Use of Adoption Technology Model to Predicting E-Learning Intention Perform among Faculty Members

Introduction

Along with fast development of technology in the world and appearance of new capabilities of information technology, there have been many changes in teaching process. There is a spreading view of teaching and learning; in this regard, E-learning is one of the prominent settings of learning in information period. In addition, research indicated E-learning could be an efficient system in teaching contexts and evaluation services (1). E-learning is a teaching learning method, but it is not the alternative to in person training; however, it is developing and is known as an effective tool in learning (2). E learning was introduced in Iran since 1996, most universities are using this technology by now, and some even turn to distance learning (3). This type of training increases efficiency of teaching process and results in higher learning qualities, easier access to large volume of information, lower educational expenses, higher quality, accurateness and validity of learning materials and higher scientific levels for students and teachers (4). Many of higher education centers try to organize and optimize E-learning to follow its procedure effectively and structurally; among them, universities tend to gain the technology for E-learning improvement (5). However, it would not be helpful unless effective factors and reasons of its adoption and application are considered. In other words, recognition of effective factors on acceptance and application of E-learning among university faculties is necessary to offer proper and practical solutions to its application among students, which may results in better learning settings (6). Different dimensions of understandings and attitudes of users should be considered in E-learning evaluation to form a useful and efficient pathology tool (7). If there were positive attitudes toward E-learning with teaching staff, there would be more motivation to use it (8). Users’ attitude and viewpoint is considered as a significantly important factor to accept and apply computer technologies (6). Liaw and Haung’s point of view, accordingly, could be used to categorize users’ attitude structure toward electronic technology to three major evaluative parts: emotions, recognition and behavioral (9). Emotional part was defined as loving or hating something special (10). In research’s about application of new technology, it would be useful to know how cognitive related factors, such as attitude, barrier, usefulness, and easy to use of technology; in this regard, adoption technology model (ATM) is one of the common models that application to predict the use of technology; ATM proposed by Davis et al (11-14). The main reason to accept technology was to introduce a basement for pursing external factors on inner believes attitudes and intentions to use technology; it is a predictive-descriptive model; therefore, managers would be able to recognize why a given system would not be accepted and offer proper reforming steps based on resulted understanding. Structure of adoption technology model includes perceived usefulness, perceived ease of use, external variables, attitude, and behavior intention (11). Perceived usefulness is defined as persons’ believe to use certain system that may improve their occupational function. Perceived ease of use refers to person’s expectation toward easiness of a given system. External variables are defined as organizational, social, systematic features of computer such as software and hard ware, teaching method and help from others to use computer system, which negatively affect person’s mental perceptions to use IT (14-16). Furthermore, most medical science universities have been paying attention to E-learning (15). In this regard, Clark suggested that it was inevitable to use technology and communication media in education (16).

The main aim of this study was determined factors related with E-learning intention among faculty members in Kermanshah University of medical science based on adoption technology model.

Methods

Participants

This descriptive-cross sectional study was conducted on 150 faculty members of Kermanshah University of Medical Science, during 2013. The sample size was calculated at 95% significant level according to the results of a pilot study and a sample of 150 was estimated. Of the population of 96, 316 (64.5%) signed the consent form and voluntarily agreed to participate in the study, which has been approved by deputy of research of Kermanshah University of Medical Sciences. Data collection based on the self-questionnaire.

Measures

Questionnaire included two sections that comprised of 36 questions: 11 demographic questions, and 25 items for ATM variable.

Demographics

Background item was designed to gather information related to age (year), gender (male, female), faculty (paramedics, health, nursing and midwifery, pharmacology, medicine and dental), education level (MSc., PhD student, PhD, MD), marital status (single, married), scientific rank (lecturer, assistant professor, associate professor and professor), electronic education background (yes, no), EDC membership (yes, no) and EDO membership (yes, no).

Adoption Technology Model Variable

The items that assessed components of the ATM used standard questionnaires (12-14), panel experts checked validity of the questionnaire and its reliability was defined with Cronbach alpha test, which is explained in the following.

Perceived usefulness included 5 items, e.g. ‘electronic education could facilitate availability of experienced professors’, answered by choosing one of the five options of ‘strongly agree’ (5 scores) to ‘strongly disagree’ (1 score). Maximum and minimum scores were 25 and 5, respectively. The higher the score, the more the perceived usefulness of electronic education was (Cronbach alpha 0.79).

Perceived ease of use included 3 items, e.g. ‘it is easy to use electronic education software’, answered through choosing one of the five options of ‘strongly agree’ (5 scores) to ‘strongly disagree’ (1 score). Maximum and minimum scores were 15 and 3, respectively. The higher the score, the more the perceived ease of use for electronic education was (Cronbach alpha 0.67).

External variables included 4 items, e.g. ‘it needs fast connection to the internet’, answered through choosing one of the five options of ‘strongly agree’ (5 scores) to ‘strongly disagree’ (1 score). Maximum and minimum scores were 20 and 5, respectively. The higher the score, the more need for external variables in electronic education (Cronbach alpha 0.80).

Attitude included 12 items, e.g. ‘electronic education pattern could increase motivation to students learning’, answered through choosing one of the five options of ‘strongly agree’ (5 scores) to ‘strongly disagree’ (1 score). Maximum and minimum scores were 60 and 12, respectively. The higher the score, the more positive the attitude to electronic education was (Cronbach alpha 0.73).

Intention included 1 item, ‘I intend E-learning education within ……’ It was answered through choosing one of the five options of ‘this term’ (5 scores), ‘next term’ (4 scores), ‘next year’ (3 scores), several next years’ (2 score) and ‘never’ (1 score), where the higher the score, the stronger the intention to E-learning education.

In addition, total Cronbach alpha of our scale was 0.80, suggesting that the internal consistency was adequate.

Statistical Analysis

Data were analyzed by SPSS version 21 using appropriate statistical tests including t-test, ANOVA, Pearson correlation and linear regression at 95% significant level.

Results

The mean age of respondents was 42.29 years [SD: 7.71], ranged from 28 to 61 years. In addition, the mean age of job history was 11.09 years, ranged from 1 to 27 years. Furthermore, 83.3 % (80/96) participants were male and 16.7 % (16/96) were female. About 96.9 % (93/96) were married and 3.1 % (3/96) were single. Regarding the educational status, 14.6 % (14/96) had MSc or Ph.D. student, 51 % (49/96) had Ph.D., and 34.4 % (33/96) were MD. Almost 5.2 % (5/96) were lecturer, 80.2 % (77/96) were assistant professor, and 14.5 % (13/96) were associate professor. 33.3 % (32/96) of respondents reported that they had attended in electronic education course. Moreover, 84.4 % (81/96) of participants reported their interest to attend electronic education courses. In addition, 28.1 % (27/96), and 32.3 % (31/96) of participant were EDC and EDO members, respectively.

Table 1 showed the relationship between demographic variables and adoption technology model constructs. In addition, table 2, indicated the mean and standard deviation in answering the items of adoption technology model about E-learning.

Table 3 shows bivariate correlations between the ATM constructs, which were statistically significant at either .05 or .01 level. The results showed that intention E-leaning was correlated with the positive attitude (r=0.464), perceived ease of use (r=0.353), and external variable (r=-0.308).

Finally, a hierarchical multiple regression analysis was performed to explain the variation in intention to E-learning, using the TAM variables. As can be seen in Table 4, ATM variables were statistically significant for predicting E-learning which, they were accounted for 46% of the variation in intention to E-learning (F: 17.385, and P<0.001).

Discussion

The aim of this study was to determine factors related to E-learning intention among faculty members based on ATM. The results of the present study indicated that perceived ease of use, external variables, and attitudes were the most influential predictors of E-learning intention among faculty members.

Maximum score gained by faculty member for attitude was 47.23% of total score, which suggested that there was no proper attitude to E-learning among participants. Zolfaghari et al (17) reported that faculty members had positive attitude to learning through E-learning systems. In addition, Naghavi indicated students and educators had positive attitudes toward E-learning (18). In addition, Mirzaei et al. reported positive attitudes toward E-learning among students of Shahid Sadoughi medical science university, Yazd, Iran (19). Khandaghi et al. (20) and Mohammadi et al. (21), also, reported similar results. Latifnejad et al. (1) showed that students had positive attitude to E-learning though they reported low levels of knowledge. Zolfaghari et al. (4) studied the efficiency of mixed E-learning system in Tehran medical science university and suggested that most students and educators had positive attitudes to modern education technology including mixed electronic education. Rashidtorabi et al in their study suggested that training over benefits of E-learning courses and supplying proper equipments to more availability to the internet could develop positive attitudes to E-learning (22). Bahadori and Yamani (23), also, reported that majority of faculty members had positive attitude to using computers and the internet in medical training.

Our findings indicated attitude toward E-learning among participants was low. In this regard, Meyers (24) suggested that the reason for improper attitudes of faculty members to E-learning was the need to attend many new training courses and change their methodology to adopt with new teaching condition. It is suggested to hold workshops to teach adoption and application of E-learning systems and introduce its advantages to advance education goals and its economic implementation by investigation centers of medical science universities.  Based on the results, only faculty members who had experience of E-learning reported meaningful proper attitude to electronic education. It could be concluded that workshops would help to improve attitudes of faculty members to implement E-learning.

There was no meaningful relationship reported among participants’ attitudes and demographic factors. This result is similar to the results reported by Mirzaei et al. (19). It seem, attitude to E-learning was not related to field of education among medical academic member and it could be considered as strength to enhance attitude to intervention studies among faculty member.

Linear regression analyses showed that perceived ease of use, external variables, and attitudes were the most influential predictors of E-learning intention participants. Several studies have reported ATM variables’ predictability to explain E-learning or information technology (IT) adoption (23-28). In this regard, Al-Gahtani reported ease of use as an effective factor on IT adoption in non-American cultures (26). In addition, Shoaei and Alavi carried out a research on librarians of Tehran technical school librarians and reported perceived ease of use and perceived usefulness are effect on IT adoption (27).

Another result from present study introduced significant role of external variables in predicting E-learning intention among participants; need to supply equipments and substructures of E-learning, and accessibility of high speed internet had highest means among other external variables. In this regard, other studies showed that system quality could affect costumers’ intention and satisfaction (29-31).

Joodi Chalan et al (32) in their study stated that, traditional patterns of medical education may be less to promotion college students learning skills. In other hand, Heidari et al (33) conducted a study on academic members of Mashhad University of medical sciences and showed the participants' did not have an appropriate attitude toward the education development organization (EDO) and the educational development center (EDC). Thus providing new training approach such as E-learning and appropriate introduce by EDC for academic members is recommended in order to improve the quality of education in universities.

Although the present study has several strengths, such as theory driven, and data collection about factors related to E-learning intention among Iranian academic members, the findings reported in this study have certain limitations. First, data collection was based on self-reporting, which is usually prone to recall bias. Second, the internal consistency the questionnaire was relatively low (α = 0.67) for assessing perceived ease of use. Third, low collaboration of faculty members in completing the questionnaire is another important limitation of this study.

Our findings indicated ATM variable were accounted for 46% of the variation in intention to E-learning. Forthemore, attitude, percieved ease of use and external variables were considered more efficient to predict behavior intention to E-learning. These points could guide education designer to design training programs to enhance E-learning application in medical science universities. 

Acknowledgements

This article is a part of research project supported by Kermanshah University of Medical Sciences. We would like to thank deputy of research of Kermanshah University of Medical Sciences for financial support of this study.

Research committee approval and financial support: Research committee of Kermanshah University of Medical Sciences, approved this study.Source of funding were Kermanshah University of Medical Sciences.

Conflict of interest: Authors have no conflict of interest.