This research aimed, firstly, to define a conceptual model that considers potential resources/challenges (Physical, Cognitive, Emotional, Social, Material, Environmental, Digital) and describes how those influence the Internet use and modify human behavior during life transitions (e.g., changing school, finding a job). Secondly, starting on that model, user profiles were outlined. Instead of grouping study participants into pre-defined groups, clustering techniques were used to group users with similar profiles. The main advantage of this methodological approach is that the participant groups, i.e., different user profiles, emerged intrinsically from the data. A cross-sectional study was proposed based on the compilation of an Online questionnaire. The sample consists of 1.524 participants. Three clusters emerged with different mean ages: young adult users (mean age = 33.83), youngest users (25.79), and oldest users (36.80). Differences were identified between all dimensions measured, particularly between youngest users and oldest users.
Martina Benvenuti, Humberto Rocha, Isabel D´ordio Dimas, Elvis Mazzoni (2022). How Life Transitions Influence People’s Use of the Internet: A Clustering Approach. Cham : Springer Nature [10.1007/978-3-031-10562-3_8].
How Life Transitions Influence People’s Use of the Internet: A Clustering Approach
Martina BenvenutiPrimo
Conceptualization
;Elvis MazzoniUltimo
Writing – Original Draft Preparation
2022
Abstract
This research aimed, firstly, to define a conceptual model that considers potential resources/challenges (Physical, Cognitive, Emotional, Social, Material, Environmental, Digital) and describes how those influence the Internet use and modify human behavior during life transitions (e.g., changing school, finding a job). Secondly, starting on that model, user profiles were outlined. Instead of grouping study participants into pre-defined groups, clustering techniques were used to group users with similar profiles. The main advantage of this methodological approach is that the participant groups, i.e., different user profiles, emerged intrinsically from the data. A cross-sectional study was proposed based on the compilation of an Online questionnaire. The sample consists of 1.524 participants. Three clusters emerged with different mean ages: young adult users (mean age = 33.83), youngest users (25.79), and oldest users (36.80). Differences were identified between all dimensions measured, particularly between youngest users and oldest users.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.