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Usability evaluation of adaptive features in smartphones
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Procedia Computer Science 112 (2017) 2185–2194
International Conference on Knowledge Based and Intelligent Information and Engineering International Conference on Knowledge Based and 2017, Intelligent Information Systems, KES2017, 6-8 September Marseille, France and Engineering Systems, KES2017, 6-8 September 2017, Marseille, France
Usability evaluation of adaptive features in smartphones Usability evaluation of adaptive features in smartphones Muhammad Waseem Iqbala , Nadeem Ahmadaa, Syed Khuram Shahzadaa a Muhammad Waseem Iqbal , Nadeem Ahmad , Syed Khuram Shahzad CS & IT Department, 1-Km Defence Road, The University of Lahore, Lahore 54000, Pakistan CS & IT Department, 1-Km Defence Road, The University of Lahore, Lahore 54000, Pakistan
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Abstract Abstract This paper presents a usability study that aims to analyze effectiveness, efficiency, and satisfaction based on the existing adaptive This paper a usability study thatfeatures aims toinclude analyze screen effectiveness, and satisfaction based on theand existing adaptive features in presents smartphones. The adaptive rotation,efficiency, voice commands, LED notifications kid mode for features in smartphones. The adaptive features screen commands,task LED notifications anda kid modetime for android and iOS platforms. The effectiveness andinclude efficiency arerotation, measuredvoice by considering completion within specific android and iOS platforms. Thethrough effectiveness efficiency are measured by considering taskexperiment completioniswithin a specific while satisfaction is measured After and Scenario Questionnaire (ASQ) technique. The carried out withtime the while satisfaction is participants. measured through Aftershows Scenario Questionnaire technique. experiment carried with the involvement of 128 The study interesting patterns(ASQ) in usability whereThe screen rotationisand voiceout commands involvement of 128 participants. The LED studynotifications shows interesting patterns feature in usability where screen and voice commands resulted in lower usability. Whereas, is a dominant having almost 88%rotation effectiveness comparing to a resulted in lower usability. Finally, Whereas,the LED notifications is athe dominant having 88% after effectiveness comparing to a non-adaptive environment. study suggests that adaptivefeature features mustalmost be applied careful analysis of user non-adaptive environment. Finally, the study suggests that the adaptive features must be applied after careful analysis of user tasks and context. tasks and context. © 2017 The Authors. Published by Elsevier B.V. © 2017 The Authors. Published by Elsevier B.V. © 2017 The under Authors. Published by B.V. Peer-review responsibility of Elsevier KES International International. Peer-review under responsibility of KES Peer-review under responsibility of KES International. Keywords: Usability patterns; adaptive mobile feature; smartphone; user experience; user satisfaction; user interface; Keywords: Usability patterns; adaptive mobile feature; smartphone; user experience; user satisfaction; user interface;
1. Introduction 1. Introduction Smartphones have become popular and most used devices in the world. Users carry them around all the time to Smartphones have become popular most used It devices in the that world. Users them 6.3 around all smartphone the time to fulfill the tasks according to their usageand requirements. is predicted there will carry be around billion fulfill the tasks according to their usage requirements. It is predicted that there will be around 6.3 billion smartphone subscribers available in 2021 [1]. Currently, the most prominent operating systems (OS) are Android (Google) and subscribers available in 2021 [1].applications Currently, (Apps) the mostwith prominent systems Android (Google) and iPhone (Apple) providing many multipleoperating features [2]. These(OS) twoare mobile operating systems iPhone (Apple) providing many applications (Apps) with multiple features [2]. These two mobile operating systems hold more than 98% of worldwide market share. As per fourth quarter of 2016, Android was leading with 80.7% hold more than 98% of worldwide market share. As per fourth quarter of 2016, Android was leading with 80.7%
Corresponding author. Tel.: +92 321 7346 428 Corresponding author. Tel.: +92 321 7346 428 E-mail address: [email protected] E-mail address: [email protected] 1877-0509 © 2017 The Authors. Published by Elsevier B.V. Peer-review©under KES International. 1877-0509 2017responsibility The Authors. of Published by Elsevier B.V. Peer-review under responsibility of KES International.
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share while iOS was the runner up with 17.7% market share 1. The user interfaces (UIs) of many Apps still have usability issues of complexity and flexibility. The physical constraints of devices have not only impact on screen size and interaction mechanism but it also contributes to several usability issues for mobile applications. Adaptive user interfaces (AUIs) provide adaptation by dealing “what information to be presented”, “how to present this information" and “how to interact with presented information” of the UI to address these usability issues [3]. Human computer interaction (HCI) is the study which provides interaction between humans and computers especially the user response time play significant role in estimation of user performance [4]. The interactive design is concerned with the required context by providing learnability, safety, utilization, efficiency, effectiveness and satisfaction to the users. The actual purpose of usability is setting the design directions to assist the user in overcoming their shortcomings. The usability metrics are created to track progress between releases, find out the competitive position, evaluate before launch and create future plans [5]. There are three type of usability issues (1) technical usability discusses the network connectivity, limited screen size and battery life of mobile devices (2) environmental usability refers to mental abilities, psychological constraints, mobility, noise, temperature and light conditions for users and (3) social usability states the issues of personalization, privacy, acceptance, comfort and adoption [6]. This study discusses the usability issues of adaptive features available in all smartphone devices (e.g. screen rotation, voice commands, LED notifications and kid mode). The usability issues are identified for automatic screen rotation that user may browse in portrait or landscape directions while the impact of incorrect orientation when users change their posture is also analyzed. It may effect on speed, efficiency and usability of users [7]. Voice commands are automatically executed without the hassles of finding and pointing on screen objects. The most well-known Apps are Apple’s Siri and Google Now that allows users to perform task by providing vocal instructions [8] [9] [10]. With the increasing number of smartphones amongst people, there is a dire need of usability study regarding alerts and notifications. The notifications are becoming most important feature to engage the users without caring their daily routine. Constant interruptions may cause inattention and hyperactivity in digitally connected society [11]. In contrast, some people feel that notifications are valuable and do not interrupt their daily life [12]. The kid mode/baby mode is an excellent feature in which children are restricted to use some applications and phone services. It is parental mode that prevents the child from accessing standard interface without password or pattern [13] [14]. AUIs are defined as systems that observe user status (context, job) to adapt variety of displays (size, visualization) and actions (implementation, interaction) to attain the user required goals. Basically, in AUIs, the interface should be adaptive according to the user rather than the users are being adaptive according to the interface [15]. AUIs provide the platform to improve the accessibility of interactive systems with large potential. Personalized user interfaces can be adaptive (system driven) or adaptable (user driven) according to the individual user’s needs and habits [16] [17]. AUI allows system to initiate changes and provides automatic responses according to the user’s context [18] [19]. While in adaptable model, the users had to map their desires, interests and demands manually with system provided interface. Any user interface that changes according to its context called context aware interface which may be adaptive or adaptable [20] [21] [22]. By adaptation, the application can accommodate the specific needs and limitations of the user [23]. In this study, there are three classes of adaptation including information, visualization and user interface for mobile adaptive features have been identified. The adaptation is influenced by the following four variables:
User based adaptation denotes to adapt the user’s preferences. Task based adaptation ensures the relevance of current task with user’s recent activity. System based adaptation refers to adjust devices with different capabilities and variables. Context based adaptation states according to the user’s current context.
In the larger perspective, this study is part of User Centered Design (UCD) approach for adaptive user interface design and development. It provides us adaptation guidelines suitable to the users and their context for various
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Apps. Furthermore, the aim of this study is to evaluate the effectiveness, efficiency and satisfaction as usability factors in adaptive environment provided by smartphones. The paper is organized in four sections where section 2 describes the background and literature review. Section 3 presents the research methodology while section 4 states the results and discussion. At the end conclusion of the study and future work is presented. 2. Background and Literature Review The term usability was used firstly in early 1980s whose primary goal is to provide guidance to product developers for the user friendly Apps development. The usability test can be performed formally or informally in specified environment [24]. Usability of smartphone Apps has become an important concern but the trade-offs by the application development have created dissatisfaction in currently available UIs [25]. The usability study usability of mobile phone Apps, conducted by Nielsen Norman group in 2009 [3] represents 59% successful completion of the task while three usability issues were identified including efficiency, screen size and text insertion. The menthol project [1] analyzed the effects of user age and gender on smartphone usage. In sample of 30,677 participants the results of average usage time for each gender concluded that female use smartphone for longer time. Another usability study in terms of response time is conducted to find out the solution of two types of “CAPTCHA” (i.e. text-based and text-number based). There were 230 internet users for experimentation on the basis of different features like age, years of internet usage, level of education and response time. By using Apriori algorithm the values of these features were used to determine the response time [26]. The studies discussed earlier are highlighting not only usability issues but also provides the impact of user context in smartphone usage. User centered design (UCD) is a methodology that focuses on the high usability and low cost products for understanding of needs, tasks, environments, preferences and limitations in user’s context. The major difference from other design philosophies is that the UCD tries to optimize the interfaces to enhance the usability and satisfaction of users. A study was conducted by Ahmed in 2014 [27] using the philosophy of UCD for illiterate and deaf people to analyze the effects and usability of online assistance. The percentage of completed tasks increased from 52% to 94%. System usability scale (SUS) specifies that their average subjective usability score enhanced from 39 to 80. The proposed interfaces provide better utility for low-literate users by translating contents in their own language. For effective usability of mobile interface, the intended tasks should be mapped with user’s mental model. The AUIs can be directly used to improve the usability and satisfaction level of user [14]. Many Apps suffer from usability issues due to their non-contextual UIs. AUIs try to enhance the usability of such Apps by catering user needs [15]. Hartman stated in 2009 [28] that the Microsoft Office hidden smart menus of prior versions (Pre-2007 version) caused many usability issues. But in its revised interface (2007-version) the menus contain predefined adaptive parts (e.g. display the most recent used items) which appear to have more beneficial for users. A mathematical e-business App “AdaptiveCalc” was developed for Android OS. The result for perfect calculations for AUI was 83.3% while 79.9% for non-AUI [20]. A mobile-map based application “MediaMaps” [3] was developed to adapt to the users according to their context. The information accuracy was calculated as 76.78% on average while user satisfaction and visualization adaptation ratio was very positive. Hanmansetty illustrated in 2004 [19] that there is also a need to develop adaptive user interface toolkits for Apps that require context aware and adaptive user interface generation to improve the usability of interfaces as well as designers. Lavie and Mayer experienced in 2010 [14] that the AUI in the context of an “In-Vehicle Telematic System” by taking twenty-four participants. They drove the cars at fixed speed approximately 30km/h and the lane position had only one level of curvature. Results indicated that the drivers using AUI were more efficient and accurate. An online survey was conducted by Cheng et al. [6] to understand the incorrect and common screen rotation usability. There were 513 smartphone and tablet users from which 91 % experienced incorrect viewing of auto rotation while 42% faced problems several times a week or more. Another study was conducted by Carlini et al. [7] for the usability of voice commands using black box and white box models/attacks. The study claimed that a verbal challenge response protocol and a machine learning approach can detect attacks with 99.8% accuracy. For mobile notifications a study “Do not disturb challenge” was performed on 12 participants. The results for
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notification checking concluded that 11 of the 12 participants checked their new notifications all the times. However, only 4 of 12 participants were agreed that the notifications are necessary part of life [11]. The study used $N method (in high accuracy level 95% to 99%) for gesture recognition in adults and kids. It showed that adults use 79% mobile phone daily but kid’s usage rate is 63%. During the completion of tasks, the children missed 50% targets more than adults and felt trouble in performing smaller tasks [13]. The above mentioned studies are providing the discussion about different models related to the adaptivity in mobile applications but these adaptive models are not specifically focusing on user context. Adaptivity can be an impairment for usability if applied in improper context. Thus, there is a need to study the usability of adaptive features in different user-contexts. In this research we are analyzing the usability of most used smartphone adaptive features for different tasks and user groups. 3. Methodology 3.1. Selection of frequent adaptive mobile features Currently, mobile devices provide variety of Apps along with multiple adaptive features [29]. The adaptivity provides significant usability to overcome the different existing issues such that easiness, information overloading, screen disorder, task completion support and limited interaction mechanism. The detailed study of 100 mostly used Apps provide us the exact vision to attain the goal. These Apps reassembled into seven categories e.g. (i) communication (ii) social networking (iii) entertainment (iv) news and information (v) utility, (vi) service provider and (vii) browsing. However, unavailable, inaccessible, desktop and windows mobile based 45 Apps excluded. Only 55 Apps selected which are freely available, accessible and most frequently used amongst users. Furthermore, 22 features are selected carefully and cross matched with supporting mobile OS to find out remarkable patterns. Different patterns are selected on the basis of available, not available, partially available and not applicable (Appendix-A). Finally, four features including screen rotation, voice commands, LED notifications and kid mode are selected due to their rich pattern-ability. 3.2. Sampling and Experimentation In this study, participants were mobile phone users with at least one year of experience. A pre-questionnaire was developed to find out the relevant participants for best user experimentation. Initially, 185 participants were found through questionnaire in which 39 users were discarded in first step due to less than one-year experience of smartphone usage. Some participants were excluded due to other reasons, specifically 9 users voluntarily not willing, 2 in vision problem and 7 unable to understand the adaptive features. Remaining 128 participants were selected for experiment and divided into four groups. Each group was assigned a specific task comprised of 32 participants in equal gender ratio. Furthermore, all tasks were performed in two sessions with and without adaptive features. The participants attended an introductory demonstration in the lab for both sessions separately for each task (excluding kid mode). They were demonstrated about the significance and measures of assigned tasks briefly. The ages of participants for first three groups ranged from 21 to 40 years and for kid mode ranged from 4 to 7 years. The detail of each task assigned to individual group is given as below: Group A: Task (Screen rotation): In this task a paragraph of 300 words was given to the users for typing. The aim was to examine whether the activity of screen rotation on and off being performed may affect the effectiveness, efficiency and satisfaction of the user. The maximum time allocated for this task was thirty minutes considering average and worst case typing speed. The experiment was observed very carefully by maintaining their record. The average time for task completion was 28.8 minutes. Group B: Task (Voice commands): Different seven commands including (i) make a call to any contact (ii) send “Hi” message to any contact (iii) play song (iv) open the gallery (v) set alarm for tomorrow at 11am (vi) turn on/off airplane mode and (vii) open calendar were allocated to users for experiment. Users were directed to perform these commands by enabling and disabling their mobile phone features. The allocated time was six minutes and calculated according to the nature of tasks that includes searching and selection of contents from the
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device. The task completion time is noted regarding the execution of each command. Average task completion time was 4.88 minutes. Group C: Task (LED notifications): Selected participants were requested to switch off their notifications for four hours consecutively with their consent. The users were interviewed to enlist their feelings particularly missing of important, urgent, delayed and emergency notifications. The received reading from the participants provided significant insight regarding LED notifications. Group D: Task (Kid mode): This experiment was managed in a school where all participants were children. The kids were directed to perform three activities such as (i) play any video (ii) play any game and (iii) make drawing of car in kid mode which was already activated. They performed enthusiastically in kid mode according to their desires. While in standard interface mode of smartphones, the kids were not performing the same task easily and anxiously. All kids were observed very keenly while using smartphone for forty minutes. The tasks were planned and executed for a class duration in the school. The average task completion time was 35.6 minutes. Table 1 shows the actual picture of groups, tasks, sub-tasks, participants, time and post task evaluation technique. Table 1 Sample groups and tasks for experiment. Group
Tasks
Sub-tasks
Group A Group B Group C Group D
Screen Rotation Voice Commands LED Notifications Kid mode
300 words paragraph typing Perform 7 different commands Dependent on notifications Perform 3 different activities
Total
Total Time in
Post Task
Participants
Minutes
Evaluation
32 32 32 32
30 06 240 40
ASQ ASQ ASQ -----
3.3. Usability Evaluation There are three parameters to measure the performance of usability such that effectiveness, efficiency and satisfaction [4]. ISO 9241-11 standard is used to measure the effectiveness and efficiency [30] whereas After Scenario Questionnaire (ASQ) is chosen for the post task evaluation to measure the user’s satisfaction. Errors may be unintended actions, slips, mistakes or omissions that a user makes while attempting a task [31]. Effectiveness is the amount of goals to be achieved and it is measured as:
Effectiven ess
Total number of tasks completed successfully * 100 Total number of tasks undertaken
(1)
The resources such as time, money or mental efforts that have to be extended to achieve the intended goals; called efficiency and can be measured as:
Time based Efficiency =
R
N
n ij
J 1
i 1
t ij
NR
(2)
Where N = The total number of tasks (goals) R = The number of users nij = The result of task i by user j; if the user successfully completes the task, then N ij = 1, if not, then Nij = 0 tij = The time spent by user j to complete task i. If the task is not successfully completed, then time is measured till the moment the user quits the task Satisfaction is measured by the amount to which user finds the use of product acceptable. Usability is dependent on the context of use and on the specific circumstances in which a product is used. The context of use consists of user’s task, hardware, software and material. There are many post task evaluation techniques available (e.g. SEQ, UME, SMEQ) [23] but in this study the satisfaction is measured through ASQ technique. The ASQ is particularly a
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short questionnaire which takes little time, easy to understand and tremendous practical considerations of participants for usability studies. It contains three questions of seven points scale (strongly disagree=1, disagree=2, somewhat disagree=3, neither agree nor disagree=4, somewhat agree=5, agree=6, strongly agree=7). with important aspects of user satisfaction with system usability. The first question shows the aspect of ease in task completion, second question provides the aspect of time to complete a task while third question analyzes the satisfaction level on capability of support information [32]. 4. Results and Discussion The study reflects that AUIs can be used to address mobile application’s usability issues. The effectiveness, efficiency and satisfaction have been calculated for four commonly used adaptive features including screen rotation, voice commands, LED notifications and kid mode. The results are presented by organizing following three usability parameters. 4.1. Effectiveness The results in Figure 1, shows the comparison of effectiveness for adaptive and non-adaptive environment considering each adaptive feature separately.
Figure 1 Effectiveness comparison chart for adaptive and non-adaptive features.
The effectiveness in the usability of screen rotation feature demonstrated that the overall average for adaptive environment is much lower. It shows a substantial difference between adaptive (56%) and non-adaptive (81%) effectiveness. With screen rotation feature, the overall gap of effectiveness measures based on gender is also considerable where female group showed lower effectiveness compared to male group. Participants felt uncomfortable while using screen rotation feature due to readjustment of smartphone keyboard. Likewise, for voice commands feature the effectiveness of female participants showed clear difference about 25% for both environments. Overall the non-adaptive environment is established high effectiveness (88%) than adaptive environment (69%). Although, the voice commands were performed without any disturbance; recognition of accent decreased its effectiveness. The LED notifications feature indicated that the effectiveness is raised in adaptive environment where male and female participants conceived about 88% participation. The significant difference for overall effectiveness is reported around 31% in both environments. It is also illustrated that participants were conscious and aware about the importance of notifications in daily life. Similarly, the calculated effectiveness is more for male and female in kid mode feature for adaptive environment. The overall effectiveness in kid mode shows major difference between adaptive and non-adaptive effectiveness (i.e. 81%=adaptive, 53%=non-adaptive). Children were excited and more energetic while using kid mode which is an excellent restricted parental feature. 4.2. Efficiency The results in Figure 2, shows that the screen rotation mode does not affect much on the efficiency of the user. Overall efficiency is low in usability of adaptive and non-adaptive screen rotation environment for genders. The
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efficiency of female participants recorded very low which is around 23% for adaptive while 41% for non-adaptive environment. It is experienced that users were inefficient while using screen rotation feature due to the adjustment of fingers on expanded keyboard in horizontal view. However, the overall efficiency in voice commands feature is 89% for adaptive environment due to easy, small and entertaining sub-tasks. The male participants do not have big difference for adaptive and non-adaptive environment. Likewise, the efficiency shows the major difference in the comparison of other features.
Figure 2 Efficiency comparison chart for adaptive and non-adaptive features.
The huge difference is observed in LED notifications feature for overall efficiency of both environments (i.e. adaptive=89%, non-adaptive=26%). The efficiency in adaptive environment is perceived very well for female participants. In kid mode feature, the overall efficiency is high in adaptive environment while gender difference is also clearly remarked. The kids were very excited during the experimental study due to their adaptive behavior. The graph indicates that the efficiency of adaptive environment is higher for voice commands, LED notifications and kid mode features. The screen rotation effects due to the nature, efficiency, usability and adaptability of task. 4.3. Satisfaction Figure 3 shows the usability comparison in terms of user satisfaction for adaptive and non-adaptive environments. The evaluation had been taken through ASQ for screen rotation, voice commands and LED notifications to measure the satisfaction of participants. The kid mode was exempted from ASQ due to its limited understandability in children.
Figure 3 Usability comparison chart for adaptive and non-adaptive features.
The overall satisfaction in the usability of screen rotation feature for non-adaptive environment is around 5.8; which is greater than adaptive environment (around 4.1). Female participants showed more dissatisfaction than male in screen rotation mode. It is also observed that the participants were feeling more comfortable to type the text with the deactivation of screen rotation mode. Likewise, the voice commands feature contains greater satisfaction level for adaptive environment. The male participants showed 5.6 satisfaction level whereas the same measures for female participants is around 6. Similarly, the LED notifications feature provides significantly greater satisfaction level for
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adaptive environment. The satisfaction level in adaptive environment for male and female participants is 6.3 and 6.6 respectively. The participants felt more comfortable and satisfied throughout the user experimentation in adaptive environment for voice command and LED notification feature. In voice commands feature both male and female satisfaction level is higher for adaptive environment due to easy and automatic usability of tasks. Moreover, LED notifications satisfaction level is also greater in adaptive environment because the user became habitual with this feature in daily routine. Conclusion and Future work In this paper we looked at the usability of adaptive features provided by the vendors in smartphones. We evaluated these adaptive features based on the effectiveness, efficiency and satisfaction. We have followed the user centered design (UCD) to analyze the usability of adaptive mobile features. In Screen rotation mode during typing the adaptive feature is 25% less effective as the user preferred to type keywords in portrait mode by using fingers of single hand. Although the adaptive feature of voice commands was almost 6% more efficient but astonishingly it was 19% less effective while interacting with user. It is found that the reason behind less effectiveness is difficulty in recognition of accent of most users. The LED notifications adaptive feature is dominantly leading in better usability with almost 88% effectiveness and 89 % efficiency. In case of kids the adaptive environment is 28% more effective and efficient than normal mode. Though the calculation of satisfaction level in kids was not possible due to their limited understanding of ASQ but they completed their tasks successfully and efficiently. Thus the adaptive environment (Kid mode) for specific user group (children) showed better usability than adaptive environment for generic users. We identified that usability issues of adaptivity still exist due to uniform adaptive features provided by the smartphone vendors regardless of user ability and task context. Currently, it is more on user’s choice to turn on or off any adaptive feature while performing specific task. The experimental result concludes that the adaptivity in user interface have greater ability to increase the usability of smartphones if applied in suitable context. We suggest that the interface should be more equipped with adaptive features to increase the usability of smartphones. It is also suggested that user and task context should be studied or sensed for switching to any adaptive environment. The user and task context analysis and its mapping to the adaptive features will be part of future work. A UCD model enriched with the context analysis and mapping functions will be devised as well. Appendix A. The Table 2 shows the patterns of features provided in different mobile applications. These applications and features are categorized into seven and four groups respectively. Table 2 Mobile adaptivity patterns in Applications-Features Matrix. LED Notification
Smart Alert
Smart Pause
Smart Stay
Eye Contact
Air Gesture
Voice Commands
Ok Goggle
Face Recognition
QR Code Reader
Kid Mode
Night Mode
Touch Disable Mode
Drive Mode
Battery Saving Mode
Color Blind Mode
P P P P P
Y Y Y Y Y
X X X X X
P P P P P
Y Y Y Y Y
P P P P P
X X P P P
Y Y Y Y Y
Y Y Y Y Y
P P P P P
P P P P P
Y Y Y Y Y
P P P P P
N N N P P
X X P P P
N N N N N
Y Y Y Y Y
N N N N N
Y N N N N
Y Y Y Y Y
Y Y Y Y Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
P
P
N
Y
N
N
Y
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
P
P
N
Y
N
N
Y
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
P
P
N
Y
N
N
Y
Y
Proximity Sensor
S-Health
Y
Raise to Awake
Line
Swift Keyboard
Apps Communication Apps SMS Y MMS Y E-mail Y WhatsApp Y Skype Y Facebook Y Messenger Viber Y
Easy Screen Turn on/off
Screen Rotation
Features
Social Networking Apps Twitter
Y
P
Y
X
P
Y
P
P
Y
N
P
P
Y
P
P
P
N
Y
N
N
P
Y
Facebook
Y
P
Y
X
P
Y
P
P
Y
N
P
P
Y
P
P
P
N
Y
N
N
P
Y
Google +
Y
P
Y
X
P
Y
P
P
Y
N
P
P
Y
P
P
P
N
Y
N
N
Y
Y
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LinkedIn
Y
P
Y
X
P
Y
P
P
Y
N
P
P
Y
P
P
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N
Y
N
N
Y
Y
Xing
Y
P
Y
X
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Y
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X
Y
N
P
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Y
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N
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Y
Y
Instagram
Y
P
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N
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N
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N
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Pinterest
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N
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Y
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X
N
Y
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Tinder
N
P
Y
X
P
Y
P
X
Y
N
P
P
Y
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N
Y
N
N
P
Y
Entertainment Apps YouTube Y Dailymotion Y SoundCloud N Mobile Games Y Netflix P Vimeo Y Dubsmash Y News and Information Apps
P P P P P P P
Y Y Y Y Y Y Y
X X X X X X X
P P P P P P P
Y Y Y Y Y Y Y
P P P P P P P
P P P P P P P
Y Y Y Y Y Y Y
N N N N N N N
P P P P P P P
P P P P P P P
Y Y Y Y Y Y Y
P P P P P P P
N N N N N P P
X X X X X X X
N N N N N N N
Y Y Y Y Y Y Y
N N N N N N N
N N P N N N N
Y Y Y Y Y Y Y
Y Y Y Y Y Y Y
BBC
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P
P
Y
Y
P
P
Y
P
N
X
N
Y
N
N
Y
Y
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
N
X
N
Y
N
N
Y
Y
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
N
P
N
Y
N
N
Y
Y
Blogger
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
N
P
N
Y
N
N
Y
Y
Kindle
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
N
N
N
Y
N
N
Y
Y
Y
P
Y
X
P
Y
P
P
Y
Y
N
P
Y
P
N
P
N
Y
N
N
Y
Y
Y
P
Y
X
P
Y
P
P
Y
Y
N
P
Y
P
N
P
N
Y
N
N
Y
Y
Y
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
X
N
Y
N
N
Y
Y
Y
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
X
N
Y
N
N
Y
Y
Google Maps Documents & Spread Sheets CPU & Memory Booster Storage Cleaner
Y
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
X
N
Y
N
Y
P
Y
Y
P
Y
X
P
Y
P
X
Y
Y
N
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Y
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X
N
Y
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N
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Y
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P
Y
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Y
P
X
Y
Y
N
P
Y
P
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N
Y
Y
P
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
X
N
Y
N
N
Y
Y
Dropbox
Y
P
Y
X
P
Y
P
P
Y
Y
N
P
Y
P
N
P
N
Y
N
N
Y
Y
Trello
Y
P
Y
X
P
Y
P
P
Y
Y
N
P
Y
P
N
P
N
Y
N
N
Y
Y
Utility Apps Google Drive Microsoft One Drive Calendar & Scheduler Calculator
Service Provider Apps Uber
N
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
P
N
Y
N
Y
P
Y
OLX
Y
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
P
P
N
Y
N
N
P
Y
E-bay
Y
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
P
N
Y
N
N
P
Y
Amazon
Y
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
P
N
Y
N
N
P
Y
PayPal Weather For. Forecasting XE Currency
P
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
P
N
Y
N
N
P
Y
P
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
P
N
Y
N
N
P
Y
P
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
P
N
Y
N
N
Y
Y
Browsing Apps Google Chrome Safari
Y Y
P P
Y Y
X X
P P
Y Y
P P
P P
Y Y
Y Y
P P
P P
Y Y
P P
P P
P P
N N
Y Y
N N
N N
P P
Y Y
Opera Mini
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
P
P
N
Y
N
N
P
Y
Mozilla Firefox
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
P
P
N
Y
N
N
P
Y
UC Browser
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
P
P
N
Y
N
N
P
Y
Legend
Y = Available
N = Non-Available
P = Partially Available
X = Not Applicable
References 1. Andone, I., Błaszkiewicz, K., Eibes, M., Trendafilov, B., Montag, C., & Markowetz, A. How age and gender affect smartphone usage. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. 2016. p. 9-12. 2. Jindal, G., & Jain, M.. A comparative study of mobile phone’s operating systems. International Journal of Computer Applications & Information Technology. 2012. 1(3). p. 10-15. 3. Wesson, J. L., Singh, A., & Van Tonder, B. Can adaptive interfaces improve the usability of mobile applications?. In Human-Computer Interaction. Springer Berlin Heidelberg. 2010. p. 187-198.
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Cena, F., Console, L., Gena, C., Goy, A., Levi, G., Modeo, S., & Torre, I. Integrating heterogeneous adaptation techniques to build a flexible and usable mobile tourist guide. AI Communications. 2006. 19(4). p. 369-384. Lewis, J. R. Usability testing. Handbook of human factors and ergonomics. IBM Software Group. Boca Raton, Florida. 2006. 12, e30. Ahmad, N., Boota, M. W., & Masoom, A. H. Smart phone application evaluation with usability testing approach. Journal of Software Engineering and Applications. 2014. 7(12), p. 1045-1054. Brodić, D., Amelio, A., & Draganov, I. R. Response Time Analysis of Text-Based CAPTCHA by Association Rules. In International Conference on Artificial Intelligence: Methodology, Systems, and Applications. Springer International Publishing. 2016. p. 78-88. Ahmad, N. People Centered HMI's for Deaf and Functionally Illiterate Users. Doctoral dissertation. Politecnico di Torino-Universität Potsdam, Germany. 2014. Hartmann, M. Challenges in Developing User-Adaptive Intelligent User Interfaces. In LWA, Telecooperation Group. Technische Universit¨at Darmstadt 64289 Darmstadt. Germany. 2009. p. ABIS-6. Syer, M. D., Nagappan, M., Hassan, A. E., & Adams, B. Revisiting prior empirical findings for mobile apps: An empirical case study on the most popular open-source android apps. In Proceedings of the Conference of the Center for Advanced Studies on Collaborative Research. IBM Corp. 2013. p. 283-297. Bevan, N. Human-computer interaction standards. Advances in Human Factors/Ergonomics. 1995. 20, p. 885-890. Lewis, J. R. An after-scenario questionnaire for usability studies: psychometric evaluation over three trials. ACM SIGCHI Bulletin. 1991. 23(4). p. 79. Roy, S., Pattnaik, P. K., & Mall, R. A quantitative approach to evaluate usability of academic websites based on human perception. Egyptian Informatics Journal. 2014. 15(3). p. 159-167.
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International Conference on Knowledge Based and Intelligent Information and Engineering International Conference on Knowledge Based and 2017, Intelligent Information Systems, KES2017, 6-8 September Marseille, France and Engineering Systems, KES2017, 6-8 September 2017, Marseille, France
Usability evaluation of adaptive features in smartphones Usability evaluation of adaptive features in smartphones Muhammad Waseem Iqbala , Nadeem Ahmadaa, Syed Khuram Shahzadaa a Muhammad Waseem Iqbal , Nadeem Ahmad , Syed Khuram Shahzad CS & IT Department, 1-Km Defence Road, The University of Lahore, Lahore 54000, Pakistan CS & IT Department, 1-Km Defence Road, The University of Lahore, Lahore 54000, Pakistan
a a
Abstract Abstract This paper presents a usability study that aims to analyze effectiveness, efficiency, and satisfaction based on the existing adaptive This paper a usability study thatfeatures aims toinclude analyze screen effectiveness, and satisfaction based on theand existing adaptive features in presents smartphones. The adaptive rotation,efficiency, voice commands, LED notifications kid mode for features in smartphones. The adaptive features screen commands,task LED notifications anda kid modetime for android and iOS platforms. The effectiveness andinclude efficiency arerotation, measuredvoice by considering completion within specific android and iOS platforms. Thethrough effectiveness efficiency are measured by considering taskexperiment completioniswithin a specific while satisfaction is measured After and Scenario Questionnaire (ASQ) technique. The carried out withtime the while satisfaction is participants. measured through Aftershows Scenario Questionnaire technique. experiment carried with the involvement of 128 The study interesting patterns(ASQ) in usability whereThe screen rotationisand voiceout commands involvement of 128 participants. The LED studynotifications shows interesting patterns feature in usability where screen and voice commands resulted in lower usability. Whereas, is a dominant having almost 88%rotation effectiveness comparing to a resulted in lower usability. Finally, Whereas,the LED notifications is athe dominant having 88% after effectiveness comparing to a non-adaptive environment. study suggests that adaptivefeature features mustalmost be applied careful analysis of user non-adaptive environment. Finally, the study suggests that the adaptive features must be applied after careful analysis of user tasks and context. tasks and context. © 2017 The Authors. Published by Elsevier B.V. © 2017 The Authors. Published by Elsevier B.V. © 2017 The under Authors. Published by B.V. Peer-review responsibility of Elsevier KES International International. Peer-review under responsibility of KES Peer-review under responsibility of KES International. Keywords: Usability patterns; adaptive mobile feature; smartphone; user experience; user satisfaction; user interface; Keywords: Usability patterns; adaptive mobile feature; smartphone; user experience; user satisfaction; user interface;
1. Introduction 1. Introduction Smartphones have become popular and most used devices in the world. Users carry them around all the time to Smartphones have become popular most used It devices in the that world. Users them 6.3 around all smartphone the time to fulfill the tasks according to their usageand requirements. is predicted there will carry be around billion fulfill the tasks according to their usage requirements. It is predicted that there will be around 6.3 billion smartphone subscribers available in 2021 [1]. Currently, the most prominent operating systems (OS) are Android (Google) and subscribers available in 2021 [1].applications Currently, (Apps) the mostwith prominent systems Android (Google) and iPhone (Apple) providing many multipleoperating features [2]. These(OS) twoare mobile operating systems iPhone (Apple) providing many applications (Apps) with multiple features [2]. These two mobile operating systems hold more than 98% of worldwide market share. As per fourth quarter of 2016, Android was leading with 80.7% hold more than 98% of worldwide market share. As per fourth quarter of 2016, Android was leading with 80.7%
Corresponding author. Tel.: +92 321 7346 428 Corresponding author. Tel.: +92 321 7346 428 E-mail address: [email protected] E-mail address: [email protected] 1877-0509 © 2017 The Authors. Published by Elsevier B.V. Peer-review©under KES International. 1877-0509 2017responsibility The Authors. of Published by Elsevier B.V. Peer-review under responsibility of KES International.
1877-0509 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of KES International 10.1016/j.procs.2017.08.258
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share while iOS was the runner up with 17.7% market share 1. The user interfaces (UIs) of many Apps still have usability issues of complexity and flexibility. The physical constraints of devices have not only impact on screen size and interaction mechanism but it also contributes to several usability issues for mobile applications. Adaptive user interfaces (AUIs) provide adaptation by dealing “what information to be presented”, “how to present this information" and “how to interact with presented information” of the UI to address these usability issues [3]. Human computer interaction (HCI) is the study which provides interaction between humans and computers especially the user response time play significant role in estimation of user performance [4]. The interactive design is concerned with the required context by providing learnability, safety, utilization, efficiency, effectiveness and satisfaction to the users. The actual purpose of usability is setting the design directions to assist the user in overcoming their shortcomings. The usability metrics are created to track progress between releases, find out the competitive position, evaluate before launch and create future plans [5]. There are three type of usability issues (1) technical usability discusses the network connectivity, limited screen size and battery life of mobile devices (2) environmental usability refers to mental abilities, psychological constraints, mobility, noise, temperature and light conditions for users and (3) social usability states the issues of personalization, privacy, acceptance, comfort and adoption [6]. This study discusses the usability issues of adaptive features available in all smartphone devices (e.g. screen rotation, voice commands, LED notifications and kid mode). The usability issues are identified for automatic screen rotation that user may browse in portrait or landscape directions while the impact of incorrect orientation when users change their posture is also analyzed. It may effect on speed, efficiency and usability of users [7]. Voice commands are automatically executed without the hassles of finding and pointing on screen objects. The most well-known Apps are Apple’s Siri and Google Now that allows users to perform task by providing vocal instructions [8] [9] [10]. With the increasing number of smartphones amongst people, there is a dire need of usability study regarding alerts and notifications. The notifications are becoming most important feature to engage the users without caring their daily routine. Constant interruptions may cause inattention and hyperactivity in digitally connected society [11]. In contrast, some people feel that notifications are valuable and do not interrupt their daily life [12]. The kid mode/baby mode is an excellent feature in which children are restricted to use some applications and phone services. It is parental mode that prevents the child from accessing standard interface without password or pattern [13] [14]. AUIs are defined as systems that observe user status (context, job) to adapt variety of displays (size, visualization) and actions (implementation, interaction) to attain the user required goals. Basically, in AUIs, the interface should be adaptive according to the user rather than the users are being adaptive according to the interface [15]. AUIs provide the platform to improve the accessibility of interactive systems with large potential. Personalized user interfaces can be adaptive (system driven) or adaptable (user driven) according to the individual user’s needs and habits [16] [17]. AUI allows system to initiate changes and provides automatic responses according to the user’s context [18] [19]. While in adaptable model, the users had to map their desires, interests and demands manually with system provided interface. Any user interface that changes according to its context called context aware interface which may be adaptive or adaptable [20] [21] [22]. By adaptation, the application can accommodate the specific needs and limitations of the user [23]. In this study, there are three classes of adaptation including information, visualization and user interface for mobile adaptive features have been identified. The adaptation is influenced by the following four variables:
User based adaptation denotes to adapt the user’s preferences. Task based adaptation ensures the relevance of current task with user’s recent activity. System based adaptation refers to adjust devices with different capabilities and variables. Context based adaptation states according to the user’s current context.
In the larger perspective, this study is part of User Centered Design (UCD) approach for adaptive user interface design and development. It provides us adaptation guidelines suitable to the users and their context for various
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Apps. Furthermore, the aim of this study is to evaluate the effectiveness, efficiency and satisfaction as usability factors in adaptive environment provided by smartphones. The paper is organized in four sections where section 2 describes the background and literature review. Section 3 presents the research methodology while section 4 states the results and discussion. At the end conclusion of the study and future work is presented. 2. Background and Literature Review The term usability was used firstly in early 1980s whose primary goal is to provide guidance to product developers for the user friendly Apps development. The usability test can be performed formally or informally in specified environment [24]. Usability of smartphone Apps has become an important concern but the trade-offs by the application development have created dissatisfaction in currently available UIs [25]. The usability study usability of mobile phone Apps, conducted by Nielsen Norman group in 2009 [3] represents 59% successful completion of the task while three usability issues were identified including efficiency, screen size and text insertion. The menthol project [1] analyzed the effects of user age and gender on smartphone usage. In sample of 30,677 participants the results of average usage time for each gender concluded that female use smartphone for longer time. Another usability study in terms of response time is conducted to find out the solution of two types of “CAPTCHA” (i.e. text-based and text-number based). There were 230 internet users for experimentation on the basis of different features like age, years of internet usage, level of education and response time. By using Apriori algorithm the values of these features were used to determine the response time [26]. The studies discussed earlier are highlighting not only usability issues but also provides the impact of user context in smartphone usage. User centered design (UCD) is a methodology that focuses on the high usability and low cost products for understanding of needs, tasks, environments, preferences and limitations in user’s context. The major difference from other design philosophies is that the UCD tries to optimize the interfaces to enhance the usability and satisfaction of users. A study was conducted by Ahmed in 2014 [27] using the philosophy of UCD for illiterate and deaf people to analyze the effects and usability of online assistance. The percentage of completed tasks increased from 52% to 94%. System usability scale (SUS) specifies that their average subjective usability score enhanced from 39 to 80. The proposed interfaces provide better utility for low-literate users by translating contents in their own language. For effective usability of mobile interface, the intended tasks should be mapped with user’s mental model. The AUIs can be directly used to improve the usability and satisfaction level of user [14]. Many Apps suffer from usability issues due to their non-contextual UIs. AUIs try to enhance the usability of such Apps by catering user needs [15]. Hartman stated in 2009 [28] that the Microsoft Office hidden smart menus of prior versions (Pre-2007 version) caused many usability issues. But in its revised interface (2007-version) the menus contain predefined adaptive parts (e.g. display the most recent used items) which appear to have more beneficial for users. A mathematical e-business App “AdaptiveCalc” was developed for Android OS. The result for perfect calculations for AUI was 83.3% while 79.9% for non-AUI [20]. A mobile-map based application “MediaMaps” [3] was developed to adapt to the users according to their context. The information accuracy was calculated as 76.78% on average while user satisfaction and visualization adaptation ratio was very positive. Hanmansetty illustrated in 2004 [19] that there is also a need to develop adaptive user interface toolkits for Apps that require context aware and adaptive user interface generation to improve the usability of interfaces as well as designers. Lavie and Mayer experienced in 2010 [14] that the AUI in the context of an “In-Vehicle Telematic System” by taking twenty-four participants. They drove the cars at fixed speed approximately 30km/h and the lane position had only one level of curvature. Results indicated that the drivers using AUI were more efficient and accurate. An online survey was conducted by Cheng et al. [6] to understand the incorrect and common screen rotation usability. There were 513 smartphone and tablet users from which 91 % experienced incorrect viewing of auto rotation while 42% faced problems several times a week or more. Another study was conducted by Carlini et al. [7] for the usability of voice commands using black box and white box models/attacks. The study claimed that a verbal challenge response protocol and a machine learning approach can detect attacks with 99.8% accuracy. For mobile notifications a study “Do not disturb challenge” was performed on 12 participants. The results for
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notification checking concluded that 11 of the 12 participants checked their new notifications all the times. However, only 4 of 12 participants were agreed that the notifications are necessary part of life [11]. The study used $N method (in high accuracy level 95% to 99%) for gesture recognition in adults and kids. It showed that adults use 79% mobile phone daily but kid’s usage rate is 63%. During the completion of tasks, the children missed 50% targets more than adults and felt trouble in performing smaller tasks [13]. The above mentioned studies are providing the discussion about different models related to the adaptivity in mobile applications but these adaptive models are not specifically focusing on user context. Adaptivity can be an impairment for usability if applied in improper context. Thus, there is a need to study the usability of adaptive features in different user-contexts. In this research we are analyzing the usability of most used smartphone adaptive features for different tasks and user groups. 3. Methodology 3.1. Selection of frequent adaptive mobile features Currently, mobile devices provide variety of Apps along with multiple adaptive features [29]. The adaptivity provides significant usability to overcome the different existing issues such that easiness, information overloading, screen disorder, task completion support and limited interaction mechanism. The detailed study of 100 mostly used Apps provide us the exact vision to attain the goal. These Apps reassembled into seven categories e.g. (i) communication (ii) social networking (iii) entertainment (iv) news and information (v) utility, (vi) service provider and (vii) browsing. However, unavailable, inaccessible, desktop and windows mobile based 45 Apps excluded. Only 55 Apps selected which are freely available, accessible and most frequently used amongst users. Furthermore, 22 features are selected carefully and cross matched with supporting mobile OS to find out remarkable patterns. Different patterns are selected on the basis of available, not available, partially available and not applicable (Appendix-A). Finally, four features including screen rotation, voice commands, LED notifications and kid mode are selected due to their rich pattern-ability. 3.2. Sampling and Experimentation In this study, participants were mobile phone users with at least one year of experience. A pre-questionnaire was developed to find out the relevant participants for best user experimentation. Initially, 185 participants were found through questionnaire in which 39 users were discarded in first step due to less than one-year experience of smartphone usage. Some participants were excluded due to other reasons, specifically 9 users voluntarily not willing, 2 in vision problem and 7 unable to understand the adaptive features. Remaining 128 participants were selected for experiment and divided into four groups. Each group was assigned a specific task comprised of 32 participants in equal gender ratio. Furthermore, all tasks were performed in two sessions with and without adaptive features. The participants attended an introductory demonstration in the lab for both sessions separately for each task (excluding kid mode). They were demonstrated about the significance and measures of assigned tasks briefly. The ages of participants for first three groups ranged from 21 to 40 years and for kid mode ranged from 4 to 7 years. The detail of each task assigned to individual group is given as below: Group A: Task (Screen rotation): In this task a paragraph of 300 words was given to the users for typing. The aim was to examine whether the activity of screen rotation on and off being performed may affect the effectiveness, efficiency and satisfaction of the user. The maximum time allocated for this task was thirty minutes considering average and worst case typing speed. The experiment was observed very carefully by maintaining their record. The average time for task completion was 28.8 minutes. Group B: Task (Voice commands): Different seven commands including (i) make a call to any contact (ii) send “Hi” message to any contact (iii) play song (iv) open the gallery (v) set alarm for tomorrow at 11am (vi) turn on/off airplane mode and (vii) open calendar were allocated to users for experiment. Users were directed to perform these commands by enabling and disabling their mobile phone features. The allocated time was six minutes and calculated according to the nature of tasks that includes searching and selection of contents from the
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device. The task completion time is noted regarding the execution of each command. Average task completion time was 4.88 minutes. Group C: Task (LED notifications): Selected participants were requested to switch off their notifications for four hours consecutively with their consent. The users were interviewed to enlist their feelings particularly missing of important, urgent, delayed and emergency notifications. The received reading from the participants provided significant insight regarding LED notifications. Group D: Task (Kid mode): This experiment was managed in a school where all participants were children. The kids were directed to perform three activities such as (i) play any video (ii) play any game and (iii) make drawing of car in kid mode which was already activated. They performed enthusiastically in kid mode according to their desires. While in standard interface mode of smartphones, the kids were not performing the same task easily and anxiously. All kids were observed very keenly while using smartphone for forty minutes. The tasks were planned and executed for a class duration in the school. The average task completion time was 35.6 minutes. Table 1 shows the actual picture of groups, tasks, sub-tasks, participants, time and post task evaluation technique. Table 1 Sample groups and tasks for experiment. Group
Tasks
Sub-tasks
Group A Group B Group C Group D
Screen Rotation Voice Commands LED Notifications Kid mode
300 words paragraph typing Perform 7 different commands Dependent on notifications Perform 3 different activities
Total
Total Time in
Post Task
Participants
Minutes
Evaluation
32 32 32 32
30 06 240 40
ASQ ASQ ASQ -----
3.3. Usability Evaluation There are three parameters to measure the performance of usability such that effectiveness, efficiency and satisfaction [4]. ISO 9241-11 standard is used to measure the effectiveness and efficiency [30] whereas After Scenario Questionnaire (ASQ) is chosen for the post task evaluation to measure the user’s satisfaction. Errors may be unintended actions, slips, mistakes or omissions that a user makes while attempting a task [31]. Effectiveness is the amount of goals to be achieved and it is measured as:
Effectiven ess
Total number of tasks completed successfully * 100 Total number of tasks undertaken
(1)
The resources such as time, money or mental efforts that have to be extended to achieve the intended goals; called efficiency and can be measured as:
Time based Efficiency =
R
N
n ij
J 1
i 1
t ij
NR
(2)
Where N = The total number of tasks (goals) R = The number of users nij = The result of task i by user j; if the user successfully completes the task, then N ij = 1, if not, then Nij = 0 tij = The time spent by user j to complete task i. If the task is not successfully completed, then time is measured till the moment the user quits the task Satisfaction is measured by the amount to which user finds the use of product acceptable. Usability is dependent on the context of use and on the specific circumstances in which a product is used. The context of use consists of user’s task, hardware, software and material. There are many post task evaluation techniques available (e.g. SEQ, UME, SMEQ) [23] but in this study the satisfaction is measured through ASQ technique. The ASQ is particularly a
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short questionnaire which takes little time, easy to understand and tremendous practical considerations of participants for usability studies. It contains three questions of seven points scale (strongly disagree=1, disagree=2, somewhat disagree=3, neither agree nor disagree=4, somewhat agree=5, agree=6, strongly agree=7). with important aspects of user satisfaction with system usability. The first question shows the aspect of ease in task completion, second question provides the aspect of time to complete a task while third question analyzes the satisfaction level on capability of support information [32]. 4. Results and Discussion The study reflects that AUIs can be used to address mobile application’s usability issues. The effectiveness, efficiency and satisfaction have been calculated for four commonly used adaptive features including screen rotation, voice commands, LED notifications and kid mode. The results are presented by organizing following three usability parameters. 4.1. Effectiveness The results in Figure 1, shows the comparison of effectiveness for adaptive and non-adaptive environment considering each adaptive feature separately.
Figure 1 Effectiveness comparison chart for adaptive and non-adaptive features.
The effectiveness in the usability of screen rotation feature demonstrated that the overall average for adaptive environment is much lower. It shows a substantial difference between adaptive (56%) and non-adaptive (81%) effectiveness. With screen rotation feature, the overall gap of effectiveness measures based on gender is also considerable where female group showed lower effectiveness compared to male group. Participants felt uncomfortable while using screen rotation feature due to readjustment of smartphone keyboard. Likewise, for voice commands feature the effectiveness of female participants showed clear difference about 25% for both environments. Overall the non-adaptive environment is established high effectiveness (88%) than adaptive environment (69%). Although, the voice commands were performed without any disturbance; recognition of accent decreased its effectiveness. The LED notifications feature indicated that the effectiveness is raised in adaptive environment where male and female participants conceived about 88% participation. The significant difference for overall effectiveness is reported around 31% in both environments. It is also illustrated that participants were conscious and aware about the importance of notifications in daily life. Similarly, the calculated effectiveness is more for male and female in kid mode feature for adaptive environment. The overall effectiveness in kid mode shows major difference between adaptive and non-adaptive effectiveness (i.e. 81%=adaptive, 53%=non-adaptive). Children were excited and more energetic while using kid mode which is an excellent restricted parental feature. 4.2. Efficiency The results in Figure 2, shows that the screen rotation mode does not affect much on the efficiency of the user. Overall efficiency is low in usability of adaptive and non-adaptive screen rotation environment for genders. The
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efficiency of female participants recorded very low which is around 23% for adaptive while 41% for non-adaptive environment. It is experienced that users were inefficient while using screen rotation feature due to the adjustment of fingers on expanded keyboard in horizontal view. However, the overall efficiency in voice commands feature is 89% for adaptive environment due to easy, small and entertaining sub-tasks. The male participants do not have big difference for adaptive and non-adaptive environment. Likewise, the efficiency shows the major difference in the comparison of other features.
Figure 2 Efficiency comparison chart for adaptive and non-adaptive features.
The huge difference is observed in LED notifications feature for overall efficiency of both environments (i.e. adaptive=89%, non-adaptive=26%). The efficiency in adaptive environment is perceived very well for female participants. In kid mode feature, the overall efficiency is high in adaptive environment while gender difference is also clearly remarked. The kids were very excited during the experimental study due to their adaptive behavior. The graph indicates that the efficiency of adaptive environment is higher for voice commands, LED notifications and kid mode features. The screen rotation effects due to the nature, efficiency, usability and adaptability of task. 4.3. Satisfaction Figure 3 shows the usability comparison in terms of user satisfaction for adaptive and non-adaptive environments. The evaluation had been taken through ASQ for screen rotation, voice commands and LED notifications to measure the satisfaction of participants. The kid mode was exempted from ASQ due to its limited understandability in children.
Figure 3 Usability comparison chart for adaptive and non-adaptive features.
The overall satisfaction in the usability of screen rotation feature for non-adaptive environment is around 5.8; which is greater than adaptive environment (around 4.1). Female participants showed more dissatisfaction than male in screen rotation mode. It is also observed that the participants were feeling more comfortable to type the text with the deactivation of screen rotation mode. Likewise, the voice commands feature contains greater satisfaction level for adaptive environment. The male participants showed 5.6 satisfaction level whereas the same measures for female participants is around 6. Similarly, the LED notifications feature provides significantly greater satisfaction level for
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adaptive environment. The satisfaction level in adaptive environment for male and female participants is 6.3 and 6.6 respectively. The participants felt more comfortable and satisfied throughout the user experimentation in adaptive environment for voice command and LED notification feature. In voice commands feature both male and female satisfaction level is higher for adaptive environment due to easy and automatic usability of tasks. Moreover, LED notifications satisfaction level is also greater in adaptive environment because the user became habitual with this feature in daily routine. Conclusion and Future work In this paper we looked at the usability of adaptive features provided by the vendors in smartphones. We evaluated these adaptive features based on the effectiveness, efficiency and satisfaction. We have followed the user centered design (UCD) to analyze the usability of adaptive mobile features. In Screen rotation mode during typing the adaptive feature is 25% less effective as the user preferred to type keywords in portrait mode by using fingers of single hand. Although the adaptive feature of voice commands was almost 6% more efficient but astonishingly it was 19% less effective while interacting with user. It is found that the reason behind less effectiveness is difficulty in recognition of accent of most users. The LED notifications adaptive feature is dominantly leading in better usability with almost 88% effectiveness and 89 % efficiency. In case of kids the adaptive environment is 28% more effective and efficient than normal mode. Though the calculation of satisfaction level in kids was not possible due to their limited understanding of ASQ but they completed their tasks successfully and efficiently. Thus the adaptive environment (Kid mode) for specific user group (children) showed better usability than adaptive environment for generic users. We identified that usability issues of adaptivity still exist due to uniform adaptive features provided by the smartphone vendors regardless of user ability and task context. Currently, it is more on user’s choice to turn on or off any adaptive feature while performing specific task. The experimental result concludes that the adaptivity in user interface have greater ability to increase the usability of smartphones if applied in suitable context. We suggest that the interface should be more equipped with adaptive features to increase the usability of smartphones. It is also suggested that user and task context should be studied or sensed for switching to any adaptive environment. The user and task context analysis and its mapping to the adaptive features will be part of future work. A UCD model enriched with the context analysis and mapping functions will be devised as well. Appendix A. The Table 2 shows the patterns of features provided in different mobile applications. These applications and features are categorized into seven and four groups respectively. Table 2 Mobile adaptivity patterns in Applications-Features Matrix. LED Notification
Smart Alert
Smart Pause
Smart Stay
Eye Contact
Air Gesture
Voice Commands
Ok Goggle
Face Recognition
QR Code Reader
Kid Mode
Night Mode
Touch Disable Mode
Drive Mode
Battery Saving Mode
Color Blind Mode
P P P P P
Y Y Y Y Y
X X X X X
P P P P P
Y Y Y Y Y
P P P P P
X X P P P
Y Y Y Y Y
Y Y Y Y Y
P P P P P
P P P P P
Y Y Y Y Y
P P P P P
N N N P P
X X P P P
N N N N N
Y Y Y Y Y
N N N N N
Y N N N N
Y Y Y Y Y
Y Y Y Y Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
P
P
N
Y
N
N
Y
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
P
P
N
Y
N
N
Y
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
P
P
N
Y
N
N
Y
Y
Proximity Sensor
S-Health
Y
Raise to Awake
Line
Swift Keyboard
Apps Communication Apps SMS Y MMS Y E-mail Y WhatsApp Y Skype Y Facebook Y Messenger Viber Y
Easy Screen Turn on/off
Screen Rotation
Features
Social Networking Apps Twitter
Y
P
Y
X
P
Y
P
P
Y
N
P
P
Y
P
P
P
N
Y
N
N
P
Y
Y
P
Y
X
P
Y
P
P
Y
N
P
P
Y
P
P
P
N
Y
N
N
P
Y
Google +
Y
P
Y
X
P
Y
P
P
Y
N
P
P
Y
P
P
P
N
Y
N
N
Y
Y
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Y
P
Y
X
P
Y
P
P
Y
N
P
P
Y
P
P
P
N
Y
N
N
Y
Y
Y
P
Y
X
P
Y
P
X
Y
N
P
P
Y
P
P
P
N
Y
N
N
Y
Y
Y
P
Y
X
P
Y
P
P
Y
N
P
P
Y
P
P
P
N
Y
N
N
P
Y
Y
P
Y
X
P
Y
P
X
Y
N
P
P
Y
P
P
X
N
Y
N
N
Y
Y
Tumblr
Y
P
Y
X
P
Y
P
X
Y
N
P
P
Y
P
P
X
N
Y
N
N
Y
Y
Tinder
N
P
Y
X
P
Y
P
X
Y
N
P
P
Y
P
P
X
N
Y
N
N
P
Y
Entertainment Apps YouTube Y Dailymotion Y SoundCloud N Mobile Games Y Netflix P Vimeo Y Dubsmash Y News and Information Apps
P P P P P P P
Y Y Y Y Y Y Y
X X X X X X X
P P P P P P P
Y Y Y Y Y Y Y
P P P P P P P
P P P P P P P
Y Y Y Y Y Y Y
N N N N N N N
P P P P P P P
P P P P P P P
Y Y Y Y Y Y Y
P P P P P P P
N N N N N P P
X X X X X X X
N N N N N N N
Y Y Y Y Y Y Y
N N N N N N N
N N P N N N N
Y Y Y Y Y Y Y
Y Y Y Y Y Y Y
BBC
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
N
X
N
Y
N
N
Y
Y
CCN
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
N
X
N
Y
N
N
Y
Y
RT
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
N
X
N
Y
N
N
Y
Y
DW
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
N
X
N
Y
N
N
Y
Y
Al Jazeera National Geographic WordPress
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
N
X
N
Y
N
N
Y
Y
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
N
X
N
Y
N
N
Y
Y
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
N
P
N
Y
N
N
Y
Y
Blogger
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
N
P
N
Y
N
N
Y
Y
Kindle
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
N
N
N
Y
N
N
Y
Y
Y
P
Y
X
P
Y
P
P
Y
Y
N
P
Y
P
N
P
N
Y
N
N
Y
Y
Y
P
Y
X
P
Y
P
P
Y
Y
N
P
Y
P
N
P
N
Y
N
N
Y
Y
Y
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
X
N
Y
N
N
Y
Y
Y
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
X
N
Y
N
N
Y
Y
Google Maps Documents & Spread Sheets CPU & Memory Booster Storage Cleaner
Y
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
X
N
Y
N
Y
P
Y
Y
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
X
N
Y
N
N
Y
Y
P
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
X
N
Y
N
N
Y
Y
P
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
X
N
Y
N
N
Y
Y
Dropbox
Y
P
Y
X
P
Y
P
P
Y
Y
N
P
Y
P
N
P
N
Y
N
N
Y
Y
Trello
Y
P
Y
X
P
Y
P
P
Y
Y
N
P
Y
P
N
P
N
Y
N
N
Y
Y
Utility Apps Google Drive Microsoft One Drive Calendar & Scheduler Calculator
Service Provider Apps Uber
N
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
P
N
Y
N
Y
P
Y
OLX
Y
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
P
P
N
Y
N
N
P
Y
E-bay
Y
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
P
N
Y
N
N
P
Y
Amazon
Y
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
P
N
Y
N
N
P
Y
PayPal Weather For. Forecasting XE Currency
P
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
P
N
Y
N
N
P
Y
P
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
P
N
Y
N
N
P
Y
P
P
Y
X
P
Y
P
X
Y
Y
N
P
Y
P
N
P
N
Y
N
N
Y
Y
Browsing Apps Google Chrome Safari
Y Y
P P
Y Y
X X
P P
Y Y
P P
P P
Y Y
Y Y
P P
P P
Y Y
P P
P P
P P
N N
Y Y
N N
N N
P P
Y Y
Opera Mini
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
P
P
N
Y
N
N
P
Y
Mozilla Firefox
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
P
P
N
Y
N
N
P
Y
UC Browser
Y
P
Y
X
P
Y
P
P
Y
Y
P
P
Y
P
P
P
N
Y
N
N
P
Y
Legend
Y = Available
N = Non-Available
P = Partially Available
X = Not Applicable
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