| Summary: |
Nowadays, there is an increased and generalised use of mobile applications (mApps) to perform the most diverse activities however, human-technology symbiosis, humanenvironment interactions, as well as privacy and security are still great challenges to be attained. Clearly, existing advances have not been enough to promote the potential that technology has in extending and improving human capabilities; or technology has evolved faster than the human could adapt to them. So, what can be done in order to tackle the aforesaid challenges and accelerate the improvement of human interactions with mApps? On average, mApps lose 65% of their users in the first week, while very popular ones lose only 35%. The success of mApps highly depends on their acceptance by the users, so usage behaviour information can help developing better mApps as well as optimizing their uptake and continued use. The study of usage logs in mApps can provide identification of typical usability problems as well as determine the extent to which the system resonates with the user or what features are most persuasive. Further, users' engagement can be determined by other factors, for instance, users' characteristics or contextual variables, indicating that particular groups or goals should be targeted differently. Testing is ideal, but testing user acceptance in a lab is usually costly as well as time and resource-consuming, and with physical constraints, moreover now with the current pandemic. Other commonly used techniques, such as user surveys with statistical analysis, could also draw biased conclusions because frequently, the correlation between mApps self-reports and log data, is not high (what you say you do, is not really what you do). New evaluation methods are required and these must include log and usage data analytics and/or implementing new frameworks for usability. AnyMApp is a proposal for an exploratory project aiming to design and implement a Digital Twin to anonymously sim  |
Summary
Nowadays, there is an increased and generalised use of mobile applications (mApps) to perform the most diverse activities however, human-technology symbiosis, humanenvironment interactions, as well as privacy and security are still great challenges to be attained. Clearly, existing advances have not been enough to promote the potential that technology has in extending and improving human capabilities; or technology has evolved faster than the human could adapt to them. So, what can be done in order to tackle the aforesaid challenges and accelerate the improvement of human interactions with mApps? On average, mApps lose 65% of their users in the first week, while very popular ones lose only 35%. The success of mApps highly depends on their acceptance by the users, so usage behaviour information can help developing better mApps as well as optimizing their uptake and continued use. The study of usage logs in mApps can provide identification of typical usability problems as well as determine the extent to which the system resonates with the user or what features are most persuasive. Further, users' engagement can be determined by other factors, for instance, users' characteristics or contextual variables, indicating that particular groups or goals should be targeted differently. Testing is ideal, but testing user acceptance in a lab is usually costly as well as time and resource-consuming, and with physical constraints, moreover now with the current pandemic. Other commonly used techniques, such as user surveys with statistical analysis, could also draw biased conclusions because frequently, the correlation between mApps self-reports and log data, is not high (what you say you do, is not really what you do). New evaluation methods are required and these must include log and usage data analytics and/or implementing new frameworks for usability. AnyMApp is a proposal for an exploratory project aiming to design and implement a Digital Twin to anonymously simulate and analyse interactions online between humans and mobile applications (fictitious or currently existing). The intention is to provide easy means, with mockup interfaces allied to anonymous survey data, to integrate useful data regarding what the user really does in specific contexts, with specific goals, and how they think/feel about it. The extraction of usage logs from those mockups will allow the exploration of human-technology relations, how these are created and possibly, maintained, and build networks and communities of interactions for further analysis. The mockups, which can simulate any domain, are to be made freely available online and widely disseminated in order to integrate different types of populations with different users' characteristics and experiences. AnyMApp will allow both research and technical communities to design and test user interactions with mApps to quickly detect usability and interactional problems, as well as test users' preferences and factors of adherence (before, during or after development). AnyMApp aims to collect and process anonymous data (no personal identifiable data), in accordance with GDPR, to mitigate the ethical and privacy concerns that
could arise from user behaviour research, but the gathered data can still be useful to help distinguishing different population segments. The main methods to develop AnyMApp comprise the exploration of: 1) what data (demographics, context) to collect in the beginning, during, and at the end of the simulation, to still maintain anonymity/privacy; 2) the most adequate methods/technologies to both collect and process logs from mockup interfaces with the use of data mining and visualization tools, to be able to aggregate data to generalize to various types of populations and characteristics; 3) the most adequate methods/technologies to both collect and process user data via online surveys and correlate with data from 2); and 4) the easiest and most successful way to disseminate the tool, recruit participants to use it, and gather enough data for subsequent analysis. Expected contributions include: 1) an open/online proof of concept of AnyMApp to easily/quickly test interactional data from real users with mApps; 2) exploring multidisciplinary methods of analysis to better find patterns, clusters, communities, behaviours, influencing factors, etc; 3) a structured online dissemination/recruitment infrastructure; and 4) a great potential to advance scientific and technical knowledge in a wide variety of domains: a) usability, security, privacy, interaction, mApps development; multidisciplinary methods of analysis; b)
personality traits, risky behaviours, victimisation, social engineering; c) easily performing risk management of mApps; and d) improving the detection of mental or other health issues as well as ageing and cognitive related matters. |