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Van TinQwise | 14-07-2017 | Article Rating | (0) reacties

The Impact of Digital Learning: Old dogs, new tricks; does age really matter?

The Impact of Digital Learning: Old dogs, new tricks; does age really matter?

By Oscar van der Horst, Team Lead Learning & Development at TinQwise

I am fortunate to help organisations in creating custom digital and blended learning solutions that are a perfect fit for organisation-, target audience- and content characteristics and needs. In most cases, somewhere, along this process, the argument pops-up that: “if the target audience is not from Generation Y or Z, we should factor in that they are not open, capable or even willing to use digital learning.”

Thinking out loud, that could be an issue. First of all, digital learning is being readily adopted in corporate learning, and second, with retirement getting pushed back across the board, that could mean that there is a large section of the workforce that has to deal with learning solutions not fitting their needs. But there is fact and fiction, so which one is this?

Leaving our opinions at the door, what do we really know and what can be proved?

The literature suggests that in a learning and design context, employee acceptance of and satisfaction with the e-learning environment is an important prerequisite for learning and for the likelihood of future use (Cheng et al., 2012; Lee et al., 2011). A paper by Sawang et al. (2013) noted that e-learning systems must be perceived positively in order to encourage adoption by employees.

Resembling popular opinion, it is often claimed that the younger generation will be more accepting of the use of technology for learning and that older employees may be less likely to embrace such approaches ( McMullin et al, 2007; Young, 2013). However, this claim has also been questioned by qualitative studies (Becker et al., 2012). So like many things, it will depend on who you are asking it.

Fun fact in this age discussion; Age is also often raised as a potential inhibitor for the adoption of new technology in organisations. Posthuma and Campion (2009) suggest that “management can hold negative stereotypes about older workers that are subtle or unconscious, yet these may affect how they think about their workers” (p. 160). Meyer (2011) found that firms with an older workforce are less likely to adopt new technologies than those with a younger workforce. In my opinion, that creates a self-fulfilling prophecy. Managers not adopting new technology, because they are (maybe not conscious) convinced the older co-workers will not adopt it. Kind of hard to adopt things that are not there to adopt in the first place.

In a recent study done by Fleming, Becker & Newton (2017) focussing on an Australian railroad company, Fleming et al. discovered vital predictors for an increased uptake of organisational e-learning and Age was not one of those. 
Those vital components were:

  •  Perceived complexity;
  • Authentic learning;
  • Technical support.

Perceived complexity
In laymens terms; Perceived complexity being the degree to which a learning solution is perceived as relatively difficult to understand and use. That finding is backed by Teo and Wong (2013) agreeing that perceived ease of use is a predictor of user satisfaction and influences intention for future use.

Authentic learning
Authentic learning being defined as learning that is useful and relevant to the real world, thus increasing transfer potential.

Technical Support
Successful implementation of any technology or innovation requires a level of support for the user. It is well-documented that most new users of e-learning will encounter initial challenges or problems that need to be resolved by technical support (Lee et al., 2011) and a lack of technical expertise to support the e-learning initiative, has been cited as one of the most important barriers to overcome in relation to e-learning (Black et al., 2007; Selim, 2007).

So surely, taking into account that this research has its bias, focussing on only one company, but with n=979, it shows a serious sample size. Fleming, Becker & Newton (2017) found low complexity, authenticity and technical support were predictors of intention for future use of organisational e-learning. Age was not a significant factor impacting either future use intentions or satisfaction with e-learning.

“When learners believe that the e-learning may be difficult or require substantial effort on their part, then it is likely that they will avoid using e-learning in the future. In addition to perceived complexity, it was also identified that learners’ experiences are enhanced when the e-learning is made personally relevant and situated within an authentic context. real-world experiences offer a level of personal relevance that leads to intention to use e-learning in the future. Satisfaction and future intention to use e-learning is positively impacted when employees become immersed in real-world situations or scenarios where learning can be realistically applied. A supportive environment in terms of supplying appropriate technical support is important for continued usage as well as meeting an organisation’s desire to make e-learning an efficient and effective means of training and development.”

Fleming, Becker & Newton (2017)

So does age matter?
It’s one piece of the puzzle, sure, but we should stop stereotyping generations and target audiences in general. Current negative stereotypes around older workers’ inability or unwillingness to engage with technologies (for learning or other purposes) do not hold true. Age should not be seen as an obstacle to e-learning use.

Those responsible for the development and implementation of e-learning would be far better served addressing the variables that are proven to impact the use of e-learning and as a great bonus; they prove to be variables over which these organisations have significant control!

So the age-argument is getting old. (yes, I said it.) Learning professionals should focus on providing learners with realistic and context-specific content and activities. Delivered in an inviting, well-supported and well-dosed package.

Thanks for your interest and feel free to leave a response.

Used sources:

Becker, K., Fleming, J. and Keijsers, W. (2012), “E-learning: ageing workforce versus technology-savvy generation”, Education + Training, Vol. 54 No. 5, pp. 385-400.

Black, E.W., Beck, D., Dawson, K., Jinks, S. and DiPietro, M. (2007), “Considering implementation and use in the adoption of an LMS in online and blended learning environments”, TechTrends,Vol. 51 No. 2, pp. 35-53.

Cheng, B., Wang, M., Moormann, J., Olaniran, B.A. and Chen, N.-S. (2012), “The effects of organizational learning environment factors on e-learning acceptance”, Computers & Education, Vol. 58 No. 3, pp. 885-899.

Fleming, J., Becker, K. and Newton, C. (2017) "Factors for successful e-learning: does age matter?", Education + Training, Vol. 59 Issue: 1, pp.76-89.

Lee, Y.-H., Hsieh, Y.-C. and Hsu, C.-N. (2011), “Adding innovation diffusion theory to the technology acceptance model: supporting employees’ intentions to use e-learning systems”, Educational Technology & Society, Vol. 14 No. 4, pp. 124-137.

McMullin, J.A., Duerden Comeau, T. and Jovic, E. (2007), “Generational affinities and discourses of difference: a case study of highly skilled information technology workers”, The British Journal of Sociology, Vol. 58 No. 2, pp. 297-316.

Meyer, J. (2011), “Workforce age and technology adoption in small and medium-sized service firms”,Small Business Economics, Vol. 37 No. 3, pp. 305-324.

Posthuma, R.A. and Campion, M.A. (2009), “Age stereotypes in the workplace: common stereotypes, moderators and future research directions”, Journal of Management, Vol. 35 No. 1, pp. 158-188.

Sawang, S., Newton, C. and Jamieson, K. (2013), “Increasing learners’ satisfaction/intention to adopt more e-learning”, Education + Training, Vol. 55 No. 1, pp. 83-105.

Selim, H.M. (2007), “Critical success factors for e-learning acceptance: confirmatory factor models”, Computers & Education, Vol. 49 No. 2, pp. 396-413.

Teo, T. and Wong, S.L. (2013), “Modeling key drivers of e-learning satisfaction among student teachers”, Journal of Educational Computing Research, Vol. 48 No. 1, pp. 71-95.

Young, K. (2013), “Changing demographics: are companies meeting the development needs of an ageing workforce?”, Development and Learning in Organizations, Vol. 27 No. 4, pp. 4-5.

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