中文    English

Journal of Library and Information Science in Agriculture ›› 2022, Vol. 34 ›› Issue (4): 30-40.doi: 10.13998/j.cnki.issn1002-1248.21-0811

Previous Articles     Next Articles

Research on the Influencing Factors of Adoption Intention of Information Technology for Pandemic Prevention Among the Elderly in Public Health Emergency

CHEN Jinghao1,3, LUO Qi2   

  1. 1. Research Center of Regional Social Governance and Innovation, Guangxi University, Nanning 530004;
    2. School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731;
    3. Big Data Institute, Wuhan University, Wuhan 430072
  • Received:2021-10-23 Online:2022-04-05 Published:2022-05-24

Abstract: [Purpose/Significance] This paper takes the elderly aged 60 and above in Guangxi as the research object to explore the factors that affect their adoption of information technology for pandemic prevention and control. It is helpful to improve the elderly's ability to apply information technology, and promote the construction and development of information technology to prevent a pandemic. [Method/Process] Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this paper added pandemic risk perception, technology anxiety, resistance to change as new variables to construct the model of influencing factors of the elderly's adoption intention of information technology for COVID-19 pandemic prevention and control, designed and collected 210 valid questionnaires, used SPSS 21.0 software to finish the descriptive statistical analysis, and used SmartPLS 3.0 software to finish the reliability and validity analysis of the questionnaires, and constructed a Structural Equation Model to verify the model. [Results/Conclusions] The study found that the risk perception, performance expectancy, effort expectancy, social influence and facilitating conditions had a significant impact on the elderly's behavioral intention of information technology for COVID-19 pandemic prevention and control , and technology anxiety, resistance to change had no significant impact on the behavioral intention. Finally, this paper puts forward countermeasures and suggestions based on the research findings.

Key words: COVID-19, health informatics, UTAUT, structural equation model

CLC Number: 

  • G206
[1] 邓莎. 健康传播视角下代际健康信息壁垒分析——以“新冠肺炎疫情”为例[J]. 新闻研究导刊, 2020, 11(24): 74-75.
DENG S.Intergenerational health information barriers from the perspective of health communication - A case study of COVID-19[J]. Journal of news research, 2020, 11(24): 74-75.
[2] NIEHAVES B, PLATTFAUT R.Internet adoption by the elderly: Employing is technology acceptance theories for understanding the age-related digital divide[J]. European journal of information systems, 2014, 23(6): 708-726.
[3] 石卫星. 老年人采纳信息技术影响因素的案例研究[J]. 淮阴工学院学报, 2017, 26(2): 67-77.
SHI W X.Case study on factors influencing the elderly's adoption of IT[J]. Journal of Huaiyin institute of technology, 2017.26(2): 76-77.
[4] 付华. 疫情背景下的“数字鸿沟”现象分析[J]. 数字通信世界, 2020(10): 53-55.
FU H.Analysis on digital divide in the context of COVID-19 epidemic[J]. Digital communication world, 2020(10): 53-55.
[5] DOGRUEL L, JOECKEL S, BOWMAN N D.The use and acceptance of new media entertainment technology by elderly users: Development of an expanded technology acceptance model[J]. Behaviour & information technology, 2015, 34(11): 1052-1063.
[6] TALUKDER M S, SORWAR G, BAO Y, et al.Predicting an- tecedents of wearable healthcare technology acceptance by elderly: A combined SEM-neural network approach[J]. Technological fore- casting and social change, 2020, 150: 119793.
[7] HOQUE R, SORWAR G.Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model[J]. International journal of medical informatics, 2017, 101: 75-84.
[8] 顾向栋. 中老年微信用户健康信息采纳研究——以新冠肺炎疫情期间为例[J]. 传媒论坛, 2020, 3(18): 142, 144.
GU X D. Health information adoption by middle-aged and elderly WeChat users - A case study during the COVID-19 pandemic[J]. Media forum, 2020, 3(18): 142, 144
[9] HASSEN H B, AYARI N, HAMDI B.A home hospitalization system based on the internet of things, fog computing and cloud computing[J]. Informatics in medicine unlocked, 2020, 20: 100368.
[10] 张大伟, 谢兴政. 代际反哺:农村中老年人信息寻求行为形成机制探索——基于突发公共卫生事件时期的实证研究[J]. 图书情报工作, 2020, 64(15): 194-203.
ZHANG D W, XIE X Z.Empirical research on the mechanism of public health emergency information seeking behavior among middle-aged and elderly people in rural areas[J]. Library and information service, 2020, 64(15): 194-203.
[11] 韩正彪, 周明峰, 岳杭. 低风险疾病情境下农村居民健康信息规避行为研究[J]. 农业图书情报学报, 2021, 33(11): 4-15.
HAN Z B, ZHOU M F, YUE H.Rural residents' health information avoidance behavior in lower risk disease context[J]. Journal of library and information science in agriculture, 2021, 33(11): 4-15.
[12] NIKOU S.Mobile technology and forgotten consumers: The youn- gelderly[J]. International journal of consumer studies, 2015, 39(4): 294-304.
[13] GUNER H, ACARTURK C.The use and acceptance of ICT by senior citizens: A comparison of technology acceptance model (TAM) for elderly and young adults[J]. Universal access in the information society, 2020, 19(2): 311-330.
[14] SHAO D, LEE I J.Acceptance and influencing factors of social virtual reality in the urban elderly[J]. Sustainability, 2020, 12(22): 9345.
[15] 李彪. 数字反哺与群体压力: 老年群体微信朋友圈使用行为影响因素研究[J]. 国际新闻界, 2020, 42(3): 32-48.
LI B.Digital feedback and group pressure: A study on the influencing factors of WeChat moments in the elderly[J]. Chinese journal of journalism & communication, 2020.42(3): 32-48.
[16] 杨秋红, 冯缨. 基于UTAUT模型的智慧养老产品使用意愿研究[J]. 老龄科学研究, 2020, 8(1): 18-31.
YANG Q H, FENG Y.Study on seniors' willingness to use smart el- dercare devices: Based on the UTAUT model[J]. Scientific research on aging, 2020, 8(1): 18-31.
[17] 吴祁. 突发公共卫生事件中农村老年人防疫信息搜寻影响因素[J]. 图书馆论坛, 2020, 40(9): 106-114.
WU Q.Factors influencing the rural elderly's epidemic prevention information seeking in public health emergencies[J]. Library tribune, 2020, 40(9): 106-114.
[18] 汤志伟, 赵迪, 罗伊晗. 公共危机事件中政务短视频公众使用的实证研究——基于新冠肺炎疫情[J]. 电子政务, 2020(8): 2-14.
TANG Z W, ZHAO D, LUO Y H.An empirical study on public use of government short videos in public crises - Based on COVID-19[J]. E-Government, 2020(8): 2-14.
[19] KHAN I U, YU Y, HAMEED Z, et al.Assessing the physicians' acceptance of e-prescribing in a developing country: An extension of the UTAUT model with moderating effect of perceived organizational support[J]. Journal of global information management(JGIM), 2018, 26(3): 121-142.
[20] SINTONEN S, IMMONEN M.Telecare services for aging people: Assessment of critical factors influencing the adoption intention[J]. Computers in human behavior, 2013, 29(4): 1307-1317.
[21] GREER S L, KING E J, DA FONSECA E M, et al. The comparative politics of COVID-19: The need to understand government responses[J]. Global public health, 2020, 15(9): 1413-1416.
[22] VENKATESH V, MORRIS M G, DAVIS G B, et al.User acceptance of information technology: Toward a unified view[J]. MIS quarterly, 2003: 425-478.
[23] CHIN W W, MARCOLIN B L, NEWSTED P R.A partial least squares latent variable modeling approach for measuring interaction effects: Results from a monte Carlo simulation study and an electronic-mail emotion/adoption study[J]. Information systems research, 2003, 14(2): 189-217.
[24] HAIR J F, BLACK W, BABIN B, et al.Multivariate data analysis (the 6th edition)[M]. New Jersey: pearson prentice hall press, 2005: 107-157.
[25] HAIR JR J F, HULT G T M, RINGLE C, et al. A primer on partial least squares structural equation modeling (PLS-SEM)[M]. London: SAGE publications, 2016.
[26] RINGLE C M, SINKOVICS R R.The use of partial least squares path modeling in international marketing[J]. Advances in international marketing, 2009, 20: 277-319.
[27] VINZI V E, CHIN W W, HENSELER J, et al.Handbook of partial least squares[M]. Berlin: Springer, 2010.
[28] 王纾. 作为中介的学生学习性投入: 大学学习经验影响学习收获的路径分析[C]//2009年首届首都高校教育学研究生学术论坛论文集, 2010: 154-167.
WANG S.Engagement as a mediator for learning: A path analysis of the impact of college student learning experience on learning outcomes[C]//The first capital university pedagogy graduate student academic forum proceedings, 2010: 154-167.
[29] MACCALLUM R C, BROWNE M W, SUGAWARA H M.Power analysis and determination of sample size for covariance structure modeling[J]. Psychological methods, 1996, 1(2): 130.
[1] GUO Weijia. Influencing Factors of Artificial Intelligence Readiness in Libraries [J]. Journal of Library and Information Science in Agriculture, 2022, 34(5): 47-56.
[2] MA Yunzhe, CUI Xu, ZHANG Xiaoyi. 24-Hour Service Quality Evaluation System of a Self-Service Library under the Background of Normal State of COVID-19 Epidemic Prevention and Control [J]. Journal of Library and Information Science in Agriculture, 2022, 34(3): 68-80.
[3] LI Jiawen, QIN Qin, KE Qing. Empirical Research on the Influencing Factors of Users' Knowledge Hiding Behavior in Virtual Question-and-Answer Community [J]. Journal of Library and Information Science in Agriculture, 2022, 34(2): 48-62.
[4] LU Duochun, HU Haibo. An Empirical Analysis on the Cognition and Service Demands of Smart Elderly Care Users: Taking Guangzhou's N Community as an Example [J]. Journal of Library and Information Science in Agriculture, 2021, 33(8): 32-44.
[5] ZHAO Lei, ZHANG Chengzhi. Difference Analysis of Research Topics in a Specific Domain Based on Different Content Levels [J]. Journal of Library and Information Science in Agriculture, 2021, 33(5): 14-27.
[6] WANG Feiyan, CAO Yunqiu, XIAO Anqi, JI Lu, KE Qing. Thematic Correlation and Contextual Factors of Netizens' Information Needs During the COVID-19 Pandemic [J]. Journal of Library and Information Science in Agriculture, 2021, 33(5): 28-39.
[7] ZHANG Xin, TIAN Xuecan, ZHANG Lu, LI Yue. User Utilization Behavior and Influencing Factors of Hospital Online Information Service: A Case Study of Hebei Province [J]. Journal of Library and Information Science in Agriculture, 2021, 33(11): 38-49.
[8] JIA Xiaoshuang, YAO Jing. The Analysis of the Role of Digital Humanities in Sudden Public Crises: Taking COVID-19 as an Example [J]. Journal of Library and Information Science in Agriculture, 2020, 32(9): 22-30.
[9] ZHAO Shuai, ZHOU Dan. Analysis on the Epidemic Situation of COVID-19 in Six Provinces Adjacent to Hubei [J]. Journal of Library and Information Science in Agriculture, 2020, 32(4): 5-14.
[10] ZHENG Yufei, WANG Zheng. Characteristics and Trends of Overseas Library Open Access Activities During the Epidemic Period [J]. Journal of Library and Information Science in Agriculture, 2020, 32(12): 20-28.
[11] LI Ziqiang, ZHANG Meiling, YE Weijiao, CHEN Youcheng. Research on APP User Behavior Based on Modified UTAUT and TTF Model ——Taking the University Mobile Library as an Example [J]. Agricultural Library and Information, 2019, 31(9): 51-59.
[12] LE Chengyi, LI Peipei, CHEN Shuilin. User Satisfaction Analysis of College Mobile Digital Library from the Perspective of User Perception [J]. Agricultural Library and Information, 2019, 31(2): 20-29.
[13] ZHAO Limei, TAO Yi. Empirical Study on Impact Factors of Online Reviews Usefulness [J]. , 2018, 30(7): 14-17.
[14] SUN Yanzi. Research on Book Lending Satisfaction of University Library from the Perspective of User Experience —A Case Study of Changzhi University [J]. , 2018, 30(7): 84-90.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!