Age, Usability, and Content on Mobile Application Usage
A thesis presented to the faculty of
the Russ College of Engineering and Technology of Ohio University
In partial fulfillment of the requirements for the degree
Master of Science
Shijing Liu August 2012
This thesis titled
Technology Acceptance Model for Determining the Effects of Age, Usability, and Content on Mobile Application Usage
by SHIJING LIU
has been approved for
the Department of Industrial and Systems Engineering and the Russ College of Engineering and Technology by
_____________________________________________ Diana J. Schwerha
Assistant Professor of Industrial and Systems Engineering
_____________________________________________ Dennis Irwin
ABSTRACT
LIU, SHIJING, M.S., August 2012, Industrial and Systems Engineering
Technology Acceptance Model for Determining the Effects of Age, Usability, and Content on Mobile Application Usage
Director of Thesis: Diana J. Schwerha
With market competition and customer needs, the development of smart phones and mobile applications is fast and changes our daily life. Meanwhile, our world
population is aging. The group of older people is the fastest growing mobile application users. This research compared the effects of age, training, different usability
characteristics between younger and older users. The Technology Acceptance Model (TAM) was used as a theoretical construct in this research. Seventeen older adults (over 50 years old) and twenty younger adults (18 – 30 years old) were recruited from the local community. Four mobile applications were tested on smart phones or similar devices. A training session was included in the experiment. Results of the experiment showed: (1) training has significant effect on the increase of TAM measures, (2) customers prefer to use mobile applications which have higher level of TAM measures, and (3) older and younger groups have different level of TAM measures. Recommendations for age targeted design considerations for mobile applications are given.
Approved: ____________________________________________________________ Diana J. Schwerha
ACKNOWLEDGEMENTS
I would like to thank everyone who helped and inspired me during my master study in Ohio University.
First and foremost, I am heartily thankful to my advisor Dr. Diana Schwerha, whose enormous help enable me to complete this research and my thesis. I would also like to show my gratitude to Department of Industrial and Systems Engineering for supporting my project and the payment for all participants. I have furthermore to thank my committee members Dr. David Koonce, Dr. Tao Yuan, and Dr. Vic Matta for their suggestions and contributions.
Especially, I would like to express my deepest gratitude to my parents for their love and support during my study in the United States. Their continuing advice and love have always encouraged me towards excellence.
TABLE OF CONTENTS Abstract...3 Acknowlegements ...4 List of Tables ...6 List of Figures...7 Chapter 1. Introduction ...8
Chapter 2. Literature Review...10
2.1. Mobile Applications Usability...10
2.2. Older Adults ...11
2.3. Usability Testing Method ...15
Chapter 3. Hypotheses ...21 Chapter 4. Methods...22 4.1. Participants ...22 4.2. Environment ...22 4.3. Devices ...22 4.4. Procedure ...22 4.5. Analysis Method...28 Chapter 5. Results ...29 5.1. Demographics...29
5.2. Hypothesis 1: Training will increase TAM measures. ...30
5.3. Hypothesis 2: Age and type of application are factors of TAM measures and usability...43
5.4. Hypothesis 3: Usability characteristics will enhance user preference for mobile applications...52
5.5. Hypothesis 4: Participants will prefer to use mobile applications which have higher level of PU, PEU, and usability...56
Chapter 6. Discussion and Conclusion ...61
6.1. Conclusion ...61
6.2. Recommendations for Improvement for Applications Used in This Study 62 6.3. Recommendation and Future Work...63
References ...65
Appendix 1: Demographic Survey and Questionnaire ...69
Appendix 2 Modified PU and PEU Scales...70
LIST OF TABLES
Table 4-1 Experimental Session Schedule ... 23
Table 5-1 Demographics of younger group ... 29
Table 5-2 Demographics of older group ... 30
Table 5-3 Baseline of TAM measures for different age groups ... 31
Table 5-4 P-Value for TAM measures by apps ... 34
Table 5-5 Paired t-tests of PU/PEU for all participants ... 37
Table 5-6 Paired t-tests of PU/PEU for younger group ... 40
Table 5-7 Paired t-tests of PU/PEU for older group ... 43
Table 5-8 ANOVA for usability of four mobile applications ... 44
Table 5-9 ANOVA for PU of four mobile applications before training ... 47
Table 5-11 ANOVA for PEU of four mobile applications before training ... 49
Table 5-12 ANOVA for PEU of four mobile applications after training ... 50
Table 5-13 ANOVA of Regression Analysis of Usability Characteristics for Younger Group ... 53
Table 5-14 Coefficients Table of Regression Analysis for Younger Group ... 54
Table 5-15 ANOVA of Regression Analysis for Usability Characteristics of Older Group ... 55
Table 5-16 Coefficients Table of Regression Analysis for Older Group ... 55
Table 5-17 ANOVA of Overall Regression Analysis... 57
Table 5-18 ANOVA of Regression Analysis for Younger Group ... 59
LIST OF FIGURES
Figure 2-1 Smart phone users in the United States. ... 13
Figure 2-2 A framework for the design and implementation of usability testing of mobile applications. ... 16
Figure 2-3 The Technology Acceptance Model (TAM) (Davis, 1989). ... 18
Figure 2-4 Senior Technology Acceptance & Adoption Model (STAM). ... 20
Figure 4-1 Metrics mapped according to usability characteristic. ... 27
Figure 5-1 Bar Charts of PU for age groups by apps before training (session 1) and after training (session 2) ... 32
Figure 5-2 Bar Charts of PEU for age groups by apps before training (session 1) and after training (session 2). ... 33
Figure 5-3 Bar Charts of PU by apps for all participants before training (session 1) and after training (session 2)... 35
Figure 5-4 Bar Charts of PEU by apps for all participants before training (session 1) and after training (session 2)... 36
Figure 5-5 Bar Charts of PU by apps for younger group before training (session 1) and after training (session 2)... 38
Figure 5-6 Bar Charts of PEU by apps for younger group before training (session 1) and after training (session 2)... 39
Figure 5-7 Bar Charts of PU by apps for older group before training (session 1) and after training (session 2). ... 41
Figure 5-8 Bar Charts of PEU by apps for older group before training (session 1) and after training (session 2)... 42
Figure 5-9 Interaction Plot of Usability for four applications by age groups. ... 45
Figure 5-10 Interaction Plot of PU for four applications by age groups. ... 48
CHAPTER 1. INTRODUCTION
With market competition and consumer need, the development of multi-function smart phones and mobile applications is fast and changes our daily life markedly. Mobile applications are common on most smart phones and consist of software that runs on a mobile device and executes certain tasks for the user of the mobile phone [1].
Our world population of older people is steadily growing [22]. The United States Bureau of the Census estimated that there will be about 72.1 million older adults in the U.S. in 2030, which is almost twice their number (40.2 million) in 2010 [5]. Our world population is aging [22], and the group of older people (over 50 years old) is the fastest growing group of mobile applications users [23]. It is challenge for designers and researchers that develop appropriate mobile applications to satisfy older users’ need and help them preserve their life quality [22].
For the design of mobile applications, an efficient tool to evaluate mobile devices and applications is important. Usability is the ease of use and learnability of a human-made object [2]. It is an elementary criterion to evaluate the efficacy of these mobile techniques. An appropriate usability evaluation method for mobile applications is
necessary [3]. For using a new technology, the Technology Acceptance Model (TAM) is an information systems theory that simulates how users accept and use a technology [13]. It utilizes two scales: Perceived Usefulness (PU) and Perceived Ease of Use (PEU).
This research will compare the effect of different usability characteristics between younger and older users. The Technology Acceptance Model will be used as a theoretical construct in this research. Two age groups, 17 older adults (older than 50 years of age)
and 20 younger adults (between 18 and 30 years of age), were be recruited from local community. Four different mobile applications were tested on smart phones (iPhone / iPod touch) in the research. These mobile applications were selected because they have both same and different usability characteristics, and we predicted that because of these differences, users will have rate them differently with respect to different level of Perceived Usefulness (PU) and Perceived Ease of Use (PEU).
There are several objectives of this research. The first objective is to perform TAM for each application and find its PU and PEU level for both older and younger people. The second objective is to evaluate different usability characteristics of each application and determine if there is any relation and connection between usability characteristics and PU/PEU. The long-term goal for this research is to provide recommendations on
CHAPTER 2. LITERATURE REVIEW
2.1. Mobile Applications Usability 2.1.1. Mobile applications
With technological development and market competition, today’s mobile phones are designed for multiple purposes beyond the typical functions such as voice calls and texting. Various features and applications are added into regular phones and smart phones. Mobile applications are common on most modern phones, and consist of
software that runs on a mobile device and executes certain tasks for the user of the mobile phone [1]. These applications are served by a number of mobile application developers, publishers and providers. Also, they have an increasing number of markets. For example, the Apple Store’s website lists thousands of iPhone applications, and these applications can be placed in several categories: calculate, entertainment, games, news, productivity, search tools, social networking, sports, travel, utilities, and weather.
2.1.2. Usability
The term usability was originally derived from the term “user friendly” [2]. Generally speaking, usability is the ease of use and learnability of a human-made object.
While many definitions of usability exist, the definition which was specified in ISO/IEC 9126-1 (2001) is now widely applied [2]. The ISO organization has developed various usability standards, and its function is to provide and impose consistency. In ISO/IEC 9126-1, usability was defined as “the capability of the software product to be understood, learned, used and be attractive to the user, when used under specified
conditions” [2]. This definition is primarily concerned with a software product, and it is suitable for mobile applications [3].
ISO/IEC 9126-1 specifies usability by the following measurable attributes [2]: • Understandability: The capability of the software product to enable the user to
understand whether the software is suitable, and how it can be used for particular tasks and conditions of use.
• Learnability: The capability of the software product to enable the user to learn its application.
• Operability: The capability of the software product to enable the user to operate and control it.
• Attractiveness: The capability of the software product to be attractive to the user. For instance the use of colors or nature of graphical design.
2.2. Older Adults
Demographically, older adults (65+ years old) are the fastest growing group worldwide [4]. By 2030, it is estimated that there will be about 72.1 million older adults in the U.S., which is almost twice their number (40.2 million) in 2010 [5]. In addition, this group consists of the fastest growing group of mobile applications users. Therefore, to face the development and market competition and to help older adults preserve their life quality and remain independent [22], it is critical that mobile application developers and designers meet the needs of older customers.
2.2.1. Smart phone users
Young people, typically the earliest adopters of new technology, comprise the majority of smart phone users. However, according to the data of smart phone users in the United States which Nielsen reported in March 2011 (Figure 1), old adults age 55+ make up approximately 20% of the market. Smart phones are finally breaking into the older adult market.
For older adults, using a smart phone is far more than fun and games. Older adults are likely to use smart phones for more serious purposes than younger users. Starting in 2011, millions of baby boomers have begun to turn 65. This generation has an
unforgettable imprint on the development of culture and technology, and they will likely accept new mobile technologies that enable them to explore and access the web in new ways.
Figure 2-1 Smart phone users in the United States.
While overall smart phone users of old adults is still a small number, it’s important to note that the older users of technology is growing, and in surprising ways. Generally, users of technology who are older than 55 year old are considered as older users for mobile device market. According to a recent research [26], older people are gaming on their phones. Around 13% of 55- to 64-year-olds and 5% of people 65 and older play games using a smart phone or standard cellphone. Old adults will embrace the new mobile technology in the same as younger people, if the technology is really good for entertainment and daily life use.
2.2.2. Limitations of older users
Users of technological products (e.g., notebook, computers, and smart phones), are required to have some basic knowledge and capability. However, there are many age-related limitations for older users. When designing mobile applications for older users, designers and providers must consider these limitations. Characteristics of older adults can be related to cognition, physical capability, and perceptual ability.
For cognitive factors, the memory functions and spatial abilities of older users, which are both important to their navigation behavior, decline with age [6 – 9]. Older adults have more difficulties than younger users with navigation and spend more time on tasks due to more detours and lost time [10].
In terms of the physical factors, a previous study [11] formed five distinct human factors that show measurable disparities between older and younger people:
(1) Learning time (=time to perform task) (2) Speed of performance
(3) Error rate
(4) Retention over time (5) Subjective satisfaction
During usability testing, these different factors should be considered. Qualitative and quantitative analysis can be performed within these factors through usability questionnaire or heuristic evaluation.
Perceptual factors include vision and hearing. During the design and development of new technology for older adults, age dependent changes in vision, such as visual acuity
(ability to resolve detail), visual accommodation (ability to focus on close objects), color vision (ability to discriminate/perceive shorter wavelengths), contrast detection (ability to detect contrast), dark adaptation (ability to adapt quickly to darker conditions), and glare (susceptibility to glare), need to be considered [11].
2.3. Usability Testing Method 2.3.1. Usability testing
Usability evaluation is an elementary activity to test or evaluate mobile devices [3]. There are various usability evaluation methods, and they can be classified into three types: usability testing, usability inquiry, and usability inspection [12]. Usability testing requires representative users to work on typical tasks using the system or the prototype [2]. It is an evaluation tool used to estimate how well users can use a specific software system. Traditional guidelines and methods used in usability testing are not applicable to mobile devices, because they focus on desktop and environment [12]. Therefore, an appropriate usability testing method for mobile applications is necessary.
Zhang and Adipat [12] provided a generic framework that includes some major issues that researchers need to consider while designing a usability test for a mobile application, as shown in Figure 2.
Figure 2-2 A framework for the design and implementation of usability testing of mobile applications.
This first stage is the testing method for usability testing of mobile applications. For the usability testing of mobile applications, laboratory experiments and field studies are selected as the two major methodologies. During a laboratory experiment,
participants are required to complete certain tasks using a mobile application in a controlled lab environment. However, in a field study, participants are allowed to use mobile applications in a real environment [12]. Both of the lab and field study have pros
and cons. Therefore, to select an appropriate methodology for usability testing should depend on its purpose and usability features.
The second stage includes the tools used for usability testing of mobile
applications. Actual mobile devices are used in both lab experiment and field studies. Besides, for laboratory study, usability tests of mobile applications in laboratories can be performed on emulators. Both tools have their pros and cons. It is more controllable that use an emulator on a desktop, but it will omit some important factors of actual mobile devices and mobile context. Researchers can collect more realistic information and data from a test on actual mobile devices in a real environment than testing on emulators.
On the third stage, selection of usability attributes which will be measured should be considered. The usability attributes (e.g., learnability, efficiency, memorability, error, and satisfaction), can be tested both in lab and field study to evaluate the mobile
applications.
The fourth stage is data collection approaches. It is much easier for the data collection in laboratory experiments than field studies. There are several traditional data collection methods have been applied in usability testing for mobile applications, such as system log, verbal protocol, interview, questionnaire, and observation. Also, some data collection approaches have been developed for field studies, such as voice-mail diaries, multiple interviews, and Web diaries. For this experiment, we have chosen to conduct a laboratory experiment with a real phone in order to capture as much ecological validity while having control within the laboratory setting.
2.3.2. Technology Acceptance Model (TAM)
The Technology Acceptance Model (TAM) (shown in Figure 3) is an information systems theory that simulates how users accept and use a technology. The Technology Acceptance Model states that usefulness and ease of use are two essential elements in describing individuals’ attitudes when using a new technology [13]. TAM is considered the most influential and widely applied theory to evaluate users’ acceptance of
information systems. TAM, originally proposed by Davis [13] and adapted from the Theory of Reasoned Action, supposes that an individual’s information systems acceptance is described by two essential variables:
• Perceived Usefulness (PU) • Perceived Ease of Use (PEU)
Figure 2-3 The Technology Acceptance Model (TAM) (Davis, 1989). Previous research has shown some utilization of TAM on usability testing, especially many empirical studies which involve user acceptance of word processors [13], spreadsheets [14], e-mail [15], voice mail [16], and telemedicine technology [17]. Also, there are some usability principles (speaking the users’ language, consistency,
minimization of the user’s memory load, flexibility and efficiency of use, aesthetic and minimalist design, chunking, progressive levels of detail, navigational feedback, etc.) and usability testing criteria (use understandable graphics and terms, displays are easy to read, and information is easy to find).
These years, some derivational technology acceptance models, which are related to mobile devices and applications, have been studied. The Mobile Phone Technology Adoption Model (MOPTAM) [18] focused factors influencing mobile phone employ such as sociology, computer-supported cooperative work, and human-computer
interaction. The Senior Technology Acceptance& Adoption Model (STAM) for mobile technology [19] (as shown in Figure 4), integrated the study on TAM for senior users [20].
Figure 2-4 Senior Technology Acceptance & Adoption Model (STAM). Based on former research, Renaud and van Biljon [19] proposed the Senior
Technology Acceptance & Adoption Model (STAM) in 2008. As shown in Figure 3, this model contained several components, e.g., user context, perceived usefulness, intention to use, experimentation and exploration, ease of learning and use, confirmed usefulness, and actual use. According to former features, acceptance or rejection will be determined by ease of learning and use, or actual use. This model related technology acceptance factors to adoption stages, and explained the reason that why many older people failed to fully accept the new technology. However, STAM is useful for other demographic groups.
CHAPTER 3. HYPOTHESES
The objectives for this research are (1) to analyze TAM measures (PU/PEU) for each application, (2) to analyze the effect of training on TAM measures, (3) to evaluate usability characteristics and determine if there is any relation between Usability
characteristics and PU/PEU, and (4) to provide recommendations on application design for different age groups for mobile designers and providers. Differences between older and younger adults’ usability of mobile applications are studied, and recommendations of different mobile applications are given. The Technology Acceptance Model (TAM) is used as a theoretical construct in this research. Three hypotheses are tested in this research:
Hypothesis 1: Training will increase TAM measures.
Hypothesis 2: Age and type of application are factors of TAM measures and usability. Hypothesis 3: Usability characteristics will enhance user preference for mobile
applications.
Hypothesis 4: Participants will prefer to use mobile applications which have higher level of PU, PEU, and usability.
CHAPTER 4. METHODS
4.1. Participants
Seventeen older adults (older than 50 years of age) and twenty younger adults (between 18 and 30 years of age) were recruited from the local community, e.g., Ohio University (Athens and Lancaster), a local hospital (O’Bleness, Holzer Clinic), the Athens Village, the Senior Center, and local civic organization in general.
All participants were required to own smart phones (iPhone) or similar devices (iPod touch), or have experience using smart phones. All participants should be able to use computers and smart phones. All participants were paid $25 for the whole
experiment, including survey and test. 4.2. Environment
All the experiments were hold in Ohio University facility and public location, e.g., Human Factors and Ergonomics Lab and Alden Library in Ohio University, and the Athens County Senior Center. The noise, light, and temperature were controllable. All devices used school or public Wi-Fi with same loading speed.
4.3. Devices
All participants used smart phones (iPhone), or similar devices (iPod touch). All smart phones were able to connect Wi-Fi.
4.4. Procedure
All participants were recruited from the local community and participated on an informed consent basis before all experimental sessions. All participants were tested in small groups or individually. All participants had completed a demographic survey and
questionnaire (Appendix 1) before all experiment session. One experimental session, which included five parts, was proposed. The whole lab session took approximately two hours. Timeline of the experimental session is shown in Table 4.1.
Table 4-1 Experimental Session Schedule
Session Part Session Time (min)
IRB Completion 10 Baseline Survey 10 First Testing 30 Training 20 Practicing 20 Second Testing 40
Completion of Forms and Payment 10
Total 130
4.4.1. Baseline Survey
In computer lab, all participants were given an initial introduction of smart phones and different mobile applications. First, they were required to finish a survey and
questionnaire (Appendix 2) to evaluate the applications they used most frequently on their smart phone.
Participants were required to download four applications on their smart phones. These applications were:
APP 2 KAYAK; APP 3 Frugal Flyer; APP 4 FOX2 Weather. 4.4.2. First Testing
All participants completed two tasks using each application which was
downloaded on their smart phones, and they were allowed to spend 5 minutes on each application.
APP 1: Kroger Co.
1. Find a Kroger store near you.
2. Find a dairy product on sale this week. APP 2: KAYAK
1. Find a hotel near you and the lowest price for one room tonight.
2. Find a one-way flight from CMH to SFO this weekend and the lowest price. APP 3: Frugal Flyer
1. Find a hotel in Athens and the lowest price for one room tonight.
2. Find a one-way flight from CMH to SFO this weekend and the lowest price. APP 4: FOX2 Weather
1. Find tomorrow’s weather condition in Athens.
2. Find the weather condition of next Tuesday in Athens.
After participants completed the two tasks for each application, they were asked to complete a survey to evaluate each mobile application based on modified PU and PEU Scales, which is shown in Appendix 2.
4.4.3. Training
After participants completed the evaluations, a training session was given by the instructor on how to use these mobile applications on their smart phones. For the needs of customers, a brief introduction and training for each mobile application was done. In order to guide the participants to use the mobile applications and perform certain tasks, the training included introduction of the functions for each application and a
demonstration of certain task on each application. The training included: 1. Explanation of what the application is and why use it could be used; 2. Details of the functions of the application;
3. How to use the application;
4. A demonstration of performing a task on the application; 5. Recommendations for using the application.
The training session took approximately 20 minutes. 4.4.4. Practice
To accept the new technology and learn how to use it, participants were given 20 minutes to practice and perform different tasks on these applications.
APP 1 Kroger Co.;
APP 2 KAYAK;
APP 3 Frugal Flyer; APP 4 FOX2 Weather.
4.4.5. Second Testing
After training and practicing on these applications, all participants were required to finish two different tasks on each application. They were allowed to have 5 minutes on each application. The following tasks in different order which in first test were given: APP 4: FOX2 Weather
1. Will it rain in the next six hours in Athens?
2. How about the weather condition in New York City next Tuesday? APP 3: Frugal Flyer
1. For this weekend, find an available car in CMH and its rental price.
2. For your summer vacation, find the price for a round-trip to NYC in July (from CMH).
APP 2: KAYAK
1. For this weekend, find an available car in CMH and its rental price.
2. For you summer vacation, find the price for a round-trip to NYC in July (from CMH).
APP 1: Kroger Co.
1. Find a coupon for Health and Pharmacy.
2. Change another store in Columbus (OH) and write down its address.
When participants completed the two tasks for each application, they completed a survey to evaluate each mobile application based on modified PU and PEU Scales again, which is shown in Appendix 2.
Then, the participants were asked to fill out a usability characteristics checklist (Appendix 3) for each mobile application they used during the experiment. This usability characteristics checklist (Appendix 3) includes twelve questions related to the four measurable attributes of usability: understandability (Question 1, 2, 3, 7), learnability (Question 1, 2, 4), operability (Question 4, 5, 6, 7, 8, 9), and attractiveness (Question 10, 11, 12). It is based on a heuristic evaluation checklist for systems (Pierotti, 2007) [25]. The four measurable attributes of usability, which are described as a metric of quality characteristics including their sub-characteristics, are shown in Figure 5 [24].
Figure 4-1 Metrics mapped according to usability characteristic. When participants finished all experimental sessions, they were paid $25 per person.
4.5. Analysis Method
Four hypotheses were tested in this experiment, and all of the four hypotheses utilized the TAM measures (PU and PEU). Sum TAM scores were used in the analyses. Hypothesis 1: Training will increase TAM measures.
To test this hypothesis, paired t-test was used to compare the difference of TAM measures before and after training overall and by age groups. Meanwhile, two-sample t-test was used to set a baseline and measure the increase of TAM measures for each age group overall and by applications.
Hypothesis 2: Age and type of application are factors of TAM measures and usability. To test this hypothesis, a two-way GLM (General Linear Model) was used. Hypothesis 3: Usability characteristics will enhance user preference for mobile
applications.
To test this hypothesis, we conducted a stepwise regression with all the usability characteristics as independent variables and user preference as the dependent variable. Hypothesis 4: Participants will prefer to use mobile applications which have higher level
of PU, PEU, and usability.
To test this hypothesis, stepwise regression analysis was used to determine if PU, PEU, and usability were predictive of user preference for mobile applications. They were done together and by age groups.
CHAPTER 5. RESULTS
5.1. Demographics
Thirty-seven participants were recruited from local community. Twenty
participants were in the younger group (18-30 years old), and seventeen participants were in the older group (50+ old). All participants owned smart phones (iPhone) or similar devices (iPod touch), or had experience in using smart phones. Their education level ranged from associate degree (some college or no college) to PhD or equivalent degree. All participants completed a demographic survey and questionnaire before the
experiment. The questionnaire included participants’ experience of using smart phones or similar devices (iPod), number of mobile applications that were downloaded on their devices, and the total hours per week they spent on the mobile applications. Table 5-1 and Table 5-2 list the descriptive data of demographics and questionnaire.
Table 5-1 Demographics of younger group
Variable Mean Std. Dev Range
Age (years) 24.55 3.502 19 – 30
Years of using smart phones 1.175 0.974 0.1 – 4
Number of downloaded
mobile applications 54.80 44.44 4 – 200
Hours per week on mobile
applications 11.18 8.79 1 – 35
Gender
Male Female
Table 5-2 Demographics of older group
Variable Mean Std. Dev Range
Age (years) 63.06 9.85 50 – 87
Years of using smart phones 1.25 1.91 0 – 7
Number of downloaded
mobile applications 11.71 11.42 0 – 35
Hours per week on mobile
applications 4.44 4.13 0 – 15
Gender
Male Female
7 10
5.2. Hypothesis 1: Training will increase TAM measures.
To test this hypothesis, a paired t-test was used to compare the difference of TAM measures before and after training overall and by age groups. Meanwhile, two-sample t-test was used to set a baseline and measure the increase of TAM measures for each age group overall and by applications.
5.2.1 Baseline for different age groups
To determine the baseline of the performance for different age groups, there was a testing session before training and practice session. During the first testing session, all participants were required to complete two tasks on four mobile applications, and none of them had used these applications before the test. First, all mobile applications were grouped and two-sample t-tests were run (see Table 5-3) to determine if baseline scores were different between age groups. These did not lead to significant results (PU: p-value
= 0.742; PEU: p-value = 0.161). To determine if there are directional differences between applications, bar charts were drawn by applications for each age group (before and after training). See Figure 5-1 and 5-2. For all the results, statistically significant results were those having a p-value < 0.05.
Table 5-3 Baseline of TAM measures for different age groups Age Group
PU PEU
Mean SD Mean SD
Younger 26.9 11.1 30.4 11.5
Figure 5-1 Bar Charts of PU for age groups by apps before training (session 1) and after training (session 2) App Session AGE 4 3 2 1 2 1 2 1 2 1 2 1 Olde r Youn ger Olde r Youn ger Olde r Youn ger Olde r Youn ger Olde r Youn ger Olde r Youn ger Olde r Youn ger Olde r Youn ger 40 30 20 10 0 M e a n o f P U Chart of Mean PU
Figure 5-2 Bar Charts of PEU for age groups by apps before training (session 1) and after training (session 2).
In Figure 5-1 and 5-2, the data indicated that directional differences between younger and older groups were not constant between applications. Therefore, t-tests were run (with unequal variances) for each application by age groups and their p-values are shown in Table 5-4. App Session AGE 4 3 2 1 2 1 2 1 2 1 2 1 Olde r Youn ger Olde r Youn ger Olde r Youn ger Olde r Youn ger Olde r Youn ger Olde r Youn ger Olde r Youn ger Olde r Youn ger 40 30 20 10 0 M e a n o f P E U
Table 5-4 P-Value for TAM measures by apps before training (session 1) and after training (session 2)
TAM Session Application 1 2 3 4 PU 1 0.719 0.944 0.074 0.108 2 0.979 0.707 0.048 0.533 PEU 1 0.994 1.000 0.054 0.288 2 0.999 1.000 0.036 0.847
In Table 5-4, significant results of p-values were bold. Results indicated that TAM measures were different for Application 3 (Frugal Flyer) by age groups, but not for the other ones.
5.2.2 Paired t-test
The experiment included one training session given between two test sessions. During the training session, the instructor gave participants an introduction for each mobile application, which included a brief introduction of the functions for each application and a demonstration of certain tasks on each application. All participants were allowed to practice different tasks on the four mobile applications. The training and practice took 40 minutes in total. To analyze the effect of training on TAM measures, bar charts of the sum scores of PEU and PU for different mobile applications (before
training, session 1, and after training, session 2) are drawn for all participants (see Figure 5-3 and 5-4). Then, a paired t-test was conducted (see Table 5-5).
Figure 5-3 Bar Charts of PU by apps for all participants before training (session 1) and after training (session 2).
App Session 4 3 2 1 2 1 2 1 2 1 2 1 1400 1200 1000 800 600 400 200 0 S u m o f P U Chart of Sum( PU )
Figure 5-4 Bar Charts of PEU by apps for all participants before training (session 1) and after training (session 2).
App Session 4 3 2 1 2 1 2 1 2 1 2 1 1400 1200 1000 800 600 400 200 0 S u m o f P EU
Table 5-5 Paired t-tests of PU/PEU for all participants before training (session 1) and after training (session 2)
Session N Mean SD SE Mean T-Value P-Value
PU_1 148 27.135 10.481 0.862
-8.72 0.000
PU_2 148 32.878 8.657 0.712
Difference 148 -5.743 8.016 0.659
95% upper bound
for Mean Difference -4.653
PEU_1 148 29.372 10.254 0.843
-7.03 0.000
PEU_2 148 33.743 8.836 0.726
Difference 148 -4.372 7.568 0.622
95% upper bound
for Mean Difference -3.342
In Figure 5-3 and 5-4, results indicated that TAM measures had increased after training. Quantitative results from Table 5-5 shows TAM measures had significant differences after training.
To determine if training had the same effect on TAM measures for different age groups, bar charts and paired t-tests were done by age groups. For the younger group, bar charts of TAM measures and the results of paired t-tests are shown in Figure 5-5 and 5-6, and Table 5-6. Results indicated that training had a significant effect on increasing TAM measures.
Figure 5-5 Bar Charts of PU by apps for younger group before training (session 1) and after training (session 2).
App Session 4 3 2 1 2 1 2 1 2 1 2 1 800 700 600 500 400 300 200 100 0 S u m o f P U Chart of Sum( PU )
Figure 5-6 Bar Charts of PEU by apps for younger group before training (session 1) and after training (session 2).
App Session 4 3 2 1 2 1 2 1 2 1 2 1 900 800 700 600 500 400 300 200 100 0 S u m o f P EU
Table 5-6 Paired t-tests of PU/PEU for younger group before training (session 1) and after training (session 2)
Session N Mean SD SE Mean T-Value P-Value
PU_1 80 26.88 11.11 1.24
-6.98 0.000
PU_2 80 32.80 9.17 1.03
Difference 80 -5.925 7.589 0.848
95% upper bound for
Mean Difference -4.513
PEU_1 80 30.44 11.49 1.28
-5.36 0.000
PEU_2 80 34.84 9.58 1.07
Difference 80 -4.400 7.336 0.820
95% upper bound for
Mean Difference -3.305
For the older group, bar charts of TAM measures and results of paired t-tests are shown in Figure 5-7 and 5-8, and Table 5-7. Results indicated that training had a
Figure 5-7 Bar Charts of PU by apps for older group before training (session 1) and after training (session 2). App Session 4 3 2 1 2 1 2 1 2 1 2 1 600 500 400 300 200 100 0 S u m o f P U Chart of Sum( PU )
Figure 5-8 Bar Charts of PEU by apps for older group before training (session 1) and after training (session 2).
App Session 4 3 2 1 2 1 2 1 2 1 2 1 600 500 400 300 200 100 0 S u m o f P EU
Table 5-7 Paired t-tests of PU/PEU for older group before training (session 1) and after training (session 2)
Session N Mean SD SE Mean T-Value P-Value
PU_1 68 27.44 9.76 1.18
-5.34 0.000
PU_2 68 32.97 8.08 0.98
Difference 68 -5.53 8.54 1.04
95% upper bound
for Mean Difference -3.80
PEU_1 68 28.12 8.49 1.03
-4.54 0.000
PEU_2 68 32.46 7.74 0.94
Difference 68 -4.338 7.887 0.956
95% upper bound
for Mean Difference -2.743
The results show that there is significant increase of TAM measures after training overall, both younger group and older group. This indicates that even a small amount of training created an improvement in perceived usefulness and perceived ease of use.
5.3. Hypothesis 2: Age and type of application are factors of TAM measures and usability.
To test this hypothesis, a two-way GLM (General Linear Model) was used. During the experiment, four mobile applications in different categories (e.g., shopping, travel, weather) were chosen. Each mobile application has different usability
characteristics, and different usability characteristics are hypothesized to differently influence PU and PEU. For different age groups, participants had different response for the same mobile application. To analyze the different response between age groups and mobile applications, the general linear model and interaction plots were done for
usability, PU, and PEU for session 2 (before and after training). Residual plots for TAM measures and usability were done, and results indicated that the residual appeared to be normally distributed.
5.3.1. ANOVA of mobile application usability
An Analysis of variance for mobile application usability was done after training. Table 5-8 shows the results for this analysis.
Table 5-8 ANOVA for usability of four mobile applications
Source DF Sum of Square (adj) Mean Square F P Application 3 7350.8 2450.3 18.50 0.000 Age 1 1260.4 1260.4 9.52 0.002 Application*Age 3 2583.6 861.2 6.50 0.000 Error 140 18542.3 132.4 Total 147 R-Sq (adj) 36.18%
Results from Table 5-8 indicated that there were significant main effects as well as interaction effects (at p<0.05 for age, application, and age * application). Post-Hoc Tukey tests indicated at 95% confidence that mean scores of usability characteristics were significantly different between age groups. For four mobile applications, application 1 (Kroger) and 2 (KAYAK) were not different, but they were different from application 3 (Frugal Flyer) and 4 (FOX2 Weather). Application 3 and 4 were different from each other. An interaction plot of usability scores for different applications by age groups is shown in Figure 5-9.
Figure 5-9 Interaction Plot of Usability for four applications by age groups. 2 1 75 70 65 60 55 50 45 AGE M e a n 1 2 3 4 App2 Interaction Plot for Usability
From the interaction plot of usability in Figure 5-9, younger and older participants had different response of usability on each application. Younger participants had
evaluated Application 1(Kroger), 2 (KAYAK), and 4 (FOX2 Weather) with a higher level on usability scores than older participants. For Application 3 (Frugal Flyer), older participants gave a higher score on mobile application usability. For all the mobile applications, younger and older participants had ranked Application 3 and 4 on usability scores in the same way; even both of Application 1 and 2 had higher level of usability than Application 3 and 4, younger and older participants had evaluated them in different way: younger participants rated Application 2 a higher score on usability than
Application 2, but older participants rated them contrary. 5.3.2. ANOVA of PU
Analysis of variance was run for perceived usefulness both before and after training. Table 5-9 and 5-10 show the analysis of PU for four mobile applications before and after training.
Table 5-9 ANOVA for PU of four mobile applications before training Source DF Sum of Square (adj) Mean Square F P Application 3 3185.20 1061.73 12.18 0.000 Age 1 11.78 11.78 0.14 0.714 Application*Age 3 604.99 201.66 2.31 0.079 Error 140 12200.69 87.15 Total 147 R-Sq (adj) 24.45%
Table 5-10 ANOVA for PU of four mobile applications after training
Source DF Sum of Square (adj) Mean Square F P Application 3 2299.08 766.36 13.36 0.000 Age 1 1.07 1.07 0.02 0.892 Application*Age 3 500.38 166.79 2.91 0.037 Error 140 8029.74 57.36 Total 147 R-Sq (adj) 23.48%
From Table 5-9 and 5-10, results for PU scores indicated although age was not a significant factor, application was a significant factor both before and after training, and the age * application was marginally significant before training and was a significant
interaction factor after training. Post-Hoc Tukey tests indicated at 95% confidence that mean scores of usability characteristics were not significantly different between age groups. For four mobile applications, only application 3 (Frugal Flyer) had different results of PU from other applications at both time points. An interaction plot of PU scores after training for different applications by age groups is shown in Figure 5-10.
Figure 5-10 Interaction Plot of PU for four applications by age groups.
From the interaction plot of PU in Figure 5-10, younger and older participants had different response of PU on each application. Younger participants had evaluated
Application 1(Kroger), 2 (KAYAK), and 4 (FOX2 Weather) with a higher level on PU scores than older participant, as same as usability interaction plot. For Application 3
2 1 40 35 30 25 AGE M e a n 1 2 3 4 App2 Interaction Plot for PU
(Frugal Flyer), older participants gave a higher score on PU. For all the mobile
applications, younger and older participants had ranked Application 2 and 3 on PU scores in the same order; but for Application 1 and 4, younger and older participants ranked them in different orders: younger participants rated Application 1 a higher score on PU than Application 4, but older participants rated them contrary.
5.3.3. ANOVA of PEU
Analysis of variance was run for Perceived Ease of Use both before and after training. Table 5-11 and 5-12 shows the ANOVA of PEU for four mobile applications before and after training.
Table 5-10 ANOVA for PEU of four mobile applications before training
Source DF Sum of Square (adj) Mean Square F P Application 3 3587.49 1195.83 16.53 0.000 Age 1 197.81 197.81 2.73 0.100 Application*Age 3 1248.46 416.15 5.75 0.001 Error 140 10127.61 72.34 Total 147 R-Sq (adj) 34.48%
Table 5-11 ANOVA for PEU of four mobile applications after training Source DF Sum of Square (adj) Mean Square F P Application 3 3177.74 1059.25 22.16 0.000 Age 1 208.49 208.49 4.36 0.039 Application*Age 3 1084.36 361.45 7.56 0.000 Error 140 6691.26 47.79 Total 147 R-Sq (adj) 38.78%
From Table 5-11 and 5-12, results for PEU scores indicated although age was not significant, application and the interaction factor (age * application) were significant before training, and all main factors and the interaction factor were significant after training (at p<0.05 level). Post-Hoc Tukey tests indicated at 95% confidence that mean scores of usability characteristics were significant different between age groups. For four mobile applications, only application 3 (Frugal Flyer) had different results of PU from other applications at both time points. An interaction plot of PEU scores after training for different applications by age groups is shown in Figure 5-11.
Figure 5-11 Interaction Plot of PEU for four applications by age groups. From the interaction plot of PEU in Figure 5-11, younger and older participants had a different response for PEU on each application. Younger participants had evaluated Application 1(Kroger), 2 (KAYAK), and 4 (FOX2 Weather) with a higher level on PEU scores than older participants, and as the same for usability and PU in the interaction plots. For Application 3 (Frugal Flyer), older participants gave a higher score on PEU. Even both of younger and older groups evaluated Application 1, 2, and 4 with a higher level on PEU, they ranked their PEU in different ways: younger participants rated Application 2 a highest score on PEU, and Application 1 was obtained a very close PEU score; but older participants rated Application 4 a highest score on PEU.
2 1 40 35 30 25 20 AGE M e a n 1 2 3 4 App2 Interaction Plot for PEU
From the results, we found that age, application, and age * application were significant factors of TAM measures and usability.
5.4. Hypothesis 3: Usability characteristics will enhance user preference for mobile applications.
To test this hypothesis, we conducted a stepwise regression with all the usability characteristics as independent variables and user preference as the dependent variable.
The different mobile applications had different usability characteristics (e.g., font size, function keys, scrolling menu). All characteristics reflected one or more aspects of system usability (e.g., understandability, learnability, operability, and attractiveness), and these aspects of usability can affect users’ preference on each application. In the usability characteristics checklist (see Appendix 3), there are 11 questions which related to four aspects of system usability. All participants were required to evaluate them in a score scale from 1 (strongly disagree) to 7 (strongly agree). Stepwise regression analyses were done for all usability characteristics for each mobile application that was tested during the experiment. Analyses were done separately for each age group.
5.4.1. Regression Analysis for Usability Characteristics for Younger Group
Results of the stepwise regression analysis of usability characteristics for younger group are shown in Equation (1). The Minitab default levels of significance were used for these analyses. The analysis is shown in Equation (1) below:
Regression Analysis Equation (1):
Preference = – 0.392 + 0.400 (3) + 0.335 (2) + 0.206 (1) + 0.110 (10) R-Sq = 65.3%
R-Sq (adj) = 63.5% P-Value = 0.000 Where:
(3) – logical menu choices and function keys; (2) – prevent user to make errors;
(1) – font size is large enough;
(10) – less steps to accomplish task, complexity.
For the younger group, the most significant usability characteristics were 3 (logical menu choices and function keys), 2 (prevent user to make errors), 1 (font size is large enough), and 10 (less steps to accomplish task, complexity). Regression analysis results usability characteristics for the younger group are shown in Table 5-13 and 5-14.
Table 5-12 ANOVA of Regression Analysis of Usability Characteristics for Younger Group Source DF SS MS F P Regression 4 202.158 50.539 35.30 0.000 Residual Error 75 107.392 1.432 Total 79 309.550
Table 5-13 Coefficients Table of Regression Analysis for Younger Group Predictor Coefficient SE Coefficient T-Value P-Value
Constant – 0.3915 0.6077 – 0.64 0.521
3 0.3997 0.1301 3.07 0.003
2 0.3553 0.1214 2.93 0.005
1 0.2061 0.1225 1.68 0.097
10 0.1096 0.0743 1.47 0.145
5.4.2. Regression Analysis for Usability Characteristics for Older Group Result of stepwise regression analysis of usability characteristics for younger group is shown in Equation (2).
Regression Analysis Equation (2):
Preference = – 0.383 + 0.483 (9) – 0.346 (1) + 0.522 (7) + 0.349 (4) R-Sq = 58.4%
R-Sq (adj) = 55.8% P-Value = 0.000 Where:
(9) – appropriate number of function keys; (1) – font size is large enough;
(7) – prompts and cues;
For the older group, the most significant usability characteristics were 9 (appropriate number of function keys), 1 (font size is large enough), 7 (prompts and cues), and 4 (scrolling menu). Results for this regression analysis are shown in Table 5-15 and 5-16.
Table 5-14 ANOVA of Regression Analysis for Usability Characteristics of Older Group
Source DF SS MS F P
Regression 4 120.907 30.227 22.13 0.000
Residual Error 63 86.034 1.366
Total 67 206.941
Table 5-15 Coefficients Table of Regression Analysis for Older Group Predictor Coefficient SE Coefficient T-Value P-Value
Constant – 0.3832 0.8102 – 0.47 0.638
9 0.4831 0.1423 3.40 0.001
1 – 0.3465 0.0731 – 4.74 0.000
7 0.5220 0.1554 3.36 0.001
4 0.3494 0.1610 2.17 0.034
From the results of regression analysis of usability characteristics, usability characteristics that have a significant effect on users’ preference and TAM measures (PU/PEU) can be determined. According to regression analysis shown in Equation (1),
there were five usability characteristics that significantly predicted overall users’ preference: scrolling menu is easy to use; appropriate number of function keys; prevent user to make errors; font size is large enough; prompts and cues.
For the younger group, from regression analysis Equation (2), there are four usability characteristics that significantly predicted users’ preference: logical menu choices and function keys; prevent user to make errors; font size is large enough; and less steps to accomplish task, complexity. For the older group, from regression analysis Equation (2), there are four usability characteristics that significantly predicted users’ preference: appropriate number of function keys; font size is large enough; prompts and cues; scrolling menu is easy to use.
5.5. Hypothesis 4: Participants will prefer to use mobile applications which have higher level of PU, PEU, and usability.
To test this hypothesis, we summed the usability score (overall and by category) and used a stepwise regression analysis to determine the relation between usability characteristics and user preference. These usability characteristics are listed in Appendix 3.
At the end of the experiment, all participants chose their preference for each mobile application in the usability checklist, on a scale with scores from 1(strongly dislike) to 7 (strongly like). To determine the relationship between preference and different TAM measures or usability characteristics, stepwise regression analyses were done for each age group and each application. To verify the correctness of the equation,
we split the overall data (both younger and older groups) into two samples, and applied regression analysis for each data sample.
5.5.1. Overall Regression Analysis
The regression equation for the combined dataset is shown in Equation (3) below: Regression Analysis Equation (3):
Preference = -1.000 + 0.109 PU + 0.0450 Usability R-Sq = 59.33%
R-Sq(adj) = 58.76% P-Value = 0.000
Table 5-17 shows the results of ANOVA for overall regression analysis.
Table 5-167 ANOVA of Overall Regression Analysis
Source DF SS MS F P
Regression 2 308.31 154.16 105.74 0.000
Residual Error 145 211.39 1.46
Total 147 519.70
For split sample 1, Regression Analysis Equation (4): Preference = -0.1547 + 0.0489 Usability + 0.079 PU R-Sq = 55.63%
R-Sq(adj) = 54.41% P-Value = 0.000
Preference = -2.0368 + 0.143 PU + 0.043 Usability R-Sq = 65.89%
R-Sq(adj) = 64.90% P-Value = 0.002
The equation was validated through the splitting technique. Based on the results from the regression analysis for all participants on four mobile applications, users’ preferences on these applications are related to PU and usability, which means that users prefer to use certain mobile applications with higher level of usefulness and usability characteristics.
5.5.2. Regression Analysis for Younger Group
In addition to the analysis on the complete data set, stepwise regression was run for the younger and older groups. These analyses are shown below in Equation (6):
Regression Analysis Equation (6):
Preference = -1.720 + 0.0771 Usability + 0.062 PU R-Sq = 71.29%
R-Sq(adj) = 70.54% P-Value = 0.002
Table 5-17 ANOVA of Regression Analysis for Younger Group
Source DF SS MS F P
Regression 2 220.67 110.33 95.58 0.000
Residual Error 77 88.88 1.15
Total 79 309.55
5.5.3. Regression Analysis for Older Group
We also ran this analysis just for the older group. This regression equation is shown below in Equation (7):
Regression Analysis Equation (7):
Preference = -0.2395 + 0.119 PU + 0.055 PEU R-Sq = 53.62%
R-Sq(adj) = 52.19% P-Value = 0.036
Table 5-18 ANOVA of Regression Analysis for Older Group
Source DF SS MS F P
Regression 2 110.958 55.479 37.57 0.000
Residual Error 65 95.983 1.477
Total 67 206.941
For the younger group, users’ preferences were related to both usability
characteristics and TAM measures. For the older group, users’ preferences were related to TAM measures (PEU/PU). The usefulness and ease of use for mobile applications have an important influence on users’ preference for older customers.
From the regression analysis equations (3), (6), (7), participants preferred to use mobile applications that have higher level of PU and usability and the equations were validated for the entire group. There is a same conclusion with younger group; for the older group, higher levels of PU and PEU led to higher scores for customer preference.
CHAPTER 6. DISCUSSION AND CONCLUSION
6.1. Conclusion
This research has utilized a modified Technology Acceptance Model (TAM) to evaluate mobile applications. According to the experimental results, training has
significant effect on the increase of TAM measures; mobile applications that have higher sum usability scores have higher level of TAM measures (PEU/PU). Also, customers prefer to use mobile applications which have higher TAM measures. This conclusion indicates that even highly intuitive applications can still benefit from usability research.
For different age groups, different usability characteristics influence on
customers’ preference. When designing mobile applications to satisfy customers’ needs, designers should consider these usability characteristics for different age groups.
Younger people prefer to use mobile applications with logical menu choices and function keys, simple design that they can complete task with fewer steps. Older people prefer the applications with appropriate number of function keys, and scrolling menu which is easy to use. For both of the two age groups, they think that font size should be large enough, and appropriate prompts and cues should be included to prevent users making errors. Also, TAM measures (PU/PEU) should be considered when designing mobile
applications. Younger people considered usability and usefulness of mobile applications when they were using new mobile applications. Older people considered both usefulness and ease of use when they were learning how to use new mobile applications.
6.2. Recommendations for Improvement for Applications Used in This Study
In this research, four mobile applications for iPhone/iPod were tested. They are in different categories: shopping (Kroger), travel (KAYAK, Frugal Flyer), and weather (FOX2 Weather). Each kind of mobile application has its unique function and usability features. To satisfy customers’ need for different age groups, designers could make several changes on these mobile applications.
For Kroger, it is a simple shopping application, and has got high level of TAM measures (PU/PEU). If the data entry room is bigger when users typing in store address, it is better for older people to use it.
For KAYAK, it is a travel application, and also has high level of TAM measures. Two functions could be changed to make it easier to use. First, scrolling button could be bigger when users choosing car pick-up time and price range. Second, certain prompts or cues should pop out if users make a mistake which will lead a non-results search.
For Frugal Flyer, it is also an application for travel, but it got the lowest level of TAM measures and usability scores. Several crucial disadvantages should be noticed. Font size of this application is extreme small for both older and younger people. Too much data should be typed in during searching. Users should choose search engine themselves after they typed in all necessary data. It takes too many steps and is not easy for users to get appropriate results, even if it supplies much comparative information from companies.
For FOX2 Weather, it is a simple weather application. The background contrast is designed nicely. But its font size is too small to be recognized. It has a long scrolling
menu which should make it easy to use, but there is no distinct sign to help users to find it, which make it worse to use.
6.3. Recommendation and Future Work
In this research, all participants were recruited from local community, and only two age groups were tested. Most of them were highly educated and had a bachelors or above. Future work could recruit more participants from different areas with different education background, and larger age range is necessary. More data points will enhance the results, and age could be set as a factor of user preference on mobile applications for future study.
This lab experiment only included one session, which had a forty minutes of training and practice. Future work could extend experiment by adding one or two more field sessions in one or two months after the original lab session. This would allow participants to spend more time learning new mobile applications and having field experience on using these applications.
Many new mobile applications are designed and published every day. The original research only chose four applications in shopping, travel, and weather. Future studies could choose more applications in different categories, like entertainment, health and medical, business, education, game, navigation, social networking, and productivity.
TAM was used as a theoretical construct, and eleven items of usability
characteristics in four aspects were tested in this research. Future work could test more usability characteristics, and use other model or testing tools to study usage of mobile applications. Different versions and products of same mobile applications could be
studied and compared, for example, applications between different kinds of smart phones, operating systems, or web vs. mobile based systems.
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