Political ideology and differences in seeking COVID-19 information on the internet: examining the comprehensive model of information seeking

IF 3.1 3区管理学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Online Information Review Pub Date : 2023-03-13 DOI:10.1108/oir-08-2022-0436

Xianlin Jin

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Abstract

PurposeGuided by the Comprehensive Model of Information Seeking (CMIS), this article identifies significant predictors that impact individuals seeking COVID-19 information. People with different political ideologies read contradictory information about the COVID-19 pandemic. However, how political ideology may affect COVID-19 information seeking remains unclear. This study explores the major information channels for individuals with different political ideologies to seek COVID-19 information. It further examines how political ideologies influence CMIS's effectiveness in predicting online health information-seeking.Design/methodology/approachThis study collected 394 completed survey responses from adults living in the United States after the 2020 lockdown. ANOVA analyses revealed the differences in salience, beliefs, information carrier characteristics, utilities and information-seeking actions between Liberals and Conservatives. Regression analyses discovered variables that predict Liberals' and Conservatives' online health information seeking.FindingsResults suggest that the internet is the top channel for COVID-19 information seeking. Compared to Conservatives, Liberals report more COVID-19 information-seeking actions. Liberals also express stronger salience, perceive higher trustworthiness of online COVID-19 information, are more likely to think of seeking online COVID-19 information as useful and helpful and report more substantial efficacy to mitigate the risk. Most CMIS variables predict Liberals' information seeking; however, only salience significantly predicts Conservatives' information seeking.Originality/valueThis article indicates that CMIS should include political ideology to refine its prediction of information seeking. These findings offer practical implications for designing health messages, enhancing information distribution and reducing the public's uncertainty.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2022-0436.

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政治意识形态与网络新冠肺炎信息搜寻的差异——信息搜寻综合模型的检验

目的在信息寻求综合模型(CMIS)的指导下，本文确定了影响寻求COVID-19信息的个人的重要预测因素。不同政治意识形态的人阅读了相互矛盾的新冠疫情信息。然而，政治意识形态如何影响COVID-19信息获取尚不清楚。本研究探讨了不同政治意识形态个体寻求新冠肺炎信息的主要信息渠道。它进一步研究了政治意识形态如何影响CMIS在预测在线健康信息搜索方面的有效性。设计/方法/方法本研究收集了2020年封锁后生活在美国的成年人填写的394份调查问卷。方差分析揭示了自由党和保守党在显著性、信念、信息载体特征、效用和信息寻求行为方面的差异。回归分析发现了预测自由党和保守党在线健康信息搜索的变量。结果表明，互联网是COVID-19信息搜索的首选渠道。与保守党相比，自由党报告了更多的COVID-19信息寻求行动。自由主义者也表现出更强的突出性，认为在线COVID-19信息的可信度更高，更有可能认为在线寻求COVID-19信息是有用的和有帮助的，并报告了更实质性的降低风险的效果。大多数CMIS变量预测自由党的信息寻求;然而，只有显著性才能显著预测保守党的信息寻求。原创性/价值本文指出，CMIS应纳入政治意识形态，以完善其对信息寻找的预测。这些发现对设计健康信息、加强信息传播和减少公众的不确定性具有实际意义。同行评议本文的同行评议历史可在:https://publons.com/publon/10.1108/OIR-08-2022-0436。

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来源期刊

Online Information Review 工程技术-计算机：信息系统

CiteScore

6.90

自引率

16.10%

发文量

审稿时长

6 months

期刊介绍： The journal provides a multi-disciplinary forum for scholars from a range of fields, including information studies/iSchools, data studies, internet studies, media and communication studies and information systems. Publishes research on the social, political and ethical aspects of emergent digital information practices and platforms, and welcomes submissions that draw upon critical and socio-technical perspectives in order to address these developments. Welcomes empirical, conceptual and methodological contributions on any topics relevant to the broad field of digital information and communication, however we are particularly interested in receiving submissions that address emerging issues around the below topics. Coverage includes (but is not limited to): •Online communities, social networking and social media, including online political communication; crowdsourcing; positive computing and wellbeing. •The social drivers and implications of emerging data practices, including open data; big data; data journeys and flows; and research data management. •Digital transformations including organisations’ use of information technologies (e.g. Internet of Things and digitisation of user experience) to improve economic and social welfare, health and wellbeing, and protect the environment. •Developments in digital scholarship and the production and use of scholarly content. •Online and digital research methods, including their ethical aspects.