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Student acceptance of mobile learning

Dissertation
Author: Robin Lee Donaldson
Abstract:
Mobile devices with Internet capabilities and applications have dramatically increased the convenience of accessing information for community college students. This study uses technology acceptance theory as a theoretical framework to examine the determinants associated with community college students' behavioral intention to use of mobile learning and mobile library resources. The acceptance of mobile learning by students and educators is critical to the successful implementation of mobile learning systems therefore it is important to understand the factors that affect student intentions to use mobile learning. This research is based on relevant technology acceptance literature and the the Unified Theory of Acceptance and Use of Technology (UTAUT). The purpose of this study is to test the determinants of the behavioral intention to use mobile learning by community college students and to discover if there exist either age or gender differences in the acceptance of mobile learning. The results indicate that performance expectancy, social influence, perceived playfulness of learning, and voluntariness of use were all significant determinants of behavioral intention to use mobile learning. Effort expectancy and self-management were not found to be significant predictor variables. This research provides useful information in understanding the drivers of acceptance for mobile learning in order to take proactive interventions for students that may be less inclined to adopt mobile learning.

v TABLE OF CONTENTS 1. INTRODUCTION ........................................................................................................ 1

1.1

C ONTEXT OF THE S TUDY ............................................................................................ 2 1.2

P ROBLEM S TATEMENT ................................................................................................ 5 1.3

R ESEARCH Q UESTIONS ............................................................................................... 6 1.4

P URPOSE OF THE S TUDY ............................................................................................. 6 1.5

S IGNIFICANCE OF THE S TUDY ..................................................................................... 8 1.6

T HE S URVEY ............................................................................................................... 9 1.7

L IMITATIONS ............................................................................................................ 10 1.8

R ESEARCH A PPROACH .............................................................................................. 11 1.9

O RGANIZATION OF D ISSERTATION ............................................................................ 12 2. LITERATURE REVIEW .......................................................................................... 13

2.

1

M OBILE L EARNING .................................................................................................. 14 2.2

B ARRIERS AND B ENEFITS ......................................................................................... 17 2.3

U SES OF M OBILE L EARNING ..................................................................................... 23 2.4

S UMMARY OF M OBILE L EARNING ............................................................................ 26 2.5

U NIFIED T HEORY OF A CCEPTANCE AND U SE OF T ECHNOLOGY M ODEL (UTAUT) .. 26 2.6

S ELF -M ANAGEMENT OF L EARNING .......................................................................... 33 2.7

P ERCEIVED P LAYFULNESS ........................................................................................ 34 2.8

T ECHNOLOGY A CCEPTANCE M ODEL (TAM) ............................................................ 35 2.9

T ECHNOLOGY A CCEPTANCE M ODEL 2

(TAM

2) ...................................................... 39 2.10

TAM3 .................................................................................................................... 40 2.11

TAM,

TAM2 AND UTAUT .................................................................................... 41 2.10

S UMMARY .............................................................................................................. 44 3. METHOD .................................................................................................................... 45

3.1

R ESEARCH D ESIGN ................................................................................................... 46 3.2

R ESEARCH Q UESTIONS ............................................................................................. 47 3.3

R ESEARCH H YPOTHESES .......................................................................................... 48 3.4

D EFINITION OF C ONSTRUCTS .................................................................................... 49 3.5

M ETHOD ................................................................................................................... 53 3.6

P OTENTIAL C RITICISM .............................................................................................. 62 4. QUANTITATIVE ANALYSIS .................................................................................. 64

4.1

I NTRODUCTION ......................................................................................................... 64 4.2

R ESULTS ................................................................................................................... 64 4.3.

A DDITIONAL A NALYSIS ........................................................................................... 74 4.4

S UMMARY ................................................................................................................ 77 5. QUALITATIVE ANALYSIS RESULTS .................................................................. 78

5.1

I NTRODUCTION ......................................................................................................... 78 5.2

D ATA C OLLECTION ................................................................................................... 79 5.3

T HEMES .................................................................................................................... 81 5.4

C ATEGORIES ............................................................................................................. 83 6. PROJECT SUMMARY ........................................................................................... 100

vi 6.1

I NTRODUCTION ....................................................................................................... 100 6.2

Q UANTITATIVE F INDINGS AND D ISCUSSION ........................................................... 101 6.3

Q UALITATIVE F INDINGS AND D ISCUSSION .............................................................. 111 7. CONCLUSION ......................................................................................................... 121

7.1

O VERVIEW .............................................................................................................. 121 7.2

C ONTRIBUTION ....................................................................................................... 122 7.3

L IMITATIONS .......................................................................................................... 124 7.4

F UTURE R ESEARCH ................................................................................................. 125 7.5

C ONCLUSION .......................................................................................................... 127 APPENDIX A: DESCRIPTIVE STATISTICS .......................................................... 128

APPENDIX B: UTAUT CONSTRUCT S AND RELATED THEORIES................ 131

APPENDIX C: JUSTIFICATIONS, STRENGTHS, AND WEAKNESSES OF METHOD ...................................................................................................................... 1 34 APPENDIX D: INTERVIEW QUESTIONS ............................................................. 138

APPENDIX E: BEHAVIORAL CONSENT .............................................................. 139 APPENDIX F: BEHAVIORAL CONSENT MEMORANDUM..…………………140 APENDIX G: DEFINITION OF MAJOR TERMS .................................................. 142

REFERENCES .............................................................................................................. 144

vii LIST OF TABLES

Table 4.1 Descriptive Sta tistics for the Participants’ Demographics……..……………. 65 Table 4.2 Descriptive St atistics for Mobile Le arning Subscales..……………………… 67 Table 4.4 Means and Standard Deviations of Intention to Use Scores by Gender …….. 69 Table 4.5 Independent Samples t-test on Intention to Use by Gender ………………… 70 Table 4.6 Regression Coefficients for Research Question 2...…………………………. 71 Table 4.7 Descriptive Statistics for Regres sion Predictors.……………………….……. 73 Table 4.8 Regression Coefficients for Research Question 3 ............................................ 73 Table 4.9 Descriptive Sta tistics for Mobile Device Personal Activities ........................... 74 Table 4.10 Descriptive Statistics for Frequency of Internet Access ................................. 75 Table 4.11 Descriptive Statistics for M obile Device Information Resources .................. 75 Table 4.12 Descriptive Statistics for Mobile Device Interest in Access Information Resources ..................................................................................................................... ..... 76 Table 4.13 Descriptive Statistics for Mobile Device Interest in Access IT Resources .... 76 Table 4.14 Descriptive Statistics for Mob ile Device Interest in Access Learning Resources ..................................................................................................................... ..... 77 Table 5.1 Themes Identified in In dividual Interview Analysis ........................................ 82 Table 5.2 Categories and Themes ..................................................................................... 83 Table 5.3 Constructs and Themes from Qualitative Analysis .......................................... 99 Table 6.1 Wang, et al., (2008) Coefficients and Donaldson, (2011) Coefficients ...........103

viii LIST OF FIGURES

Figure 2.1 UTAUT Model………...…….……………………………………………… 27 Figure 2.2 Technology Acceptance Model (TAM).……………………………………. 36

Figure 3.1 Proposed Model………………………………………..…………………….46 Figure 4.1 Distribution of Females’ Intention to Use Scores..…………………………. 68 Figure 4.2 Distribution of Males’ Intention to Use Scores.…………………………….. 69 Figure 4.7 Scatter Plot for Research Question 2..………………………………………. 71

Figure 6.1 Beta Coefficients for Donaldson (2011)…………………... ……………….102

ix ABSTRACT

Mobile devices with Internet capabilitie s and applications have dramatically increased the convenience of accessing informa tion for community college students. This study uses technology acceptance theory as a theoretical framework to examine the determinants associated with community college students’ behavioral intention to use of mobile learning and mobile library resour ces. The acceptance of mobile learning by students and educators is critical to the successful implementation of mobile learning systems therefore it is important to understand th e factors that affect student intentions to use mobile learning. This research is base d on relevant technology acceptance literature and the the Unified Theory of Accepta nce and Use of Technology (UTAUT). The purpose of this study is to test the de terminants of the beha vioral intention to use mobile learning by community college stude nts and to discover if there exist either age or gender differences in the acceptance of mobile learning. The results indicate that performance expectancy, social influence, perceived playfulness of learning, and voluntariness of use were all significant determinants of behavioral intention to use mobile learning. Effort expectancy and self-m anagement were not found to be significant predictor variables. This research provides useful information in understanding the drivers of acceptance for mob ile learning in order to take proactive interventions for students that may be less inclin ed to adopt mobile learning.

1

1. INTRODUCTION

This study uses technology acceptance theory as a theoretical framework to examine the determinants associated with community college students’ use of mobile learning and mobile library resources in a community college setting. Information technology and the Internet have dramatica lly increased the convenience of accessing information for the students and general public. Community colleges have begun to experiment with the application of mobile technology. However, there are segments of the population who have neither access to nor have accepted recent innovative information technology such as mobile le arning, mobile access to the Internet, and mobile information access (John Horrigan, 2008a; John Horrigan, 2008b; Jones & Fox, 2009; Madden, 2008). Wang and Shih (2008) suggest we need to ensure that there are no groups underrepresented or without adequate access to information. Marshall (2008) further suggests that the di gital divide between those with or without innovative technology should be furt her investigated. The rapid development of mobile tec hnology and higher education student and faculty ownership of mobile devices with Internet access have expanded communication methods, opportunities for collaboration, acce ss to traditional learning, and access to information resources. Innovations in cell phones and other device s allow students to have mobile access to academic email, lib rary staff, podcasts, videos, Internet information resources, course documents, a nd peer collaboration on projects. However, mobile learning and mobile technology acceptance research using information technology theories such as the Technology Acceptance Model (TAM) or the Unified Theory of Acceptance and Use of Technol ogy (UTAUT) is limited (Pendersen & Ling, 2003). In particular, there is a lack of rese arch using technology acceptance theories on whether community college students plan to us e or are currently using mobile devices to support their learning or to access the resour ces provided by higher education libraries.

2 Later in chapter 2 and elsewhere te chnology acceptance th eories TAM and UTAUT are discussed in detail . Appendix E provides definitions of major terms. This study contributes to the existing body of knowledge on technology acceptance theory by determining if the UTAUT constructs and the additional constructs voluntariness of use, perceived playfulness, and self-management of learning are factors in community college students’ intention to use m obile learning. The results from this study indicated that together the predictors account for .75 percent of the varian ce in behavioral intention to use mobile learning.

1.1 Context of the Study

Information systems (Davis, Bagozzi, & Warshaw, 1992), library information studies researchers and pract itioners (J.A. Kim, 2005; Park, Roman, Lee, & Chung, 2009; Spacey, Goulding, & Murray, 2004; Starkweat her & Wallin, 1999; Totolo, 2007), and education researchers (Cetr on, 2007; Dasgupta, Granger, & McGarry, 2002; Lin, Chan, & Jin, 2004; Williams, 2009) have paid consider able attention to technology acceptance. Hendrick and Brown (1984) define technology acceptance

as a person’s psychological state in regards to their voluntary use of or intention to use a specific technology. Venkatesh, et. al., (2003) descri be research into technology adoption, acceptance, and use as “the most mature research area in contemporary information systems research literature” (p. 426). However, technology acceptance research re lated to mobile information technologies using information systems (IS) theory is very limited, and researchers such as Wang, W u, and Wang (2008) suggest that further research is needed on the acceptance and use of mobile learning using traditional IS models. Mobile technologies provide new methods for accessing and interacting with information and broaden the means of comm unication and collaboration among students and between faculty and students. Chapter two discusses m obile learning in higher education in detail. The design, development, distribution, implementation, and support of technology are expensive investments for institutions such as community colleges. The only way innovative information technology will reach its full potential and support this is if

3 students and faculty accept and value it. There are numerous IS theories and models designed to assist in understanding how and why people accept technology and the intention to use and actual use of it. For instance, technology acceptance theory has been used as a framework to examine library staff attitudes toward the Internet (Spacey, et al., 2004), acceptance of web-based subscription databases (J.-A. Kim, 2005), acceptance of digital libraries in developing countries (Park, et al., 2009), faculty responses to library technology (Starkweather & Wallin, 1999), social inclusion of digital libraries in academic and clinical settings (Adams, Blandford, & Lunt, 2005), and the role of self- efficacy in electronic library usage (Aafaqi & Ramayah, 2004).. Venkates et. al. (2003) describe technology acceptance research as the most mature research area in IS literature. Taylor and Todd (1995b) state that assessing the value of information technology to organizations (e.g., colleges, universities, libraries, public schools) and understanding the determinants of that value are keys to acceptance, integration, and use of the technology. To address this concern, there have been numerous theories and models designed to assist in understanding information technology acceptance, seeking, exchange, and use. Researchers have used theories like the Technology Acceptance Model (TAM), the Theory of Reasoned Action (TRA), The Theory of Planned Behavior (TPB), and Diffusion of Innovation to better understand the diffusion of innovations, how and why technology is accepted, and the intentions of individuals to use and the use of technology (I. Ajzen & Fishbein, 1980; Dillon & Morris, 1996; Lee, Kozar, & Larsen, 2003; Lucas & Spitler, 1999; Rogers, 1995; Venkatesh, et al., 2003). The UTAUT technology acceptance theory forms the theoretical foundation for this dissertation. The reason for its selection, development of UTAUT, its eight IS theoretical influences, key constructs, strengths, and limitations are discussed chapter 2. The UTAUT theory helps explain the individual user’s intentions to use an information system his or her actual usage behavior (Venkatesh, et al., 2003). The model, first proposed by Venkatesh, et.al., (2003), demonstrates great promise for understanding acceptance of information systems. When UTAUT was developed, research had shown that models such as TAM could successfully predict the acceptance of an innovation in roughly 40% of the cases (S. Taylor & P. A. Todd, 1995b; Venkatesh & Davis, 2000).

4 TAM is considered one of the most robus t and notable technology acceptance models. First outlined in Davis’s (1986) doctoral dissertation, TAM propos es that perceived usefulness and perceived ease of use on the indi vidual level are determining factors for an individual’s intention to use a system in the workplace. Chapter 2 discusses TAM in detail. The UTAUT theory was chosen as the theoretical basis for this dissertation’s research into determinants of mobile learning acceptance for the following reasons: 

UTAUT is an empirically validated model that integrates cons tructs from eight key information technology acceptance models. 

Researchers see UTUAT to be superior to previous technology acceptance metrics (Moran, 2006). It can explain to a highe r degree the variance of intention (as much as 70% in user behavioral intenti on to use and actual use) than the other eight major theoretical models. 

Venkatesh, et. al., (2003) vali date the questionnaire inst rument with performance expectancy, effort expectancy, social infl uence, and facilitating conditions as the four core determinants of intention 

This study adds an explanation for gaps in the use of mobile technology and data about the readiness of students to ad opt mobile technology in their academic setting to the literature on community co llege student behavioral intention to use and actual use of mobile technology. Findi ngs from this research also provide faculty, library information studies (LIS) st aff, and other academic staff with key information to use to design training, marketing strategies, and mobile applications learning. These in terventions can specifically target users slower to accept and use new mobile technology. This can help facilitate successful new mobile technology servi ces (Van Biljon, 2006).

5 1.2 Problem Statement

This study focuses primarily on answeri ng the following questions: What factors affect community college stude nts’ intention to use mobile devices for learning? How applicable is UTAUT and the additional va riables, voluntariness of use, perceived playfulness and self-management of learning in explaining stude nt behavioral intention to use mobile devices for learning? The potential impact of mobile devices on higher education, our understanding of the issues surrounding the use of mobile technology for provi ding access to library and information resources, and their impact on lifelong learning opportuni ties are unclear and still evolving (Kukulska-Hulme, 2007). Attemp ts to apply information adoption models to explain student use and intention to us e audio, video, mobile services, and mobile learning have been limited and need furthe r investigation to determine whether these models need modification to address m obile technology acceptance (Pendersen & Ling, 2003). Researchers suggest that m obile learning may have unique characteristics that traditional technology acceptance models may not fully address and have called for further research in this area (Pende rsen & Ling, 2003; Wang, Wu, & Wang, 2009). Wang, Wu, and Wang (2008) recommend future m obile learning research include usage behavior, all UTAUT independent variables, voluntariness of use, perceived playfulness, and self-management of learning. A review of the literature provides limited empirical research on the use of mobile learning in higher education such as accessing library or course-related podcasts using mobile devices (Fernandez, Simo, & Sallan, 2009; McKinney, Dyck, & Luber, 2008) using technology acceptance as a theoretical framework. More research is n eeded to determine whether st udents perceive a benefit to using innovative technology su ch as mobile devices for learning and accessing library resources (Spencer & Hughan, 2008). The followi ng section details the specific research questions associated with this study.

6 1.3 Research Questions

The following research questions are desi gned to cumulatively answer the larger question: Are the UTAUT constructs and the additional variables, voluntariness of use, perceived playfulness and self-managemen t of learning, significant predictors of community college students’ behavioral inten tion to use and actual us e of mobile devices for learning. Research questions one through three are addressed in the quantitative portion and research question f our is addressed in the qual itative portion of this study. Research Question 1.

Is there a statistical ly significant difference between males and females on the behavioral intention to use mobile learning? Research Question 2 . Is there a statistically signif icant relationship between the participants age and their inte ntion to use mobile learning? Research Question 3.

Are the following independent variab les significant predictors of the behavioral intention to use mobile learning: performance expectancy, effort expectancy, social influence, voluntarine ss of use, perceived playfulness, self- management of learning, and facilitating conditions? Research Question 4 . What factors do students identify as influencing their use mobile learning?

1.4 Purpose of the Study

The purpose of this study is to test the determinants of the acceptance and use of mobile learning by community college student s. The proposed research model is based on relevant technology acceptance litera ture. The UTAUT model as proposed by Venkatesh, et. Al. (2003) is the theoretical foundation. Howe ver, the UTAUT model may not fully address the unique context of mob ile information systems (Wang, et al., 2009). This study examines pre-existing data result s from a North Florida community college survey in which UTAUT was extended with th e additional constructs of voluntariness of

7 use, perceived playfulness, and self-man agement of learning. This study seeks to determine if the UTAUT and additional cons tructs are factors in community college students’ intention to use and actual use of mobile learning. Chapters 2 and 3 provide information on UTAUT, the additional constr ucts, and their appr opriateness for this research. Quantitative data were drawn from archival data from a survey administered at a North Florida community college. The survey consists of demographic questions, the UTAUT survey instrument modified to a ddress mobile learning, questions on three additional constructs, and questions regard ing student use of information technology, library, and learning resources. Chapter 3 pr ovides information regarding the survey instrument. While the college limited its analys is to percentages, averages, and one open- ended question, this study analyzes the data in more depth to gain an understanding of the significance of the UTAUT c onstructs, self management of learning, perceived playfulness, and voluntariness of use. The analysis of the data is discussed in 1.9 Research Approach. The researcher conducted interviews with twenty students from the same North Florida community college to add depth to the survey results. The researcher’s data analysis of the archival survey data guided the development of interview questions. The archival data analysis reflects the followi ng modifications of UTAUT made to address the unique characteristics of mobile technology and the resear ch needs of the community college: 1.

The independent variables voluntariness of use, perceived playfulness, and self- management of learning were added to the four UTAUT constructs: performance expectancy, effort expectancy, social in fluence, and facilitating conditions. 2.

Age, gender, experience, and voluntariness of use were not used as modifiers. The modifying effects of these four items ar e not the focus of the college and were beyond the scope of this dissertation.

8 1.5 Significance of the Study

The findings from this research e xpand the existing body of knowledge by determining whether the original UTAUT and additional independent variables voluntariness of use, perceived playfuln ess and self-management of learning are significant predictors of the intention to use mobile learning in community college undergraduate students. The resu lts of this study found that the proposed omnibus model was a significant predictor of behavioral intentions to use mobile learning (R 2 =.75). This indicates that together the predictors account for a signifi cant amount of variation in behavioral intention to use mobile learning. The individual predic tors ease of use and self-management of learning were not found to be significant predic tors of behavioral intention. Voluntariness of use was found to be a significant ne gative predictor of behavioral intention. Mobile learning in higher education is still in the be ginning stages of implementation, and the concepts and instru ctional issues surrounding mobile learning are evolving and require further resear ch (Kukulska-Hulme, 2007). Understanding student and faculty acceptance and use of mobile, information service, and communication technology is essential to the successful delivery of academic, organizational, library, and instructional in formation. Before investing limited funds in developing mobile services and content, it is important that an institution be able to anticipate and account for the f actors that influence student s’ technology acceptance. If students fail to accept the mobile technology offe red to them in the academic setting, they will also fail to use the technology to seek and exchange information, thereby wasting the organizational funds. This study will also benefit community college stakeholders. The information gleaned from the results of this study will provide administrators, educators, and librarians with knowledge of dete rminants of student intention to use and actual use of mobile devices to access academic cont ent in a community college setting. Administrators: This study can assist college admini strators and IT support staff with information useful for planning implementation of mobile learning se rvices and support.

9 It will also provide informa tion on student perceptions of mobile technology use, actual usage, level of expected support, and me thods to address stud ent resistance and acceptance.

Educators: This study provides educators with additional knowledge and information on general mobile learning. It offe rs data from both archival survey data and interviews conducted by this researcher to explain how students currently use mobile devices. Furthermore, it identifies academic-related c ontent that students currently use or would like to have access to on mobile devices.

Librarians : This research provides academic libra rians information on mobile learning and the types of information students obtain on mobile devices. In addition, it provides data identifying library resources students use or would like to access to on mobile devices.

1.6 The Survey Originally I intended to c onduct the survey out of whic h the quantitative data was derived as part of my dissertation. I helped to develop the survey, but after developing some of the questions for it, it was not under my control. I worked at the institution as an Instructional Technologist in the Center for Instructional T echnology. Prior to the semester in which I planned to begin my res earch, the college deemed it necessary for the Center for Teaching Excellence to implement a mobile learning survey to both students and faculty. The Vice President of Academic Affairs and other administrative staff were aware of and supported my plan for mobile learning research. Unfortunately, the semester before I was to re quest IRB approval and begin th e quantitative portion of my dissertation, the college decided they were not in a position to wait for IRB approval. One of my duties as an Instructi onal Technologist in the Center for Instructional Technology included identifying new instru ctional technology and guida nce in its evaluation. The Director of the Center for Teaching Excellen ce asked me to assist her department in developing questions for a mobile learni ng survey for students and faculty. After evaluation and discussion, the director supported the incorporation of the UTUAT constructs, perceived playfulness, and self-m anagement of learning. It was also decided

10 that voluntariness of use would also be examin ed as a predictor variab le. I served strictly as an expert in the area of mobile and inst ructional techno logy. The Center for Teaching Excellence and a representative from student counseling were in charge of the final decisions on the questions and all compone nts of the administration of the survey.

1.7 Limitations

This study has the following limitations that can likely be remedied in future research: 1.

The results and their implications come from a single community college. Results may not be generalizable to other community colleges. 2.

Convenience sampling has potential bias a nd may not be generalizable to other colleges. 3.

Responses are limited by the participants’ willingness to honestly self-report and ability to reliably recall. 4.

The study was cross-sectional. However, re search suggests that user perceptions change over time as they gain experi ence and training (Mathieson, Peacock, & Chin, 2001; Venkatesh, et al., 2003 5.

The study is geographically limited to the United States. 6.

The predictors identif ied in this dissertation may not be found to be predictors in other mobile learning research. This single study should not rule out self- management of learning and effort expect ancy as predictor variables in other mobile learning studies.

To improve generalizability and better identify similarities and differences, future research should use the same survey instrument and randomly sample community colleges throughout the United States. Further research is needed to determine whether or not longitudinal evidence fo r the validity of this dissertation’s findings exists. For

11 instance, researchers could administer the survey at the beginning and end of the same term see how students’ perceptions change over time as they gain experience and training. Other limitations are discussed as needed throughout this dissertation.

1.8 Research Approach

Research began by exploring current studi es on technology acceptance theory in IS and mobile learning in higher education and academic libraries. Review of technology acceptance theory focused on the eight theoretical models outlined in UTAUT: Theory of Reasoned Action (TRA), Technology Accepta nce Model (TAM), Motivational Model (MM), Theory of Planned Behavior, (TPB ) and Decomposed TPB; Model of PC Utilization (MPCU); Innovation Diffusion Theory (IDT); and Social Cognitive Theory (SCT). Both a quantitative and a qualitative com ponent are used to examine students’ behavioral intention to use and actual use of mobile learning. This includes a quantitative, descriptive, and comparative research design using cross-sectional survey data. Survey data is employed to estimate population charac teristics and to explor e the significance of predictor variables. The quant itative data derives from ar chival data derived from a UTAUT survey administered at a north Fl orida community college. The survey was supplemented with three additional constr ucts: voluntariness of use, perceived playfulness, and self-management of learning. Quantitative data analysis used SPSS 15.0 and consisted of two primary stages. First, descriptive statistics were calculat ed on all variables. Means and standard deviations were calculated for variables on a ratio or interval scale. Frequencies and percentages were provided for nominal or ordinal scaled va riables. The second stage of the quantitative analyses presented inferentia l statistics used to test the research hypotheses. Details on survey data analysis are provided in chapters 4 and 6. Additional qualitative data is derived from interviews. Details of interview and interview data analysis are discussed in chapters 5 and 6.

12 The survey subjects consist of students at a North Florida community college with a full-time enrollment of 10491 students. To obtain a range of responses, freshmen and sophomores were selected from courses with a wide variety of academic and career disciplines.

Full document contains 168 pages
Abstract: Mobile devices with Internet capabilities and applications have dramatically increased the convenience of accessing information for community college students. This study uses technology acceptance theory as a theoretical framework to examine the determinants associated with community college students' behavioral intention to use of mobile learning and mobile library resources. The acceptance of mobile learning by students and educators is critical to the successful implementation of mobile learning systems therefore it is important to understand the factors that affect student intentions to use mobile learning. This research is based on relevant technology acceptance literature and the the Unified Theory of Acceptance and Use of Technology (UTAUT). The purpose of this study is to test the determinants of the behavioral intention to use mobile learning by community college students and to discover if there exist either age or gender differences in the acceptance of mobile learning. The results indicate that performance expectancy, social influence, perceived playfulness of learning, and voluntariness of use were all significant determinants of behavioral intention to use mobile learning. Effort expectancy and self-management were not found to be significant predictor variables. This research provides useful information in understanding the drivers of acceptance for mobile learning in order to take proactive interventions for students that may be less inclined to adopt mobile learning.