You are currently viewing a new version of our website. To view the old version click .
MDPIMDPI
Search all articles
Buildings
Download PDF
  • Article
  • Open Access

18 December 2024

Level of Acceptance of E-Learning Courses for Upgrading Digital Skill Sets Among Built Environment Professionals

,
and
1
Faculty of Architecture, Building and Planning, The University of Melbourne, Melbourne, VIC 3010, Australia
2
Department of the Built Environment, College of Design and Engineering, National University of Singapore, Singapore 117566, Singapore
*
Author to whom correspondence should be addressed.
Buildings2024, 14(12), 4017;https://doi.org/10.3390/buildings14124017
This article belongs to the Section Construction Management, and Computers & Digitization
Version Notes
Order Reprints

Abstract

E-learning has emerged as a new way of training employees, making the digital upgrading process more efficient and economically viable. This study aimed to assess the perceptions of built environment (BE) professionals concerning e-learning courses aimed at digital upskilling and to identify the positive and negative influences on the level of acceptance of such e-learning courses. Having identified the influencing factors, a self-completed questionnaire was a good approach for this study. A questionnaire was distributed to BE professionals in Singapore. Over four weeks from the middle of December 2022 to early January 2023, 78 valid responses were collected. The results indicated that individual demographics (such as age, gender, and education level) other than years of computer experience using e-learning do not encourage or deter BE professionals from accepting e-learning courses. This study also identified fifteen positive factors that increase the level of acceptance, with the top-rated factors being usefulness to a BE professional’s job scope, increased efficiency at work, potential pay increment, and supportive work culture. This study also identified ten significant negative factors, with the most important ones being cost, compatibility issues with work systems, and negative instructor characteristics.

1. Introduction

In the current economic environment, many governments and companies require their workforce to constantly keep up with the changing trends in response to globalization, so that employees can be equipped with the relevant skills for their work. According to a report by the World Economic Forum [1], by 2025, 50% of employees will need reskilling due to adopting new technologies. With an aging population and slower growth in the workforce, there is an increased urgency to adopt new technologies to drive productivity. On a positive note, according to [1], a number of countries have, in recent years, developed innovative funding mechanisms to finance the reskilling of workers, including France’s “MyskillsAccount” scheme and Denmark’s introduction of funding programs to support worker reskilling and continuous learning. Similarly, in Singapore, investment in human capital via courses alone is not sufficient, and Singapore should make use of technology to deliver these courses [2], driving the productivity of the workforce [3] and sustaining healthy growth coupled with improved living standards. In the face of strong competition, companies are looking for ways to stay ahead of the game, resulting in a rise in the adoption of e-learning to develop a highly skilled and knowledgeable workforce [4,5]. In the telecommunication sector, Singtel, a regional multinational corporation, is an example of a Singaporean company that has adopted e-learning [6] to train its staff in Internet Protocol (IP) skills, maintaining the employees’ competitiveness in a globalized market. Similarly, IBM offers over 3000 courses in its learning labs for employees in Singapore, and the Singapore General Hospital has e-learning tailored for its mature workers to support the government initiative and prolong the employment of older workers. However, e-learning courses tailored for built environment (BE) professionals have not been researched. It is crucial for these professionals to take up training courses to upgrade themselves with the relevant digital skills needed. COVID-19 has accelerated the speed of the digital transformation [7]. However, BE professionals might struggle to find time to attend courses to equip themselves with digital skills because of the demanding nature of the construction industry [8], which is known for long working hours and stressful job requirements [9].
In the face of such a problem, e-learning platforms represent a good alternative, complementing the traditional face-to-face sessions conducted for such courses and creating a hybrid learning environment. E-learning in the corporate setting is an area of interest for research worldwide, given that it brings about advantages such as increased flexibility and accessibility [10,11]. According to [12], e-learning is able to tailor lessons to meet each learner’s interests and needs. Having the training content portioned in doable amounts helps workers to strike a balance between taking up relevant skills in a rapidly evolving economy and their hectic work schedule. In addition, a given amount of content takes a shorter duration to teach via e-learning, resulting in savings for both the company and the employees. Widespread adoption of e-learning contributes to Singapore’s goal of becoming an information and communications (Infocom) hub with a competitive workforce, aiding in the process of lifelong learning, where upgrading skills becomes more economically viable and feasible for both employers and employees.
However, there is limited literature on e-learning with regard to employees’ attitudes and intentions to use e-learning in the workplace [11,13]. There is also limited research conducted locally regarding BE professionals’ attitudes and perceptions toward e-learning courses for digitally upgrading skill sets. This research aims to fill this gap by establishing the level of acceptance of e-learning courses through analyzing the factors that could either positively or negatively affect a BE professional’s level of acceptance toward e-learning courses aimed at upgrading digital skill sets in the workplace. Successfully identifying these factors will potentially aid in driving the demand for e-learning to be utilized for the upgrading of digital skill sets by BE professionals, ensuring that the BE workforce stays competitive and does not lag behind in a digital society.

2. E-Learning in the Built Environment

E-learning is defined as a new approach to the learning process that utilizes electronic media and devices as tools to enhance access to training, communication, and interaction [14]. Rosenberg [15] noted that the ongoing advancement of information and communications technology (ICT) has resulted in the emergence of e-learning as a new type of employee training in the workplace. As e-learning continues to grow at a rapid pace, organizations are treating e-learning as a way to help employees enhance their skill sets, with the end goal of boosting morale and retention rates [16]. In addition, more companies are of the opinion that e-learning provides accessible and more affordable courses that suit their needs [9] in the form of learning that can occur on demand [17].
In Singapore, the Ministry of Manpower [18] defines workplace-related e-learning courses as a form of organized learning that takes place online, led by an instructor. The content is designed with specific learning goals in mind, and individuals typically participate in this type of training for their current roles or for future career opportunities. According to the Infocom Media Development Authority [19], many companies in Singapore have been investing in e-learning for corporate training since 2001. E-learning is highly valued in Singapore, as it can help in the development of the workforce needed for Singapore to progress toward becoming a knowledge-based economy. However, this has not been the case in the built environment sector, where, as [20] observed, e-learning does not appear to have been as widely adopted. There is, however, great potential for e-learning to be taken advantage of in the sector. An industry digital plan (IDP) was charted for the construction and facilities management (CFM) sector in Singapore, where a growing disparity in digital proficiency among stakeholders in the construction ecosystem was identified as one of the major trends impacting the BE sector.

3. Factors Influencing E-Learning in the BE Workplace

3.1. Framework

This study contextualizes the technology acceptance model (TAM) framework to an e-learning context. The TAM has been utilized in studies that investigate the adoption behavior of different computer technologies and systems [21,22]. Given that e-learning is a technological platform that assists with learning and teaching, and given that multiple studies have utilized and extended the TAM for research in an e-learning context, the use of the TAM is feasible for this study. It is worth mentioning that the TAM framework’s belief set comprises two factors—perceived ease of use (PEU) and perceived usefulness (PU); both are crucial antecedents of behavioral intention to use technology. Ref. [23] noted that the TAM framework needs to be utilized to measure the acceptance of e-learning, because the benefits that e-learning brings to the workplace will not be recognized unless employees first accept it as a tool for learning. Measuring the acceptance of e-learning courses is important in order to ensure that the resources that are bought are not wasted. In this study, we use willingness to pay for the use of e-learning as a factor.
However, the TAM has its own limitations, as external variables are not accounted for as much, and the TAM can increase its prediction accuracy by extending its external variables [24]. Hence, for this study, we intend to seek external variables that would affect users’ behavior toward e-learning. A few studies [25,26,27] have extended the TAM model, adding external variables appropriate to the respective research studies in order to increase the external validity of the TAM model and to gain a deeper understanding of the factors affecting PEU and PU, and thus the acceptance of e-learning courses (see Table 1).
Table 1. Summary of the external variables.
Further, the Unified Theory of Acceptance and Use of Technology (UTAUT) of [28] is considered for integrating with the external variables discussed above. The UTAUT itself incorporates multiple models of technology acceptance [36]. First, the UTAUT has proven to be robust and has also been applied to studies that evaluate the acceptance of e-learning in a workplace environment [23,37,38]. In Venkatesh et al.’s [39] words, the UTAUT model can only continue progressing if it is integrated with other theories to analyze technologies utilized in an organizational context. Ref. [40] integrated the UTAUT with other theoretical models to investigate the intention to use e-learning in the workplace.

3.2. Influencing Factors

In this study, we hypothesize that the four variables from the UTAUT are determinants that increase an individual’s acceptance level. These four variables are Performance Expectancy (PE), Effort Expectancy (EE), Facilitation Conditions (FCs), and Social Influence (SI). On the other hand, negative influences bring about an opposing effect. We use the variables that are associated with the extended TAM (see Table 1). This section summarizes the positive and negative factors influencing end users’ willingness to purchase e-learning courses (see Table 2 and Table 3).
Table 2. Positive factors influencing end users’ willingness to purchase e-learning courses.
Table 3. Negative factors influencing end users’ willingness to purchase e-learning courses.

4. Method

4.1. Overview

The key objective of this study is to identify the influencing factors that can positively or negatively influence a BE professional’s level of acceptance toward e-learning courses. The e-learning courses considered here do not include those offered by the employer in the workplace but do include courses commercially available on the market, which users can subscribe to and pay for. This study attempts to understand the factors that might increase the levels of adoption of such e-learning courses, increasing employee productivity and satisfaction in the workplace [60]. Having identified the influencing factors, a self-completed questionnaire is a good approach for this study, not only because it is an affordable and practical method to collect data from a large number of respondents but also because previous studies [25] have adopted this method.

4.2. Questionnaire Development

The questionnaire consists of three sections. The questions in section one gather information on the respondent’s demographics. In section two, the questions seek to understand the level of adoption of e-learning in the workplace and to determine how receptive BE professionals are toward taking up such e-learning courses. The questions in section three aim to identify the respondent’s attitudes to the proposed positive and negative influencing factors, determining the significance and importance of each factor. Respondents were then asked to rate each influencing factor based on their perception using a four-point Likert scale: (1) Strongly Disagree, (2) Disagree, (3) Agree, (4) Strongly Agree. Respondents tend to use the midpoint as a “dumping ground” when they are responding to items that are unclear, unknown, or socially unacceptable. As a result, a four-point Likert scale is preferred to help to mitigate the issue of social desirability bias in the responses.
  • Demographic factors
Previous studies [61,62] noted that demographic characteristics can help to account for participation in e-learning. The factors that account for actual participation in e-learning are gender, age, and level of education [63,64]. Other factors, such as job tenure and computer experience, are also found to account for e-learning adoption in the workplace [27]. Therefore, we hypothesize that these demographic factors have an impact on BE professionals’ acceptance of e-learning courses.
  • Influencing factors
Based on the literature review, we draw on the extended variables associated with the TAM and the UTAUT framework, for which we identified seventeen factors (see Table 2 and Table 3). We ask respondents whether the positive influencing factors make them more willing to pay for e-learning courses. We similarly ask if the negative factors drive down their willingness to pay for e-learning courses. After developing the questionnaire, a pilot survey is recommended to identify areas for improvement by detecting possible mistakes [65]. The pilot survey helps to check if the respondents are able to have a clear understanding of the questions. After addressing the inaccuracies and gaps, the actual survey can then be published to collect data [65]. A pilot test was conducted among five BE professionals to check for any inconsistencies, errors, unclear phrasing, and technical issues. Changes were made according to the feedback.

4.3. Sampling

The target population is working adults in the BE sector. It is hard to estimate the size of this group, so we adopted the convenient approach of mass-sending to BE professionals on LinkedIn. The questionnaire was sent out for a period of four weeks from the middle of December 2022 to early January 2023. Over a period of four weeks, there were a total of 82 responses, with 78 valid and 4 invalid responses.

4.4. Data Analysis

Cronbach’s alpha was applied to test for the internal reliability of the positive and negative influences. Both the positive (α = 0.956) and negative (α = 0.902) factors had excellent internal consistencies. We then applied the Chi-square test and the Cramer’s V (CV), testing the significance of each factor. We computed the mean and the standard deviation of the statistically significant factors.

5. Results

5.1. Profile of Respondents

The respondents’ profile in terms of gender, age, education qualifications, and individual income status is shown in Table 4. Of the 78 valid responses, there were slightly more females (51%) than males (49%). Moreover, 68% of the respondents are young, based on the National Youth Council’s [66] definition of youths as those between the age of 15 and 35 years old. Table 4 suggests a generally well-educated sample, with about 68% holding an undergraduate degree and 15% having a postgraduate degree or above. Respondents with less than five years of experience using a computer for e-learning are categorized as having low experience in using a computer for e-learning; these constituted about 35%. On the other hand, those who had five years or more experience using computer for e-learning are classified as having high experience in using a computer for e-learning; these accounted for 65%.
Table 4. Profile of the respondents.

5.2. Demographic Influence on Acceptance of E-Learning

A statistical analysis was performed on the responses to determine whether the sample demographics have an effect on individual acceptance of e-learning. Suitable statistical tests were used, depending on the variable’s level of measurement (see Table 5). The results show that there is no association between the three individual characteristics (gender, age, and education level) and an individual’s willingness to pay (WTP) (p > 0.05). The only demographic factor that is an exception is computer experience, with a p-value below 0.05. It can thus be concluded that an individual’s computer experience does have a significant effect on an individual’s level of acceptance of e-learning at a 5% level of significance.
Table 5. Summary of the results of the statistical tests.

5.3. Awareness, Adoption, and Willingness to Pay

The results in Table 6 show that there is a high level of awareness of e-learning courses among BE professionals. This is unsurprising, as Singapore is a highly digitalized economy. However, the level of adoption does not match up with the level of awareness, with only 64% of the respondents’ companies adopting e-learning courses to upgrade digital skill sets. On a positive note, Table 6 illustrates that a significant percentage (96%) of BE professionals are open to the adoption of e-learning courses for digital upskilling. These results could suggest that there is good potential for BE professionals to accept and adopt e-learning courses in the workplace. Moreover, 86% of the respondents believe that blended learning will be more suitable than fully online e-learning when aiming to upgrade digital skill sets in the BE sector.
Table 6. Awareness, level of adoption, and willingness to pay.
The survey results highlight that there is a high level of acceptance toward the adoption of e-learning courses for upgrading digital skill sets, with 92% of the respondents indicating that they are willing to pay. Table 6 also shows that slightly over 70% of the respondents would prefer to pay a price ranging from SGD 100–499 to SGD 500–1000. Among those who are willing to purchase, less than 20% are willing to pay more than SGD 1000 for the e-learning courses.

5.4. Positive Influencing Factors Enhance the Acceptance Level of E-Learning Courses for Upgrading Digital Skill Sets

  • Most significant positive factors
According to Table 7, all the positive influencing factors except for SI1 and SI2 attained a p-value < 0.05. It can therefore be concluded that the majority of positive factors do significantly affect a BE professional’s acceptance of e-learning courses for upgrading digital skill sets. The ranking in Table 7 makes it clear that all the positive factors had a mean value greater than three, and the top ranked factors are PE3 “useful in my job”, PE4 “to accomplish my work tasks more quickly”, and PE2 “get a raise at work”. Interestingly, the top three factors fall under the “performance expectancy” category.
Table 7. Positive factors influencing the respondents’ willingness to purchase e-learning courses.

5.5. Negative Influencing Factors Lower the Acceptance Level of E-Learning Courses for Upgrading Digital Skill Sets

Similarly, the results of the Chi-square test showed that a number of factors are statistically insignificant, having a p-value greater than 0.05. These were thus excluded from further analysis. As shown in Table 8, the most significant negative factors pertain to the cost (CO1), compatibility (CP1), and instructors (IC1–IC3).
Table 8. Negative factors influencing the respondents’ willingness to purchase e-learning courses.

6. Discussions

6.1. Overall

The results indicated that individual demographic items, other than years of computer experience using e-learning, do not encourage or deter BE professionals from accepting e-learning courses to upgrade digital skill sets in the workplace. This finding is in line with the results of previous studies showing that age [67] is not a significant factor. The finding is, however, not in agreement with [63,64], who noted that gender and level of education are significant factors, contrary to our results. The years of computer experience using e-learning has a significant effect on a BE professional’s level of acceptance of e-learning courses.

6.2. Positive and Negative Factors

This study also mapped out the factors that affected professionals’ acceptance of e-learning courses. It may also be concluded that fifteen proposed positive and fifteen negative influencing factors can significantly affect a BE professional’s level of acceptance of the e-learning courses. The top three positive influences include factors that are related to how the courses are beneficial to their job scope, enabling one to be more efficient at completing work tasks and increasing the chances of getting a pay raise. These findings are in line with previous research showing that performance usefulness affects attitudes toward e-learning systems [43,45]. In other words, performance usefulness is what they most want to obtain from e-learning courses. On the other hand, factors associated with facilitation conditions are relatively poorly ranked, which seems to suggest that system-related factors are not key positive influences on BE professionals’ decision making in engaging in e-learning courses. The most important negative factors are the cost, the incompatibility of such courses with other systems at work, and negative instructor characteristics. It is commonly accepted that the costs of technology can be high and unpredictable, with the potential of the technology becoming outdated [16]. This supports the observation of [49] that the lower cost associated with strong funding support from the government helps to further increase employees’ level of acceptance of e-learning courses. As for the compatibility, it seems most of the respondents do not face compatibility issues between the e-learning system and the systems that they use at work. This could be because in Singapore, where technology is more digitally advanced than in other countries [68], interoperability issues between systems are less likely to be an issue. Previous research reminds us that incompatibility issues result in e-learning systems being perceived as not easy to use and not useful at work, potentially resulting in lower acceptance of the e-learning system in the workplace. Finally, our findings support [54], who found a significant relationship between instructor characteristics and learners’ satisfaction with e-learning; when instructors lack the positive traits described by [23] above, learners are expected to have lower motivational and satisfaction levels throughout the e-learning process.

7. Implications and Contributions

7.1. Managerial Implications

  • Organizations in the built environment sector
Firstly, from an organizational perspective, it is good if upper management is supportive of employees taking up e-learning courses, encouraging them to do so. This is evident in the findings, which show that both positive influences such as SI4 “company is supportive of me utilizing e-learning” and negative influences such as OS1 “my boss does not understand the benefits that can be achieved by using an e-learning system” are acknowledged. Such support can come in the form of monetary support, such as funding for courses, which can help mitigate the issue of the cost having a negative influence on acceptance. However, support can also come in the form of support from management, such as positively regarding taking courses during work time, and can aid in one’s career progression within the company. Companies can start by implementing e-learning policies where employees are reviewed based on their effective use of the digital skill sets they have gained from these courses. A pay increment scheme can be tied to the contributions they make to the company based on the knowledge they have gained from courses, and certifications can also be given to aid BE professionals with their current role and future career prospects. To ensure that the quality of the instructors is up to par, companies can seek feedback from their employees for each round of e-learning courses, shortlisting the best e-learning service provider in order to ensure that the courses provide value for money and are not a waste of time. In a digitalized society, such certifications will be applicable to not just one organization but to multiple organizations in Singapore.
  • E-learning providers
This study is also beneficial to e-learning providers, as far as the influencing factors are concerned. The top-rated driving factors indicate that providers really need to focus on content that can help end users’ workplace performance. More hands-on, problem-solving content will result in higher willingness to pay for it. On the other hand, the negative factors indicate that the characteristics of the person who delivers the content are perceived as equally important, particularly in the area of updating information, quality instructions, and assessment. A lack of good instructor characteristics in these areas will deter learners from effectively engaging with e-learning. The challenge for e-learning providers is to engage high-quality and experienced instructors for content delivery. Often, potential learners will check the reviews of the course and the instructors in advance, so ratings and reputations do play a role. A trial video showcasing quality content presented by experienced and committed instructors will grab potential learners’ attention.
  • Government support
Government funding is noted as a key positive influence, as a high cost is acknowledged to have a detrimental impact on end users’ acceptance level. SkillsFuture Singapore (SSG), overseen by the Ministry of Education, is a nationwide effort to help citizens realize their full potential in life. SSG aims for working adults to gain access to high-quality industry-tailored training over the course of their careers, with the end goal being to meet the demands of respective sectors. For example, a project management course in the built environment may have a course fee of USD 1000; a government subsidy may pay for as much as 90% of this cost, allowing users to pay as little as USD 100 [69]. With an SSG credit of USD 500, Singaporeans can then easily offset the portion of the online course fees remaining after government subsidies.

7.2. Theoretical Contributions

This research adds to the existing literature on e-learning in the BE sector and addresses the research in the literature concerning employee’s attitudes and intentions to use e-learning in the workplace. This study offers insight into the key factors that can encourage or discourage BE professionals from taking up such courses in order to digitally upgrade themselves in a digitalized society. Our study thus helps to lay the groundwork for future possible studies in the field of e-learning adoption in Singapore. These findings can aid leaders and employers within organizations to effectively implement such e-learning courses for the purpose of upgrading digital skill sets and increasing the productivity and efficiency of workers. The findings are of interest to e-learning providers as well, particularly in terms of the system, content, instructors, and others. As the growth of digitalization is expected to quicken in the future, this study will help organizations and the government to better comprehend the needs of employees, mitigating the negative factors that are faced in the adoption of e-learning courses for digital upskilling in the workplace and, overall, accelerating the rate of adoption.

8. Conclusions

Given the low productivity within the BE sector, coupled with the rapid digitalization and strong global competition, it is important for BE professionals to leverage the power of technology to increase productivity. Taking up e-learning courses helps to ensure that the workforce is able to upskill and upgrade with relevant digital skill sets in the shortest amount of time and with the fewest resources needed. There is a strong need for e-learning use to accelerate within the BE sector in order to not only keep up with other sectors in Singapore but also to compete on a global scale. With the majority of BE professionals welcoming the adoption of these e-learning courses, BE companies should explore the feasibility of offering such e-learning courses to upgrade the skills of BE professionals, ensuring that the BE sector does not lag behind in the digitalized society. This study does have certain limitations, despite meeting its aims and objectives. Firstly, due to time constraints, this study only managed to collate a total of 78 valid responses. With each respondent coming from different sectors of the built environment, their opinions might differ due to their own personal experiences. The results thus might not be universally generalizable. Secondly, the conceptual framework is contextualized to BE professionals’ perceptions within the built environment, so our results and findings might only be applicable to the BE sector. Further research thus needs to be conducted to determine if this framework can be applied to other sectors or across countries.

Author Contributions

Conceptualization, L.S.P. and K.W.J.; methodology, L.S.P. and K.W.J.; formal analysis, G.S.; writing—original draft preparation, K.W.J.; writing—review and editing, G.S. and L.S.P.; supervision, L.S.P.; project administration, K.W.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Schwab, K.; Zahidi, S. The Future of Jobs Report 2020; World Economic Forum: Geneva, Switzerland, 2020. [Google Scholar]
  2. Robinson, E. Globalisation in a Small Open Economy: The Singapore Experience; BIS Paper No. 100u, 21 December 2018. Available online: https://ssrn.com/abstract=3332105 (accessed on 21 February 2024).
  3. Ofori, G.; Zhang, Z.; Ling, F.Y. Initiatives that enable Singapore contractors to improve construction productivity. Built Environ. Proj. Asset Manag. 2021, 11, 785–803. [Google Scholar] [CrossRef]
  4. Derouin, R.E.; Fritzsche, B.A.; Salas, E. E-learning in organizations. J. Manag. 2005, 31, 920–940. [Google Scholar] [CrossRef]
  5. Rossett, A. The ASTD E-Learning Handbook; McGraw-Hill Education: New York, NY, USA, 2002. [Google Scholar]
  6. Bashar, M.I.; Khan, H. E-Learning in Singapore: A Brief Assessment; U21Global Working Paper Series, No. 003/2007, 1 April 2007. Available online: https://ssrn.com/abstract=1601611 (accessed on 21 February 2024). [CrossRef][Green Version]
  7. Döhring, B.; Hristov, A.; Maier, C.; Roeger, W.; Thum-Thysen, A. COVID-19 acceleration in digitalisation, aggregate productivity growth and the functional income distribution. Int. Econ. Econ. Policy 2021, 18, 571–604. [Google Scholar] [CrossRef]
  8. Ling, F.Y.Y.; Ho, S.W.K. Understanding and impressions of jobs in the construction industry by young adults in Singapore. J. Prof. Issues Eng. Educ. Pract. 2013, 139, 109–115. [Google Scholar] [CrossRef]
  9. Zhang, S.; Sunindijo, R.Y.; Frimpong, S.; Su, Z. Work stressors, coping strategies, and poor mental health in the Chinese construction industry. Saf. Sci. 2023, 159, 106039. [Google Scholar] [CrossRef]
  10. Seale, J.; Cooper, M. E-learning and accessibility: An exploration of the potential role of generic pedagogical tools. Comput. Educ. 2010, 54, 1107–1116. [Google Scholar] [CrossRef]
  11. Keller, C.; Cernerud, L. Students’ perceptions of e-learning in university education. J. Educ. Media 2002, 27, 55–67. [Google Scholar]
  12. Thyagharajan, K.K.; Nayak, R. Adaptive content creation for personalized e-learning using web services. J. Appl. Sci. Res. 2007, 3, 828–836. [Google Scholar]
  13. Liaw, S.-S.; Huang, H.-M.; Chen, G.-D. Surveying instructor and learner attitudes toward e-learning. Comput. Educ. 2007, 49, 1066–1080. [Google Scholar] [CrossRef]
  14. Servage, L. Strategizing for workplace e-learning: Some critical considerations. J. Workplace Learn. 2005, 17, 304–317. [Google Scholar] [CrossRef]
  15. Rosenberg, M.J. Beyond E-Learning: Approaches and Technologies to Enhance Organizational Knowledge, Learning, and Performance; John Wiley & Sons: San Francisco, CA, USA, 2005. [Google Scholar]
  16. Ali, G.E.; Magalhaes, R. Barriers to implementing e-learning: A Kuwaiti case study. Int. J. Train. Dev. 2008, 12, 36–53. [Google Scholar] [CrossRef]
  17. Govindasamy, T. Successful implementation of e-learning: Pedagogical considerations. Internet High. Educ. 2001, 4, 287–299. [Google Scholar] [CrossRef]
  18. MOM. Online Training in Singapore. 2022. Available online: https://stats.mom.gov.sg/Pages/e-learning-in-Singapore.aspx (accessed on 22 February 2023).
  19. IMDA. The E-Learning Scene in Singapore. Available online: https://www.imda.gov.sg/resources/press-releases-factsheets-and-speeches/archived/ida/speeches/2001/20061218103646 (accessed on 20 February 2023).
  20. Dowdle, D.L. Engaging Learners: The Development of Effective E-learning Applications for Students of the Built Environment. Archit. Eng. Des. Manag. 2006, 2, 31–48. [Google Scholar] [CrossRef]
  21. Park, N.; Roman, R.; Lee, S.; Chung, J.E. User acceptance of a digital library system in developing countries: An application of the Technology Acceptance Model. Int. J. Inf. Manag. 2009, 29, 196–209. [Google Scholar] [CrossRef]
  22. Liu, I.-F.; Chen, M.C.; Sun, Y.S.; Wible, D.; Kuo, C.-H. Extending the TAM model to explore the factors that affect intention to use an online learning community. Comput. Educ. 2010, 54, 600–610. [Google Scholar] [CrossRef]
  23. Lee, B.-C.; Yoon, J.-O.; Lee, I. Learners’ acceptance of e-learning in South Korea: Theories and results. Comput. Educ. 2009, 53, 1320–1329. [Google Scholar] [CrossRef]
  24. Taherdoost, H.; Masrom, M. An examination of smart card technology acceptance using adoption model. In Proceedings of the ITI 2009 31st International Conference on Information Technology Interfaces, Cavtat, Croatia, 22–25 June 2009; pp. 329–334. [Google Scholar]
  25. Lee, Y.-H.; Hsieh, Y.-C.; Chen, Y.-H. An investigation of employees’ use of e-learning systems: Applying the technology acceptance model. Behav. Inf. Technol. 2013, 32, 173–189. [Google Scholar] [CrossRef]
  26. Igbaria, M.; Guimaraes, T.; Davis, G.B. Testing the determinants of microcomputer usage via a structural equation model. J. Manag. Inf. Syst. 1995, 11, 87–114. [Google Scholar] [CrossRef]
  27. Purnomo, S.H.; Lee, Y.-H. E-learning adoption in the banking workplace in Indonesia: An empirical study. Inf. Dev. 2013, 29, 138–153. [Google Scholar] [CrossRef]
  28. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User acceptance of information technology: Toward a unified view. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef]
  29. Nelson, D.L. Individual adjustment to information-driven technologies: A critical review. MIS Q. 1990, 14, 79–98. [Google Scholar] [CrossRef]
  30. Bandura, A. Self-efficacy: Toward a unifying theory of behavioral change. Psychol. Rev. 1977, 84, 191. [Google Scholar] [CrossRef] [PubMed]
  31. Konradt, U.; Christophersen, T.; Schaeffer-Kuelz, U. Predicting user satisfaction, strain and system usage of employee self-services. Int. J. Hum.-Comput. Stud. 2006, 64, 1141–1153. [Google Scholar] [CrossRef]
  32. Webster, J.; Heian, J.B.; Michelman, J.E. Computer Training and Computer Anxiety in the Educational Process: An Experimental Analysis. In Proceedings of the 11th International Conference on Information Systems, ICIS 1990, Copenhagen, Denmark, 16–19 December 1990. [Google Scholar]
  33. Saadé, R.G.; Kira, D. Mediating the impact of technology usage on perceived ease of use by anxiety. Comput. Educ. 2007, 49, 1189–1204. [Google Scholar] [CrossRef]
  34. Rogers, E.M. Diffusion of Innovations; Free Press of Glencoe: New York, NY, USA, 1962. [Google Scholar]
  35. Yoo, S.J.; Han, S.-H. The effect of the attitude towards e-learning: The employees’ intention to use e-learning in the workplace. Int. J. E-Learn. 2013, 12, 425–438. [Google Scholar]
  36. Oshlyansky, L.; Cairns, P.; Thimbleby, H. Validating the Unified Theory of Acceptance and Use of Technology (UTAUT) tool cross-culturally. In Proceedings of the HCI 2007 The 21st British HCI Group Annual Conference University of Lancaster, Lancaster, UK, 3–7 September 2007; pp. 1–4. [Google Scholar]
  37. Borotis, S.; Poulymenakou, A. E-Learning Acceptance in Workplace Training: The Case of a Greek Bank; AIS eLibrary: Athens, Greek, 2009. [Google Scholar]
  38. Lee, Y.-H.; Hsieh, Y.-C.; Ma, C.-Y. A model of organizational employees’e-learning systems acceptance. Knowl.-Based Syst. 2011, 24, 355–366. [Google Scholar] [CrossRef]
  39. Venkatesh, V.; Thong, J.Y.; Xu, X. Unified theory of acceptance and use of technology: A synthesis and the road ahead. J. Assoc. Inf. Syst. 2016, 17, 328–376. [Google Scholar] [CrossRef]
  40. Yoo, S.J.; Han, S.-H.; Huang, W. The roles of intrinsic motivators and extrinsic motivators in promoting e-learning in the workplace: A case from South Korea. Comput. Hum. Behav. 2012, 28, 942–950. [Google Scholar] [CrossRef]
  41. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef]
  42. Teo, T.; Noyes, J. Explaining the intention to use technology among pre-service teachers: A multi-group analysis of the Unified Theory of Acceptance and Use of Technology. Interact. Learn. Environ. 2014, 22, 51–66. [Google Scholar] [CrossRef]
  43. Ngai, E.W.; Poon, J.; Chan, Y.H. Empirical examination of the adoption of WebCT using TAM. Comput. Educ. 2007, 48, 250–267. [Google Scholar] [CrossRef]
  44. Compeau, D.; Higgins, C.A.; Huff, S. Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Q. 1999, 23, 145–158. [Google Scholar] [CrossRef]
  45. Liu, S.-H.; Liao, H.-L.; Pratt, J.A. Impact of media richness and flow on e-learning technology acceptance. Comput. Educ. 2009, 52, 599–607. [Google Scholar] [CrossRef]
  46. Thompson, R.L.; Higgins, C.A.; Howell, J.M. Personal computing: Toward a conceptual model of utilization. MIS Q. 1991, 15, 125–143. [Google Scholar] [CrossRef]
  47. Teo, T. Modelling technology acceptance in education: A study of pre-service teachers. Comput. Educ. 2009, 52, 302–312. [Google Scholar] [CrossRef]
  48. Venkatesh, V.; Davis, F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef]
  49. Agrawal, R.; Wankhede, V.A.; Nair, R.S. Analysis of drivers of digital learning in COVID-19 and post-COVID-19 scenario using an ISM approach. J. Inst. Eng. (India) Ser. B 2021, 102, 1143–1155. [Google Scholar] [CrossRef]
  50. SkillsFuture Singapore. New Pilot Initiative to Support Learning Anytime, Anywhere. Available online: https://www.skillsfuture.gov.sg/newsroom/new-pilot-initiative-to-support-learning-anytime-anywhere (accessed on 21 February 2024).
  51. Singapore Business Review. Continuous Lifelong Learning with NTUC LearningHub’s LXP; Singapore Business Review: Singapore, 2023. [Google Scholar]
  52. Jones, N.; Fitzgibbon, K. For better or worse? The marriage of key skills development and on-line learning. J. Vocat. Educ. Train. 2002, 54, 395–412. [Google Scholar] [CrossRef]
  53. Kanuka, H.; Rourke, L. Exploring amplifications and reductions associated with e-learning: Conversations with leaders of e-learning programs. Technol. Pedagog. Educ. 2008, 17, 5–15. [Google Scholar] [CrossRef]
  54. Sun, P.-C.; Tsai, R.J.; Finger, G.; Chen, Y.-Y.; Yeh, D. What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Comput. Educ. 2008, 50, 1183–1202. [Google Scholar] [CrossRef]
  55. McFarland, D.J.; Hamilton, D. Adding contextual specificity to the technology acceptance model. Comput. Hum. Behav. 2006, 22, 427–447. [Google Scholar] [CrossRef]
  56. Igbaria, M.; Iivari, J. The effects of self-efficacy on computer usage. Omega 1995, 23, 587–605. [Google Scholar] [CrossRef]
  57. Piccoli, G.; Ahmad, R.; Ives, B. Web-based virtual learning environments: A research framework and a preliminary assessment of effectiveness in basic IT skills training. MIS Q. 2001, 25, 401–426. [Google Scholar] [CrossRef]
  58. Chau, P.Y.; Hu, P.J.H. Information technology acceptance by individual professionals: A model comparison approach. Decis. Sci. 2001, 32, 699–719. [Google Scholar] [CrossRef]
  59. Murray, D. E-Learning for the Workplace: Creating Canada’s Lifelong Learners; Conference Board of Canada: Ottawa, ON, Canada, 2001. [Google Scholar]
  60. Marczyk, G.R.; DeMatteo, D.; Festinger, D. Essentials of Research Design and Methodology; John Wiley & Sons: Hoboken, NJ, USA, 2010. [Google Scholar]
  61. Orvis, K.A.; Fisher, S.L.; Wasserman, M.E. Power to the people: Using learner control to improve trainee reactions and learning in web-based instructional environments. J. Appl. Psychol. 2009, 94, 960. [Google Scholar] [CrossRef]
  62. Fuller, R.M.; Vician, C.; Brown, S.A. E-learning and individual characteristics: The role of computer anxiety and communication apprehension. J. Comput. Inf. Syst. 2006, 46, 103–115. [Google Scholar] [CrossRef]
  63. Digilio, A.H. Web-Based Instruction Adjusts to the Individual Needs of Adult Learners. J. Instr. Deliv. Syst. 1998, 12, 26–28. [Google Scholar]
  64. Jones, S. Internet Goes to College: How Students Are Living in the Future with Today’s Technology; Pew Internet and American Life Project: Washington, DC, USA, 2008. [Google Scholar]
  65. Taherdoost, H. Sampling methods in research methodology; how to choose a sampling technique for research. Int. J. Acad. Res. Manag. (IJARM) 2016, 5, 18–27. [Google Scholar] [CrossRef]
  66. Here for Youth. Ministry of Culture Community and Youth. Available online: https://www.mccy.gov.sg/sector/youth#:~:text=In%20Singapore%2C%20youths%20are%20defined,aged%2015%2D35%20years%20old (accessed on 3 December 2024).
  67. Fleming, J.; Becker, K.; Newton, C. Factors for successful e-learning: Does age matter? Educ.+ Train. 2017, 59, 76–89. [Google Scholar] [CrossRef]
  68. Ng, K. Singapore 4th in global digital competitiveness, top in Asia. The Straits Times, 28 September 2022. Available online: https://www.straitstimes.com/business/economy/singapore-4th-in-global-digital-competitiveness-top-in-asia (accessed on 21 February 2024).
  69. NTUC LearningHub. How to Claim SkillsFuture Credit. Available online: https://www.ntuclearninghub.com/en/funding-claims-old/ntuc-skillsfuture-credit-claims (accessed on 21 February 2024).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Multiple requests from the same IP address are counted as one view.
Download PDF
Climatic Influences on the Environmental Performances of Residential Buildings: A Comparative Case Study in Turkey
Previous
The Supplier Selection of Prefabricated Component Production Line: A Lean-Based AHP–Improved VIKOR Framework
Next