Identifying the Types of Digital Footprint Data Used to Predict Psychographic and Human Behaviour

Autor:	Muhammad Syafiq Mohd Pozi, Chua Chy Ren, Aliff Nawi, Zalmizy Hussin, Nurfatin Syahirah Norsaidi
Rok vydání:	2020
Předmět:	Computer science 05 social sciences Network data 050301 education Unstructured data Linked data Data type Data science Event data 0502 economics and business Digital footprint Social media 0503 education Psychographic 050203 business & management
Zdroj:	Digital Libraries at Times of Massive Societal Transition ISBN: 9783030644512 ICADL
DOI:	10.1007/978-3-030-64452-9_26
Popis:	Digital footprints can be defined any data related to any online activity. When engaging, the user leaves digital footprints that can be tracked across a range of digital activities, such as web explorer, checked-in location, YouTube, photo-tag and record purchase. Indeed, the use of all social media applications is also part of the digital footprint. This research was, therefore conducted to classify the types of digital footprint data used to predict psychographic and human behaviour. A systematic analysis of 48 studies was undertaken to examine which form of digital footprint was taken into account in ongoing research. The results show that there are different types of data from digital footprints, such as structured data, unstructured data, geographic data, time-series data, event data, network data, and linked data. In conclusion, the use of digital footprint data is a practically new way of completing research into predicting psychographic and human behaviour. The use of digital footprint data also provides a tremendous opportunity for enriching insights into human behaviour.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::474ef1dc0eff88655ec803b93b4f3a60 https://doi.org/10.1007/978-3-030-64452-9_26 Zobrazit plný text záznamu