«THE EFFECT OF TECHNOLOGY ACCEPTANCE ON UNDERGRADUATE STUDENTS’ USAGE OF WEBCT AS A COLLABORATIVE TOOL by HUEI-HSUAN YANG B.A. National Chung Hsing ...»
THE EFFECT OF TECHNOLOGY ACCEPTANCE ON UNDERGRADUATE STUDENTS’
USAGE OF WEBCT AS A COLLABORATIVE TOOL
B.A. National Chung Hsing University, 2001
M.B.A. Concordia University Wisconsin, 2003
A dissertation submitted in partial fulfillment of the requirements
for the degree of Doctor in Philosophy
in the College of Education
at the University of Central Florida
Orlando, Florida Summer Term Major Professor: Stephen A. Sivo © 2007 Huei-Hsuan Yang i ABSTRACT The purpose of this research study was to use the Technology Acceptance Model (Pan, 2003) for re-examination of the relationships between students’ attitude toward the use of WebCT and the relevance of the actual usage in light of social presence and sociability. By using Technology Acceptance Model (TAM) developed by F. Davis (1989), this study focused on variables such as perceived usefulness, perceived ease of use, computer self-efficacy, subjective norms, attitude and actual use of WebCT to account for the effect towards the achievement in the exam which is an outcome variable. The data were collected over three different time periods during the spring semester of 2007 to find how these results changed over time. The participants were the students who enrolled in the business marketing course (Principle of marketing) at the University of Central Florida in spring, 2007. The course was divided to three sections: on-campus, video-streaming and online classes. Although there were three different delivery methods, there was only one instructor and they used same material for all sections so the results were used to compare the differences from three classes. The study was conducted by using instruments to measure perceived usefulness, perceived ease of use, computer self-efficacy, subjective norms, actual use, attitude, sociability, social presence and an additional demographic instrument.
Path analysis in SAS and repeated measures ANOVA in SPSS v15.0 for Windows were used to analyze the data. The results suggest that the hypothesized extended model was a good fit. The model did indicate that students’ attitude toward WebCT were determinants of the exam grades.
The findings of path analysis indicated that the research did support TAM. Perceived ease of use, perceived usefulness, subjective norms and computer self-efficacy all affected to students’ attitude ii toward WebCT and actual usage. Sociability and social presence, which were added to the model, were both factors to influence students’ attitude, too.
completed without your constant support and love. To my loving sister, Huei-Yi, who is always proud of me with or without this accomplishment. Nothing can be more gratifying than sharing this moment with you.
There are a number of people who have played an important part in getting me to this point.
First, I would like to express my great appreciation to Dr. Stephen A. Sivo who believed in me more than I believed in myself, showing great interest in being my committee chair. I cannot thank him enough for showing me the fascinating world of statistics. His encouragement is the keystone of my success. I will never forget Dr. Gary Orwig for his continuous support and being there for me whenever I needed him from the day I came to UCF. He was supportive in myriad of ways. I thank him from the bottom of my heart. I would like to thank Dr. Mike Robinson for his recommendations and suggestions to shape this dissertation to its final outcome. I must thank Dr. Carolyn Massiah, who so kindly facilitated the students participation in my study which could not have been completed without those participants in her course. Her thoughtfulness made the process easier for me while I was doing the survey. Her leadership and direction in seeing me through this dissertation process has helped me greatly in honing my research skills. Her interest in reading my dissertation encouraged me to do a good job. I am grateful to have this wonderful team of committee members to shape this dissertation to its current state.
I acknowledge my deepest gratitude to the memory of my beloved parents, who would have been proud of the biggest accomplishment of their daughter and I thank them for their unending blessings to make it all possible. Lastly, special thanks go to my lovely friends, Peggy, Jane and Divya, whose encouragement and support make this lonely dissertation journey more delightful.
I am so lucky to have these fabulous friends for one Taiwanese to study abroad alone in USA.
CHAPTER 1: INTRODUCTION
Background and Introduction
Purpose and Objectives of the Study
Relevance of the Study
Limitations of the Study
Assumptions of the Study
Definition of Terms
CHAPTER 2: LITERATURE REVIEW
The Technology Acceptance Model
Sociability and Social Presence
CHAPTER 3: RESEARCH METHODOLOGY
Design of the Study
Study Population and Sample Selection
Data Collection Instrument
Subjective Norms Instrument
System Actual Use Instrument
Sociability and Social Presence Instrument
Student Demographic Instrument
Data Collection Procedures
CHAPTER 4: RESULTS
Research Question 1
Research Question 2
Research Question 3
Research Question 4
Research Question 5
Perceived Ease of Use
Attitude Toward WebCT
CHAPTER 5: DISCUSSION
Purpose of the Study
Research Question 1
Research Question 2
Research Question 3
Research Question 4
Research Question 5
The Significant Findings of the Study
Further Research Recommendations
APPENDIX A: QUESTIONNAIRE
APPENDIX B: IRB APPROVAL LETTER
APPENDIX C: INFORMED CONSENT LETTER
LIST OF REFERENCES
Figure 1. The Hypothesized Technology Model
Figure 2. TAM 1
Figure 3. TAM 2
Figure 4. TAM 3
Figure 5. Perceived Usefulness Plots
Figure 6. Perceived Ease of Use Plots
Figure 7. Subjective Norm Plots
Figure 8. Actual use Plots
Figure 9. Sociability Plots
Figure 10. Social Presence Plots
Table 1. Internal Consistency Reliability Coefficients.
Table 2. Path Equations for Time 1
Table 3. Squared Multiple Correlation Time 1
Table 4. Path Equations for Time 2
Table 5. Squared Multiple Correlation Time 2
Table 6. Path Equations for Time 3
Table 7. Squared Multiple Correlation Time 3
Table 8. The Change of Beta Over Time: Time1-Time2-Time3
Table 9. Descriptive Statistics: Perceived Usefulness
Table 10. Tests of Within-Subjects Contrasts: Perceived Usefulness
Table 11. Tests of Within-Subjects Contrasts: Perceived Ease of Use
Table 12. Tests of Between-Subjects Effects: Perceived Ease of Use
Table 13. Tests of Within-Subjects Contrasts: Attitude
Table 14. Tests of Between-Subjects Effects: Attitude
Table 15. Multiple Comparisons: Attitude
Table 16. Tests of Within-Subjects Contrasts: Computer Self-efficacy
Table 17. Tests of Between-Subjects Effects: Computer Self-efficacy
Table 18. Multiple Comparisons: Computer Self-efficacy
Table 19. Tests of Within-Subjects Contrasts: Subjective Norms
Table 20. Tests of Between-Subjects Effects: Subjective Norms
Table 21. Multiple Comparisons: Subjective Norms
Table 23. Tests of Between-Subjects Effects: Actual Use
Table 24. Multiple Comparisons: Actual Use
Table 25. Tests of Within-Subjects Contrasts: Sociability
Table 26. Tests of Between-Subjects Effects: Sociability
Table 27. Multiple Comparisons: Sociability
Table 28. Tests of Within-Subjects Contrasts: Social Presence
Table 29. Descriptive Statistics: Social Presence
Technology has challenged the boundaries of educational structures that have traditionally facilitated and supported learning (Garmer & Firestone, 1996). The teaching and learning process has been dramatically altered by the convergence of a variety of technological, instructional and pedagogical developments in recent times (Bonk & King, 1998; Marina, 2001;
Smith, 2002). New and innovative teaching strategies have been developed especially in the area of computer technology. Hoffman (2002) stated that the educational opportunities are now accessible and not restrained by geography, time, family and money. Instructional technology has changed the way learners make choices about when to learn, how to learn and where to learn (Ling, Arger, Smallwood, Toomey, Kirkpatrick & Banard, 2001). Technology has become an integral part of higher education, enabling students to access information rapidly and visually (Smith, 2002).
Jonassen and Reeves (1996) wrote about computer based cognitive tools and learning environments that were developed to function as intellectual partners to enable and facilitate critical thinking and higher order learning. Such technologies could have become common places for accessing information or tools for analyzing the world, interpreting and organizing personal knowledge and representing knowledge to others. In 2007, Driscoll also declared that technology has played a key role in various types of communication within the classroom. The changing means of communication could have taken place and had a real impact on learning.
Ellis, Gibbs and Rein (1991) stated that it is appropriate to think of the groupware spectrum with different systems at different points on the multidimensional spectrum. These kinds of systems also can use asynchronous and synchronous distributed interaction to enhance communication and collaboration within a real-time or non-real-time interaction.
The Technology Acceptance Model (TAM) has been widely used by researchers and practitioners to predict and explain user acceptance of information technologies (King & He, 2006; Legris, Ingham & Collerette, 2003; Ma & Liu, 2004; Schepers & Wetzels, 2007). The TAM debates system usage intentions, attitude and behavior as a function of perceived usefulness (PU) and perceived ease of use (PEU). Pan (2003) used the TAM to examine the WebCT usage from a student perspective. He received a positive attitude response that WebCT was easy to use, useful, and the model fits actual use and student’s end-of-course grade. The online environment has been shown to be different from the traditional face-to-face course (Yang & Liu, 2007).
Gunawardena and Zittle (1997) found that social presence influences online learners’ satisfaction. Interpersonal or social interaction occurs especially when learners have social feedback from the instructor or their peers through personal encouragement and motivational assistance. Studies have shown that social presence has a significant impact on improved learning, collaboration and satisfaction (Garrison & Anderson, 2003; Grnawardena & Zittle, 1997; Hackman & Walker, 1990; Richardson & Swan, 2003; Uziel, 2007). Smith (2006) validated that the effects of social presence and sociability on the overall TAM model were strong and suggested that these variables do influence users' perceptions of perceived ease of use significantly. Social presence theory is an important factor of distance education.
The purpose of this research study was to use the TAM (Pan, 2003) for re-examination of the relationships between students’ attitude (AT) toward the use of WebCT and the relevance of the actual usage (AU) in light of social presence (SP) and sociability (S). This study anticipated finding evidence of students’ attitude toward how WebCT influenced their use of the system to improve their learning environment. Previous research indicated that the validity and reliability of how the TAM measures PEU and PU with computer self-efficacy (CSE) (Lee, 2002;
McAauley & Courneys, 1993) and subjective norms (SN) (Fisher, 1990; Wolski & Jackson, 1999); these were latent factors that would be measured to determine the differences between different types of classes using WebCT. Social presence has also been found to influence student persistent satisfaction in online learning (Arbaugh, 2001; Richardson & Swan; 2003, Smith, 2006)
1. How well does the initial Technology Acceptance Model (Time 1) explain the students’ grades, actual use of WebCT and attitude toward WebCT?
2. How well does the initial Technology Acceptance Model (Time 2) explain the students’ grades, actual use of WebCT and attitude toward WebCT?
3. How well does the initial Technology Acceptance Model (Time 3) explain the students’ grades, actual use of WebCT and attitude toward WebCT?
4. How do the results obtained from the Technology Acceptance Model (TAM) change over
5. How do perceived usefulness (PU), perceived ease of use (PEU), attitude toward WebCT (AT), computer self-efficacy (CSE), subjective norm (SN), sociability (S) and social presence (SP) change over time by three sections of the course?