COLLEGE STUDENT’S ACCEPTANCE OF TABLET PERSONAL COMPUTERS: A MODIFICATION OF THE UNIFIED THEORY OF ACCEPTANCE
AND USE OF TECHNOLOGY MODEL by
Mark, J. Moran
A Dissertation Presented in Partial Fulfillment Of the Requirements for the Degree
Doctor of Philosophy
Capella University August 2006
COLLEGE STUDENT’S ACCEPTANCE OF TABLET PERSONAL COMPUTERS: A MODIFICATION OF THE UNIFIED THEORY OF ACCEPTANCE
AND USE OF TECHNOLOGY MODEL by
Mark, J. Moran has been approved
August 2006
APPROVED:
CLIFF BUTLER, Ph.D., Faculty Mentor and Chair VALERIE COXON, Ph.D., Committee Member
MARK HAWKES, Ph.D., Committee Member ACCEPTED AND SIGNED:
__________________________________________ CLIFF BUTLER, Ph.D.
__________________________________________ Kurt Linberg, Ph.D.
Information technology can enhance the learning process for post secondary students. Many universities have implemented ubiquitous or required notebook or tablet personal
computing for their students but have not studied the acceptance of the technology by their student populations. This research examines student acceptance of mobile computing devices using a modification of the “Unified Theory of Acceptance and Use of Technology (UTAUT)” recently developed by leading researchers in the technology acceptance field. The objective of the study is to evaluate students’ acceptance of Tablet
PC (TPC) as a mean to forecast, explain, and improve usage patterns of UTAUT in alternate contexts. The research contributes to UTAUT’s theoretical validity and empirical applicability and to the management of information technology (IT) based
Dedication
This dissertation is dedicated to my family who has been giving me their unwavering support throughout this life-changing experience. To my wife, Mary, who has been so strong and supportive all through this educational journey. To my children, Matthew and
Marin, you are my inspiration to reach beyond my potential and reach goals I thought unattainable. I love you all so much.
iii Acknowledgments
First, I would like to extend a sincere thank you to Dr. Cliff Butler, who has been a compassionate and diligent mentor during my voyage at Capella University. I thank you for the guidance and support as my dissertation chair, professor, and mentor. Your work ethic and leadership inspire me and I look forward to continuing our relationship for many years to come.
I want to thank Dr. Valerie Coxon for agreeing to join my committee in a time of need, and for your many insightful comments during the comprehensive and dissertation process. Dr. Coxon, your supportive remarks often were inspirational and kept me moving forward. Dr. Mark Hawkes, thank you for joining me on this trek your
knowledge of instructional technology in education was critical to this task. Because of all of you I look forward to joining my academic peer in the pursuit of knowledge. I wish you all the best in life.
Finally, I would like to thank Dr. Omar El-Gayar for being the catalyst that spurred my dissertation process. Your tireless efforts at research and academia are something I will strive to attain. Thanks for all your contribution to this dissertation especially in the area of technology acceptance and the analysis of the data.
iv Table of Contents
Acknowledgments iii
List of Tables viii
List of Figures x
CHAPTER 1: INTRODUCTION 2
Wireless Data Communication 3
The Device 5
Pen Based Computing Background 7
The Adoption of Technological Innovations 9
Objective of the Study 10
Research Questions 11
Significance of the Study 13
Scope of the Study 14
Study Context 14
CHAPTER 2: LITERATURE REVIEW 16
v
History of Technology Acceptance Models 17
The Technology Acceptance Model 20
The Unified Theory of Acceptance and Use of Technology Model 24
UTAUT Supporting Research 27
CHAPTER 3: RESEARCH DESIGN 28
Performance Expectancy 31 Effort Expectancy 32 Social Influence 32 Facilitating Conditions 32 Supplementary Variables 33 Pilot Study 33 The Survey 35 Sample Size 40
Survey Sample Population 40
Human Subjects Protection 41
vi
Data Analysis Methodology 50
Measures 52
CHAPTER 4: DATA ANALYSIS 54
Data Sample 54
Data Validation 59
Model Validity 73
Reliability 74
Construct Validity 77
Partial Least Squares 82
Model Analysis 82
Structural Model Analysis 86
Freshman vs. Upper Classmen 88
CHAPTER 5: SUMMARY & CONCLUSIONS 93
Discussion 98
Moderating Conditions 100
vii
Future research 102
REFERENCES 105
APPENDIX A: SELECTION OF COURSES TO SURVEY 113
APPENDIX B: SURVEY QUESTIONS 116
viii List of Tables
Table 1. Theory of Planned Behavior Constructs...19
Table 2. TAM Research...23
Table 3. UTAUT Components...26
Table 4. UTAUT Moderators ...26
Table 5. Performance Expectancy Questions ...44
Table 6. Effort Expectancy Questions ...45
Table 7. Attitude Toward Using Technology Questions ...45
Table 8. Social Influence Questions ...46
Table 9. Facilitating Conditions Questions...46
Table 10. Behavior Intent Questions ...47
Table 11. Self Efficacy Questions ...48
Table 12. Anxiety Questions...48
Table 13. Usage and Other Information Questions ...49
Table 14. Scale Reliabilities ...52
Table15a. Statistical Analysis of the Variables ...60
Table15b. Statistical Analysis (continued) ...61
Table 16. Cross tab report for PE1 by Gender...63
Table 17. Cross tab report for PE1 by Class Status. ...64
Table 18. T test and p Values for Participating Groups and PE1 ...66
Table 19. Cross tab report for PE1 by College Major ...67
ix
Table 21. Cross tab report for PE1 by First Computer Use. ...69
Table 22. Basic statistics for Computer Experience and PE1...70
Table 23. T test and p values for Participant Groups and PE1 ...70
Table 24. Cross tab report for PE1 by First Tablet PC Use...71
Table 25. Basic Statistics for Tablet PC Use and PE1...72
Table 26. T-test and p-Values for Tablet PC Use and PE1 ...72
Table 27a. Reliability of Performance Expectancy Construct Variables ...75
Table 27b. Reliability of EE, ATUT, SI, FC, SE Variables ...76
Table 28. Reliability of ANX, BI, & USE Variables...77
Table 29. Internal Consistency Factor Analysis for PE1 – PE10 ...79
Table 30. Correlation Coefficients for Performance Expectancy Indicators...80
Table 31. Internal Consistency & Factor Loading Analysis for other Indicators...81
Table 32. Individual Loadings, Weights, and Internal Consistencies...84
Table 33. AVE Scores and Correlation of Latent Variables...86
Table 34. Comparison of Freshman and Upper Classman Model Contributions ...91
Table 35. Comparison of All, Freshman, & Upper Class Models...92
Table A-1. Course on which Survey Tool was Administered ...114
x List of Figures
Figure 1. Theory of Planned Behavior...18
Figure 2. Technology Acceptance Model...20
Figure 3. Extended Technology Acceptance Model...22
Figure 4. Unified Theory of Acceptance and Use of Technology (UTAUT)...25
Figure 5. Dissertation Research Model...30
Figure 6. Research Page Link ...36
Figure 7. Web Survey Link...37
Figure 8. Questions and Concerns Information ...38
Figure 9. Web Survey Instrument...39
Figure 10. Research Relationship Equations ...53
Figure 11. Survey Participants Class Status ...55
Figure 12. Survey Participants College Affiliation...56
Figure 13. Survey Participants First Computer Use ...57
Figure 14. Survey Participants Length of Use of Table PC...58
Figure 15. Histogram of PE1 ...62
Figure 16. Tablet PC Structural Model...88
Figure 17. Freshman PLS-Graph Model...89
CHAPTER 1: INTRODUCTION
The application of computer technology in collegiate classrooms has been
demonstrated to improve teaching when used appropriately (Surry & Land, 2000). In the past few years many universities have introduced mobile computing to their campus as a way to improve the productivity of and communication between students and faculty. But some faculty have raised concerns about the distractions caused by mobile computer hardware (Groves & Zemel, 2000). However, even with philosophical differences among faculty, many universities including Bentley College (Lowe, 2004), Notre Dame (Abbott, 2004), University of Texas (Mock, 2004), and the University of Washington (Willis & Miertschin, 2004) have implemented, or are in the process of starting, mobile computing initiatives.
Some colleges and universities have adopted computing initiatives that require every student to acquire their own portable computing device or in some cases participate in experiments with university supplied computers, more than fifty colleges and
universities have, or are in the process of, implementing various mobile computing initiatives. A website administered by Dr. Ray Brown, of Valley City State University, contains a list of over seventy institutions that are involved in various levels of mobile computing implementation (Brown, 2000). Many of these implementations included re-engineering of the institutions’ data communication facilities. Several of these have decided to adopt a specialized development of notebook computers that allow pen based data entry and screen manipulation.
Tablet Personal Computer (TPC) based mobile computing initiatives have been documented in the literature with programs ranging from a preliminary pilot study at the University of Houston (Willis & Miertschin, 2004) investigating TPCs in a mobile learning laboratory used by faculty. A university that integrates the TPC into student teacher interaction is the University of Washington where a Classroom Feedback System (CFS) is being used to give students the ability to provide feedback and ask real time questions during an instructor mediated lecture (Steel, et al., 2003). Every student enrolled in Bentley College gets a TPC (Lowe, 2004). Other universities with TPC programs include Purdue, MIT, Temple, Seton Hall, Chatham, and many others (Brown, 2000) (Wachsmuth, 2003).
One of these institutions to make significant commitment to ubiquitous, mobile computing is a small Midwest public university located in South Dakota. This institution started investigating pen based mobile computing in 2002 when thirteen wireless access points were installed on their campus (DSU goes wireless, 2002). Approximately twenty Center of Excellence (CEX) students were given tablet PC devices and given the
assignment to investigate the device as a learning instrument. The initial project was found to be beneficial resulting in expansion of both wireless network availability and students with table PCs. A task force was then organized to examine strategies for taking mobile computing to scale at the university, and to investigate computing device options. In the fall of 2004, this university moved wireless mobile computing from a small
number of Center of Excellence students and scaled it to include all first and second year enrolled students (Knowlton, 2004). This initiative required each full time student to
lease a wireless, mobile tablet/laptop personal computing device. The program has been entitled the wireless mobile computing initiative (WMCI). By the spring of 2006 all students at this university should have their own TPC (Zolnowsky, 2006).
Wireless Data Communication
Wireless networks, by themselves, can not support the typical communication traffic on a modern campus. The wireless aspect supports a continuous communication environment but a high speed wired network backbone must be in place to support the broadband applications that present applications require. Modern college student’s use many bandwidth hungry applications such as instant messaging, music downloads and peer-to-peer programs (Henderson, Kotz, & Abyzov, 2004). These data intensive applications make it difficult to a consistent quality of service (QOS) for all of the developing applications.
Over the past decade the nation’s commercial, academic, and telecommunications sectors have made considerable improvements in their telecommunications infrastructure (Decusatis, 2002). According to Dr. Kenneth Green (Green, 2004), who has been
conducting surveys of college campus computer use since 1990, roughly 4 out of 5 private and public four year colleges claim they have functional wireless LANs that can be used by their students, faculty and staff in parts or all of their campus facilities. This expansion of wireless connectivity capacity has been augmented with many universities adopting pen based computing initiatives.
Considering the mobility of laptops and TPCs in a wireless LAN environment steps need to be taken to allow the mobile computing device to keep one Internet
Protocol (IP) as the user moves across campus. Each access point covers about an area with a radius of about 100 feet so computing devices moving from one area of campus to another will move through several different AP zones. Most campuses need to segment their networks to reduce data traffic congestion in any one zone or building. Mobile users will move from their original zone to other zones as they move across campus requiring them to obtain a new IP address for each subnet. Obviously this would not be an
acceptable requirement of a WLAN. The solution is to place all the wireless access points on their own virtual LAN (VLAN) (Ciampa, 2001). Using a VLAN all wireless devices exist in the same LAN segment so they are not required to change their IP address until they leave the campus area.
The development of mature wired campus networks has enabled many universities to provide both faculty and students with a ubiquitous wireless mobile
computing environment (Barkhuus, 2005). Initially mobile notebook computers filled this need but gradually tablet PC devices have become more prevalent on college campuses. Tablet PC technology is the newest development of pen based computing.
Security is an issue with wireless networks. Most wireless standards require some configuration to provide a secure communication tool. Many university networks require user authentication before a user is allowed access to the information on a network. A common frame work to evaluate security is referred to as the CIA (Confidentiality, Integrity, and Availability) of security (Maconachy, Schou, Ragsdale, & Welch, 2001). Confidentiality addresses issues associated with the unauthorized disclosure of
alteration of the information being transmitted. Availability refers to the issues associated with ensuring that information is reliably provided to authorize users.
The provision of adequate security in wireless networks requires that the network be configured to require user identity. That can be accomplished by using provisions built into existing wireless networking standards. 802.11b and g can be set to support security equivalent to desktop computer workstation by using windows equivalent privacy (WEP) and wireless application protocol (WAP). User authentication can be accomplished by using proprietary applications or 802.1 which is built into Microsoft windows XP. Any organization planning on adding wireless access to a network must evaluate their desired level of security and implement solutions to ensure a secure computing environment.
The Device
The tablet PC is the current state of pen based computing. Since their initial release in 2002 TPCs have gradually gained acceptance as a useful tool for educators, professionals, and casual users. The devices are currently in their third generation and now possess sufficient computing power to put them on par with the average desktop computer (Mock, 2004). The TPC device is essentially an x86 microprocessor based notebook with an active screen digitizer running Windows XP Tablet Edition.
This is a new version of Windows XP with add-ons that support pen-based input. Microsoft became the leading provider of software for TPC devices when it introduced the “Tablet PC edition” of Windows XP in 2002 (Microsoft, 2002). The pen can be used as a navigation tool and an input device that allows users to write on the screen using digital ink. This digital ink can be stored directly as a graphic or it can be converted to
text. A TPC can execute any program written for the Windows XP operating system without a translation as needed on most personal data assistant (PDA) devices.
Models available now are either slate or convertible devices. A slate form factor is similar to a PDA with a larger screen and, usually includes, a detachable keyboard. The convertible TPC form factor is very similar to a traditional notebook computer with the screen display attached with a single swivel hinge to the main portable PC body that can rotate 360 degrees allowing the screen to lie flat on the keyboard, with the screen up, to emulate a slate arrangement. Several manufacturers have slate and convertible notebook tablet models available (Tablet PC 2004 Quick Comparison, 2004).
The TPC is usually used by the instructor in a classroom setting as a presentation device that eliminates the need for a blackboard or whiteboard. The presentation is
typically projected onto a screen using an overhead projection device. With products such as Microsoft OneNote, PowerPoint, or Journal, the instructor has the ability to use
prepared lecture notes or slides and annotate them on the fly. Teaching faculty at this university are able to connect to the projection device wirelessly which eliminates the need of providing a TPC docking station in each presentation classroom. All students also have a TPC and, at the discretion of the instructor, can assume control of the projector allowing them to share what is on their TPC desktop with other class participants. This capability fosters an “active learning” environment where students are actively
participating in the presentation which has been shown to increase learning (Barkhuus, 2005).
Pen Based Computing Background
Many information systems professionals view the TPC to be the next release of an existing technology. Pen based computing has been around since the 1950’s. The Semi-Automated Ground Environment (SAGE) air defense system used a “light gun” to interact with the computer screen (Ray, 2002). Computerized automated drafting
programs in the 1970’s used light pens or pen based drawing tablets to manipulate objects on the computer screen (Fisher, 1999).
The first pen based desktop computer system was introduced by Wang in 1988. This computer allowed users to annotate screen captures with “ink” from an electronic pen on an opaque tablet attached to a serial port of the computer (Francik 1991). This Wang Freestyle later allowed voice recordings to be attached to a pen event that could then be emailed to another Wang user.
The first tablet computer introduced was the GRiDPad in 1989 by Jeff Hawkins, the founder of Palm and Handspring. It was a PC (Intel based) computing device that used a pen attached by a cord. GRiDPad could recognize hand printed characters and was used for data collection, such as filling out forms. GRiDPad devices were used by several state police agencies and some specialized form filler applications (Jones, 2002).
In 1993 Apple jumped onto the pen based computing bandwagon with the Newton. This device was the first palm based computing device and was marketed by Apple from 1993 to 1998. The official name of the device was the “MessagePad” but the devices operating system called Newton (Apple Newton, 2006) became the devices
name. The Apple iPod is the latest version of this device since it operates by using the original Newton operating system.
The most successful pen based device, prior to the TPC, was the Palm Pilot. Jeff Hawkins, originally of Grid computing, founded the company in 1992. Later the
company was acquired by US Robotics and soon after that by 3com, the communications company (Dillion, 1998). The Pilot was essentially a personal organizer device capable of storing thousands of phone numbers and addresses. The device suffers from a number of limitations including; small viewing screen, low processor power, insufficient memory, and crippled applications. But the devices became popular with frequent travelers due to their convenience features. Computer Business Review reports that makers shipped nearly one million units in 2005 and projects that nearly two million units will be sold in 2006 (Fujitsu claims top tablet spot in EMEA, 2006).
In the eighteen years since the original GRiD computer introduction computer hardware has significantly come down in price while micro processors have become much more powerful. For these reason, and other developments of computer hardware components, TPC devices are more affordable and powerful. The entry of Microsoft into the market in 2000 (Gates, 2000) is the most recent event in TPC development. The combination of a larger screen, increased computing capacity, and the way people use computers today contribute the success of TPCs. With the ubiquitous availability of the Internet more users are reading information on their mobile computing devices (Taylor & Todd, 2001). Students use their devices to read email, news, web pages and other
(Abbott, 2004). As the TPC models have matured they have become very price
competitive with their notebook counterparts. Jeff Van West, of Microsoft, estimates that a TPC will cost about $200-700 more than a comparably equipped notebook computer (West, 2005). When the performance, features, and convenience of use are considered the TPC is a viable alternative to notebook computing devices.
The Adoption of Technological Innovations
Many information systems (IS) researchers have published on various theories that could be used to explain the adoption of information technology innovations. These theories include; the technology acceptance model (TAM) (Davis, Bagozzi, & Warshaw, 1989); the theory of reasoned action (TRA) (Fishbein & Ajzen, 1975); the theory of planned behavior (TPB) (Ajzen, 1991) among others which are modifications or developments of these models. The various lines of research are more extensively discussed in chapters two and three in this dissertation.
The models were developed to help estimate and measure IS innovation success. An estimate of the success rate of new IS technology implementation projects since the 1980s is about 50% (Westland & Clark, 2000). Explaining the adoption of new
information technologies has been described as the most mature research area in
contemporary information systems research literature (Hu, Chau, Sheng, & Tam, 1999). Research in this area has generated adoption metrics that can be used to determine the probability of successful implementation of information system initiatives. The combination of these metrics into a single model entitled the “Unified Theory of Acceptance and Use of Technology” (UTAUT) was proposed by several of the fields
leading researchers (Venkatesh, Morris, Davis, & Davis, 2003). The models used for technology adoption were able to successfully predict the acceptance of an innovation in only 40%of the cases (Davis et al., 1989) (Taylor & Todd, 2001) (Venkatesh & Davis, 2000). The new proposed model UTAUT has been demonstrated to be up to 70%
accurate at predicting user acceptance of information technology innovations (Venkatesh et al., 2003). By generating a significantly higher percentage of technology innovation success the UTUAT is deemed a superior metric than the prior metrics.
Objective of the Study
The objective of this study is to measure the acceptance of TPCs by students at this university. This setting provides a unique context for the study of adoption of technological innovations because of the ubiquity of TPCs, and wireless computing in general on the campus. The primary instrument used to gather adoption data is a web survey based on the variables defined in the UTAUT and other TAM studies. The UTAUT constructs are performance expectancy, effort expectancy, social influence, and facilitating conditions. Other technology research variables included are self efficacy, attitude towards using technology, and anxiety. These were added to ensure a thorough investigation of the acceptance of technology in this environment. The survey instrument was constructed to enable the researcher to determine differences among the students acceptance of TPC based on their major area of study, their year of attendance at the university, and their experience with computers including tablet PCs. The acceptance of TPC was measured using the UTAUT model proposed by Venkatesh and Davis
presented in chapter two and three of this dissertation. The primary research question guiding this research is to understanding the level and rate of technological buy-in (adoption) at this campus in order to identify the aspects of the environment that most contribute to the adoption process, and identify the support structures (social,
environmental, etc.) that facilitate this process.
Research Questions
The primary reason for the study is to measure if college students, at this campus, accept the wireless mobile computing initiative. The second question examined is the efficiency of technology adoption as determined by the UTAUT model. The third
question addressed by this study is the impact of the various UTAUT variables, and other variables not included in the UTAUT model, on user acceptance of the TPC.
From a null hypothesis and alternative hypothesis approach, the research questions are expressed below.
H01: University students, in the study environment, reject the Tablet PC.
H02: The Unified Theory of Acceptance and Use of Technology (UTAUT) does not predict the successful acceptance of the Tablet PC
H03: The constructs of the UTAUT will not demonstrate an effect on users acceptance of the tablet PC.
H04: Computer self efficacy does not impact students acceptance of the Tablet PC.
H05: Anxiety about computer use does not impact students’ acceptance of the Tablet PC
H06: Students use pattern of the Tablet PC does not impact their acceptance of the device.
The alternate hypotheses are listed below.
Ha1: University students, in the study environment, accept the Tablet PC. Ha2: The Unified Theory of Acceptance and Use of Technology (UTAUT) does
predict the successful acceptance of the Tablet PC
Ha3: The constructs of the UTAUT will demonstrate an effect on user acceptance of the tablet PC.
Ha4: Computer self efficacy does have an impact on students acceptance of the Tablet PC.
Ha5: Anxiety about computer use does have an impact on students’ acceptance of the Tablet PC.
Ha6 Students use of the Tablet PC does not impact student’s acceptance of the device.
The survey questions were mapped to constructs of the UTAUT model with some constructs included from the TAM to measure the acceptance or rejection of the
individual hypothesis. The mapping of the questions is described in Chapter 3 of this dissertation.
Significance of the Study
There are several studies that focus on the adoption of information technology beginning in 1975 with the theory of planned behavior by Ajzen (Fishbein & Ajzen, 1975). The technology acceptance model was proposed by Davis in 1989 (Davis, 1989) followed by several studies that offer support and suggested modification of the TAM model (Mathieson, 1991) (Legris, Ingham, & Collerette, 2003). This is a significant project because it will study a relatively new model, UTAUT, to determine the
acceptance of an information technology initiative by college students. This dissertation will test the validity of the UTAUT model in a context that is unique to other study settings.
Many corporate and educational institutions have the existing infrastructure to support a ubiquitous wireless computing environment. This study will support the migration to a more mobile computing environment by identifying the structural and contextual factors that facilitate the adoption of wireless technology and mobile computing devices. There are many benefits, and some disadvantages, of ubiquitous computing in a wireless environment. Ubiquitous wireless access to electronic
classrooms and overhead projectors allow teachers to use “all the things teachers can use to enrich their presentations with multimedia” (Burton, 2004, p. 55).
The study could aid academic institutions with their decisions whether or not to implement a new information system technology. The findings of this research can
indicate areas that could improve the acceptance of technology implementations.
Although this study does not investigate the financial impact of TPC initiatives it may aid academic institutions with the decision of adopting this new technology.
Scope of the Study
This study examines the acceptance of TPC by students newly enrolled in the fall of 2005. The results of the study should not be applied to all university students as other educational environments may lead to different acceptance decisions. The population size was chosen to attempt to provide some statistical significance to the study but the best predictive efficiency of the UTAUT model is only 70%. Technology adoption scenarios are not correctly predicted in nearly 30% of the cases. An examination of the studies conducted using technology adoption models reveals that many have been conducted on undergraduate and graduate students. A fair number of research has been conducted using IS adoption models on business services such as mobile internet (Pederson & Ling, 2002), text messaging, contact services, mobile payment (Pederson, Nysveen, &
Thorbjornsen, 2003), mobile gaming, and mobile parking services (Pederson & Nysveen, 2003).
Study Context
This study was conducted at a small Midwestern university where freshman and sophomore students are required to lease a TPC since the fall semester of 2004. In subsequent years new freshman, and transfer students have been required to lease a TPC. The result of this initiative is that all freshman, sophomore, and junior students at this
university have TPCs in 2006. This college has a long tradition of supporting data communication and networking innovations appearing in the top ten most wired campuses in the year 2000 (Schmidt, 2000).
CHAPTER 2: LITERATURE REVIEW
Information systems research literature is rich with articles about organizational and individual acceptance of IS innovations. Explaining how end user chooses to accept technical innovations require psychology based theories. Social Cognitive Theory (SCT) is a broad psychometric research area that studies the factors involved in individual decision making. SCT distinguishes itself from traditional social learning theory by incorporating mental processing (cognition) into the interpretation of observational learning. Albert Bandura, of Stanford University, has led development of SCT since the 1960’s. His research is important to technology acceptance models because he laid the foundation allowing us to understand human behavior. Bandura postulates that human behavior is a triadic, dynamic, and reciprocal interaction of three factors: personal factors, behavior, and the environment (Bandura, 1977, 1986). While some social scientists propose that behavior is a result of consequences, SCT postulates that goal-directed and self-regulation processes play a large part in how we react to different situations. Furthermore, SCT suggests that there are both direct and indirect effects of reinforcement t hat learners conscientiously choose. Bandura’s research stimulated researchers to study techniques for promoting organizational change and measuring the success of change.
Recently Bandura has moved his primary research to health psychology (Bandura, 2002). A new branch of research has developed that use the principles of social
cognitivism proposed by Bandura, and others, to measure the acceptance of technical innovations. These theories can be collectively referred to as technology acceptance
models. This chapter discusses the history and progression of technology acceptance models in depth. The unified theory of acceptance and use of technology (UTAUT) model including its underlying construction, previous applications, and its similarity to other implementation models is also discussed.
History of Technology Acceptance Models
Technology Acceptance Models (TAM) have been developed to measure system use, acceptance, and user satisfaction of those systems (Davis, Bagozzi, & Warshaw, 1989). The Davis model specifically focuses on information systems use and is based on the theory of reasoned action (TRA) originally introduced by Ajzen and Fishbein in the early 80’s (Ajzen & Fishbein, 1980) and further refined by Ajzen as the extended TRA in 1991 (Ajzen, 1991).
TRA is a technology acceptance model that can be used to predict behavior in a wide variety of situations, not just the adoption of information systems technology. Ajzen states that an individual’s beliefs influence his/her attitude towards various situations. The users’ attitude joins with subjective norms to shape the behavior intentions of each individual. This theory was further refined and called the theory of planned behavior (TPB) which is also titled the extended theory of reasoned action. The TPB is a general behavior model which can be used to study broader acceptance situations than the TAM but it has been applied to information systems studies (Mathieson, 1991) & (Taylor & Todd, 2001).
Figure 1 illustrates the theory of planned behavior. The model helps to explain how to affect the behavior of people. Ajzen proposed the model to predict deliberate behavior, because behavior can be deliberate and planned.
Figure 1. Theory of Planned Behavior
Note. From Ajzen, I. (1991). “The Theory of Planned Behavior.” by Izak Ajzen, 1991, Organizational Behavior and Human Decision Processes, p. 50, 179-211.
TPB includes many factors, or constructs, used to determine users’ acceptance of innovations. The three considerations are behavioral beliefs, normative beliefs, and control beliefs. These are the users core beliefs about the consequences of the action, the expectations of others, and beliefs about how the user controls, or does not control, the end result of the behavior. Table 1 further describes the model parameters.
Table 1. TPB Constructs (Ajzen, 1991)
Construct Description
Attitude Toward the Behavior The user’s evaluation of the desirability of his or her using the system
Subjective Norm The individual’s perception of social pressure to perform the behavior.
Perceived Behavioral Control The individual’s perception of his or her control over performance of the behavior.
Intention The impact of the first three constructs, attitudes, on the strength of an individual’s intent to perform the behavior.
Behavior Belief the subjective probability that the behavior will lead to a particular outcome
Outcome Evaluation A rating of the desirability of the outcome Normative Belief The individual’s perception of a referent other’s
opinion about the individual’s performance
Control Belief The perception of the availability of skills, resources and opportunities
Behavior The outcome of the process.
The complexity of TPB model limits its use in information systems research. TPBs include more variables than may be important in most information systems
technology implementations (Taylor & Todd, 2001). Some of the variables that have been removed from the TPB have shown up in more modern models such as the
influence of people considered significant by the participants. These factors are important to modern acceptance models.
The Technology Acceptance Model
The TAM model, and its derivations, gradually became the accepted model for research in information systems adoption cases. Debate and refinement of technology adoption models has continued in IS research literature. The advantage of a TAM is that it is specifically designed to address the acceptance of IS technology. The TAM model replaced the first three attitudinal constructs from the TPB with two technology
acceptance measures perceived usefulness and perceived ease of use. This was done in an attempt to simplify the model making prediction of acceptance easier to predict.
Figure 2. The Technology Acceptance Model.
Note. From “Extending the Technology Acceptance Model: The Influence of Perceived User Resources,” by Mathieson, K., Peacock, E., & Chin, W. W. (2001). The Data Base for Advances in Information Systems, 32(2), p. 90.
The two theoretical components that are the foundation of Davis’s TAM model (Davis et al., 1989, p. 985). The first is perceived usefulness (PU) and perceived ease of use (PEOU). Davis defines usefulness as “the degree to which a person believes that using a particular system would enhance his or her job performance.” Davis goes on to define perceived ease of use as “the degree to which a person believes that using a particular system would be free of effort” (Davis et al., 1989, p. 985).
A recognized limitation of TAM is that it does not take into consideration any barriers that would prevent an individual from adopting a particular information systems technology (Taylor & Todd, 2001). These variables that are not included in TAM are system design characteristics, training, support, and decision maker characteristics.
The research studying modifying factors to Davis’s technology acceptance model attempt to improve upon it by adding user resources and restrictions to the model.
Mathieson termed these factors as external control factors. These external factors include subjective norm, voluntariness, job relevance, output quality, and result demonstrability (Mathieson, 1991, p. 87). By adding robustness to the model the researchers hope to improve the predictive value of the tool. Recently Venkatesh et al. published
improvement in the prediction of acceptance from 17% to 42% (Venkatesh et al., 2003, p. 439).
Venkatesh and Davis attempted to incorporate these discussions of external factors into a TAM model when they proposed a model in 2000 extending the TAM. Their new model incorporates additional theoretical variables that include social
influence processes and cognitive processes. Figure 3 illustrates the result of the extended model.
Figure 3. Extension of the Technology Acceptance Model.
Note. From. “A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies,” by Venkatesh, V., & Davis, F. D. (2000) Management Science, 46(2), p. 190.
Examination of current literature on technology acceptance does indicate that additional factors need to be included that were not in original TAM models. Examples of the types of variables are demographics, managerial knowledge, social factors, environmental characteristics, and task-related characteristics (Pijpers, 2001). Some examples of possible additional factors are the motivational factors introduced by Vallerand (Vallerand, 1997). The perception that users will want to perform an activity “because it is perceived to be instrumental in achieving valued outcomes that are distinct from the activity itself, such as improved job performance, pay, or promotions are
extrinsic while the perception that users will want to perform the activity “for no apparent reinforcement other that the process of performing the activity per se” (Davis et al., 1992, p. 1112).
Information Systems research has validated the TAM, and its derivations. Table 2 presents an abbreviated list of researchers and dates that have used the TAM model to study their technology acceptance research problems.
Table 2
TAM Research (Legris, Ingham, & Collerette, 2003)
Year Author Model Used
________________________________________________________________________
1989 Davis TAM
1992 Davis & Bagozzi Extended TAM
1991 Mathieson TAM & TPB
1995 Taylor& Todd TAM + subjective norm
1997 Jackson TAM + validation tools
1998 Bajaj et. al. TAM + loop back adjust
1999 Hu et al. TAM
2000 Venkatesh & Davis TAM + subjective norms
2002 Hwang & M. Yi TAM + goal orientation, CSE
This body of research validates and extends the application of TAM. But TAM is only capable of predicting technology adoption success between 30% (Meister &
Compeau, 2002) and 40%of the cases (Venkatesh & Davis, 2000). As a result of this, researchers have searched for better technology acceptance models that can deliver a
higher prediction of success (Legris et al., 2003) (Plouffe, Hulland, & Vandenbosch, 2001). The call for a modified model that incorporates both human and social variables led to the development of an extended TAM and eventually the Unified Theory of Acceptance and Use of Technology model.
The Unified Theory of Acceptance and Use of Technology Model
The result of the discussion and debate over the best technology acceptance tool resulted in several tools being available to IS researchers. As many as eight models have received support in recent literature. The September 2003 issue of the MIS Quarterly has addressed this issue (Venkatesh, Morris, Davis, & Davis, 2003). This research article examines the current state of knowledge in this area comparing similarities and differences in the current models. The Unified Theory of Acceptance and Use of
Technology (UTAUT) model resulted from this study. Dr. Venkatesh et al. attempted to validate the tool by testing UTAUT on historical data from previous TAM researchers.
Figure 4. UTAUT Model (Venkatesh et al., 2003, p. 447)
Note. From ”Theories Used in IS Research” Website, www.istheory.yourku.ca.
Figure four illustrates the UTAUT model that compiles all the variables found in the eight existing models and a selected subset of additional constructs. Venkatesh et al. then validated the model using both existing data, from the previous TAM studies, and data obtained in two new surveys. The UTAUT model postulates that three direct variables (performance expectancy, effect expectancy, and social influence) determine the behavioral intent of technology use and a direct determinant of usage behavior in facilitating conditions. The model integrates four moderating factors (gender, age, experience, and voluntariness) having varying influence the primary constructs. In summary, the UTAUT model has condensed the 32 variables found in eight existing models into four main effects and four moderating factors.
Table 3. UTAUT Components
Construct Description
Performance Expectancy (PE) Degree to which an individual believes that using the system will help attain gains in job performance Effort Expectancy (EE) The degree of ease associated with the use of the
system
Social Influence (SI) The degree to which an individual perceives that important others believe he or she should use the new system
Facilitating Conditions (FC) The degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system.
Table 4. UTAUT Moderators
Construct Description
Gender Gender roles have a strong psychological basis and are enduring.
Age Age has an effect on attitudes.
Experience Effort is expected to be more important in the early stages of new behavior.
The moderating factors have influence on the four model constructs. Gender and Age influence performance expectance, effort expectance, and social influence. Age and experience moderate the facilitating conditions. Experience moderates effort expectancy, social influence, and facilitating conditions. Voluntariness of use moderates the effect of social influence in UTAUT. The combinations of the constructs and moderating factors have increased the predictive efficiency to 70%, a major improvement over previous TAM model rates (Venkatesh et al., 2003).
UTAUT Supporting Research
Research is currently being conducted to test the UTAUT model. Dr. John Anderson and Dr. Paul Schwager, of East Carolina University are examining an application of the UTAUT model that is being presented at AMCIS conference this coming summer in Acapulco, Mexico (Anderson & Schwager, 2006). Dr. Christer Calrsson et al (Carlsson, Carlsson, Hyvönen, Puhakainen, & Walden, 2006) have studied the adoption of wireless mobile communication in Europe with UTAUT. Dr. Monica Garfield used the UTAUT tool to analyze the acceptance of table PCs at Bentley College (Garfield, 2005).
CHAPTER 3: RESEARCH DESIGN
The objective of this study was to measure the acceptance of TPCs by college students at a small Midwestern University. The expectations are that the survey will provide evidence of the acceptance of the devices by students at this research site. The research questions guiding this study are:
H01: University students, in the study environment, reject the Tablet PC.
H02: The Unified Theory of Acceptance and Use of Technology (UTAUT) does not predict the successful acceptance of the Tablet PC
H03: The constructs of the UTAUT will not demonstrate an effect on users’ acceptance of the tablet PC.
H04: Computer self efficacy does not impact students acceptance of the Tablet PC.
H05: Anxiety does not impact student’s acceptance of the Tablet PC
H06: Students use pattern of the Tablet PC does not impact their acceptance of the device
The alternate hypotheses are listed below.
HA2: The Unified Theory of Acceptance and Use of Technology (UTAUT) does predict the successful acceptance of the Tablet PC
HA3: The constructs of the UTAUT will demonstrate an effect on user acceptance of the tablet PC.
HA4: Computer self efficacy does have an impact on students acceptance of the Tablet PC.
HA5: Anxiety does impact student’s acceptance of the Tablet PC
HA6: Students use of the Tablet PC does not impact student’s acceptance of the device.
The participants of this study were college students who had been using the device since the fall of 2005. The sample of student participants is over three hundred students. The population of students who enrolled at the university for the fall semester of 2005 was 356. The number of subjects available to be surveyed was lower due to
dropouts and transfers to other educational institutions and the timing of the sampling which was at the beginning of the summer semester. The researcher approached the instructors of the various courses that would be a normal progression for this student population and obtained their permission to conduct the survey during a scheduled class period.
The instrument used to gather adoption data was a web survey based on the variables defined in this study. The survey tool presented questions based on the Unified
Theory of Acceptance and Use of Technology (UTAUT) Model, and since this is a relatively new model, the survey included questions addressing constructs that were present in the original Davis Technology Acceptance model (TAM) dealing with
computer self efficacy, anxiety, and usage. These questions were added as suggested by a group of technology acceptance
researchers.
Figure 5: Dissertation Research Model (Moran, 2006)
Note. Figure created with PLS-Graph from the hypothesized research model.
The UTAUT model theorizes that four constructs have a significant determination on user acceptance of IT innovations (Venkatesh et al., 2003). These variables are
performance expectancy, effort expectancy, social influence, and facilitating conditions. These constructs are moderated, in varying degrees, by gender, age, experience, and voluntariness of use. Figure 5 graphically displays the relationship between interacting variables used for this study.
Performance Expectancy
Performance expectancy is defined as the degree to which the student believes that using the TPC will help him or her to accomplish the various academic assignment at a typical university. Venkatesh (Venkatesh et al., 2003) postulates that performance expectancy is the strongest of the four constructs in his model. This theory is support by other researchers publishing papers on acceptance models (Agarwal & Prasad, 1998, Compeau & Higgins, 1995; Taylor & Todd, 2001) [APA style references indicate that multiple references given at the end of a sentence be enclosed within the same
parentheses]. Performance expectancy will be measured using ten questions that focus on task accomplishment. These questions are only slightly modified versions of questions used consistently in most prominent research publications dealing with TAM. Recent studies have determined that this construct may have a gender bias (Lynott &
McCandless, 2000, Venkatesh et al., 2003) determined that the effect performance expectancy was moderated by age and age such that it was more important to younger male workers in particular.
Effort Expectancy
Effort expectancy is defined as the degree of ease associated with the use of the TPC. This construct will be measure by asking eight questions based on the common current literature set. Some researchers suggest that there are gender differences related to roles in life (Lynott et al. 2000). Researchers in technology acceptance have addressed these criteria (Venkatesh & Morris, 2000) thus a gender effect is expected in this study and will be measured by determining the gender of the survey participants.
Social Influence
Social influence is the degree to which an individual perceives that important others believe he/she should use the new systems. This construct deals with the notion that the individuals behavior is influenced by the way in which they believe others will view them as a result of using the technology. The importance of social factors becomes more significant in mandated environments is postulated by Venkatesh and Davis in their 2000 publication (Venkatesh & Davis, 2000). In mandatory adoption settings social influence appears to be significant only in the early stages with its effect eroding over time.
Facilitating Conditions
Facilitating conditions are defined as the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the TPC. Five common TAM research questions will be used to measure this construct. Venkatesh et al.
published in 2003 that this variable was not significant as a determinant of intention. However this variable was retained because of discussion pertaining to its importance in other publications (Taylor & Todd, 1995).
Supplementary Variables
The TAM model is a simpler model than the UTAUT but includes constructs that address other personal and situational variables that may be pertinent in this study. The additional variables added to the study from TAM are; attitude toward using technology (ATUT), self-efficacy (SE), and Anxiety (ANX). These variables will be measured by asking five to six questions dealing with each area that have been modified to address tablet PC use. In addition, the researcher is interested in the usage of the TPC as a pen based mobile computing platform so questions are asked to determine the usage of the device. These questions were constructed with the help of a group of researchers interested in tablet PCs in an educational environment.
Pilot Study
A pilot study was conducted to test the survey instrument with a small group of upper class students enrolled in a one credit FrontPage application class. The survey used in the pilot study contained approximately sixty statements pertaining to the various constructs included in the dissertation model. The researcher asked the participants to complete the sample survey during normal class meeting time. Following the group survey completion, which was approximately fifteen minutes, the group of participants and the researcher discussed the survey instrument for areas that could be improved.
Areas of discussion included; confusion about what the survey was; confusion about the wording of some statements, and the length of the survey.
Following the first test of the survey the researcher made the recommended changes to the instrument. The preliminary material, before participants entered the survey, was reworded to clarify the reasons for the research and the confidentiality of the participants. Six survey statements that were thought to be confusing or redundant were removed and many statements were reworded to clarify the statements. Another result of the test was reorganization of the questions into construct groups allowing a full set of statements to be answered on one screen. The final addition was the elimination of all the submit buttons and replacing them with a submit command button at the end of the survey.
The modified survey tool was re-evaluated by the pilot group in a subsequent class session with discussion following the second trial run. The pilot group was pleased with the changes and suggested a desire that participants may wish to know the final result. This resulted in a research link being established on the researcher’s website that students can visit to view the survey results and publications based on data obtained by the survey. Survey modifications were based on information found at web survey design hosted by San Diego State University (Hoffman, 2006). Specifically the layout of the final survey design is based on information found in an excellent book by Don Dillman of Washington State University (Dillman, 1999).
The Survey
After the survey instrument and the scope of the research had been approved by the university Human Subjects Committee, then all subsequent survey participants were given identical survey forms. Participants were assured response anonymity by not being required to provide identifying information on the survey. The survey instrument was made available to the participants via the World Wide Web. An increasing number of research studies are being conducted in this fashion (Gall, Gall, & Borg, 2003). There is a tremendous benefit of using a web survey over paper survey because the responses are transferred automatically to a database eliminating clerical errors. The researcher prepared both the survey instrument and the response database in preparation for survey administration.
The survey instrument was constructed using Hypertext Markup Language (HTML) and Active Server Pages (ASP), and the database utilized Access, from the Microsoft Office Suite, to capture and store the data. The survey tool was demonstrated to faculty and staff both at research university and another tablet university, where it was critiqued resulting in a more streamlined tool. Initially the survey was deemed to be too long and several questions were removed in an attempt to eliminate fatigue from
adversely affecting survey results.
Based on the expert committee input, the survey layout was changed to include multiple related questions into a single screen and the length of the survey was limited to five screens. The survey instrument was beta-tested by administration to sixteen students who were enrolled in an intermediate Microsoft Access course. These upperclassmen also
considered the design and format of the survey and made suggestions that resulted in limiting the surveys length to five screens.
The questions or statements used in survey instrument can be examined in appendix B. Figure 6 illustrates the webpage link utilized in the study.
Figure 6. Research Page Link (http://www.homepages.dsu.edu/moranm/Research/research_page.htm)
Survey participants accessed the tool by clicking on the “TabletPC Survey” link on the lower left hand corner of the figure. When this link was selected the screen displayed in Figure 7 appeared explaining the purpose of the survey. When the participants clicked that link they are indicating informed consent. Participants were allowed to exit the process or enter the actual survey instrument. Survey participants were
in a class setting and were guided to the instrument by the survey coordinator who is the primary researcher.
The screen below (Figure 8) was displayed to provide survey participants with links to responsible individuals, and Capella University, if the participants had any questions, or concerns, about the survey or the process. If participants clicked on a link they were directed to the appropriate email address for the responsible person or a mailing address and phone number were displayed.
When survey participants clicked the “TabletPC Survey” button they were taken to the Website containing the survey instrument. Figure 8 shows the first screen the participants saw containing the first set of questions.
Figure 9. Web Survey Instrument
After survey completion the participants submitted their selections to the survey database by selecting a command button. The instrument and database are stored on a secure web server administered by computing services staff at the participant university.
The survey questions were broken down by construct, survey layout, and human subjects’ committee approval documentation are available from the researcher.
Sample Size
One method of determining the necessary sample size for a survey is based on the error the researcher is willing to accept. It is common in the social sciences to try to achieve a statistical power of at least 95% confidence or an alpha of 0.05. Using the method and a table provided by Cohen (Cohen, 1988, p. 52) it was determined that a sample of at least 175 participants would be needed to achieve 95% confidence. One of the benefits of using PLS-Graph is that it can resample the initial data set enlarging it thus reducing overall sample requirements. Guidelines provided with PLS-Graph recommend a sample size equal to the larger of two possibilities: (1) ten times the number of
indicators on the most formative construct, in this study ten times the ten indicators of performance expectancy or one hundred participants, or (2) ten time the largest number of antecedent constructs used to determine a dependent variable, in this study ten times six, the number of constructs used to determine behavior intent. In all cases the 263 survey participants is greater than the calculated sample size.
Survey Sample Population
This survey tool was administered to 361 students at the research site, refer to appendix A for the exact number of students in each class. The participant pool were students enrolled in the following courses
Introduction to computers (CSC 105) – about one hundred eighty students Introduction to visual basic (CIS 130)– about one hundred fifty students Computer hardware and networks (CIS 351)– about ninety students
Computer science 2 (CSC 150)– about forty students
Management of Information Systems (CIS 325) – about thirty students
These classes were selected for the survey because they contained students who had been using the TPC since August 2005; it was postulated that most of these students were introduced to the device during the fall semester of 2005. Students who are enrolled in more than one of the classes surveyed were instructed to not complete the survey by the survey administrator. The survey was administered during normal class meeting times by the researcher who verified that no participant completed the instrument more than once. The survey was conducted during normal class sessions during the last ten minutes of class using each student’s TPC. The time required to complete the survey was five to seven minutes.
Human Subjects Protection
The human subjects research committee (HSC) at the subject university
determined that this research is exempt from the rules governing the protection of human subjects because of the method used to obtain and record the information. This exemption is based on information published in the Code of Federal Regulations Title 34
Department of Education PART 97 - Protection of Human Subjects (Code of Federal Regulations Title 34 Department of Education PART 97 - Protection of Human Subjects, 2004). The rules of exemption state that
“(2) Research involving the use of educational tests (cognitive, diagnostic, aptitude, achievement), survey procedures, interview procedures or
(i) Information obtained is recorded in such a manner that human subjects can be identified, directly or through identifiers linked to the subjects; and (ii) Any disclosure of the human subjects' responses outside the research could reasonably place the subjects at risk of criminal or civil liability or be damaging to the subjects' financial standing, employability, or
reputation.” (PART 97.101, paragraph b2)
The exemption was granted because survey is a common educational tool for this university and no information will be recorded that could be used to identify the subjects. The survey information will be maintained and protected by the primary investigator for the duration of its use and retained for five years.
The two class sections taught by the researcher have enrollments of thirty six of the approximately three hundred survey target audience. These participants will be compared to the results from the rest of the survey to determine if they can be included in the statistical calculations based on the similarity of survey
responses. The difference will be evaluated in chapter four by comparing the mean, standard deviation, and variance, using a t-test, of the two groups. Since these sections are under the instruction of the primary investigator there may be an influence from the instructor.
Survey Questions
Venkatesh et al. (2003) in the September issue of the MIS Quarterly used a survey comprised of thirty one questions to support the UTAUT model. The questions were derived from the eight models analyzed in the paper. The Venkatesh team used the survey instrument in two studies to validate the new model. They studied a financial
service company that was primarily involved in research and a retail electronics company whose primary functional area was customer service.
The survey instrument contained four questions addressing each of the technology acceptance areas. The eight variables are performance expectancy, effort expectancy, attitude toward using technology, social influence, facilitating conditions, self-efficacy, anxiety, and behavior intention to use the system. The validation process used in the Venkatesh publication eliminated three of these variables which were found to not be significant determinants of use. The Venkatesh eliminated variables are attitude toward using technology, self-efficacy, and anxiety.
The survey used at the research site utilizes a model similar to that used in the Venkatesh publication in that it contains questions pertaining to the constructs of the UTAUT. This dissertation survey includes questions pertaining to constructs that the researcher believes are important in this environment. There are also questions measuring the four moderating UTAUT items, questions pertaining to the TAM model, and question that address the participants age and computer experience including computer experience prior to their enrollment in college. Survey participants were asked to indicate their response to each statement using a seven item likert scale with one representing a strong disagreement and seven being a strong agreement with the statement.
Table 5 lists the questions that are being used to measure performance expectancy.
Table 5. Performance Expectancy Questions
Question Item
Using the Tablet PC in my classes would enable me to accomplish tasks more quickly [font size not consistent with the other table]
Using the Tablet PC in my classes would hamper my performance (reverse scored) Using the Tablet PC in my classes would increase my productivity
Using the Tablet PC in my classes would hamper my effectiveness in class (reverse scored) Using the Tablet PC in my classes would make it easier to do my homework
Using the Tablet PC in my classes would hamper the quality of the work I do (reverse scored) Using the Tablet PC in my classes would cause my classmates perceive me as competent Using the Tablet PC in my classes would increase the instructors respect for me
Using the Tablet PC in my classes would decrease my chances of getting a good grade (reverse scored) Using the Tablet PC in my classes would be useful in my classes
Table 6 lists the statements that are being used to measure effort expectancy with appropriate responses on a seven item Likert scale.
Table 6. Effort Expectancy Questions
Question Item
Learning to operate the Tablet PC is easy for me.
I find it easy to get the Tablet PC to do what I want it to do
My interaction with the Tablet PC would be clear and understandable I find the Tablet PC to be flexible to interact with
It is easy for me to become skillful at using the Tablet PC I find the Tablet PC easy to use
Using the Tablet PC takes too much time from my normal duties.
Working with the Tablet PC is so complicated, and difficult to understand
Table 7 lists the statements that are being used to measure attitude toward using technology with appropriate responses on a seven item Likert scale.
Table 7. Attitude toward using technology Questions
Question Item
Using the Tablet PC is a good idea
I dislike the idea of using the Tablet PC (reverse scored) Using the Tablet PC is pleasant
The Tablet PC makes schoolwork more interesting Using the Tablet PC is fun
Table 8 lists the statements that are being used to measure social influence with appropriate responses on a seven item Likert scale.
Table 8. Social Influence Questions
Question Item
People who influence my behavior think that I should use the Tablet PC. People who are important to me think that I should use the Tablet PC. Professors in this university have been helpful in the use of the Table PCs My advisor is very supportive of the use of the Tablet PC for my class. In general, the university has supported the use of the Tablet PC. Having the Tablet PC is a status symbol in my university.
Table 9 lists the statements that are being used to measure facilitating conditions with appropriate responses on a seven item Likert scale.
Table 9. Facilitating Conditions Questions
Question Item
I have the resources necessary to use the Tablet PC. I have the knowledge necessary to use the Tablet PC.
The Tablet PC is not compatible with other computer systems I use. The help desk is available for assistance with the Tablet PC difficulties. Using the Tablet PC fits into my work style.
Table 10 lists the statements that are being used to measure behavioral intention with appropriate responses on a seven item Likert scale.
Table 10. Behavioral Intention Questions
Question Item
Whenever possible, I intend to use the Tablet PC in my studies I perceive using the Tablet PC as Involuntary
I plan to use the Tablet PC in the next three months.
To the extent possible, I would use Tablet PC to do different things (school or not school) related
To the extent possible, I would use Tablet PC in my studies frequently.
In addition to the statements regarding constructs included in the UTAUT model this research added statements that address additional variables included in many
technology acceptance models which are self efficacy, anxiety, and usage. The researcher postulates that including these construct will both strengthen the study and improve the support for the UTAUT.
Table 11 list the statements used to measure self efficacy, responses are in a seven item likert scale with one representing a strong disagreement and seven being a strong agreement with the statement..
Table 11. Self Efficacy
Question Item
I could complete a task using the Tablet PC if there was no one around to tell me what to do as I go.
I could complete a task using the Tablet PC if I had seen someone else demonstrate how it could be used
I could complete a task using the Tablet PC if I could call someone to help if I got stuck I could complete a task using the Tablet PC if I had a lot of time to complete the job. I could complete a task using the Tablet PC if J had just the built in help facility for assistance
Table 12 list the statements used to measure anxiety with appropriate responses on a seven item Likert scale.
Table 12. Anxiety Questions
Question Item
I feel apprehensive about using the Tablet PC.
It scares me to think that I could lose a lot of information by using the Tablet PC and pressing the wrong key.
I hesitate using the Tablet PC for fear of making mistakes I cannot correct The Tablet PC is somewhat intimidating to me.