 pm and I didn’t feel comfortable travelling to the other side of town alone”. Finally, the identity context also includes contextual properties that are least commonly adopted, such as: . country of origin; . purpose of trip; . attractions last visited by tourists; . the time a place was last visited; . favourite cuisine; . information-seeking trend; . age; . affordable price range; . number of repeated trips; . agenda; . shopping list; . refreshment and dietary preferences; and . preferred level of detail for information. 5.3 Location (proximity) The property of current location refers to the existing physical position of tourists. This is a widely adopted property and is supported by all ten mobile tourism applications reviewed in our work. Traditional media, websites and printed materials do not


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support this because these media do not have positioning technology, which is essential to provide this contextual information. This property is also confirmed by Raper et al. (2007) and Jose et al. (2003), who emphasised the importance of using current location as the way to measure the relevancy of information. Tourists’ nearby attractions is also a commonly supported property that refers to places of interest near the tourist’s current location. It was adopted by CRUMPET (Poslad et al., 2001), COMPASS (Setten et al., 2004), GUIDE (Cheverst et al., 2000a) and Ubiqmuseum (Cano et al., 2006). All mobile tourism applications had classified nearby attractions under the location context. The focus group participants were concerned about how to get to places of interest and whether the journey was handicap-friendly. They also wanted to know the cheapest as well as the fastest available mode of transport. Some wanted to know whether it was within walking distance and along their planned route. All agreed that this information could only be available given their current location. From the current location, the mobile tourism application would be able to calculate the path, cost and time needed to get to the destination and work out whether it was within walking distance. They also agreed that the location of the place and transportation schedule is part of the domain information. The focus group also gathered comments about the location context, which included: I would like to know the shopping malls near me. I would also like to know the distance between my current location and the nearby mall; it should be measured by walking time, driving time or the number of blocks away, and not kilometres.

These comments are supported by Mountain and Macfarlane (2007), who included travelling time as a context property in the design of their mobile information retrieval solution. Likewise, the properties of distance between location and services and travelling direction are also confirmed by Mountain and Macfarlane (2007), who took into consideration the distance between the users’ current location and the information source. In addition, our analyses show that there was also a set of less commonly adopted contextual properties. They included age of last measured location, travelling speed, distance between location and services and travelling direction. 5.4 Environmental The property of weather includes descriptions on temperature, humidity, climate and air quality. Setten et al. (2004) was the only mobile tourism application that supported this property and had placed it under the environmental context. Their design was consistent with the official tourism websites of Singapore (see: www.visitsingapore. com), Toronto (see: www.torontotourism.com) and Australia (see: www.australia.com), which also published information on their current weather. Setten et al. (2004) also included the property of traffic condition to take into consideration the current flow of traffic. If there was a traffic jam along the tour route, their application would be able to detect this and recommend a different route. When selecting places of interest, the focus group participants wanted to know the availability of facilities such as height limitations in a theme park and types of liquor served in a restaurant. The participants also highlighted that they were concerned with

the current weather. They also wanted to know how to get to a place of interest using the quickest route, thus requiring information about the current road conditions such as traffic jams. Both properties of weather and traffic/road conditions were supported by focus group findings. One participant explained: “When the weather is nice and cool, I might select restaurants with alfresco dining and enjoy a hot beverage. However, when the weather is hot, I would prefer cold drinks in an air-conditioned environment”. Another participant suggested: “It may be nice to have recommendations on suitable clothing”. Most participants agreed that the weather would affect their selection of restaurants. One commented: “When it is freezing cold, I will fill my tour agenda with indoor activities such as museum visits and shopping. But if it is warm, I may go for wind surfing”. Another added: “Of course, if there is a traffic jam along the route to the beach, we can change our destination”. 5.5 Social Travelling companions constitutes a property supported by GUIDE (Cheverst et al., 2002) to describe people who are travelling together with tourists. Their design suggested different places of interest depending on their travelling companions. Other tourists nearby was also a property in the social context. Cheverst et al. (2002) incorporated features indicating people near the tourists. The design allowed tourists to prompt these people for comments and ratings of the places of interest where they were located. For example, a tourist could prompt another person located at a nearby cafe´ for comments. The comments given would be ranked by the relevancy, such as travelling companions with the assumption that those travelling with family would appreciate comments from tourists with similar travel companions. It was also interesting to find that all focus group participants wanted to read about comments and ratings by other tourists. Two of them said they make decisions by considering what others were doing. They would visit popular places of interest. They decided that what others recommend should be classified as part of the social context. One participant said: “It is probably due to some kind of herd mentality . . . but it is a natural social behaviour to find out what others are doing and to follow the crowd”. This observation is supported by Raper (2007), who indicated that people have a social urge to join in with others if they are interested in the activity taking place. Another participant also suggested: The buddy finder service provided by the Singtel mobile service providers is useful during the tour because I like to make new friends during my trip. The new friends/buddies can then make recommendation on what is worth visiting and where to get value-for-money souvenirs.

In this context type, there were also two least commonly supported properties. They were the group name, supported by Cheverst et al. (2002), and group interest, supported by Poslad et al. (2001). 6. Discussion and conclusion This work addresses the lack of understanding of context and tourists’ goals. It reviews the contextual information supporting mobile tourism applications and gathers tourists’ information needs through a focus group study. Based on our findings, this paper proposes a contextual information model – TILES.


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Existing work such as GUIDE and COMPASS (Cheverst et al., 2000a, b, c; Setten et al., 2004) have emphasised sensing and adapting information based on samples of contextual information. Work on organising contextual information such as CONON (Wang et al., 2004) has focused on organising portions of contextual information into a model to support their context-based design. To the best of our knowledge, there is little work that puts together a comprehensive list of context types and contextual properties that can be used in the context-based design of mobile tourism application. Thus, the TILES model is proposed to fill this gap. The TILES model can also used by researchers to design new methods of meeting users’ information needs in the tourism domain through context-aware applications. It also adds to and supports existing research in other related areas such as working on effective information retrieval methods in mobile tourism applications. The findings on tourist information needs in the social context can also be used in the field of social networking, for example deciding on the social contextual information to be presented to social network users. The search criteria, shown in the search branch of the mind-map (section 4.2), will help mobile service providers to design useful search functions in their applications. An in-depth understanding of tourist preferences and tourists’ information needs will also help providers to deliver better services to their users, for example filtering information according to tourists’ preferences. There are some limitations that should be addressed in future work. In the review, contextual information is extracted from existing mobile tourism applications. The analysis process was challenging because the designers of these mobile tourism applications classified the contextual properties under different context types. The analysis work was done without inter-coder agreement, but was mitigated by triangulation through other methods such as focus groups. Nevertheless, in future work, inter-coder analysis will be introduced to confirm and validate the findings. Future work will also include refining the contextual information framework (TILES) and exploring ways to fill the gaps of existing mobile tourism applications. The focus group was conducted through a group of participants with travelling experience. Our analysis is based on their general travelling experiences; it does not include differences in their information needs by gender, age group and type of travel, such as leisure or business. Thus, there is also a need to further understand and analyse contextual information based on specific tourist profiles. This study forms part of our ongoing research to determine effective mobile user interface designs supporting features associated with contextual information. This paper proposed a set of contextual information needed to support mobile tourism applications, which will be used as the basis of the design of our user interface in the next stage of work. This next stage will focus on using social contextual information to filter information. It is expected to lead to the development of effective user interface designs for visualising and manipulating social contextual information. This aspect of work on social context based design and research is important as we embrace social computing on an increasing basis in the future. References Albers, M. and Kim, L. (2002), “Web design issues when searching for information using handheld interfaces”, Technical Communication, Vol. 49 No. 3, pp. 314-29.

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