User characteristics and performance with automated mobile phone

Int. J. Mobile Communications, Vol. 6, No. 1, 2008

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User characteristics and performance with automated mobile phone systems Mark Howell and Steve Love* School of Information Systems, Computing and Mathematics, Brunel University, West London, Uxbridge UB8 PH, UK Fax: +44(0)1895-251686 E-mail: [email protected] E-mail: [email protected] *Corresponding author

Mark Turner Department of Psychology, University of Portsmouth, King Henry Building, Portsmouth, PO1 2DY, UK E-mail: [email protected] Abstract: The aim of this study was to investigate the impact of individual differences (such as gender and attitude towards mobile phone use in public places) on the usability of a speech-activated mobile city guide service in various context of use (e.g. café, train). A Wizard of Oz methodology was used to provide the service functionality for the mobile city guide service. Participants in the study completed specific tasks over a six-week period in public and private locations. The results highlight the importance of considering the effects of individual differences on the context of use in system design and evaluation. Keywords: context of use; individual differences; mobile phone services; usability. Reference to this paper should be made as follows: Howell, M., Love, S. and Turner, M. (2008) ‘User characteristics and performance with automated mobile phone systems’, Int. J. Mobile Communications, Vol. 6, No. 1, pp.1–15. Biographical notes: Dr. Mark Howell has recently become a Usability Specialist for the BBC interactive television design team, and is a Member of the User Experience Design team at BBC New Media and Technology. His research interests include the study of context of use, interface metaphors and individual differences. Dr. Steve Love works in the School of Information Systems, Computing and Mathematics at Brunel University, West London, UK. His research interests primarily focus on looking at the social impact of mobile technology on people’s behaviour and its effect on their perception of the usability of mobile services and applications. Copyright © 2008 Inderscience Enterprises Ltd.

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M. Howell, S. Love and M. Turner Dr. Mark Turner is a Senior Lecturer in the Department of Psychology at the University of Portsmouth, UK. His research interests include the applied study of cognitive ability, for example, while driving, when using mobile communications and new display technologies.

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Introduction

The increased use in mobile telecommunications (particularly mobile phone use) has led to a growth in research which indicates that the use of mobile communications are influencing how we go about our daily lives from both a social and an economic perspective (Love, 2005). A crucial aspect for consideration in the development of these services and applications is context of use, as people have to feel comfortable and also feel they are able to use their mobile phones in different settings. Context of use has been defined as “the circumstances in which the interactive product is expected to operate” (Preece, Rogers and Sharp, 2002, p.207). This includes the social, technical, organisational and physical environment. Another definition proposes two interpretations for the concept of context of use (Dourish, 2004). On one hand, the author suggests that it is a technical term offering system designers new ways to conceptualise the relationship between human action and the computer systems that support it. On the other hand, it is also a concept drawn from social science, drawing attention to certain aspects of the social environment. For example, the majority of calls being made by mobile phone users take place on the streets, public transport, shops and in restaurants (Wei and Leung, 1999). This has led to the phenomenon whereby mobile phones now occupy concurrent social spaces, spaces with norms of behaviour that sometimes conflict, such as the space of the mobile phone user, and the virtual space where the conversation takes place (Palen, Salzman and Youngs, 2000). This has resulted in a lively public debate about what is acceptable and unacceptable in relation to mobile phone use in public places and the development of a concept known as social usability. Social usability can be defined here as the environmental and social factors such as current location, expectations and interpretations of behaviour that influence people’s own behaviour and attitude towards the use of mobile technology in the social situation they are currently located (Love, 2005). An example of social usability is provided by (Ling, 2002). In this study, Ling found that people perceived mobile phone use in places such as restaurants as unacceptable, partly because people tend to talk louder than usual when using mobile phones and, as a result of this, individuals located near mobile phone users felt coerced into eavesdropping into their conversation. This seems to suggest that people believe there should be some form of ‘mobile etiquette’ for the use of mobile phones in public places. As the demand for location-based services increases, there is a concomitant need for designers and developers of mobile services and applications to be aware of the impact that context of use has on people’s perception of the usability of a device as they move around in dynamic mobile environments with changing needs and requirements. According to some researchers (AlShaali and Varshney, 2005), the usability aspects that should be considered in relation to mobile commerce include how information is organised and browsed by users using mobile devices such as mobile phones and Personal Digital Assistants (PDAs). It is essential that designers reduce information

User characteristics and performance with automated mobile phone systems

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complexity to accommodate the small screen size without any significant loss of content. The authors also point out that users often find it very difficult to find the information they are looking for in mobile applications and services. This can be due, in part, to service providers not providing adequate flexibility when it comes to the exchange of information between different platforms, such as downloading information from the internet on to a desktop PC and downloading similar information on to a mobile phone as you walk along the street. Other usability aspects that should be considered in the design of location-based mobile applications and services include interaction styles. For example, in a noisy street environment, it may not be convenient to try to converse with a speech-based mobile phone service due to recognition problems on the part of the system. In this case, the user should have the opportunity to switch to a different interaction mode such as text input or stylus entry. In addition, flexible interaction styles would also allow a user to interact with a mobile service with some privacy when it comes to input confidential information in a public location. Issues of privacy are one of the key areas of ongoing research work in the area of location-based services. For example, the results of one study propose a new mobile authentication scheme for mobile users, which takes into consideration aspects such as wireless network in a public setting by providing both user and system authentication procedures (Chang, Lee and Chang, 2006). This would improve users’ perception of the trustworthiness and reliability of a mobile service, key usability requirements for the successful uptake of any application or service. This approach may help address issues of security and privacy for both individuals and organisations that have been caused by the use of Radio Frequency Identification (RFID) tags to locate the whereabouts of individuals. For example, as an individual walks down a shopping precinct, they could be bombarded by messages to their mobile phone telling them of the latest sales offers. In another context, RFID could be used by employers to track the movements of their workers throughout the day. For a comprehensive review of the security and privacy issues raised by RFID use and possible countermeasures, see Shih, Lin and Lin (2005). From the above, it would appear that for mobile commerce to meet the needs of users and service providers alike, especially in the context of location-based services, there is the need for a location-based business service model. This model should focus on local business orientation and take into consideration the dynamic context of use of mobile commerce users (Wang and Wang, 2005). The study reported in this paper sought to explore the dynamic context of use by focussing specifically on the characteristics of the users and their effects on their perception of the usability of location-based mobile application. In relation to this, researchers have recognised the importance of analysing context of use and found that although many interactive systems exhibited high levels of usability during laboratory evaluations, this was the not the case when transferred to the real world (Whiteside, Bennett and Holtzblatt, 1988). However, there are considerable challenges involved with evaluating the usability of mobile systems, involving the many activities and demands that can occur simultaneously and randomly, which makes it difficult to model the interactions between the activities, domains of use and users (Maguire, 2001).The practical problems concerning observation, data collection and the limited means of controlling variables also exist (Kjeldskov and Skov, 2003). Despite these challenges, support exists for the added value of field-based testing of mobile systems

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M. Howell, S. Love and M. Turner

(Abowd and Mynatt, 2000; Brewster, 2002). These benefits have, however, recently been questioned (Kjeldskov et al., 2004). Another factor that needs to be considered is the impact of individual differences. The majority of user interfaces are designed with only a generic, ideal user in mind (Chen, Czerwinski and Macredie, 2000). However, the impact of individual differences (such as age, gender, cognitive ability and memory) on users’ performance and perception of the usability of a service or application have been well documented in human–computer interaction literature (Tun and Wingfield, 1997; Palmquist, 2001; Goodman, Brewster and Gray, 2004). As yet, there have been few studies published exploring the impact of individual differences on the context of use of mobile devices and applications. This is surprising given the fact that mobile devices are typically used in highly dynamic contexts, with other people occupying the users’ physical surroundings (Beck et al., 2003), and activities such as walking while operating the device placing heavy demands on both the working memory and visuo-spatial resources (Garden, Cornoldi and Logie, 2002). When a mobile phone is used in public, physical aspects of the environment that can impact on usability include visual distractions (e.g. real-world navigation to avoid other people in the street) and auditory information (e.g. traffic noise). In addition, the presence of other people, whether their actions interrupt the user or not and whether their presence affects the users’ actual or perceived ability to perform the task they are currently undertaking, may interact as social constraints on the interaction. An example of this can be seen in a study which found that personality characteristics such as extraversion and introversion had a significant effect on users’ attitude towards mobile phone use in public places such as cafes (Love and Kewley, 2005). Therefore, the main aim of the experiment reported in this paper was to investigate the degree to which individual differences may be used to predict the usability of a speech-based mobile phone service in both private and public locations. The reason why a speech-based mobile phone system was used is that although mobile phone usage is widespread, social norms concerning the appropriate and timely use of mobile phones in public locations have yet to be established, especially in relation to location-based services, and individuals are left to decide for themselves what constitutes considerate usage. A key aspect of this is how the individual perceives the functionality of the mobile service in the dynamic mobile context that they find themselves located, as this has significant consequences for mobile commerce. Therefore, with this in mind, the following hypothesis was proposed: Hypothesis 1. Individual differences such as age, gender, working memory, verbal ability, spatial ability, cognitive style, mobile phone, telephone and computing experience, and attitude towards mobile phone usage in public and private places will have a significant impact on users’ perception of the usability of an automated mobile phone service.

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Method

2.1 Prototype design In order to carry out this study, a mock mobile city guide service was constructed using a hierarchically structured menu system, consisting of five levels of dialogue prompts with


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36 information-containing nodes at the fifth level. The service provided information about city venues under categories such as ‘Nightlife’ and ‘Eating out’. The service was designed as a single webpage, with hyperlinks linking to sound files, and the experimenter activating the sound files requested by participants. In addition, participant interactions with the service were logged using TrueActive monitoring software (TrueActive Corporation, Kennewick, WA). On accessing the service for the first time, the user heard a welcome message at level 1 of the service, and had to navigate to level 5 in order to find the specific information they were looking for. After locating the information, users were either given the option of hearing venue location and contact details, or the option of saving these details to voicemail to be accessed later. A Wizard of Oz Methodology was used to simulate the functionality of a speech recognition engine. This technique is commonly used for the simulation of interfaces that are currently only partially built and therefore may not be fully functional. The basic idea behind this technique is that a ‘hidden’ experimenter [known as the wizard, named after the character in the book called the Wizard of Oz (Baum, 1900) takes on the role of the computer system in a simulated human–computer interaction (Fraser and Gilbert, 1991)]. Table 1 provides an overview of the service and an example of the dialogue from each level of the service. Table 1

Interface objects, menu options and dialogue for the service used in study

Level

Objects

Options

Example dialogue

1

Three options

One, two and three

Welcome to the telephone city guide service. There are three options available. For Nightlife select option 1, for Eating Out select option 2 and for Arts and Entertainment select option 3. Say 1, 2 or 3.

2

Two options

One and two

Option 1, Nightlife. There are two categories of Nightlife available. For Music select option 1, and for Drinking select option 2. Say 1 or 2.

3

Three options

One, two and three

Option 2, Drinking. There are three categories of Drinking available. For Pubs select option 1, for Wine Bars select option 2, and for Pre-Club Bars select option 3. Say 1, 2 or 3.

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Three options

One, two and three

Option 1, Pubs. There are three categories of Pubs available. For Pubs that close at 11 pm, select option 1, for Pubs that close at 12 midnight select option 2, and for Pubs that close at 1 am select option 3. Say 1, 2 or 3.

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City information No menu options

Option 2, The Pubs that close at 12 midnight are The Beach and The Quadrant.

2.2 Experimental design The experimental design was a 7 u 2 mixed factorial design with two within-subjects factors. The first within-subjects factor was location, which consisted of two levels: private and public. Participants were told that a private location would be considered as somewhere quiet, indoors, and where they were alone, for example, their bedroom, whereas a public location would be considered as somewhere outside their home and accessible to the public, for example, a café. In order to control the settings, an additional

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requirement was for users to be stationary and not directly engaged in other tasks. The final choice of location was determined by the participant, and so was effectively subjectively ‘private’ or ‘public’ to them. Table 2 below shows the actual locations used by participants in this study to make their mobile phone calls. Table 2

Percentage of calls for both private and public locations

Private location Location

Public location Frequency

Percentage

Location

Frequency

Percentage

Bedroom

86

51.2

Street

67

39.9

Lounge

64

38.1

Main road

16

9.5

Classroom

7

4.2

Corridor

11

6.5

Kitchen

6

3.6

Pedestrian zone

10

6.0

Car

3

1.8

Shopping mall

10

6.0

Office

2

1.2

Shopping street

9

5.4

Building foyer

8

4.8

Car park

8

4.8

Café

7

4.2

Park

5

3.0

Launderette

5

3.0

Bar

4

2.4

Supermarket

4

2.4

Train

4

2.4

The second within-subjects factor was the trial consisting of seven testing sessions conducted at weekly intervals. Repeated testing with the service over seven weeks was conducted in order to establish a reliable estimate of system performance. Each participant completed a control trial using the service in a private location, and then six experimental trials using the service. On experimental trials, participants were assigned to one of two locations (private or public), which alternated weekly. The trial location was counterbalanced across trials and between experimental groups, and although factors such as background noise could not be controlled, it may be assumed that such factors might balance out across weeks and conditions such that their net effect would be minimised across any observed main statistical effects (Robson, 2002).

2.3 Participants Fifty-six people took part in the study, consisting of undergraduate students aged between 18 and 36 (mean age 21), of whom 14 were male and 42 were female. The participants were matched for age and gender, and were divided equally between the two experimental groups. All participants were recruited from the participant pool at the University of Portsmouth psychology department.


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2.4 Data collection Attitude data, using the measures described in Table 3 later, was collected after each trial using a Likert scale questionnaire. Table 3

Attitude measures collected for the study

System response accuracy

Refers to how accurately the service recognised user input, and provided a response that matched the users expectations.

Likeability

Refers to whether the user found the service useful and pleasant, and whether they would choose to use it again.

Cognitive demand

Refers to the users opinion about the amount of effort necessary to use the service, and how they felt as a result of expending this effort.

Annoyance

Refers to the degree to which the user found the service repetitive, boring, irritating or frustrating.

Habitability

Refers to whether the users conceptual model of the service was sufficient to inform them of what to do and what the service was doing.

Speed

Refers to how quickly the user perceived the service as responding to their input, and to the overall duration of the interaction.

The following performance measures were also collected during the study. See Table 4: Table 4

Performance measures collected

Time

The time to complete a task was calculated as a percentage of the total length of the service dialogue (a lower score was taken to indicate a better performance).

Prompt interrupts

The number of times the dialogue was interrupted with a response was calculated as a percentage of the total number of dialogue nodes used (a higher score was taken to indicate a better performance).

Nodes

The number of dialogue nodes used to complete a task was calculated as a percentage of the optimum number – the shortest route (a lower score was taken to indicate a better performance).

A technographic questionnaire was used to gather data about age, gender and previous mobile phone, telephone and computing experience. The AH4 test (Heim, 1970) was used to measure verbal and spatial ability. The Cognitive Styles Analysis (Riding, 1991) was used to measure cognitive style. The Baddeley working memory test (Baddeley, 1986) was used to measure working memory, and a questionnaire was used to gather data about participants’ attitudes towards mobile phone usage in public places. An example of the type of questions asked in relation to this is given below in Figure 1. Figure 1

Example of the questionnaire format used in study

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2.5 Procedure In a preliminary session, participants completed the technographic questionnaire, the AH4 test, the Cognitive Styles Analysis, the working memory test and the mobile phone attitude questionnaire. Participants were then given the experimental materials needed to complete the experiment: a participant information form outlining the experimental procedure; a response scale to refer to when verbally responding to the Likert questionnaire statements; a practice task sheet; and a task sheet containing the task to be completed during each of the seven trials. Each task required the participants to find a specific piece of information, for example, ‘Find the names of two pubs that close at 1 am and then exit the service’. The tasks became progressively more difficult across the trials, with later tasks comprising two sub-tasks that required participants to go backwards as well as forwards through the service structure, for example, ‘Find the names of two midrange Italian restaurants, and then find the names of two pop concerts and then exit the service’. All participants were informed that they could interrupt the service with a response at any time. For the next part of the experiment, participants were called on their mobile phones and were required to attempt one task each week using the city guide service. The experimenter activated the service messages and prompts, and the participants responded verbally with their menu selections. On completion of each testing session, participants verbally responded to the Likert questionnaire statements, and were asked to identify where they were when they used the service, about any visual or auditory distractions they experienced and whether they perceived these distractions as negatively affecting their performance. This procedure was repeated over the following six weeks at approximately weekly intervals. After the final testing session, participants were interviewed regarding their experience of using the service.

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Results

To investigate the association between the measured individual differences and context of use, multiple regression analyses were performed. In order to carry out this analysis, the performance and attitude measures were used to produce a composite measure of usability: effectiveness, efficiency and satisfaction. The performance measures of interest were performance in private locations and performance in public locations. The three performance measures analysed for this experiment (time, prompts and nodes) were recorded using different scales with different ranges. In order to reduce these three measures into one performance mean score for private and one for public, it was necessary to transform the measures onto a common scale in order to make them comparable. Individual participant raw scores on each measure were therefore converted into z-scores, and an overall performance mean for each participant was calculated by taking a mean of the three z-scores for each context of use. The overall mean score for performance in private locations for the service will, therefore, be referred to as private performance; the overall mean score for performance in public locations for the service will be referred to as public performance. In order to investigate the association between the measured individual differences and performance in private and public locations for the automated mobile city guide service, two multiple regression analyses were performed. The criterion variables used in successive analyses


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were the private performance score and the public performance score. The predictor variables used for each multiple regression were age, gender, previous mobile phone experience, previous fixed-line telephone experience, previous computing experience, working memory, verbal ability, spatial ability, cognitive style and attitude towards mobile phone usage in public. For private performance, the overall model was not significant [F(10,17) = 1.57; p = 0.20], and the adjusted R2 value of 0.18 showed that the predictor variables accounted for 18% of the variation in the private performance score. None of the individual predictor variables were significantly related to the performance score. Table 5 shows the significance levels for all of the predictor variables. Table 5

Predictors of private performance for the service

Predictor variables Age

Standardised Beta Coefficient 0.39

Gender

0.002

t 1.83

Significance 0.09

0.01

0.99

0.19

0.83

0.42

0.07

0.25

0.81

Spatial ability

0.47

1.45

0.17

Cognitive style

0.29

1.09

0.29

0.15

0.70

0.50

Working memory Verbal ability

Mobile experience Telephone experience

0.02

0.10

0.93

Computing experience

0.09

0.41

0.69

Mean mobile attitude

0.01

0.07

0.95

For public performance, the overall model was not significant [F(10,17) = 2.39; p = 0.06], and the adjusted R2 value of 0.34 showed that the predictor variables accounted for 34% of the variation in the public performance score. Gender was found to be a significant predictor of standard public performance (p = 0.03), but the remaining individual predictor variables did not account for a significant variance in the public performance score. Table 6 shows the significance levels for all of the predictor variables. Table 6

Predictors of public performance for the service

Predictor variables

Standardised Beta Coefficient

t

Significance

0.11

0.60

0.56

Gender

–0.45

–2.31

0.03

Working memory

–0.20

–0.98

0.34

Verbal ability

–0.12

–0.47

0.64

Spatial ability

0.60

2.08

0.05

Cognitive style

–0.09

–0.39

0.70

0.07

0.38

0.71

Age

Mobile experience Telephone experience

0.29

1.44

0.17


–0.08

–0.39

0.70


0.25

1.36

0.19

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In order to investigate the association between the attitude measures and the measured individual differences, a further set of multiple regression analyses were performed. The attitude measures of interest were attitude in private locations and attitude in public locations for the service. These scores were calculated by taking a mean of the six attitude measures for each participant, resulting in a score of between 1 and 7. The overall mean score for attitude towards the service in private locations will be referred to as private attitude; the overall mean score for attitude towards the service in public locations will be referred to as public attitude. Two multiple regression analyses were performed. The criterion variables used in successive analyses were the private attitude score and the public attitude score. The predictor variables used for each multiple regression were the same as those used for the multiple regressions performed for the performance measures. For private attitude, the overall model was significant [F(10,17) = 3.07; p = 0.02), and the adjusted R2 value of 0.43 showed that the predictor variables accounted for 43% of the variation in the attitude score. Gender (p = 0.01), working memory (p = 0.04) and previous telephone experience (p = 0.03) were found to be significant predictors of attitude, but the remaining predictor variables did not account for a significant variance in the standard private attitude score. Table 7 shows the significance levels for all of the predictor variables. Table 7

Predictors of private attitude for the service

Predictor variables Age Gender Working memory


t

Significance

2.07

0.05

0.51

2.86

0.01

0.43

2.24

0.04

0.37

Verbal ability

0.13

0.58

0.57

Spatial ability

0.40

1.51

0.15

Cognitive style

0.30

1.35

0.19

Mobile experience

0.02

0.13

0.90

Telephone experience

0.43

2.32

0.03


0.05

0.29

0.78


0.02

0.09

0.93

For public attitude, the overall model was significant [F(10,17) = 5.35; p < 0.01], and the adjusted R2 value of 0.62 showed that the predictor variables accounted for 62% of the variation in the attitude score. Age (p = 0.03), gender (p < 0.01), working memory (p = 0.01) and previous telephone experience (p = 0.01) were found to be significant predictors of attitude, but the remaining predictor variables did not account for a significant variance in the public attitude score. Table 8 shows the significance levels for all of the predictor variables. Table 9 provides a summary of the individual differences that were found to be significant predictors of performance with, and attitude towards the standard service and the office filing system service, in private locations and in public locations.

User characteristics and performance with automated mobile phone systems 11 Table 8

Predictors of public attitude for the service

Predictor variables


t

Significance

Age

0.35

2.42

0.03

Gender

0.52

3.55

0.01

–0.48

–3.07

0.01

0.17

0.91

0.38

Working memory Verbal ability Spatial ability

0.26

1.17

0.26

Cognitive style

–0.17

–0.92

0.37

Mobile experience

0.06

0.41

0.69

Telephone experience

–0.44

–2.92

0.01


–0.19

–1.25

0.23


0.01

0.05

0.96

Table 9

Summary of significant predictors of performance and attitude

Location

Attitude

Performance

Private

Gender, working memory, telephone experience

No predictors

Public

Age, gender, working memory, telephone experience

Gender

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Conclusion and discussion

The extent to which participants’ individual differences could be used to predict both attitudes towards and performance with the services was affected by the service and the location in which the service was used. In terms of the service, there were a range of predictor variables. Therefore, the results of this study extend support to the experimental hypothesis proposed for this study. Gender, working memory and previous telephone experience were predictors of attitude towards the mobile city guide service in both private and public locations. Female participants perceived the service more positively, as did participants with low working memory and participants who were infrequent users of fixed-line telephones. These results suggest a persistent trend for male participants to dislike the service, regardless of location. The association between working memory and attitude was unexpected, as it may have been predicted that participants with good working memory would have perceived the service more positively due to their enhanced ability to remember the service messages and prompts. A possible explanation may be that participants with high working memory found the service easy to use and found the menu options easy to remember, but as a result of this focussed on other aspects of the service, such as the actual user experience. They may not have liked the service or found it tedious to use and therefore, despite having no difficulties completing the tasks with the service, perceived it negatively. The results for previous telephone experience may relate to participant’s experience with telephone-based interaction, and with automated phone services. Participants who use telephones infrequently may lack confidence in using the telephone and prefer a rigid structured style of interaction that is task-oriented, which matches the type of limited interaction offered by the service.

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The only location-specific difference for the attitude measures was that age emerged as a predictor of improved perceptions towards the service in public locations, but was not a predictor of attitude in private locations. However, due to the narrow spread of ages across the participant sample, the effect of age will not be discussed as an important predictor. There were no significant predictors of performance with the service in private locations, but gender was a predictor of performance in public locations, with males performing better than females. This suggests that context of use interacts with participant gender to affect performance with the service. When the service was used in private, there was no gender-related association between the attitude and performance measures, and the poorer perceptions towards the service by males were not associated with any subsequent performance detriment. However, in public there appears to have been an association between the effects of gender and attitude and performance. When used in public, males perceived the service more negatively than females, but their performance was better than female performance. That this association existed in public, but not in private, may be the result of the additional physical distractions and social pressures when interacting in public, which male participants may have been better able to effectively deal with, leading to better levels of performance. Overall, the results indicate that individual differences, such as gender, do have an impact on individual’s use of mobile phones in certain contexts of use. This could be due, in part, to the idea of Beck et al. (2003) that as mobile phones is used in dynamic contexts (such as walking down the street), other people occupy the user’s physical surroundings and this feeling of being crowded (even temporarily) could ‘put off’ certain people from using their phone. In a study looking at social space and ATM use (Kaya and Erkip, 1999), the authors found that females preferred to have a greater distance between themselves and other people (especially males) in comparison to men. Therefore, the presence of others in close proximity could be seen by the users as placing a social constraint on the interaction in this case (perhaps due to security concerns). In relation to mobile phone use, this feeling of social constraint could also be due to the perceived nature of the call being made or received (e.g. private call from a loved one). In fact, anecdotal evidence obtained from discussions with participants at the end of the study reported in this paper suggests that some females were concerned with issues of privacy when using a location-based service such as the mobile city guide, especially if any of the services required a monetary transaction (e.g. booking a ticket for a concert). Therefore, this conflict between physical space and conversational space provides a clear example of one of the ways that public use of mobile phones could be perceived negatively depending on the individual characteristics of the mobile phone user. One of the limitations of this study was the fact that it was a simulated service that participants were asked to use. This resulted in no performance degradation due to wireless connectivity problems. As users of wireless technology know, this is often not the case and one can envisage that poor connectivity in a city centre would have a serious impact on users’ perception of the usability of a location-based service such as the mobile city guide service. An area for future research in relation to this would be how to ensure that the user knows where they are in an interaction if they loose connectivity and are then re-connected. Another limitation and area for future research will be to investigate the attitude and performance of people in older age groups when using mobile phone services in public locations. This is an important factor to consider, as research evidence indicates that although this sector of the population is growing, older people are still often

User characteristics and performance with automated mobile phone systems 13 excluded in the design considerations of product designers in the technological sector (Czaja and Lee, 2003). The major contribution of this paper to this area of research is that it draws attention to the fact that as the number of 3G services such as location-based services increases, there is a need to understand in what way individual differences have an impact on the way people use mobile technology in various contexts of use. This requires designers of applications and services to investigate how these user characteristics interact with the dynamic physical and social environment in which the devices will be used. Implicitly related to this is the concomitant need to develop robust research methodologies that will allow us to fully investigate the interaction between mobile technology and context of use. These issues are important to consider as mobile service providers focus more and more on personalising the services that they offer to their customers. In this case, it is important that mobile commerce business models can segment their market to ensure that they can package services which are personalised to specific groups of users based on salient individual differences. This is essential for the success of contextual marketing in mobile commerce (Lee and Jun, 2007). In addition, as part of this location-based business service model, it is essential that quality-of-service parameters (Seth, Gupta and Momaya, 2007) are also included to ensure that business always takes into consideration the needs of users in the design of new mobile services and applications. The failure of a business to adopt an efficient and effective mobile commerce business will see them lose out to their competitors as customers become increasingly aware of the quality of service provided elsewhere.

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