Tag Archives: m-Learning

Using mobile learning for corporate training: A contextual framework

This is an excerpt from one my my latest chapters on the use of digital media.

Suggested citation: Butler, A., Camilleri, M. A., Creed, A., & Zutshi, A. (2021). The use of mobile learning technologies for corporate training and development: A contextual framework. In M. A. Camilleri (Ed.), Strategic corporate communication in the digital age. Bingley: Emerald, pp. 115-130. DOI: 10.1108/978-1-80071-264-520211007

Photo by Daniel Korpai on Unsplash

There are a number of factors that can have an effect on the successful implementation of mobile learning (m-learning) for training and development purposes, including their course content, learning outcomes, the users’ perceived ease of use, usefulness and enjoyment, among other issues.

The individuals’ accessibility to these technologies or their spatial environment can also have an effect on their engagement with m-learning. Moreover, there may be certain distractions in the environment that can disrupt m-learning and/or decrease their effectiveness.

Csikszentmihalyi’s (1975) flow theory suggests that individuals can be completely focused on specific tasks (Csikszentmihalyi, Aduhamdeh & Nakamura 2014). They may immerse themselves in their training and development through m-learning. Of course, they have to be in the right environment where there are no distractions. Hence, the contextual setting of m-learning can influence its effectiveness. For example, experiential learning theory suggests that individuals learn through their ongoing interactions with their surrounding environment as they find meanings to problems and develop their understanding (Illeris, 2007). Similarly, Kolb’s (1984) learning theory posits that knowledge may result from a combination of direct experiences and socially acquired understandings (Matthews & Candy 1999). Laouris and Eteokleous (2005) discuss about the critical factors that could influence the outcomes of m-learning.

Hence, this contribution builds on these theoretical insights and on the findings from this study. The authors of this chapter put forward a contextual framework for m-learning. They identify the specific factors, including; accessibility and cost; the usefulness of the learning content; the ease of use of the technology; time; extrinsic and intrinsic motivations (e.g. rewards and perceived enjoyment, among others); integration with other learning approaches; individual learning styles and predispositions; and spatial issues and the surrounding environment, as featured here:

A prepublication version of this contribution is available here: https://www.researchgate.net/publication/344337930_The_Use_of_Mobile_Learning_Technologies_for_Corporate_Training_and_Development_A_Contextual_Framework

The authors argue that these eight contextual factors can have an effect on the successful implementation of m-learning.

  1. Time: This relates to the time that the users dedicate to learn to use and to engage in m-learning.
  2. Spatial issues and the environment: These relate to the physical location of the user when they access m-learning content.
  3. The usefulness of the learning content: The learning content (video, audio, written, or a combination of these) has to be useful to improve the mobile users’ knowledge, skills and competences.
  4. Ease of use of the technology: The m-learning technology has to be easy to use. It may (not) be connected to wireless networks (if it is, there should not be connectivity problems when accessing the content). The m-learning technology may require passive or active learning (for example, reading and/or interacting through games).
  5. Individual learning styles and predispositions: The m-learning technology should consider the individuals’ age, cognitive knowledge (e.g. memory); skills; visual, auditory and/or kinaesthetic abilities, as well as their preferences toward certain technologies. The technology may require interaction with peers or facilitators in synchronous, or asynchronous modes (these issues will depend on the learning outcomes of the mentioned technology).
  6. Extrinsic and intrinsic motivations: Organisations and professionals should also consider extrinsic and intrinsic motivations to entice the mobile users to use the m-learning technology.
  7. Accessibility and cost: These relate to the accessibility and cost of the m-learning technology. It can be available through different mobile platforms. It may be used by wide range of users (who have different learning needs) for different purposes. The software and/or hardware ought to be reasonable priced.
  8. Integration with other learning approaches: The m-learning technology ought to be complemented and blended with offline teaching approaches.

This proposed framework represents different contextual factors that can have an effect on the successful implementation of learner-centred corporate education (see Grant, 2019; Janson, Söllner & Leimeister, 2019). These eight factors are influencing the effectiveness of m-learning during the training and development of human resources. Hence the arrows are pointing inwards. However, the factors in the outer circle are related to each other and they can lead to further considerations. M-leaners may choose a short video over a longer podcast to learning or revise depending on the content or their situation. There are innumerable other examples of contextual learning due to the diversity of people, organizations and learning resources, objects and opportunities. For example, time is related to the spatial issues and the environment. The mobile users will use their downtimes wisely at the office, at home, or whilst commuting to and from work if they engage with m-learning applications. Their down time may provide them with an opportunity to improve their learning journey.

Conclusions and implications

The contextual factors for mobile learning encompass a variety of dimensions including time, spatial issues and the environment, the usefulness of the learning content and the ease of use of the technology, individual learning styles and predispositions, extrinsic and intrinsic motivations, accessibility and cost, as well as integration with other learning approaches.  The authors posit that this comprehensive framework can support businesses in their human resources training and development. It enables them to identify all the contextual factors that can have an effect on the successful roll out of m-learning designs.

This chapter has featured a critical review of the relevant literature and has presented the findings from an empirical research. The data for this study was gathered through quantitative and qualitative methodologies. The researchers have disseminated a survey questionnaire among course participants and have organised semi-structured interview sessions with corporate training participants. In sum, this study reported that the younger course participants were more likely to embrace the m-learning technologies than their older counterparts. They suggested that they were using laptops, hybrids as well as smartphones and tablets to engage with m-learning applications at home and when they are out and about. These recent developments have led many businesses to utilize mobile technologies to engage with their employees or to use them for their training and development purposes.

Therefore, this contribution has identified the contextual factors that should be taken into account by businesses and/or by training organisations. Thus, the authors have presented their proposed framework for mobile learning. This framework is substantiated by their empirical research and by relevant theoretical underpinnings that are focused on m-learning.

The authors are well aware that every study has its inherent limitations. In this case, this sample was small, but it was sufficient for the purposes of this exploratory study. Future studies may include larger sampling frames and/or may use different research designs. The researchers believe that there is still a knowledge gap in academia on this topic. For the time being, just a few studies have explored the use of mobile learning among businesses. The mobile learning technologies can be rolled out for the training and development of corporate employees. The training organisations can encourage their course participants to engage in self-directed learning and development through formal, informal or micro learning contexts. Corporate educators and services providers of continuous professional training and development can use the mobile learning applications to improve the employees’ skills and competences. This may in turn lead to increased organisational productivities and competitiveness.

This chapter was published in Strategic Corporate Communication in the Digital Age.

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The future of marketing is mobile…

mobile

An IBM (2012) technology trends survey indicated that mobile devices could increase the productivities and efficiencies of organisations. This study showed that mobile software was the second most “in demand” area for research and development. In addition, Gartner BI Hype Cycle (2012) also anticipated that mobile analytics was one of the latest technologies that may potentially disrupt the business intelligence market. At the same time, the market for mobile advertising is escalating at a very fast pace. Interestingly, eMarketer (2012) had predicted that mobile advertising shall experience a surge from an estimated $2.6 billion in 2012 to more than $10.8 billion in 2016. Evidently, there are niche areas for professional growth, particularly in this specialised field; as more and more individuals are increasingly creating new applications for mobile operating systems.

Recent advances in mobile communication and geo-positioning technologies have presented marketers with a new way how to target consumers based on their location. Location-targeted mobile advertising involves the provision of ad messages to cellular subscribers based on their geographic locations. This digital technology allows marketers to deliver ads and coupons that are customised to individual consumers’ tastes, geographic location and time of day. Given the ubiquity of mobile devices, location-targeted mobile advertising seems to offer tremendous marketing benefits.

In addition, many businesses are commonly utilising applications, including browser cookies that track consumers through their mobile devices as they move out and about. Once these users leave these sites, the products or services that they had viewed online will be shown to them again in advertisements, across different websites. Hence, businesses are using browsing session data combined with the consumers’ purchase history to deliver “suitable” items that consumers like. Therefore, savvy brands are becoming increasingly proficient in personalising their offerings as they collect, classify and use large data volumes on their consumers’ behaviours. As more consumers carry smartphones with them, they are (or may be) receiving compelling offers that instantaneously pop up on their mobile devices.

For instance, consumers are continuously using social networks and indicating their geo location as they use mobile apps. This same data can be used to identify where people tend to gather — information that could be useful in predicting real estate prices et cetera. This information is valuable to brands as they seek to improve their consumer engagement and marketing efforts. Businesses are using mobile devices and networks to capture important consumer data. Smart phones and tablets that are wifi-enabled interact with networks and convey information to network providers and ISPs. This year, more brands shall be using mobile devices and networks as a sort of sensor data – to acquire relevant information on their consumers’ digital behaviours and physical movements. These businesses have become increasingly interactive through the proliferation of near-field communication (NFC). Basically, embedded chips in the customers’ mobile phones are exchanging data with retailers’ items possessing the NFC tags. It is envisaged that mobile wallet transactions using NFC technologies are expected to reach $110 billion, by the year 2017. The latest Android and Microsoft smartphones have already include these NFC capabilities. Moreover, a recent patent application by Apple has revealed its plans to include NFC capabilities in their next products. This will inevitably lead to an increase in the use of mobile wallets (GSMA, 2015). Undoubtedly, the growth of such data-driven, digital technologies is adding value to customer-centric marketing. Therefore, analytics can enable businesses to provide a deeper personalisation of content and offers to specific customers.

Apparently, there are promising revenue streams in the mobile app market. Both Apple and Android are offering paid or free ad-supported apps in many categories. There are also companies that have developed apps for business intelligence. For example, enterprise / industry-specific apps, e-commerce apps and social apps. Evidently, the lightweight programming models of the current web services (e.g., HTML, XML, CSS, Ajax, Flash, J2E) as well as the maturing mobile development platforms such as Android and iOS have also contributed to the rapid proliferation of mobile applications (Chen et al., 2012). Moreover, researchers are increasingly exploring mobile sensing apps that are location-aware and activity-sensitive.

Possible future research avenues include mobile social innovation for m-learning; (Sharples, Taylor and Vavoula, 2010; Motiwalla, 2007), mobile social networking and crowd-sourcing (Lane et al., 2010), mobile visualisation (Corchado and Herrero, 2011), personalisation and behavioural modelling for mobile apps in gamification (Ha et al., 2007), mobile advertising and social media marketing (Bart et al., 2014; Yang et al., 2013). Google’s (2015) current projects include gesture and touch interaction; activity-based and context-aware computing; recommendation of social and activity streams; analytics of social media engagements, and end-user programming (Dai, Rzeszotarski, Paritosh and Chi, 2015;  Fowler, Partridge, Chelba, Bi, Ouyang and Zhai, 2015; Zhong, Weber, Burkhardt, Weaver and Bigham, 2015; Brzozowski, Adams and Chi, 2015).

 

References:

Bart, Y., Stephen, A. T., & Sarvary, M. (2014). Which products are best suited to mobile advertising? A field study of mobile display advertising effects on consumer attitudes and intentions. Journal of Marketing Research, 51(3), 270-285.

Brzozowski, M. J., Adams, P., & Chi, E. H. (2015, April). Google+ Communities as Plazas and Topic Boards. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 3779-3788). ACM. Retrieved May 22nd, 2015, from http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43453.pdf

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188.

Corchado, E., & Herrero, Á. (2011). Neural visualization of network traffic data for intrusion detection. Applied Soft Computing, 11(2), 2042-2056.

Dai, P., Rzeszotarski, J. M., Paritosh, P., & Chi, E. H. (2015). And Now for Something Completely Different: Improving Crowdsourcing Workflows with Micro-Diversions. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (pp. 628-638). ACM. Retrieved May 17th, 2015, from http://dl.acm.org/citation.cfm?id=2675260

eMarketer (2012). eMarketer in the News: June 1, 2012 Retrieved January 28th, 2015, from http://www.emarketer.com/newsroom/index.php/emarketer-news-june-1-2012/

Fowler, A., Partridge, K., Chelba, C., Bi, X., Ouyang, T., & Zhai, S. (2015, April). Effects of Language Modeling and its Personalization on Touchscreen Typing Performance. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 649-658). ACM. Retrieved May15th, 2015, from http://cslu.ohsu.edu/~fowlera/Fowler_CHI2015.pdf

Gartner (2012). Big Data Drives Rapid Changes in Infrastructure and $232 Billion in IT Spending Through 2016. Retrieved January 20th, 2015, from https://www.gartner.com/doc/2195915/big-data-drives-rapid-changes

Google (2015). Human-Computer Interaction and Visualization Research at Google. Retrieved May 20th, 2015, from http://research.google.com/pubs/Human-ComputerInteractionandVisualization.html

Ha, I., Yoon, Y., & Choi, M. (2007). Determinants of adoption of mobile games under mobile broadband wireless access environment. Information & Management, 44(3), 276-286.

IBM (2012) Tech Trends Report. Fast track to the future. Retrieved May15th, 2015, from http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?subtype=XB&infotype=PM&appname=CHQE_XI_XI_USEN&htmlfid=XIE12346USEN&attachment=XIE12346USEN.PDF#loaded

Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of mobile phone sensing. Communications Magazine, IEEE, 48(9), 140-150.

Motiwalla, L. F. (2007). Mobile learning: A framework and evaluation. Computers & Education, 49(3), 581-596.

Sharples, M., Taylor, J., & Vavoula, G. (2010). A theory of learning for the mobile age. In Medienbildung in neuen Kulturräumen (pp. 87-99). VS Verlag für Sozialwissenschaften.

Yang, B., Kim, Y., & Yoo, C. (2013). The integrated mobile advertising model: The effects of technology-and emotion-based evaluations. Journal of Business Research, 66(9), 1345-1352.

Zhong, Y., Weber, A., Burkhardt, C., Weaver, P., & Bigham, J. P. (2015). Enhancing Android accessibility for users with hand tremor by reducing fine pointing and steady tapping. In Proceedings of the 12th Web for All Conference (p. 29). ACM. Retrieved Ma7 20th, 2015, from http://dl.acm.org/citation.cfm?id=2747277

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