The changing global trends have urged workers to constantly grow their knowledge and skill and develop their critical thinking. Therefore, companies and organizations must seek an effective way of achieving competitive advantages to attain sustainable employee development. Corporate sectors have implemented virtual training using e-learning. Human Capital Report Adoption of E-learning 2019 by HRDF highlights the importance of e-learning adoption in workplace training. E-learning in the workplace provides many more opportunities for employees to enroll in training courses that increase productivity and boost job performance. 'Research focuses on the effectiveness between them and classroom training (Beinicke & Kyndt, 2020) and employee's perception of e-learning (Shirmila & Udhayarekha, 2022). However, the study regarding older employees' intention to use e-learning in Malaysia remained unclear, while corporate training in the workplace aimed to produce reliable and accessible learning content (Garg & Sharma, 2020).
Existing studies focusing on the behavioural intention of using online training portals such as e-learning systems mainly focus on students and working adults instead of mature-aged workers. These workers are important assets to an organization as mature workers have more organizational knowledge due to their long employment (Rudolph & Tommey, 2015). According to 2019 HRDF's Human Capital Report Issue August 2019, mature-aged workers in Malaysia aged 45 and above have a lower participation rate in organizational training in 2017 and 2018 compared to younger age groups due to a lack of awareness and participation to develop self-potential and improve productivity. Mature-aged workers are often associated with groups with low confidence in using digital technology (Zheng et al., 2016; Peek et al., 2016; Reneland-Forsman, 2018). However, there have been contradicting findings on technology self-efficacy among older adults. Older adults do not necessarily have low Internet self-efficacy (Chung et al., 2010) and react positively to e-learning. It is vital to address the gaps by finding out how perceived ease of use, perceived usefulness, and self-efficacy in terms of accessing e-learning and using computers will affect mature-aged workers' behavioural intention in the workplace to use training websites such as e-learning for self-learning and skill upgrade to achieve job performance goals.
This paper is organized into sections. Section two discusses related studies. Section three discusses the research hypothesis and models. Section four discusses the methodology. Section five discusses the research findings, and the last section concludes the paper. The research questions are: (i) Is there any relationship between PEU and BI in using online training portals? (ii) Is there any relationship between PU and BI using online training portals? (iii) Is there any relationship between SE and BI in using an online training portal?
Literature Review
HRDF Human Capital Report August 2019 stated that mature-aged workers have low participation in training in all Organisation for Economic Co-operation and Development (OECD) countries, including Malaysia, Singapore, Indonesia, and others, as they have less access to training opportunities. Figure 1 shows that the participation rate of mature-aged workers aged between 45–64 in the year 2017 and 2018 are below 20%.
E-learning in the workplace
The adoption of e-learning in the workplace through a training approach for up-skilling and reskilling is vital to ensure Malaysia's workforce is well-equipped with the necessary skills and knowledge. E-learning helps to foster learning activity across the entire organization, even among those who are not in the best position to access the more traditional means of workplace learning. As such, e-learning can be a motivator for employers rather than personal interests (Labre & Lauzier, 2021). E-learning can bridge the gap between learning and work by leveraging workplace technologies. Both employers and employees recognize that e-learning will diminish the narrowing gap between work and home, and between work and learning. Employees can stay competitive with the assistance of lifelong learning and workplace education. Therefore, workplace learning and training activities play a great role in enhancing individual and organizational performance. There are several learning perspectives in workplace settings such as individual and organization. Individuals can perform self-regulated learning if they can identify their learning needs with suitable support. Dindana et al. (2020) emphasized that individual and organizational learning needs, which have strongly tied to work performance, are the main contributing factors for successful e-learning in the workplace.
Older Adults in Technology Use
There are misconceptions about older workers, such that they are less proficient in using modern digital technologies than younger workers, and do not favor technologies. According to McIntosh (2020), there are stereotypes among old workers, such as resistance to change and intolerance of technological advances. Young (2013) claimed that the technology acceptance in learning for younger employees is higher as compared to older employees, who may be less likely to adapt to it. Misconceptions and stereotypes periphrastically affect older worker's SE and intention to use technology.
Older people found using technology stressful as they might not have much experience and felt uneasy using it. This might be why organizations with a higher number of older workers will choose not to adopt new technologies. These older workers are not agile and are likely to make mistakes while using new technology. As for them to explore online learning such as e-learning, they require more time to understand and pick up the knowledge about the technology before completing the assignments (Ravichandran et al., 2015).
Prior studies identified the older adults' educational level as one of the critical factors for Internet use and internet experience (Hargittai et al., 2019; König et al., 2018; Kämpfen & Maurer, 2018; Schehl et al., 2019; Macedo, 2017; König et al., 2018; Hunsaker & Hargittai, 2018). Older adults with higher education are more likely to regard the Internet as a useful tool for learning. Besides that, experience in using digital technology (Lee & Coughlin, 2015; Salloum et al., 2019), such as the Internet is also one of the factors that affects older adults in adopting technology. Prior research studied online activities of older adults, such as shopping, entertainment, banking, and health-related tasks (Nimrod, 2018; Hong & Cho, 2017).
Workers' Behaviour to Use E-learning in the Workplace
Teoh and Tan (2020) implemented the Social cognitive theory(SCT) and Technology Acceptance Model(TAM) to predict the Malaysia manufacturing engineers' BI of using e-learning in the workplace. The results showed that the computer self-efficacy of the engineers would have an immediate impact on the ease of use and usefulness of e-learning, thus influencing their BI. Although the prior experience does not directly impact their BI, it will affect the PU towards using e-learning. The study by Shirmila and Udhayarekha (2022) shows that SE is one of the significant contributors towards the effectiveness of e-learning. Jabeen and Sadique (2020) investigated PEU, PU, and SE as factors affecting e-learning adoption in the educational and corporate sectors. Also, learners with high self-confidence in handling e-learning systems, are likely to opt for e-learning environments.
However, there are not many studies on the behavioural intention of older workers in using e-learning. As Zheng et al. (2016) reported, older workers have low confidence in using digital technology. Reneland-Forsman (2018) investigated that older adults lack SE in computer use, indirectly affecting their abilities to use digital technology to meet their job performance needs and self-learning.
Self-Efficacy in Online Learning Environment
SE in e-learning represents the dimensions of perceived behavioral control construct in the theory of planned behavior (TPB). A person with SE has a strong tendency to complete the e-learning programs as the person will put more effort, be more persistent, and work hard to overcome barriers to training. Adnan et al. (2019) highlighted that the CSE is also an important characteristic of the trainees in the e-learning environment as it positively impacts learning achievement. Several studies focus on multiple variants of SE in an online learning environment, namely CSE, CUSE, Online Learning Self-Efficacy Scale (OLSES), and Web-based Learning Self-Efficacy (WBLSE).
CUSE was an enhanced version of CSE introduced by Cassidy and Eachus (2002). It is used to study one's confidence in the use of computers, including peripheral devices (Alqurashi, 2016). CUSE includes the user's gender, user's experience with the computer, use of software packages, computer training, and computer ownership. Ferdousi (2019) found that CUSE 'greatly impacts learners' intention to use digital technology in their learning. Zimmerman and Kulikowich (2016) proposed OLSES to study 338 postsecondary students with and without online learning experience. The results showed that students with high OLSE tend to succeed in online learning courses. WBLSE was introduced by Nahm and Resnick (2008) to assess the older 'adult's ability to use Web-based health learning modules.
Employees have varied technological backgrounds, e.g., computer experience (Williams & Williams, 2010) and thus different technological self-efficacy levels (Todd, 2019), which affects their intention to assess e-learning (Lu et al., 2016; Kim & Park, 2018; Downey & Kher, 2015; Teoh & Tan, 2020). Laganà(2008) highlighted the importance of training on computer and internet use for older adults with less internet experience to increase their CUSE.
Industry at all levels has undergone rounds of transformation and entered the era of digitization (Petrillo et al., 2018). Training in the workplace escalates to prepare them to adapt to Industry 4.0 as it will prepare Malaysia for Fourth Industrial Revolution. Malaysia is having a critical labor insufficiency issue due to the deteriorating employment participation rate from its aging population. Training in manufacturing industries is important as it can address labor shortage issues and utilize skillful older workers. Besides that, skill upgrades are needed to cater to the dynamic global business and trade challenges. Local talents in this context will need to possess qualities that will help them achieve global competitiveness and put them on a level playing field with their competitors. As workers age, their injuries are likely to be more severe. Older workers are at less risk for injury than younger workers, but injuries among older workers are more likely to be fatal.
Existing research on digital technology adoption among older adults did not include e-learning technologies. Instead, most focus on entertainment, shopping, news, information, health services, and others. A study by Niehaves and Plattfaut (2014) stated that age would moderate performance expectancy and BI for Internet adoption among senior citizens aged 65 and above. Prior experience in using e-learning will affect PU and, thus the users' BI to use e-learning (Teoh & Tan, 2020). However, there is no study on prior experience in using e-learning among older workers. Further, most of the studies (König et al., 2018; Macedo, 2017; Kämpfen & Maurer, 2018; Schehl et al., 2019; Hargittai et al., 2019) in Internet use among older adults or workers are not in Malaysia context.
Existing studies focusing on BI to use e-learning systems are mostly focusing on students (Humida et al., 2021; Samaila et al., 2022; Rajeh et al., 2021; Kundu, 2020) and working adults in general (Teoh & Tan, 2020; Kapo et al., 2020), instead of older workers. Although a study by Samaila et al. (2022) does not target working adults, its target audience contains postgraduate students aged between 45 and above with the flexibility of a learning schedule that fits well with their work (Kite et al., 2020).
Apart from some postgraduate students who are working adults pursuing their studies, other working adults in the industries did not participate in furthering their studies, and there are older workers among them. Although studies focus on the impact of perceived ease of use and perceived usefulness among working adults' behavioural intention in using e-learning, they did not focus on older workers, which are one of the important assets to an organization. Although there are studies on SE and PEU among older adults using e-learning (Chu, 2010), most of the studies targeting older adults focus on Internet use (Niehaves & Plattfaut, 2014; König et al., 2018; Macedo, 2017) and social media (Lüders & Brandtzæg, 2017). Besides that, there have been contradicting findings on technology self-efficacy where older adults do not necessarily have low Internet efficacy, as reported by Chung et al. (2010), and older workers have been shown to react positively to technological innovations, including e-learning.
Research Hypothesis and Models
Abdullah and Ward (2016) agreed that SE, subjective norm, enjoyment, computer anxiety, and prior experience are among the widely used external variables in learners' perception towards e-learning systems.
Perceived Usefulness (PU)
The perceived usefulness can be regarded as "a person's perception that the product or new technology can effectively improve the efficiency of completing a specific task using a product or new technology". The user will use any systems that contain the technology deemed useful to them (Huang, 2021). Older people may require more time to understand and pick up technology-related knowledge, although some are reluctant to use modern technologies due to technophobia. Therefore, the following hypotheses were formulated to determine the relationship between PU and BI of mature-aged workers using modern education technology, such as online training portals to upgrade their knowledge.
H0: There is no relationship between PU and BI in using the online training portal
H1: There is a relationship between PU and BI in using the online training portal
Perceived ease of use (PEU)
The perceived ease of use can be regarded as "the degree to which an individual believes a product or new technology is easy to use and easy to learn". A user will act more positively towards using a technology if he or she believes that they can use it easily (Huang, 2021; Malureanu et al., 2021). Nurakun et al. (2018) stated that the level of ease of use for online training portals, such as LMS, will influence the student's perception of LMS. Meanwhile, other studies, such as Teoh and Tan (2020), and Kapo et al. (2020), have highlighted the relationship between workers' perceived ease of use and their behavioural intention to use e-learning. With the above analysis, the following hypotheses can be formulated.
H0: There is no relationship between PEU and BI to use the online training portal.
H1: There is a relationship between PU and BI in using the online training portal
Self-efficacy (SE)
Peek et al. (2016) stated that older adults tend to fear needing assistance when operating ICT devices, which showed that they have low technology self-efficacy. Macedo (2017) and König et al. (2018) emphasized the need for internet experience among older adult workers to use e-learning. Teoh and Tan (2020) predict manufacturing engineers' intention of using e-learning according to their prior experience in using e-learning. However, the study by Lüders & Brandtzæg (2017) showed that SE indirectly affects older adults' BI to use one of the web services such as Social Networking Sites (SNS). Further, there are existing studies that have identified several types of SE that influenced BI of using e-learning, including Computer User Self-Efficacy (CUSE), Online Learning Self-efficacy (OLSE), and Web-Based Learning Self-Efficacy (WBLSE).
Cassidy and Eachus developed CUSE (2002), an enhanced CSE that includes the user's experience with computers, gender, software package usage, computer training, and computer ownership. Unlike CSE, the CUSE scale comprises more comprehensive questionnaires that identify the respondent's detailed past computer experience, including usage of software, and computer ownership. This will provide a better understanding on the influence of respondents' past computer experience on their usage of digital technology. It showed that CUSE greatly influences adults' BI towards computer use and digital technology. In this study context, CUSE is one of the contributing factors in influencing older adults' BI towards computer use and digital technology usage in their workplace, which helps them upgrade their skills and boost their competencies. OLSE scale was developed by Zimmerman and Kulikowich (2016) and consisted of 22 instruments. These instruments consider training environment, time management, and technology use. OLSE also measures the learners' confidence in their capability to perform complex technological tasks (Calaguas & Consunji, 2021). WBLSE, developed by (Nahm & Resnick, 2008), consists of eight questionnaires. It is used to identify older adults' experience with the Internet and computer-based programs. WBLSE covers the physical conditions of the older adult, the assistance provided, and the encouragement provided to the older adult.
This research will adapt CUSE, OLSE and WBLSE questionnaires as CUSE can be used to measure older workers' computer past experiences while OLSE and WBLSE measure older workers' online learning self-efficacy with regards to the online learning experience. Thus, these instruments will contribute to better adoption and usage of an online training portal, including e-learning for older workers. Based on the above statements, the following hypotheses can be formulated.
H0: There is no relationship between SE and BI to use the online training portal.
H1: There is a relationship between SE and BI using the online training portal.