The BeVID method

The transformation to digital technologies in the world of service creates new opportunities for observing human social and behavioural activities as data becomes more available for both practice and academic research.  To date, most of the studies in psychology, marketing and behavioural economics are intention or attitude based, and behavioural research explains human behaviour through the lens of social preferences, heuristics and norms. Scientific findings are mainly taken from surveys or laboratory experiments and not from real life behaviours. Epistemologically, these studies frequently face the tension of being either clearly object–oriented or perspective-driven. Methods are often causality or quasi causality focused.  

Advancement in digital technologies and sensors is enabling data to be collected from actual physical day to day interactions in addition to activities on the Internet. There is now data from spending and social media postings, music listens on Spotify, Fitbit sleeps and activities, all of which provide opportunities to observe and analyse authentic human behaviours through data, without being intrusive. How could such data empower the development of research projects and new scientific research tools and methods in social sciences? This is the question we pose in our study as we craft the new methodology on Behavioural Visibility in Data (BeViD).

The ability for individuals to share their own data has only been possible recently, with the HAT (the Hub of All Things) technology that enables individuals to collect their own data from a range of Internet services and IoT devices and then donate or exchange that data. The BeViD method includes the steps of any scientific process: observation; developing hypothesis; making predictions as well as testing the predictions through analysis of other individual BeVID records and finally developing theory. 

The following projects are initiatives to document the process of creating the BeVID methodology, with the aim of crafting it into a robust approach for social sciences. The projects will document how to set up respondent panels, create rules and put in place tests for reliability, and validity. They investigate classification algorithms (based on decision- trees, neural networks or fuzzy logic techniques) to ensure higher levels of accuracy in monitoring relevant behavioural activities. The research rigour of the new BeViD methodology must ensure unbiased experimental design, analysis, interpretation, and reporting of results so that it can be a significant contribution to research and society.

BEVID research methodology workshop will be discussed at the CADE Forum in Venice, see


Project 1: BEVID research and Classification algorithms

Led by Professor Tatiana Chameeva Bouzdine

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Project 2: Social and ethical issues of BeVID methodology

Led by Dr Susan Wakenshaw

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Project 3: BEVID experiment and research set up 

Led by Dr Lalitha Dhamotharan