Tag Archives: Mobile Applications

The functionality and usability features of mobile apps

This is an excerpt from one of my latest publications.

Suggested citation: Camilleri, M.A., Troise, C. & Kozak, M. (2023). Functionality and usability features of ubiquitous mobile technologies: The acceptance of interactive travel apps. Journal of Hospitality and Tourism Technology, https://doi.org/10.1108/JHTT-12-2021-0345 Available from: https://www.researchgate.net/publication/366633583_Functionality_and_usability_features_of_ubiquitous_mobile_technologies_The_acceptance_of_interactive_travel_apps.

(C) DrMarkCamilleri.com

Prior studies relied on specific theoretical frameworks like the Interactive Technology Adoption Model – ITAM (Camilleri and Kozak, 2022), elaboration likelihood model (ELM), information adoption model (IAM) and/or technology acceptance model (TAM), among others, to better understand which factors are having an impact on the individuals’ engagement with digital media or information technologies.

In this case, this research identifies the factors that are influencing the adoption of travel apps, in the aftermath of COVID-19. It examines the effects of information quality and source credibility (these measures are drawn from IAM framework), as well as of technical functionality, relating to electronic service quality (eSERVQUAL), on the individuals’ perceptions about the usefulness of these mobile technologies and on their intentions to continue using them on a habitual basis (the latter two factors are used in TAM models), to shed light on the consumers’ beliefs about their usability and functionality features.

This study suggests that consumers are valuing the quality of the digital content that is presented to them through these mobile technologies. Apparently, they are perceiving that the sources (who are curating the content) were knowledgeable and proficient in the upkeep and maintenance of their apps. Moreover, they are appreciating their functional attributes including their instrumental utility and appealing designs. Evidently, these factors are influencing their intentions to use the travel apps in the future. They may even lead them to purchase travel and hospitality services. Furthermore, they can have an impact on their social facilitation behaviors like positive publicity (via electronic word of mouth like online reviews, as well as in-person/offline), among other outcomes.

This contribution implies that there is scope for future researchers to incorporate a functionality factor in addition to ITAM, IAM and/or TAM ‘usability’ constructs to investigate the individuals’ dispositions to utilize technological innovations and to adopt their information. It confirms that the functionality features including their ease of use, responsiveness, organized layout and technical capabilities can trigger users to increase their app engagement on a habitual basis.

Practical recommendations

The results from this study reveal that the respondents hold positive perceptions toward interactive travel apps. In the main, they indicate that these mobile technologies feature high quality content, are organized, work well, offer a good selection of products and are easy to use.

This research posits that mobile users appreciate the quality of information that is presented to them through the travel apps, in terms of their completed-ness, accuracy and timeliness of information. Yet, the findings show that there is room for improvement. There is scope for service providers (and for the curators of their travel apps) to increase their credentials on source trustworthiness and expertise among consumers.

The results suggest that information quality had a more significant effect on the respondents’ perceived usefulness of travel apps than source credibility. Moreover, they also suggest that consumers are willing to engage with travel apps as they believe that they offer seamless functionality features, including customization capabilities and fast loading screens. Most probably, the respondents are cognizant that they offer differentiated pricing options on flights, hotels and cars, from various service providers. They may be aware that many travel apps also enable their users to access their itineraries even when they are offline and allow them to keep a track record of their reward points (e.g. of frequent flyer programs) on every booking.

In this day and age, consumers can utilize mobile devices to access asynchronous content in webpages, including detailed information on tourism service providers, transportation services, tours to attractions, the provision of amenities in tourist destinations, frequently answered questions, efficient booking engines with high resolution images and videos, quick loading and navigation, detailed maps, as well as with qualitative reviews and quantitative ratings. Very often they can even be accessed through different languages.

A number of travel apps allow their users to log in with a secure, random password authentication method, to keep a track record of their credit card details and past transactions. Most of them are also sending price alerts as well as push notifications that remind consumers about their past searches. These services are adding value to the electronic service quality as opposed to unsolicited promotional messages, that are not always related to the consumers’ interests.

Generally, customers expect travel and tourism service providers to respond to their online queries in an instantaneous manner. They are increasingly demanding web chat services to resolve queries, as soon as possible, preferably in real time.

Tourism and hospitality service providers are already using augmented reality (AR) and virtual reality (VR) software, to improve their consumers’ online experiences and to emphasize their brand positioning as high-quality service providers. In the foreseeable future, it is very likely that practitioners could avail themselves of Metaverse technologies that could teleport consumers in the cyberspace, to lure them to book their flight, stays, car rentals or tours. Online (and mobile) users may be using electronic personas, called avatars to move them around virtual spaces and to engage with other users, when they are in the Metaverse.

This interactive technology is poised to enhance its users’ immersive experiences, in terms of their sensory inputs, definitions of space and points of access to information, particularly those that work with VR headsets. Hence, travel and hospitality businesses could avail themselves of such interactive technologies to gain a competitive advantage.

You can access this paper in its entirety, via: https://www.researchgate.net/publication/366633583_Functionality_and_usability_features_of_ubiquitous_mobile_technologies_The_acceptance_of_interactive_travel_apps


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The future of marketing is 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).



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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