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Key Terms in Education Technology Literature

This is an excerpt from one of my latest contributions, entitled: “The Use of Mobile Learning Technologies in Primary Education”.

edtech(The Image has been adapted from Buzzle.com)

 

  • The ‘Constructivist-Based learning’ is a learning theory claiming that individuals construct their knowledge and understandings through experiencing things.
  • The ‘Digital Learning Resources’ include digitally formatted, educational materials like; graphics, images or photos, audio and video, simulations and animation technologies, that are used to support students to achieve their learning outcomes.
  • The ‘Digital Games-Based Learning’ (DGBL) involves the use of educational video games that can be accessed through computer-based applications. DGBL are usually aimed to improve the students’ learning outcomes by balancing educational content and gameplay.
  • The ‘Discovery-Based Learning’ is a constructivist-based approach to education as students seek to learn through continuous inquiry and experience.
  • The ‘Learning Outcomes’ are assessment tools that measure the students’ achievement at the end of a course or program.
  • ‘Mobile Learning’ (M-Learning) is a term that describes how individuals learn through mobile, portable devices, including smart phones, laptops and/or tablets.
  • The ‘Serious Games’ refer to games that are used in industries like; education, health care, engineering, urban planning, politics and defence, among other areas. Such games are usually designed for training purpose other than pure entertainment.
  • The ‘Ubiquitous Technology’ involves the use of wireless sensor networks that disseminate information in real time, from virtually everywhere.

 

ADDITIONAL READING

  1. Bakker, M., van den Heuvel-Panhuizen, M., & Robitzsch, A. (2015). Effects of playing mathematics computer games on primary school students’ multiplicative reasoning ability. Contemporary Educational Psychology40, 55-71.
  2. Blatchford, P., Baines, E., & Pellegrini, A. (2003). The social context of school playground games: Sex and ethnic differences, and changes over time after entry to junior school. British Journal of Developmental Psychology21(4), 481-505.
  3. Bottino, R. M., Ferlino, L., Ott, M., & Tavella, M. (2007). Developing strategic and reasoning abilities with computer games at primary school level. Computers & Education49(4), 1272-1286.
  4. Camilleri, M.A. & Camilleri, A. (2017). The Students’ Perceptions of Digital Game-Based Learning. In Pivec, M. & Grundler, J. (Ed.)11th European Conference on Games Based Learning (October). Proceedings, pp. 52-62, H JOANNEUM University of Applied Science, Graz, Austria, pp 56-62. http://toc.proceedings.com/36738webtoc.pdf https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3087801
  5. Camilleri, A.C. & Camilleri, M.A. (2019). The Students Intrinsic and Extrinsic Motivations to Engage with Digital Learning Games. In Shun-Wing N.G., Fun, T.S. & Shi, Y. (Eds.) 5th International Conference on Education and Training Technologies (ICETT 2019). Seoul, South Korea (May, 2019). International Economics Development and Research Center (IEDRC). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3339158
  6. Camilleri, A.C. & Camilleri, M.A. (2019). The Students’ Perceived Use, Ease of Use and Enjoyment of Educational Games at Home and at School. 13th Annual International Technology, Education and Development Conference. Valencia, Spain (March 2019). International Academy of Technology, Education and Development (IATED). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3339163
  7. Camilleri, M.A. & Camilleri, A.C. (2019). Student-Centred Learning through Serious Games. 13th Annual International Technology, Education and Development Conference. Valencia, Spain (March 2019). International Academy of Technology, Education and Development (IATED). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3339166
  8. De Aguilera, M., & Mendiz, A. (2003). Video games and education:(Education in the Face of a “Parallel School”). Computers in Entertainment (CIE)1(1), 1-14.
  9. Hainey, T., Connolly, T. M., Boyle, E. A., Wilson, A., & Razak, A. (2016). A systematic literature review of games-based learning empirical evidence in primary education. Computers & Education102, 202-223.
  10. Hromek, R., & Roffey, S. (2009). Promoting Social and Emotional Learning With Games: “It’s Fun and We Learn Things”. Simulation & Gaming40(5), 626-644.
  11. Lim, C. P. (2008). Global citizenship education, school curriculum and games: Learning Mathematics, English and Science as a global citizen. Computers & Education51(3), 1073-1093.
  12. McFarlane, A., Sparrowhawk, A., & Heald, Y. (2002). Report on the educational use of games. TEEM (Teachers evaluating educational multimedia), Teem, Cambridge, UK. pp.1-26. http://consilr.info.uaic.ro/uploads_lt4el/resources/pdfengReport%20on%20the%20educational%20use%20of%20games.pdf
  13. Miller, D. J., & Robertson, D. P. (2010). Using a games console in the primary classroom: Effects of ‘Brain Training’programme on computation and self‐British Journal of Educational Technology41(2), 242-255.
  14. Pellegrini, A. D., Blatchford, P., Kato, K., & Baines, E. (2004). A short‐term longitudinal study of children’s playground games in primary school: Implications for adjustment to school and social adjustment in the USA and the UK. Social Development13(1), 107-123.
  15. Tüzün, H., Yılmaz-Soylu, M., Karakuş, T., İnal, Y., & Kızılkaya, G. (2009). The effects of computer games on primary school students’ achievement and motivation in geography learning. Computers & Education52(1), 68-77.

 

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Filed under digital games, Digital Learning Resources, digital media, education technology, Higher Education, Mobile, mobile learning, online

The Students’ Engagement with Mobile Learning Technologies

These are excerpts from our latest academic article.

How to Cite: Camilleri, M.A. & Camilleri, A.C. (2019). The Students’ Readiness to Engage with Mobile Learning Apps. Interactive Technology and Smart Education. https://www.emerald.com/insight/content/doi/10.1108/ITSE-06-2019-0027/full/html


Hand-held mobile devices such as smart phones and tablets allow individuals, including students, to access and review online (educational) content from virtually anywhere. The mobile applications (apps) can provide instant access to the schools’ learning resources (Camilleri & Camilleri, 2019b; Sánchez & Isaías, 2017; Cheon, Lee, Crooks & Song, 2012). Therefore, they are increasingly being utilized in the context of primary education to improve the student experience. Relevant theoretical underpinnings reported that more primary level students are utilizing mobile learning technologies to engage with their instructors (Rodríguez, Riaza & Gómez, 2017; Sánchez & Isaías, 2018). Notwithstanding, it is much easier for the younger pupils to mobile apps to read eBooks, as hard-copy textbooks need to be carried in their bags. Arguably, the proliferation of portable technologies like tablets are lighter and less bulky than laptop computers. Hence, primary school students can easily use mobile technologies anywhere, beyond the traditional classroom environment (Rodríguez et al., 2017). Currently, there is a wide variety of educational apps that are readily available on a wide array of mobile devices (Chee, Yahaya, Ibrahim &Hasan, 2017; Domingo & Garganté, 2016). Such interactive technologies can improve the delivery of quality education as teachers provide direct feedback to their students, in real time. Some of the mobile apps can even engage primary school students in immersive learning experiences (Camilleri & Camilleri,2019c; Isaias, Reis, Coutinho & Lencastre, 2017).

On the other hand, other academic literature posited that some students may not want to engage in mobile learning. Very often, commentators implied that the mobile technologies have their own limitations (Cheon et al., 2012; Wang, Wu & Wang, 2009). A few practitioners contended that mobile devices had small screens with low resolutions. Alternatively, some argued about their slow connection speeds, or pointed out that they lacked standardization features  (Sánchez & Isaías, 2017; Camilleri & Camilleri,2017).

As a matter of fact, Android, Apple and Microsoft Windows have different operating systems. As a result, learning apps may have to be customized to be compatible with such systems. Moreover, individuals, including primary school students may hold different attitudes towards the use of mobile devices. There may be students who may be motivated to engage with mobile technologies (Sánchez & Isaias, 2018; Ciampa, 2014) as they use these devices to play games, watch videos, or to chat with their friends, online (Wang et al., 2009). In this case, the primary school students may use their mobile devices for hedonic reasons, rather than to engage in mobile learning activities. Such usage of the mobile technologies can possibly result in undesired educational outcomes. Nevertheless, those primary level students who already own or have instant access to a mobile device may easily become habitual users of this technology; as they use it for different purposes. However, there is still limited research in academia that explores these students’ readiness to engage in mobile learning at home, and at school.


Results

The findings in this study are consistent with the argument that digital natives are increasingly immersing themselves in digital technologies (Bourgonjon et al., 2010), including educational games (Camilleri & Camilleri,2019; Ge & Ifenthaler, 2018; Carvalho et al., 2015, Wouters et al., 2013). However, the results have shown that there was no significant relationship between the perceived ease of the gameplay and the children’s enjoyment in them. Furthermore, the stepwise regression analysis revealed that there was no significant relationship between the normative expectations and the children’s engagement with the educational apps; although it was evident (from the descriptive statistics) that the parents were encouraging their children to play the games at home and at school. This research relied on previously tried and tested measures that were drawn from the educational technology literature in order to explore the hypothesized relationships. There is a common tendency in academic literature to treat the validity and reliability of quantitative measures from highly cited empirical papers as given.

Future studies may use different sampling frames, research designs and methodologies to explore this topic. To the best of our knowledge, there is no other empirical study that has validated the technology acceptance model within a primary school setting. Further work is needed to replicate the findings of this research in a similar context.


References (the full bibliography of this paper)

Ajzen, I. (1991), “The theory of planned behavior”, Organization Behaviour and Human Decision Processes, Vol. 50, No. 2, pp. 179-211.

Bourgonjon, J., Valcke, M., Soetaert, R., and Schellens, T. (2010), “Students’ perceptions about the use of educational games in the classroom”, Computers & Education, Vol. 54, No. 4, pp. 1145-1156.

Burguillo, J.C. (2010), “Using game theory and competition-based learning to stimulate student motivation and performance”, Computers & Education, Vol. 55, No. 2, pp. 566-575.

Camilleri, M.A. and Camilleri, A. (2017a), “The Technology Acceptance of Mobile Applications in Education”, In Sánchez, I.A. & Isaias, P. (Eds) 13th International Conference on Mobile Learning (Budapest, 11th April). Proceedings, pp 41-48. International Association for Development of the Information Society.

Camilleri, M.A., and Camilleri, A.C. (2017b), “Digital learning resources and ubiquitous technologies in education”, Technology, Knowledge and Learning, Vol. 22, No. 1, pp. 65-82.

Camilleri, M. A., and  Camilleri, A. (2019a), “Student Centred Learning Through Serious Games”, 13th Annual International Technology, Education and Development Conference. Valencia, Spain (March, 2019). International Academy of Technology, Education and Development (IATED).

Camilleri, A.C., and Camilleri, M.A. (2019b), “Mobile Learning via Educational Apps: An Interpretative Study”. In Shun-Wing N.G., Fun, T.S. & Shi, Y. (Eds.) 5th International Conference on Education and Training Technologies (ICETT 2019). Seoul, South Korea (May, 2019). International Economics Development and Research Center (IEDRC).

Camilleri, A.C., and Camilleri, M.A. (2019c), “The Students Intrinsic and Extrinsic Motivations to Engage with Digital Learning Games”, In Shun-Wing N.G., Fun, T.S. & Shi, Y. (Eds.) 5th International Conference on Education and Training Technologies (ICETT 2019). Seoul, South Korea (May, 2019). International Economics Development and Research Center (IEDRC).

Carvalho, M.B., Bellotti, F., Berta, R., De Gloria, A., Sedano, C.I., Hauge, H.B., Hu, J., and Rauterberg, M. (2015), “An activity theory-based model for serious games analysis and conceptual design”, Computers & Education, Vol. 87, pp.166-181.

Chang, C.T., Hajiyev, J., and Su, C.R. (2017), “Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach”, Computers & Education, Vol. 111, pp. 128-143.

Chee, K. N., Yahaya, N., Ibrahim, N. H., and Hasan, M. N. (2017). Review of mobile learning trends 2010-2015: A meta-analysis. Journal of Educational Technology & Society20(2), 113-126.

Chen, K. C. and Jang, S. J. (2010), “Motivation in online learning: Testing a model of self-determination theory”, Computers in Human Behavior, Vol. 26, No. 4, pp. 741-752.

Cheon, J., Lee, S., Crooks, S. M. and Song, J. (2012), “An investigation of mobile learning readiness in higher education based on the theory of planned behavior”, Computers & Education, Vol. 59, No. 3, pp. 1054-1064.

Ciampa, K. (2014), “Learning in a mobile age: an investigation of student motivation”, Journal of Computer Assisted Learning, Vol. 30, No. 1, pp. 82-96.

Connolly, T.M., Boyle, E.A., MacArthur, E.  Hainey, T., and Boyle, J.M. (2012), “A systematic literature review of empirical evidence on computer games and serious games”, Computers & Education, Vol. 59, No. 2, pp. 661-686.

Davis, F.D. (1989), “Perceived usefulness, perceived ease of use, and user acceptance of information technology”, MIS Quarterly, Vol. 13, No. 3, pp. 319-340.

Davis, F.D., Bagozzi, R.P., and Warshaw, P.R. (1989), “User acceptance of computer technology: a comparison of two theoretical models”, Management Science, Vol. 35, No. 8, pp. 982-1003.

Dickey, M.D. (2011), “Murder on Grimm Isle: The impact of game narrative design in an educational game‐based learning environment”, British Journal of Education Technology, Vol. 42, No.  3, pp. 456-469.

Domingo, M. G. and Garganté, A. B. (2016). Exploring the use of educational technology in primary education: Teachers’ perception of mobile technology learning impacts and applications’ use in the classroom. Computers in Human Behavior, Vol. 56, pp. 21-28.

Dunne, Á., Lawlor, M. A., and Rowley, J. (2010), “Young people’s use of online social networking sites–a uses and gratifications perspective”, Journal of Research in International Marketing,. Vol. 4, No. 1, pp.  46-58.

Ge, X., and Ifenthaler, D. (2018), “Designing engaging educational games and assessing engagement in game-based learning”, In Gamification in Education: Breakthroughs in Research and Practice, IGI Global, Hershey, USA, pp. 1-19.

Harris, J. Mishra, P., and Koehler, M. (2009), “Teachers’ technological pedagogical content knowledge and learning activity types: Curriculum-based technology integration reframed”, Journal of Research on Technology in Education, Vol. 41, No. 4, pp. 393-416.

Huang, W.H., Huang, W.Y., and Tschopp, J. (2010), “Sustaining iterative game playing processes in DGBL: The relationship between motivational processing and outcome processing”,  Computers & Education, Vol. 55, No. 2, pp. 789-97.

Hwang, G.J., and Wu, P.H.  (2012), “Advancements and trends in digital game‐based learning research: a review of publications in selected journals from 2001 to 2010”, British. Journal of Education Technology, Vol. 43, No. 1, pp. E6-E10.

Isaias, P., Reis, F., Coutinho, C. and Lencastre, J. A. (2017), “Empathic technologies for distance/mobile learning: An empirical research based on the unified theory of acceptance and use of technology (UTAUT)”, Interactive Technology and Smart Education, Vol. 14, No. 2, pp. 159-180.

Lee, M. K., Cheung, C. M., and Chen, Z. (2005), “Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation”, Information & Management,. Vol. 42, No. 8, pp. 1095-1104.

Li, H., Liu, Y., Xu, X., Heikkilä, J., and Van Der Heijden, H. (2015), “Modeling hedonic is continuance through the uses and gratifications theory: An empirical study in online games”, Computers in Human Behavior, Vol. 48, pp. 261-272.

Park, S.Y. (2009), “An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning”, Education. Technology & Society, Vol. 12, No. 3, pp. 150-162.

Park, S. Y., Nam, M. W., and Cha, S. B. (2012), “University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model”, British Journal of Education Technology, Vol. 43, No. 4, pp. 592-605.

Rodríguez, A. I., Riaza, B. G., & Gómez, M. C. S. (2017), “Collaborative learning and mobile devices: An educational experience in Primary Education”, Computers in Human Behavior, Vol. 72, pp. 664-677.

Ryan, R. M., and Deci, E. L. (2000), “Intrinsic and extrinsic motivations: Classic definitions and new directions”, Contemporary Education Psychology, Vol. 25, No. 1, pp. 54-67.

Sánchez, I. A., & Isaías, P. (2017), “Proceedings of the International Association for Development of the Information Society (IADIS)”, International Conference on Mobile Learning (13th, Budapest, Hungary, April 10-12, 2017). International Association for Development of the Information Society.

Sánchez, I. A., & Isaias, P. (2018), “Proceedings of the International Association for Development of the Information Society (IADIS)”, International Conference on Mobile Learning (14th, Lisbon, Portugal, April 14-16, 2018). International Association for Development of the Information Society.

Teo, T., Beng Lee, C., Sing Chai, C., and Wong, S.L. (2009), “Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the Technology Acceptance Model (TAM)”, Computers & Education, Vol. 53, No. 3, pp. 1000-1009.

Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003), “User acceptance of information technology: Toward a unified view”, MIS Quarterly, Vol. 27, No. 3, pp. 425-478.

Venkatesh, V., Thong, Y.T.L., and Xu, X. (2012), “Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology”, MIS Quarterly, Vol. 36, No.1, pp. 157-178.

Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009), “Investigating the determinants and age and gender differences in the acceptance of mobile learning”, British Journal of Educational technology, Vol. 40, No. 1, pp. 92-118.

Wouters, P., Van Nimwegen, C., Van Oostendorp, H., and Van Der Spek, E.D. (2013), “A meta-analysis of the cognitive and motivational effects of serious games”,  Journal of Education Psychology,  Vol. 105, No.  2, pp. 249-266.


Related Publications

Camilleri, M.A. & Camilleri, A.C. (2019). The Acceptance and Use of Mobile Learning Applications in Higher Education. In Pfennig, A. & Chen, K.C. (Eds.) 3rd International Conference on Education and eLearning (ICEEL2019), Barcelona, Spain.

Camilleri, A.C. & Camilleri, M.A. (2019). The Students’ Perceived Use, Ease of Use and Enjoyment of Educational Games at Home and at School. 13th Annual International Technology, Education and Development Conference. Valencia, Spain (March, 2019). International Academy of Technology, Education and Development (IATED).Download this paper

Camilleri, M.A. & Camilleri, A. (2017). The Students’ Perceptions of Digital Game-Based Learning. In Pivec, M. & Grundler, J. (Ed.) 11th European Conference on Games Based Learning  (October). Proceedings, pp. 52-62, H JOANNEUM University of Applied Science, Graz, Austria, pp 56-62. http://toc.proceedings.com/36738webtoc.pdf Download this paper

Camilleri, M.A. & Camilleri, A. (2017). Measuring The Educators’ Behavioural Intention, Perceived Use And Ease Of Use Of Mobile Technologies. In Wood, G. (Ed) Re-connecting management research with the disciplines: Shaping the research agenda for the social sciences (University of Warwick, September). Proceedings, pp., British Academy of Management, UK. http://conference.bam.ac.uk/BAM2017/htdocs/conference_papers.php?track_name=%20Knowledge%20and%20Learning Download this paper

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Targeted Segmentation Through Mobile Marketing

mobile

The mobile is an effective channel to reach out to many users. The mobile devices, including smart phones and tablets could increase the productivities and efficiencies of organisations. For the time being, the mobile applications (apps) are an “in demand” area for research and development. Gartner (2015) anticipated that mobile analytics was going to be one of the latest technologies that could disrupt business intelligence. In fact, the market for advertising on mobile is still escalating at a fast pace. Moreover, there are niche areas for professional growth, as more and more individuals are increasingly creating new applications for many purposes on 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 (Camilleri, 2016). Location-targeted mobile advertising involves the provision of ad messages to mobile data subscribers. This digital technology allows marketers to deliver ads and coupons that are customised to individual consumers’ tastes, geographic location and the time of day. Given the ubiquity of mobile devices, location-targeted mobile advertising are increasingly offering 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. Very often, when users leave the sites they visited, the products or services they viewed will be shown to them again in advertisements, across different websites. Hence, many companies are using browsing session data combined with the consumers’ purchase history to deliver “suitable” items that consumers like. There are also tourism businesses who are personalising their offerings as they collect, classify and use large data volumes on the consumers’ behaviours. As more consumers carry smartphones with them, they may be easily targeted with compelling offers that instantaneously pop-up on their mobile devices.

For instance, consumers are continuously using social networks which are indicating their geo location, as they use mobile apps. This same data can be used to identify where people tend to gather — this information that could be very useful. This information is valuable to brands as they seek to improve their consumer engagement and marketing efforts. It may appear that businesses are using mobile devices and networks to capture important consumer data. For instance, smart phones and tablets that are wifi-enabled interact with networks and convey information to network providers and ISPs. This year, more businesses 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 the retailers’ items possessing the NFC tags. The latest iPhone, Android and Microsoft smartphones have already included these NFC ca­pabilities. This development has recently led to the use of mobile wallets. The growth of such data-driven, digital technologies is surely adding value to the customer-centric marketing. Therefore, analytics can enable businesses to provide a deeper personalisation of content and offers to specific customers.

The geo-based marketing message or offer is delivered at the right time, and at the right place. The brands that hold customer data can gain a competitive edge over their rivals. Of course, firms will need more than transaction history and loyalty schemes to be effective at this. They may require both socio-demographic and geo-data that new mobile technologies are capable of gathering.

For instance, many mobile service companies are partnering with local cinemas, in response to the location-targeted mobile advertising; as cinema-goers often inquire about movie information, and they may book tickets and select their seats through their mobile app. The consumers who are physically situated within a given geographic proximity of the participating cinemas could be receiving location-targeted mobile ads. The cinemas’ ads will inform prospects what movies they are playing and could explain how to purchase tickets through their phone. The consumers may also call the cinemas’ hotlines to get more information from a customer service representative. Besides location-targeted advertising, the mobile companies can also promote movie ticket sales via mobile ads that arte targeted to individuals, according to their behaviour (not by location). Therefore, the companies may direct mobile-ad messages to those consumers who had previously responded to previous mobile ads (and to others who had already purchased movie tickets, in the past months). Moreover, the cinema companies could also promote movies via Facebook Messenger Ads if they logged in the companies’ website, via their Facebook account. The mobile users might receive instant message ads via pop-up windows whenever they log into the corporate site of their service provider.

It is envisaged that such data points will only increase as the multi-billion dollar advertising monopolies are built on big data and analytics that can help businesses personalise immersive ads to target individual customers. The use of credit card transactions is also complementing geo-targeting and Google Maps, with ads; as the physical purchases are increasingly demanding personalisation, fulfillment and convenience. Consumers and employees alike are willing to give up their data for value. Therefore, the businesses need to reassure their customers through concise disclosures on how they will use personal data. They should clarify the purpose of maintaining consumer data, as they should provide simple user controls to opt in and out of different levels of data sharing. This way, they could establish a trust-worthy relationship with customers and prospects.

Companies are already personalising their mobile shopping experience based on the user situation and history. Tomorrow’s tourism businesses are expected to customise their user experiences of applications and web interfaces, according to the specific needs of each segment. Big data and analytics capabilities are increasingly allowing businesses to fully leverage their rich data from a range of new digital touchpoints and to turn this into high impact interactions. Those businesses that are able to reorient their marketing and product-development efforts around digital customer segments and behaviours will be in a position to tap into the hyper-growth that mobile, social media and the wearables market are currently experiencing.

References:

Camilleri, M. A. (2016). Using big data for customer centric marketing. Using Big Data for Customer-Centric Marketing. In Evans, C. (Ed.) Handbook of Research on Open Data Innovations in Business and Government, IGI Global, Hershey, PA, USA. https://www.um.edu.mt/library/oar/bitstream/handle/123456789/10682/Using%20Big%20Data%20for%20Customer-centric%20Marketing.pdf?sequence=3&isAllowed=y

Gartner (2015) Gartner Says Power Shift in Business Intelligence and Analytics Will Fuel Disruption. http://www.gartner.com/newsroom/id/2970917

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Tourism and Technology: What the future holds for travel distribution?

mobile.pngThe development of digital media technologies, particularly the internet and social media are offering a wide range of possibilities to the travel industry. These latest technological advances have enabled many travel businesses, including airlines and hotels to manage their distribution channels in a more efficient and economical way.

With the changing landscape of travel e-commerce and the ubiquity of IT solutions which gather, store, and analyse data in a variety of ways; airlines have improved their ability to monitor their performance across channels. Very often, they are in a position to quickly adjust offers. Their prices are usually based on a variety of situations and circumstances, as they optimise communications and transactions.

By using big data and analytics on their customer behaviours, many travel businesses are taking advantage of channel-based distribution. Hence, the distribution networks have come a long way from the ticket counter. Evidently, travel and tourism businesses are leveraging themselves with data-driven marketing, as they seek new customers and prospects. For example, they may increase their profitability from high-yield customers as they are using elaborated pricing and revenue management systems. The travel distribution is evolving from its current passive, rigid, and technology-centric state to a more flexible, dynamic, and passenger-centric environment which we call ‘Active Distribution’.

Any changes in the tourism distributive systems may be stimulated by external macro factors such as politics and trade, global and national economies, technological innovations and access to them, et cetera. The airline industry could also be effected by increased competition from low-cost carriers, merger and acquisitions, and fuel costs, among other issues. However, the commercial future of the tourism industry may also be influenced by other factors, including travel distribution.

Tourism businesses can possibly become even more effective in how they sell their products and services, particularly if they deliver positive customer experiences. Tourists perceive value in customer-centric businesses. Most probably, in future, there will be significant improvements in terms of technologically enhanced customer services.

Tomorrow’s businesses will be serving passengers from geographically-diverse regions.  There will be more travellers from emerging markets and developing economies. The travel distribution systems will have to cater for senior citizens, as there are aging populations in many countries.

The distributive channels must be designed to accommodate a divergent nature of users. Tourism service providers and their intermediaries have to provide engaging, intuitive shopping experiences that tap into the traveller’s discretionary purchases.

The businesses will need to embrace new technologies and flexible distribution processes, as outmoded distribution components will be replaced. It is envisaged that the distributive systems will be increasingly relying on mobile devices as these technologies enable consumer interaction with speech and voice recognition software.

The tourism businesses will leverage themselves with artificial intelligence which could facilitate dynamic pricing as well as personalisation of services.

The distributive  systems could interface with virtual  reality software to help businesses merchandise their products in captivating customer experiences.

The third-party retailers will continue to form part of the distribution mix. However, many service providers will be using their direct channels to reach their targeted customers.

There will probably be fewer market intermediaries and online travel agencies will see significant declines.

It is very likely, that airlines will not have to pre-file volumes of defined fares through third-parties as they may not rely on inventory buckets to manage their selling capacity. The airlines must recognise the need to invest in new internal selling systems. Today’s passenger service systems lack the flexibility that airlines require. They are not adequate enough to serve  the airlines’ flexible and dynamic sales environments. These systems could be replaced with modular retailing platforms. Full Retailing Platforms (FRPs) will allows airlines to take back the control they require to be better retailers through any distribution channel (IATA, 2016).

However, Google, the multinational technology company, could be playing a much larger role in travel distribution. The technology giant could participate in, and possibly disrupt the tourism industry if it becomes an online travel agency. whether through acquisition or by launching a product of its own. In fact, its travel product, Google Flights is increasing in popularity among travellers.

Moreover, there have been recent developments in online payment facilities. Undoubtedly, there will further improvements in this area, as well. Payment providers like M-Pesa, Alipay, and PayPal will probably become more important.

In the foreseeable future, the travel marketplace will surely introduce new technologies and capabilities as multiple venture capital firms are increasingly investing in disruptive innovation.

There may be new businesses which could penetrate the market, including private air service operators who could provide “on-demand” airline services; alternatively, technology companies could develop or acquire their meta-search engines or online travel agencies.

Undoubtedly, the travel and tourism businesses need to find ways that intentionally overturn decades of outdated, distribution practices. The distribution community can choose to innovate and disrupt, or allow others to be leading innovators.

 

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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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