Vulpe Simona, Crăciun Andrei
Faculty of Sociology and Social Work, University of Bucharest, 9 Schitu Măgureanu, Bucharest, Romania.
Eur J Ageing. 2019 Jun 18;17(1):125-134. doi: 10.1007/s10433-019-00520-2. eCollection 2020 Mar.
Filling a gap in our understanding of how senior citizens use information and communication technologies (ICTs), we identified several profiles of technology communication use among European seniors (aged 65+). These profiles include: , and We outline the importance of a broader distinction, one that surpasses the non-user and user dichotomy, and explores the singularities of the seniors who overcome the challenge of adopting and using ICT. We consider the concept as a starting point for the theoretical background that we reviewed in order to explain the process through which senior citizens accept and adopt this technology. Analysing data gathered within the Eurobarometer (Standard Eurobarometer 84 Autumn 2015-media use in the European Union. https://dbk.gesis.org/dbksearch/sdesc2.asp?no=6642, 2015), we applied K-Means Cluster analysis and discriminant analysis in order to identify three types of older Internet users. We run the analysis on a sample of 4404 respondents aged between 65 and 99 years. Our results help with increasing the adequacy of Digital Single Market policies for European seniors, as well as with more suitably targeting senior for social care and medical care programmes in the digital environment. Providing suggestions for further research, we argue for an in-depth classification of ICT users, based on characteristics such as gender, education, ethnicity or social class.
为填补我们对老年人如何使用信息通信技术(ICT)理解上的空白,我们确定了欧洲老年人(65岁及以上)中几种技术通信使用情况的概况。这些概况包括: 、 和 。我们概述了一种更广泛区分的重要性,这种区分超越了非用户和用户的二分法,并探讨了那些克服采用和使用ICT挑战的老年人的独特之处。我们将 概念作为我们所回顾的理论背景的起点,以便解释老年人接受和采用这项技术的过程。通过分析在欧洲晴雨表(2015年秋季标准欧洲晴雨表84——欧盟媒体使用情况。https://dbk.gesis.org/dbksearch/sdesc2.asp?no=6642, 2015)中收集的数据,我们应用K均值聚类分析和判别分析来识别三种类型的老年互联网用户。我们对4404名年龄在65岁至99岁之间的受访者样本进行了分析。我们的结果有助于提高数字单一市场政策对欧洲老年人的适用性,以及在数字环境中更适当地为老年人制定社会护理和医疗保健计划。我们为进一步研究提供建议,主张基于性别、教育程度、种族或社会阶层等特征对ICT用户进行深入分类。