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25 Nov
2019

Professional Development | Good Grade Guarantee!

Research on Factors that are Related to Online Professional Development
Research on Factors that are Related to Online Professional Development Use
Research studies have stressed the importance of factors that are related to employees’ participation and acceptance of online professional development. One of the many models used to measure employees’ acceptance of new technology is the UTAUT model (Kijsanayotin et al., 2009; Yoo & Huang, 2016; Yoo & Han, 2013). Moreover, focusing on online professional development in corporate contexts involved an emphasis on the significance of employees’ intention as a determining factor of successful acceptance and use of new technologies (Hur & Im, 2013; Yoo & Huang, 2016). Behavioral intention regarding using online professional development has been found as the most influential factor in employees’ technology acceptance (Kijsanayotin et al., 2009; Yoo & Han, 2013). For instance, Yoo and Han (2013) mentioned that online learning acceptance correlates strongly with employees’ intention. Overall intention to lead individuals into powerful online training is necessary for corporate employees to adopt and use online learning (Hur & Im, 2013). Factors that play a significant role in predicting users’ intentions regarding using online professional development are performance expectancy, effort expectancy, social influence, and facilitating conditions which is demonstrated by Kijsanayotin et al. (2009). Kijsanayotin, Pannarunothai, and Speedie (2009) collected data concerning health IT online professional development in Thailand, and they examined its adoption among employees who were working in different positions in health-related fields. They wanted to know how the understanding of employees and organization affected users’ acceptance of technology. They mainly examined the determinants that influenced the employees’ acceptance and use of technology.  Their survey was a questionnaire, and the questions targeted information about the employees’ intention to adopt new technology. They used the constructs of Venkatesh et al.’s (2003) model, which measured the five variables: performance expectancy, effort expectancy, social influence, facilitating conditions, and voluntariness. The participants were randomly selected from 1,607 community health centers around 13 provinces in Thailand. Their analyses benefited from using an updated version of SPSS multivariate analysis, which utilized multiple regression, discriminant analysis, logistic regression, and analysis of variance. The authors concluded that the factors predicting employees’ acceptance consisted of performance expectancy, effort expectancy, social influence, facilitating conditions, and voluntariness, and they emphasized that among these factors performance expectancy was considered the most significant predictor of the employees’ intention. Finally, it was confirmed by the results of the study that the UTAUT model was a valid research framework for investigating and exploring healthcare employees’ intentions when adopting new technology for work or training purposes.Furthermore, there is a strong positive relationship between performance expectancy and attitudes towards using online professional development, which indirectly affects employees’ intention to use online training (Yoo & Han, 2013). Performance expectancy, on the other hand, is considered another statistically significant factor in predicting employees’ intention to use online training. The UTAUT model is used extensively to examine employees’ acceptance of online learning and it is a valid research model. Also, Yoo and Huang mentioned that it is important to investigate employees’ acceptance and use of technology before implementation of online training, noting that no matter how well-designed an online course is, it will not be useful to the employees unless they show acceptance toward using online learning. Therefore, the authors tested the employees’ intention to use online learning in three different workplace environments as a technology tool to improve professional performance. For their methodology, the authors used convenience sampling because the employees were available and ready to be studied, and Yoo and Huang collected the data by means of an electronic survey. They pointed out that the collection of data from the three corporate workplaces included participants who were already taking online courses to improve their professional development. UTAUT model constructs were used to measure the adoption and use of online learning among employees in the three work settings. The constructs of performance expectancy, effort expectancy, social influence, facilitating conditions, and intention were used as variables in this study. In addition to UTAUT model, the authors employed another instrument called the “learning organization questionnaire” (p. 579) that measured the extent to which empowerment and creating learning opportunities affects an organization. The findings showed that the employees’ use of online learning impacted corporate learning development. Similarly, the acceptance of online learning among employees can positively increase the development of companies into learning organizations.  The authors found that to promote or develop learning organizations, employees should first accept and use online learning as their training choice. Yoo and Huang mentioned that the acceptance of technology in the workplace for learning is vital for developing learning plans that can improve employees’ professional performance in many different areas. In terms of limitations, the authors mentioned that future studies are needed to obtain qualitative findings, which would add in-depth details about the issue. Also, they recommended further studies should be conducted in other regions and cultures. Acceptance of technology in the corporate workplace for learning is vital because without users’ acceptance learning would be useless.Moreover, studies have included the demographic factors of age and gender, considering them as influential and significant in predicting employees’ acceptance and usage behavior. For example, Karaaslan (2013) tested whether males and females differ in online learning usage and satisfaction with its content.  There are significant differences between males and females regarding their use and satisfaction of online professional development programs. The results showed that male employees tended to use online learning more than female employees because male employees have higher technological skills than female employees. However, a high percentage of males and females showed that they were not satisfied with the online training content provided. The findings showed Karaaslan (2013) correlated with Ching and Hung (2011) regarding the importance of including gender as a factor for measuring employees’ intention regarding technology usage.
The UTAUT Model as Theoretical Framework
The Unified Theory of Acceptance and Use of Technology (UTAUT) is a technology acceptance model that was founded by Venkatesh, Morris, Davis, and Davis (2003) (see figure 1below). The main goal of UTAUT model is to explain users’ intents toward new technology (Yoo & Han 2013; Min el at. 2008). Numerous studies (Kijsanayotin et al., 2009; Yoo & Han, 2013; Yoo & Huang, 2016) utilized the UTAUT as the theoretical framework that guided their studies to explain users’ intention toward using new educational technology. These studies provided evidence that the UTAUT model is a valid research framework for investigating users’ intentions regarding the adoption and use of new information technology. This section will explain why technology acceptance theories are needed. Also, it will give historical information about the UTAUT model and how

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