2010 International Conference on User Science and Engineering
Level of Acceptance and Factors Influencing Students’ Intention to Use UCSI University’s E-mail System Mohamed Yamin1, Yvonne Lee2 Faculty of Management and Information Technology UCSI University Kuala Lumpur, Malaysia E-mail:
[email protected],
[email protected] implementation of an information system is very much dependent on the acceptance level of the system by its users. The main objective of this study is to explore the level of acceptance of the newly implemented student e-mail system at UCSI University. The study also seeks to discover relationships between influencing factors and students’ intention to use the e-mail system.
Abstract—The study explores the level of acceptance and factors influencing the acceptance of the newly implemented student email system at UCSI University, Malaysia. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was modified and used to determine the factors that influence students’ intention to use the e-mail system. 357 students participated in the study from three faculties of the University. The study found out that the level of acceptance of the student e-mail system was low amongst the students of UCSI University. The results indicate that Performance Expectancy, Effort Expectancy, Attitude, Social Influence, Facilitating Conditions, and Self Efficacy have a significant relationship with Behavioral Intention. However, results of the regression analysis indicate that only Performance Expectancy, Effort Expectancy, and Attitude are significant in influencing Behavioral Intention. Recommendations were given to increase the acceptance of the student e-mail system among students based on the findings.
A. “Microsoft Live@edu” vs. “Google Apps Education Edition” Microsoft was the first to tap in to solve the limitations of the student e-mail systems when it launched “Microsoft Live@edu” providing a free web-based e-mail system for educational institutions in 2005. Google followed shortly in 2006, offering Google’s version of the e-mail system known as “Google Apps Education Edition” [3].Microsoft provides free e-mail accounts, a suite of online tools such as shared calendars, documents, and workspaces [4]. Educational institutions enrolling for Microsoft Live@edu are provided with free hosted, co-branded communication and collaboration services for students, alumni and staff [5]. The web-based email application of Microsoft; Windows Live Hotmail is bundled with the package including 10 gigabytes (GB) of email storage.
Keywords-Level of acceptance; UCSI University’s student email system; web-based e-mail; UTAUT
I.
INTRODUCTION
With the advent of the internet, web-based e-mail became one of the most popular activities for internet users. The benefits and importance of e-mail were recognized by all types of organizations including educational institutions. Many universities and colleges in Malaysia use web-based student email systems. Web-based e-mail systems allow users to login to their e-mail accounts using a web browser via internet. Popular e-mail systems used in educational institutions in Malaysia include Zimbra, SquirrelMail, and IceWarp.
Likewise, Google’s package includes its popular Gmail format e-mail, Google Calendar (shared calendaring) and Google Docs (online document creation & sharing) [6]. The email system includes 7 gigabytes (GB) of storage, built-in chat, innovative search, and IMAP. Like Microsoft, Google Apps Education Edition is a free suite of hosted communication and collaboration applications designed for educational institutions.
However, most of these webmail systems do have its drawbacks. Limited inbox capacity, virus vulnerability, and in most cases, the price of acquiring and maintaining these systems are costly [1]. To overcome the drawbacks, UCSI University migrated from its old SquirrelMail system to “Google Apps Education Edition” – a free web-based e-mail system hosted by Google on the internet. Since the rollout of the new student e-mail system in May 2009, no research has been conducted to see how well the system has been accepted by students. According to Venkatesh et al., [2] successful
CFP1087L-ART/ 978-1-4244-9049-3/ © 2010 IEEE
At virtually no cost, the services offered by Microsoft or Google yields financials benefits for educational institutions. The services help IT/ICT Departments to reduce maintenance costs and spend less time maintaining e-mail systems and more time on strategic initiatives [5]. Other benefits for students include having a common, familiar tool for them to communicate and collaborate with peers and faculty anywhere anytime. However, the service providers have the right to include advertisements which may not be favorable for users.
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II.
Even though TRA is believed to be one of the most fundamental and influential theories of human behavior [2], Sheppard et al. [19] disagreed with the theory claiming that more than half of the research they reviewed investigated activities for which the model was not designed. Sheppard et al. [19] argue that there are three limiting conditions on the use of attitudes and subjective norms to predict intentions and the use of intentions to predict the performance of behavior. They include goals versus behaviors; the choice among alternatives; and intention versus estimates. Work by Hale et al. [18] point out the theory excludes a wide range of behaviors such as those that are spontaneous, impulsive, habitual, the result of cravings, or simply scripted or mindless. Despite the criticisms, Davis et al. [10] applied TRA to individual acceptance of technology and found that the variance explained was largely consistent with other studies that had used TRA in the context of other behaviors.
LITERATURE REVIEW
Researchers have studied various theories that could be used to explain the adoption of information technology innovations. Prominent theories include the Technology Acceptance Model (TAM) developed by Davis et al. [7]; the Theory of Reasoned Action (TRA) developed by Fishbein and Ajzen [8]; the Theory of Planned Behavior (TPB) developed by Ajzen [9]. Among these models, the Technology Acceptance Model (TAM) is considered to be the most robust, parsimonious, and influential model in explaining acceptance of information technology by its users [7, 10, 11]. However, Mathieson et al. [12] criticized the TAM stating that TAM was limited to the fact that the model assumes usage is volitional, that is, there are no barriers that would prevent an individual from using an information system if he or she chose to do so. Nevertheless, there might be situations where users are confronted with lack of time, money, etc which may prevent them from using the system [13].
B. Technology Acceptance Model (TAM) Extending the TRA developed by Fishbein and Ajzen [8], Davis [10] designed the Technology Acceptance Model (TAM) to predict information technology acceptance and usage on the job. Unlike TRA, TAM focused on information systems context and excluded the Attitude construct in order to better explain intention parsimoniously. Davis [10] suggested that user’s motivation can be explained by perceived ease of use, perceived usefulness, and attitude toward using the system. He hypothesized that the attitude of a user toward a system was a major factor in determining whether the user will actually use or reject the system. The attitude of the user was considered to be influenced by perceived usefulness and perceived ease of use where perceived ease of use had a direct influence on perceived usefulness.
To address the shortcomings of TAM, several researchers including Agarwal and Prasad [14], Mathieson et al. [12], Shih [15], Musa [16], and Tung et al. [17] have attempted to improve the TAM model. These studies focused to modify the original TAM by overcoming TAM’s limitations. However, it resulted in more fragmentation of the TAM. Furthermore, researchers adopting any of these models had to make a choice from a large number of models and found that they must “pick and choose” constructs across the modified models, or choose a “favored model” and largely ignore the contributions from alternative models [13]. To address this issue Venkatesh et al. [2] proposed a new model called the Unified Theory of Acceptance and Use of Technology (UTAUT) that overcomes the above mentioned limitations.
While many researchers have confirmed the robustness of TAM, several other researchers have also highlighted important limitations of the model [20]. To measure system use, TAM advocated on using self-reported usage data instead of real actual usage data. However, some researchers believe that self reported usage data is a subjective measure and hence is unreliable in measuring actual use of a system [21, 22]. Apart from this, the theoretical foundation of the TAM has also been criticized. According to Bagozzi [23], TAM showed a poor theoretical relationship among different constructs. He remarked that TAM was a deterministic model, and hence, an individual’s act was assumed to be totally determined by his or her intention to act. However, Bagozzi [23] asserts that a person’s intention could be subjected to evaluation and reflection, which might direct the person to reformulate his or her intention, and even to take a different course of action. Hence, he concluded that TAM was not appropriate for explaining and predicting use of a system.
A. Theory of Reasoned Action (TRA) Fishbein and Ajzen [8] proposed the Theory of Reasoned Action (TRA), which was drawn from the field of social psychology. The study began as the theory of attitude, which led to the study of attitude and behavior. According to Hale et al. [18], the theory was, “born largely out of frustration with traditional attitude-behavior research, much of which found weak correlations between attitude measures and performance of volitional behaviors”. The theory focused on predicting Behavioral Intention, spanning predictions of attitude and predictions of behavior. TRA consists of the constructs Behavioral Intention (BI), Attitude (A), and Subjective Norms (SN). The theory suggests that a person's Behavioral Intention is dependent on the person's Attitude about the behavior and Subjective Norms (BI = A + SN). It is argued that if a person intends to do a behavior, then the person will probably do it. The intention of a person is directed by attitude towards the behavior and the subjective norm. Behavioral intention measures a person’s relative strength of intention to perform a behavior. Attitude comprises of beliefs about the consequences of engaging in a behavior, where as subjective norm is the combination of perceived expectations from relevant individuals or groups along with intentions to comply with these expectations [8].
To address shortcomings, Venkatesh and Davis [24] extended the TAM to explain perceived usefulness and usage intentions in terms of Social Influence and cognitive instrumental processes. Consequently, a new model TAM2 was established and was tested in both voluntary and mandatory settings. Using the TAM 2, Venkatesh and Davis [24] were able to explain in more detail the reasons for a particular system to be useful for its users.
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accurate in predicting user acceptance of information technology innovations [2]. UTAUT was validated in a longitudinal study.
C. Theory of Planned Behavior (TPB) Azjen [9] proposed the Theory of Planned Behavior (TPB) extending the TRA. The additional construct included in TPB apart from the constructs of TRA is the construct of perceived behavioral control. In TPB, perceived behavioral control is hypothesized to be an additional determinant of intention of behavior. Perceived behavioral control is also influenced by control beliefs and perceived facilitation. Control beliefs include perceived availability of skills, resources, and opportunities. Perceived facilitation belief is defined as an individual’s assessment of available resources to the achievement of a given set of outcomes.
UTAUT Model has been widely used by many researchers in different settings to explain the adoption of technologies. In recent years, the model has gained popularity in education settings as well. El-Gayar and Moran [27] applied a modified UTAUT Model to evaluate students’ acceptance of Tablet PCs as a means to forecast, explain, and improve usage pattern of Tablet PC in education. Study by Marchewka et al. [28] to find out student perceptions (in terms of applying the UTAUT Model) on the use of Blackboard® (Course Management Software) found mixed support for the model’s reliability of the scale items representing the UTAUT constructs and the hypothesized relationships. Jong and Wang [29] used a modified UTAUT model to determine technology acceptance of a web-based learning system of Taiwan technical university students. A study conducted in UCSI University to evaluate students’ acceptance of blogging also found strong support for the UTAUT model [30].
Mathieson [25] conducted an experiment to compare TPB with TAM and found out that both models performed well in predicting intention to use an information system. However, his findings also revealed that while TPB provides more specific information that can better guide development, TAM had a slight empirical advantage. The advantage was that TAM was easier to apply. He believed that due to simplicity and ease of implementation TAM was favored instead of TPB or TRA.
E. Conceptual Model The conceptual model (see Fig. 2) formulated for this study will focus on identifying the relationship between Performance Expectancy, Effort Expectancy, Social Influence, Attitude, Facilitating Conditions, Self Efficacy, Anxiety, Voluntariness of Use and students’ intention to use UCSI University’ student e-mail system.
D. Unified Theory of Acceptance and Use of Technology (UTAUT) The UTAUT model proposed by Venkatesh et al. [2] (see Fig. 1) integrates the fragmented technology acceptance models. The UTAUT model is a unified theoretical model that captures the essential elements of eight previously established models (i.e., the TAM, TRA, the motivational model, the TPB, a model combining the TAM and the TPB, the model of personal computer utilization, the innovation diffusion theory, and the social cognitive theory). The UTAUT aims to explain user intentions to use an information system and subsequent usage behavior. Performance Expectancy
Effort Expectancy Behavioral Intention
Use Behavior
Figure 2. Conceptual Model
Social Influence
III.
Facilitating Conditions
Gender
Age
Experience
METHOD
A. Research Instrument The survey questionnaire was mainly based on the constructs of the UTAUT model developed by Venkatesh et al. [2]. The questionnaire consisted of two sections. Part A comprised of the questions adopted from the UTAUT model. Part B consisted of demographic questions. The UTAUT questions in Part A were sub divided into nine sub categories. They were, Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Anxiety, and Voluntariness of Use, each covering four questions. Attitude covered five questions while Self Efficacy and Behavioral Intention covered three questions. A five point Likert scale was used to administer the UTAUT questions.
Voluntariness of use
Figure 1. UTAUT Model
According to the UTAUT model, four constructs are deemed to be direct determinants of user acceptance and usage behavior: (1) Performance Expectancy, (2) Effort Expectancy, (3) Social Influence, and (4) Facilitating Conditions. Perceived usefulness and ease of use which was originally used in the TAM study were incorporated as Performance Expectancy and Effort Expectancy in the UTAUT model. According to the UATUT model, Effort Expectancy is more salient in the early stages of using a new technology. In contrast to previous technology acceptance models which were able to predict user acceptance of an innovation with about 40 percent accuracy [7, 26], UTAUT was found 70 percent
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B. Target Population and Sampling The target population for this study was the total number of active user accounts created in the UCSI University student email system. According to the Computer Services Department of UCSI University, a total of 5,000 user accounts were created for students as at May 2010. The total student population of UCSI University comprised of ten faculties. Cluster sampling was used to select faculties where each faculty was considered as a cluster. However, due to the geographic disparity, three faculties in Sarawak and Terengganu campus were not included. Systematic sampling was used to select the faculties for this study from the seven faculties in Kuala Lumpur campus. After arranging the seven faculties in alphabetical order, every second faculty was selected.
A. Demographic Data Majority of the respondents were females totaling to 248 respondents (69.47%) while male respondents were 109 (30.53%). The respondents’ age varied from 17 years to 27 years. The mean age of all students was 21.37 years. There were a total of 262 local students (73.39%) and 95 international students (26.61%). Among the respondents, most respondents were studying for their Bachelor’s Degree which was 343 students (96.08%). 7 students (1.96%) were studying in Foundation while 5 students (1.40%) were from the Diploma programs. 2 students (0.56%) were studying for their Masters Degree.
Performance Expectancy
2.54
Low
Effort Expectancy
3.08
Moderate
Attitude
2.98
Low
Social Influence
2.82
Low
Facilitating Conditions
3.08
Moderate
Anxiety
2.31
Low
Voluntariness of Use
3.30
Moderate
Behavioral Intention
2.79
Low
The Standardized Beta Coefficient indicates the measure of contribution of each variable to the model. The results show that Attitude has the largest value (B=0.206) indicating that Attitude had the highest impact (among all the independent variables) in determining behavioral intention to use UCSI student e-mail. Effort Expectancy had the second highest value (B=0.183) followed by Performance Expectancy (B=0.133). The t-value for Attitude, Effort Expectancy, and Performance Expectancy was 3.130, 2.763, and 2.024 respectively. Attitude had a significant p-value of 0.002 while Effort Expectancy and Performance Expectancy yielded a p-value of 0.006 and 0.044 respectively.
LEVEL OF ACCEPTANCE OF THE STUDENT E-MAIL SYSTEM Level
Low
Regression analysis was carried out to determine the relationship with the independent variables (Performance Expectancy, Effort Expectancy, Attitude, Social Influence, Facilitating Conditions, Self Efficacy, Anxiety, and Voluntariness of Use) and the dependent variable (Behavioral Intention).
B. Level of Acceptance To explore the acceptance level of the student e-mail system, mean ratings were calculated. It was observed that the overall acceptance of the student e-mail system was low (see Table 1).
Mean Ratinga
2.97
C. Correlation and Regression Analysis Pearson Product-Moment Correlation was used to investigate the relationship between the independent variables and the dependent variable. According to the correlation analysis, Performance Expectancy, Effort Expectancy, Attitude, Social Influence, Facilitating Conditions, and Self Efficacy was positively correlated with Behavioral Intention where correlation coefficient was equal to 0.414, 0.426, 0.453, 0.371, 0.309, 0.269 respectively. All of the mentioned correlations were significant at the 0.01 level. However, Anxiety and Voluntariness of Use were observed to have an insignificant correlation with Behavioral Intention where the correlation coefficient was 0.033 and 0.008 respectively.
RESULTS AND DISCUSSION
Construct
Self Efficacy
Low scores for constructs such as Performance Expectancy, Attitude, and Behavioral Intention indicates that students have not realized the benefits of the system and their attitude is not positive. Hence, students did not have a high intention of using the e-mail system. The cause of the low acceptance to use the system by students may probably be because of the lack of awareness and the fact that the students do not believe there is a requirement to use the system.
A total of 357 students participated in the study. The students were selected based on the relative population size of the faculties studied. Hence, 89 students were selected from the FoAS, 207 students were from FoMIT and 61 students were selected from FoMSSD.
TABLE I.
Level
a. Rated on a scale of 1 (very low) to 5 (very high)
The selected faculties were the Faculty of Applied Sciences (FoAS), Faculty of Management and Information Technology (FoMIT), and Faculty of Music, Social Sciences, and Design (FoMSSD).
IV.
Mean Ratinga
Construct
D. Conclusion and Recommendations The study explored the level of acceptance of the student email system at UCSI University and established factors that influence the acceptance by using a modified UTAUT model by Venkatesh et al. [2]. Being the first study to explore the acceptance of the newly implemented student e-mail system at UCSI University, this study has shed light on the current
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the conventional sending and receiving of e-mail messages. For example, the built in chat function, shared online calendars, online document collaboration, and the feature to create web sites, will facilitate students to communicate and manage their group assignments more efficiently, and plan and manage activities of their University clubs and associations.
situation. The study found out that the level of acceptance of the student e-mail system was low. However, strong support was found to the independent variables Attitude, Effort Expectancy, and Performance Expectancy to predict the Behavioral Intention to use the student e-mail system. In light of the findings the following recommendations have been put forward. •
Students should be instilled with a sense of importance of using the student e-mail system. Students should be made aware that important notices or messages from the University such as messages from the Finance Office, Records Office, Student Affairs Office, Corporate Affairs Office, Library, and Co-Operative Education and Career Services (CECS) will be sent to the UCSI University student e-mails.
•
Lecturers and staff of UCSI University can play a vital role in advocating the student e-mail system. For example, lecturers can encourage students to use the student e-mail system when sending e-mails to lecturers and staff. This will allow authentication of the identity of the student. Lecturers can also organize learning activities utilizing the web site feature where all students could participate.
•
•
Future research can expand this research by including the moderating variables, gender, age, and experience proposed in the UTAUT model which was not included in this study. It would also be imperative to conduct a follow up study on the acceptance of student e-mail system, after the recommended actions have been taken to measure the effectiveness of the recommendations.
In order to create more awareness, it is proposed that more promotional activities be carried out. For example, a demonstration on how to use the features of the student e-mail system could be provided during the University Orientation Days at the beginning of each semester (for new students). Promotional messages can be put on the electronic bulletin boards located throughout the University. Tutorial videos on how to use different features of the student e-mail system can also be uploaded to UCSI University’s YouTube channel. In addition, step by step guides or manuals can be distributed to students to increase usability.
•
Students may not be aware of the new features enhancing ease of use. For example, the student e-mail system holds a comprehensive address book where all active UCSI University students are listed. The auto complete feature in the compose message screen suggests e-mail addresses when the name of any student is keyed in. Hence, an e-mail can be sent to a student even if the sender does not know the recipient’s e-mail address, but by typing the recipient’s name. Hence, the new features that help ease of use of the system should be explained to students.
•
Another new feature in the student e-mail system is the tasks, and collaborative functions. It would be easier for students to organize their tasks with their peers and collaborate online by using features such as calendar, documents. Hence, it is proposed that the ease of use of these features should be emphasized when explaining or promoting the system to students.
•
It is recommended that the benefits of the new student e-mail system should be promoted to the students for them to accept the system. There are a lot of new features in the new student e-mail system apart from
Students should be convinced of the value of using the student e-mail system. The student e-mail system allows students to add multiple e-mail accounts so that other e-mail addresses owned by students can be integrated into the student e-mail system which helps to manage all e-mails addresses in one platform [31]. Having one platform for all e-mails saves time because there is no need to log in to multiple e-mail service providers’ web sites.
REFERENCES [1]
K. Conn, The internet and the law: What educators need to know. Virginia: Association for Supervision and Curriculum Development, 2002. [2] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, "User acceptance of Information Technology: Toward a unified view," MIS Quarterly, vol. 27, pp. 425-478, 2003. [3] K. Joly, “E-mail, Gmail, Hotmail, and beyond,” University Business Magazine, [Online], August 2008, Accessed May 10, 2010, http://www.universitybusiness.com/viewarticle.aspx?articleid=1111. [4] Microsoft Corporation, “School email and more online tools for your students – less work, lower cost for you,” [Online] Last modified April 27, 2010, http://www.microsoft.com/liveatedu/free-emailaccounts.aspx?locale=en-US&country=US. [5] Microsoft Corporation, “Microsoft Live@Edu,” [Online], Last modified May 10, 2010, http://www.microsoft.com/education/solutions/liveedu.aspx. [6] Google Inc, “Google Apps for education - free hosted email (Gmail) for EDU,” [Online], Last modified April 27, 2010, http://www.google.com/a/help/intl/en/edu/index.html. [7] F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, "User acceptance of computer technology: A comparison of two theoretical models," Management Science, vol. 35, pp. 982-1003, 1989. [8] M. Fishbein and I. Ajzen, Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley Publishing Company, 1975. [9] I. Ajzen, "The theory of planned behavior," Organizational behavior and human decision processes, vol. 50, pp. 179-211, 1991. [10] F. D. Davis, "Perceived usefulness, perceived ease of use, and user acceptance of information technology," MIS Quarterly, vol. 13, p. 319−339, 1989. [11] M. Igbaria, T. Guimaraes, and G. B. Davis, "Testing the determinants of microcomputer usage via a structural equation model," Journal of Management Information Systems, vol. 11, pp. 87-114, 1995. [12] K. Mathieson, E. Peacock, and W. W. Chin, "Extending the technology acceptance model: The influence of perceived user resources," The database for advances in information systems, vol. 32, pp. 86-112, 2001.
41
[23] R. P. Bagozzi, "The legacy of the technology acceptance model and a proposal for a paradigm shift," Journal of thr Association of Information Systems, vol. 8, pp. 244-254, 2007. [24] V. Venkatesh and F. D. Davis, "A theoretical extension of the technology acceptance model: Four longitudinal field studies," Management Science, vol. 46, pp. 186-204, 2000. [25] K. Mathieson, "Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior," Information Systems Research, vol. 2, pp. 173-191, 1991. [26] S. Taylor and P. A. Todd, "Understanding information technology usage: A test of competing models," Information Systems Research, vol. 6, pp. 144-176, 1995. [27] O. F. El-Gayar and M. Moran, "College students' acceptance of Tablet PCs: An application of the UTAUT Model," Dakota State University, pp. 2845-2850, 2006. [28] J. T. Marchewka, C. Liu, and K. Kostiwa, "An application of the UTAUT model for understanding student perceptions using Course Management Software," Communications of the IIMA, vol. 7, pp. 93104, 2007. [29] D. Jong and T. S. Wang, "Student acceptance of web-based learning system," Paper presented at the 2009 International Symposium on Web Information Systems and Applications (WISA’09), Nanchang, People’s Republic of China, May 22-24, 2009. [30] S. Ismail, "Students acceptance in using Blog as learning tool in an international private university," Paper presented at the ICI9 International Conference on Information, Kuala Lumpur, Malaysia, August 12-13, 2009. [31] J. Sohn, “Tip: Check and reply from multiple email addresses in Gmail,” The Official Gmail Blog, [Online], June 12, 2009, Accessed May 10, 2010, http://gmailblog.blogspot.com/2009/06/tip-check-and-reply-frommultiple-email.html.
[13] W. H. Loo, P. H. P. Yeow, and S. C. Chong, "User acceptance of Malaysian government multipurpose smartcard applications," Government Information Quarterly, vol. 26, pp. 358-367, 2009. [14] R. Agarwal and J. Prasad, "A conceptual and operational definition of personal innovativeness in the domain of information technology," Information Systems Research, vol. 9, pp. 204-215, 1998. [15] H. P. Shih, "Extended technology acceptance model of internet utilization behavior," Information and Management, vol. 41, pp. 719729, 2004. [16] P. F. Musa, "Making a case for modifying the technology acceptance model to account for limited accessibility in developing countries," Information technology for development, vol. 12, pp. 213-224, 2006. [17] F. C. Tung, S. C. Chang, and C. M. Chou, "An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry," International Journal of Medical Informatics, vol. 77, pp. 324-335 2008. [18] J. L. Hale, B. J. Householder, and K. L. Greene, "The theory of reason action," in The persuasion handbook: developments in theory and practice, ed California: Sage Publications, Inc., 2002, pp. 259-288. [19] B. H. Sheppard, J. Hartwick, and P. R. Warshaw, "The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research," The Journal of Consumer Research, vol. 15, pp. 325-343, 1988. [20] M. Y. Chuttur, "Overview of the technology acceptance model: Origins, developments and future directions," Sprouts: Working Papers on Information Systems, vol. 9, pp. 1-22, 2009. [21] P. Legris, J. Ingham, and P. Collerette, "Why do people use information technology?: A critical review of the technology acceptance model," Information and Management, vol. 40, pp. 191-204, 2003. [22] S. Y. Yousafzai, F. R. Gordon, and J. G. Pallister, "Technology acceptance: A meta-analysis of the TAM: Part 1," Journal of Modelling in Management, vol. 2, pp. 251-280, 2007.
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