Technology Acceptance Model for Determining the Effects of. Age, Usability, and Content on Mobile Application Usage. A thesis presented to

Transcription

1 Technology Acceptance Model for Determining the Effects of 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 0 0 Shijing Liu. All Rights Reserved.

2 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 Dean, Russ College of Engineering and Technology

3 3 ABSTRACT LIU, SHIJING, M.S., August 0, 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 (8 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: () training has significant effect on the increase of TAM measures, () 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 Assistant Professor of Industrial and Systems Engineering

4 4 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.

5 5 TABLE OF CONTENTS Abstract...3 Acknowlegements...4 List of Tables...6 List of Figures...7 Chapter. Introduction...8 Chapter. Literature Review Mobile Applications Usability Older Adults Usability Testing Method...5 Chapter 3. Hypotheses... Chapter 4. Methods Participants Environment Devices Procedure Analysis Method...8 Chapter 5. Results Demographics Hypothesis : Training will increase TAM measures Hypothesis : 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...56 Chapter 6. Discussion and Conclusion Conclusion Recommendations for Improvement for Applications Used in This Study Recommendation and Future Work...63 References...65 Appendix : Demographic Survey and Questionnaire...69 Appendix Modified PU and PEU Scales...70 Appendix 3 Usability Characteristics Checklist...7

6 6 LIST OF TABLES Table 4- Experimental Session Schedule... 3 Table 5- Demographics of younger group... 9 Table 5- Demographics of older group Table 5-3 Baseline of TAM measures for different age groups... 3 Table 5-4 P-Value for TAM measures by apps Table 5-5 Paired t-tests of PU/PEU for all participants Table 5-6 Paired t-tests of PU/PEU for younger group Table 5-7 Paired t-tests of PU/PEU for older group Table 5-8 ANOVA for usability of four mobile applications Table 5-9 ANOVA for PU of four mobile applications before training Table 5- ANOVA for PEU of four mobile applications before training Table 5- ANOVA for PEU of four mobile applications after training Table 5-3 ANOVA of Regression Analysis of Usability Characteristics for Younger Group Table 5-4 Coefficients Table of Regression Analysis for Younger Group Table 5-5 ANOVA of Regression Analysis for Usability Characteristics of Older Group Table 5-6 Coefficients Table of Regression Analysis for Older Group Table 5-7 ANOVA of Overall Regression Analysis Table 5-8 ANOVA of Regression Analysis for Younger Group Table 5-9 ANOVA of Regression Analysis for Older Group... 60

7 7 LIST OF FIGURES Figure - Smart phone users in the United States Figure - A framework for the design and implementation of usability testing of mobile applications Figure -3 The Technology Acceptance Model (TAM) (Davis, 989) Figure -4 Senior Technology Acceptance & Adoption Model (STAM) Figure 4- Metrics mapped according to usability characteristic Figure 5- Bar Charts of PU for age groups by apps before training (session ) and after training (session )... 3 Figure 5- Bar Charts of PEU for age groups by apps before training (session ) and after training (session ) Figure 5-3 Bar Charts of PU by apps for all participants before training (session ) and after training (session ) Figure 5-4 Bar Charts of PEU by apps for all participants before training (session ) and after training (session ) Figure 5-5 Bar Charts of PU by apps for younger group before training (session ) and after training (session ) Figure 5-6 Bar Charts of PEU by apps for younger group before training (session ) and after training (session ) Figure 5-7 Bar Charts of PU by apps for older group before training (session ) and after training (session ) Figure 5-8 Bar Charts of PEU by apps for older group before training (session ) and after training (session )... 4 Figure 5-9 Interaction Plot of Usability for four applications by age groups Figure 5-0 Interaction Plot of PU for four applications by age groups Figure 5- Interaction Plot of PEU for four applications by age groups.... 5

8 8 CHAPTER. 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 []. Our world population of older people is steadily growing []. The United States Bureau of the Census estimated that there will be about 7. million older adults in the U.S. in 030, which is almost twice their number (40. million) in 00 [5]. Our world population is aging [], and the group of older people (over 50 years old) is the fastest growing group of mobile applications users [3]. It is challenge for designers and researchers that develop appropriate mobile applications to satisfy older users need and help them preserve their life quality []. 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 humanmade object []. 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 [3]. 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, 7 older adults (older than 50 years of age)

9 9 and 0 younger adults (between 8 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 application design for different age groups for mobile designers and providers.

10 0 CHAPTER. LITERATURE REVIEW.. Mobile Applications Usability... 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 []. 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.... Usability The term usability was originally derived from the term user friendly []. 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 96- (00) is now widely applied []. The ISO organization has developed various usability standards, and its function is to provide and impose consistency. In ISO/IEC 96-, 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

11 conditions []. This definition is primarily concerned with a software product, and it is suitable for mobile applications [3]. ISO/IEC 96- specifies usability by the following measurable attributes []: 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... Older Adults Demographically, older adults (65+ years old) are the fastest growing group worldwide [4]. By 030, it is estimated that there will be about 7. million older adults in the U.S., which is almost twice their number (40. million) in 00 [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 [], it is critical that mobile application developers and designers meet the needs of older customers.

12 ... 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 0 (Figure ), old adults age 55+ make up approximately 0% 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 0, 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.

13 3 Figure - 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 [6], older people are gaming on their phones. Around 3% 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.

14 4... 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 agerelated 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 [0]. In terms of the physical factors, a previous study [] formed five distinct human factors that show measurable disparities between older and younger people: () Learning time (=time to perform task) () 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

15 5 (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 []..3. Usability Testing Method.3.. 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 []. Usability testing requires representative users to work on typical tasks using the system or the prototype []. 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 []. Therefore, an appropriate usability testing method for mobile applications is necessary. Zhang and Adipat [] 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.

16 6 Figure - 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 []. Both of the lab and field study have pros

17 7 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 voic 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.

18 8.3.. 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 [3]. TAM is considered the most influential and widely applied theory to evaluate users acceptance of information systems. TAM, originally proposed by Davis [3] 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 -3 The Technology Acceptance Model (TAM) (Davis, 989). Previous research has shown some utilization of TAM on usability testing, especially many empirical studies which involve user acceptance of word processors [3], spreadsheets [4], [5], voice mail [6], and telemedicine technology [7]. Also, there are some usability principles (speaking the users language, consistency,

19 9 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) [8] 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 [9] (as shown in Figure 4), integrated the study on TAM for senior users [0].

20 0 Figure -4 Senior Technology Acceptance & Adoption Model (STAM). Based on former research, Renaud and van Biljon [9] proposed the Senior Technology Acceptance & Adoption Model (STAM) in 008. 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.

21 CHAPTER 3. HYPOTHESES The objectives for this research are () to analyze TAM measures (PU/PEU) for each application, () 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 : Training will increase TAM measures. Hypothesis : 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.

22 CHAPTER 4. METHODS 4.. Participants Seventeen older adults (older than 50 years of age) and twenty younger adults (between 8 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 $5 for the whole experiment, including survey and test. 4.. 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 Devices All participants used smart phones (iphone), or similar devices (ipod touch). All smart phones were able to connect Wi-Fi 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

23 3 questionnaire (Appendix ) 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.. Table 4- Experimental Session Schedule Session Part Session Time (min) IRB Completion 0 Baseline Survey 0 First Testing 30 Training 0 Practicing 0 Second Testing 40 Completion of Forms and Payment 0 Total 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 ) 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 Kroger Co.;

24 4 APP APP 3 APP 4 KAYAK; Frugal Flyer; FOX Weather 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 : Kroger Co.. Find a Kroger store near you.. Find a dairy product on sale this week. APP : KAYAK. Find a hotel near you and the lowest price for one room tonight.. Find a one-way flight from CMH to SFO this weekend and the lowest price. APP 3: Frugal Flyer. Find a hotel in Athens and the lowest price for one room tonight.. Find a one-way flight from CMH to SFO this weekend and the lowest price. APP 4: FOX Weather. Find tomorrow s weather condition in Athens.. 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.

25 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:. Explanation of what the application is and why use it could be used;. 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 0 minutes Practice To accept the new technology and learn how to use it, participants were given 0 minutes to practice and perform different tasks on these applications. APP APP APP 3 APP 4 Kroger Co.; KAYAK; Frugal Flyer; FOX Weather.

26 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: FOX Weather. Will it rain in the next six hours in Athens?. How about the weather condition in New York City next Tuesday? APP 3: Frugal Flyer. For this weekend, find an available car in CMH and its rental price.. For your summer vacation, find the price for a round-trip to NYC in July (from CMH). APP : KAYAK. For this weekend, find an available car in CMH and its rental price.. For you summer vacation, find the price for a round-trip to NYC in July (from CMH). APP : Kroger Co.. Find a coupon for Health and Pharmacy.. 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.

27 7 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,, 3, 7), learnability (Question,, 4), operability (Question 4, 5, 6, 7, 8, 9), and attractiveness (Question 0,, ). It is based on a heuristic evaluation checklist for systems (Pierotti, 007) [5]. The four measurable attributes of usability, which are described as a metric of quality characteristics including their sub-characteristics, are shown in Figure 5 [4]. Figure 4- Metrics mapped according to usability characteristic. person. When participants finished all experimental sessions, they were paid $5 per

28 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 : 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 : 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.

29 9 CHAPTER 5. RESULTS 5.. Demographics Thirty-seven participants were recruited from local community. Twenty participants were in the younger group (8-30 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- and Table 5- list the descriptive data of demographics and questionnaire. Table 5- Demographics of younger group Variable Mean Std. Dev Range Age (years) Years of using smart phones Number of downloaded mobile applications Hours per week on mobile applications Gender Male Female 9

30 30 Table 5- Demographics of older group Variable Mean Std. Dev Range Age (years) Years of using smart phones Number of downloaded mobile applications Hours per week on mobile applications Gender Male Female Hypothesis : 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.. 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

31 3 = 0.74; PEU: p-value = 0.6). 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- and 5-. For all the results, statistically significant results were those having a p-value < Table 5-3 Baseline of TAM measures for different age groups Age Group PU PEU Mean SD Mean SD Younger Older

32 Mean of PU 3 40 Chart of Mean PU AGE Session App 0 Younger Older Younger Older Younger Older Younger Older Younger Older 3 Younger Older Younger Older 4 Younger Older Figure 5- Bar Charts of PU for age groups by apps before training (session ) and after training (session )

33 Mean of PEU 33 Chart of Mean PEU AGE Session App 0 Younger Older Younger Older Younger Older Younger Older Younger Older 3 Younger Older Younger Older 4 Younger Older Figure 5- Bar Charts of PEU for age groups by apps before training (session ) and after training (session ). In Figure 5- and 5-, 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.

34 34 Table 5-4 P-Value for TAM measures by apps before training (session ) and after training (session ) TAM Session Application 3 4 PU PEU 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.. 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, and after training, session ) are drawn for all participants (see Figure 5-3 and 5-4). Then, a paired t-test was conducted (see Table 5-5).

35 Sum of PU Chart of Sum( PU ) Session App 3 4 Figure 5-3 Bar Charts of PU by apps for all participants before training (session ) and after training (session ).

36 Sum of PEU 36 Chart of Sum( PEU ) Session App 3 4 Figure 5-4 Bar Charts of PEU by apps for all participants before training (session ) and after training (session ).

37 37 Table 5-5 Paired t-tests of PU/PEU for all participants before training (session ) and after training (session ) Session N Mean SD SE Mean T-Value P-Value PU_ PU_ Difference % upper bound for Mean Difference PEU_ PEU_ Difference % upper bound for Mean Difference 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.

38 Sum of PU Chart of Sum( PU ) Session App 3 4 Figure 5-5 Bar Charts of PU by apps for younger group before training (session ) and after training (session ).

39 Sum of PEU Chart of Sum( PEU ) Session App 3 4 Figure 5-6 Bar Charts of PEU by apps for younger group before training (session ) and after training (session ).

40 40 Table 5-6 Paired t-tests of PU/PEU for younger group before training (session ) and after training (session ) Session N Mean SD SE Mean T-Value P-Value PU_ PU_ Difference 95% upper bound for Mean Difference PEU_ PEU_ Difference 95% upper bound for Mean Difference 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 significant effect on increasing TAM measures.

41 Sum of PU 4 Chart of Sum( PU ) Session App 3 4 Figure 5-7 Bar Charts of PU by apps for older group before training (session ) and after training (session ).

42 Sum of PEU 4 Chart of Sum( PEU ) Session App 3 4 Figure 5-8 Bar Charts of PEU by apps for older group before training (session ) and after training (session ).

43 43 Table 5-7 Paired t-tests of PU/PEU for older group before training (session ) and after training (session ) Session N Mean SD SE Mean T-Value P-Value PU_ PU_ Difference 95% upper bound for Mean Difference PEU_ PEU_ Difference 95% upper bound for Mean Difference 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 Hypothesis : 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

44 44 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 (before and after training). Residual plots for TAM measures and usability were done, and results indicated that the residual appeared to be normally distributed 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 Age Application*Age Error Total 47 R-Sq (adj) 36.8%

45 Mean 45 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 (Kroger) and (KAYAK) were not different, but they were different from application 3 (Frugal Flyer) and 4 (FOX 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. Interaction Plot for Usability Data Means App AGE Figure 5-9 Interaction Plot of Usability for four applications by age groups.

46 46 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 (Kroger), (KAYAK), and 4 (FOX 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 and had higher level of usability than Application 3 and 4, younger and older participants had evaluated them in different way: younger participants rated Application a higher score on usability than Application, but older participants rated them contrary ANOVA of PU Analysis of variance was run for perceived usefulness both before and after training. Table 5-9 and 5-0 show the analysis of PU for four mobile applications before and after training.

47 47 Table 5-9 ANOVA for PU of four mobile applications before training Source DF Sum of Mean F P Square (adj) Square Application Age Application*Age Error Total 47 R-Sq (adj) 4.45% Table 5-0 ANOVA for PU of four mobile applications after training Source DF Sum of Mean F P Square (adj) Square Application Age Application*Age Error Total 47 R-Sq (adj) 3.48% From Table 5-9 and 5-0, 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

48 Mean 48 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 Interaction Plot for PU Data Means App AGE Figure 5-0 Interaction Plot of PU for four applications by age groups. From the interaction plot of PU in Figure 5-0, younger and older participants had different response of PU on each application. Younger participants had evaluated Application (Kroger), (KAYAK), and 4 (FOX Weather) with a higher level on PU scores than older participant, as same as usability interaction plot. For Application 3

49 49 (Frugal Flyer), older participants gave a higher score on PU. For all the mobile applications, younger and older participants had ranked Application and 3 on PU scores in the same order; but for Application and 4, younger and older participants ranked them in different orders: younger participants rated Application a higher score on PU than Application 4, but older participants rated them contrary ANOVA of PEU Analysis of variance was run for Perceived Ease of Use both before and after training. Table 5- and 5- shows the ANOVA of PEU for four mobile applications before and after training. Table 5-0 ANOVA for PEU of four mobile applications before training Source DF Sum of Square (adj) Mean Square F P Application Age Application*Age Error Total 47 R-Sq (adj) 34.48%

50 50 Table 5- ANOVA for PEU of four mobile applications after training Source DF Sum of Square (adj) Mean Square F P Application Age Application*Age Error Total 47 R-Sq (adj) 38.78% From Table 5- and 5-, 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-.

51 Mean 5 Interaction Plot for PEU Data Means App AGE Figure 5- Interaction Plot of PEU for four applications by age groups. From the interaction plot of PEU in Figure 5-, younger and older participants had a different response for PEU on each application. Younger participants had evaluated Application (Kroger), (KAYAK), and 4 (FOX 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,, and 4 with a higher level on PEU, they ranked their PEU in different ways: younger participants rated Application a highest score on PEU, and Application was obtained a very close PEU score; but older participants rated Application 4 a highest score on PEU.

52 5 From the results, we found that age, application, and age * application were significant factors of TAM measures and usability 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 questions which related to four aspects of system usability. All participants were required to evaluate them in a score scale from (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 Regression Analysis for Usability Characteristics for Younger Group Results of the stepwise regression analysis of usability characteristics for younger group are shown in Equation (). The Minitab default levels of significance were used for these analyses. The analysis is shown in Equation () below: Regression Analysis Equation (): Preference = (3) () () (0) R-Sq = 65.3%

53 53 R-Sq (adj) = 63.5% P-Value = Where: (3) logical menu choices and function keys; () prevent user to make errors; () font size is large enough; (0) less steps to accomplish task, complexity. For the younger group, the most significant usability characteristics were 3 (logical menu choices and function keys), (prevent user to make errors), (font size is large enough), and 0 (less steps to accomplish task, complexity). Regression analysis results usability characteristics for the younger group are shown in Table 5-3 and 5-4. Table 5- ANOVA of Regression Analysis of Usability Characteristics for Younger Group Source DF SS MS F P Regression Residual Error Total

54 54 Table 5-3 Coefficients Table of Regression Analysis for Younger Group Predictor Coefficient SE Coefficient T-Value P-Value Constant Regression Analysis for Usability Characteristics for Older Group Result of stepwise regression analysis of usability characteristics for younger group is shown in Equation (). Regression Analysis Equation (): Preference = (9) () (7) (4) R-Sq = 58.4% R-Sq (adj) = 55.8% P-Value = Where: (9) appropriate number of function keys; () font size is large enough; (7) prompts and cues; (4) scrolling menu is easy to use.

55 55 For the older group, the most significant usability characteristics were 9 (appropriate number of function keys), (font size is large enough), 7 (prompts and cues), and 4 (scrolling menu). Results for this regression analysis are shown in Table 5-5 and 5-6. Table 5-4 ANOVA of Regression Analysis for Usability Characteristics of Older Group Source DF SS MS F P Regression Residual Error Total Table 5-5 Coefficients Table of Regression Analysis for Older Group Predictor Coefficient SE Coefficient T-Value P-Value Constant 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 (),

56 56 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 (), 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 (), 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 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 (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,