ArticlePDF Available

From information seeking to information avoidance: Understanding the health information behavior during a global health crisis


Abstract and Figures

Individuals seek information for informed decision-making, and they consult a variety of information sources nowadays. However, studies show that information from multiple sources can lead to information overload, which then creates negative psychological and behavioral responses. Drawing on the Stimulus-Organism-Response (S-OR) framework, we propose a model to understand the effect of information seeking, information sources, and information overload (Stimuli) on information anxiety (psychological organism), and consequent behavioral response, information avoidance during the global health crisis (COVID-19). The proposed model was tested using partial least square structural equation modeling (PLS-SEM) for which data were collected from 321 Finnish adults using an online survey. People found to seek information from traditional sources such as mass media, print media, and online sources such as official websites and websites of newspapers and forums. Social media and personal networks were not the preferred sources. On the other hand, among different information sources, social media exposure has a significant relationship with information overload as well as information anxiety. Besides, information overload also predicted information anxiety, which further resulted in information avoidance.
Content may be subject to copyright.
Information Processing and Management 58 (2021) 102440
0306-4573/© 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license
From information seeking to information avoidance:
Understanding the health information behavior during a global
health crisis
Saira Hanif Soroya
, Ali Farooq
, Khalid Mahmood
, Jouni Isoaho
, Shan-e Zara
Department of Information Management, University of the Punjab, Lahore, Pakistan
Department of Future Technologies, Faculty of Science & Engineering, University of Turku. Finland
Information seeking
Information overload
Information anxiety
Information avoidance
Individuals seek information for informed decision-making, and they consult a variety of infor-
mation sources nowadays. However, studies show that information from multiple sources can
lead to information overload, which then creates negative psychological and behavioral re-
sponses. Drawing on the Stimulus-Organism-Response (S-O-R) framework, we propose a model to
understand the effect of information seeking, information sources, and information overload
(Stimuli) on information anxiety (psychological organism), and consequent behavioral response,
information avoidance during the global health crisis (COVID-19). The proposed model was
tested using partial least square structural equation modeling (PLS-SEM) for which data were
collected from 321 Finnish adults using an online survey. People found to seek information from
traditional sources such as mass media, print media, and online sources such as ofcial websites
and websites of newspapers and forums. Social media and personal networks were not the
preferred sources. On the other hand, among different information sources, social media exposure
has a signicant relationship with information overload as well as information anxiety. Besides,
information overload also predicted information anxiety, which further resulted in information
1. Introduction
The quick spread of the novel coronavirus disease (COVID-19) has disrupted life across the globe. It has brought a halt to worldwide
trade, movement, and socialization in society. Soon after the World Health Organization (WHO) declared COVID-19 a global
pandemic, the governments issued advisories to their people to restrict the spread of COVID-19. These advisories contained recom-
mendations such as travel restrictions, closure of educational institutions, marketplaces, and public places, along with recommending
social isolation to restrict the spread (Fang, Nie, & Penny, 2020; Wilder-Smith & Freedman, 2020). Governments used mass media,
print media, and the internet to mobilize the community, convey precautionary measures to the people, and inform them about the
supportive measures and channels. The people themselves resorted to different information sources, predominantly internet-based
sources, to learn about the COVID-19. Being health conscious amid the emerging uncertainty, people immediately started
* Corresponding author.
E-mail addresses: (S.H. Soroya), alifar@utu. (A. Farooq), (K. Mahmood), jisoaho@utu. (J. Isoaho), (S.-e. Zara).
Contents lists available at ScienceDirect
Information Processing and Management
journal homepage:
Received 14 July 2020; Received in revised form 9 November 2020; Accepted 14 November 2020
Information Processing and Management 58 (2021) 102440
searching for COVID-19 information, such as its symptoms and precautionary measures (Bento et al., 2020). According to Statista
(2020), people used various sources to keep themselves informed about the COVID-19. Among these sources, mass media (TV and
radio), print media (newspapers and magazines), social media (Facebook, Twitter, etc.), search engines such as Google, family, and
friends, and scientic and ofcial websites are prominent. The available statistics from Google trends also conrm that people
worldwide were actively seeking COVID-19 related information online (Fig. 1).
In the event of a public health emergency like COVID-19, or a disaster, the information sources help people make sense of the
situation, learn precautionary measures, and reduce anxiety caused by the uncertain situation during a disaster or disease outbreak
(Chao, Xue, Liu, Yang, & Hall, 2020). While helpful, information sources, especially mass media, print media, and Internet-based
sources, can create new problems. The content available from these sources may amplify the risk perceptions and fear, especially
when individuals cannot discern between real and fake news, adversely affecting the mental health and well-being of the masses (Laato
S., Islam A.N., Islam M.N. & Whelan E, 2020a; Kasperson et al., 1998). Another negative outcome of a multitude of information
sources, in general, is information overload. This situation occurs when handling and processing a wealth of information from multiple
information sources become cumbersome, leading to information overload (Beaudoin, 2008). This overload of information has been
found to create stress, fatigue, exhaustion, and even discontinuation of the use of information sources in recent studies (Fu, Li, Liu,
Pirkkalainen & Salo, 2020; Guo, Lu, Kuang & Wang, 2020 ; Matthews, Karsay, Schmuck & Stevic, 2020). Students found information
overload as a cause of psychological stress (Eppler, 2015), negative emotions (Zhang, Ma, Zhang & Wang, 2020), negative effects,
depressive symptoms, trait anxiety, and trait anger (Swar, Hameed & Reychav, 2017). Studies also show that information overload, in
general, adversely affects human information processing capacity (Eppler & Mengis, 2004). It can even lead to discontinuation of
information seeking (Swar et al., 2017), the use of information sources (Zhang et al., 2020), and, ultimately, information avoidance
(Chae, 2016). High levels of uncertainty and newspaper stories about treatment create health information overload (Jensen et al.,
In the context of COVID-19, if information sources become a source of mental ill-being or start creating information overload, the
result could be detrimental to societys collective measures against the COVID-19. For example, people will start avoiding information
seeking and consulting different information sources. In this way, they will not be updated with the changing situation. To better
understand the role of information sources, researchers have started looking into the role of different information sources towards
psychological well-being and coping behavior during the COVID-19. A recent study on COVID-19 shows that social media is associated
with adverse psychological outcomes, while no such relation was found with traditional media (mass and print media) (Chao etal.,
2020). Another study shows that exposure to a variety of information sources results in information overload, which negatively affects
coping measures related to COVID-19, as well as the intention to take coping behavior (Farooq, Laato, & Islam, 2020). The study above
also shows that information overload was higher among the individuals who used social media as a source of information for
COVID-19. Further, it has been found that information overload results in cyberchondria, an excessive and chronically worrying state
of feeling ill (Laato, Islam, Farooq & Dhir, 2020b). An important area that still needs investigation is the impact of different information
sources on information behavior during the COVID-19. In the context of COVID-19, it is important to nd answers to questions such as,
what information sources create information overload during COVID-19? And, how does information overload affect information
behavior in the COVID-19 context? Further, it has been suggested to conduct context-specic inquiries (such as type of disease, setting,
and user groups) to understand health-related information-seeking behavior (Pian, Song, & Zhang, 2020).
In line with Pian et al. (2020) suggestions and the questions raised above, this study aims to complement the existing research on
information overload in general and COVID-19, in particular, by addressing two aspects. First, to investigate the effect of a variety of
information sources on information overload. Second, to study the consequences of information load explicitly related to information
behavior. In this regard, using the Stimulus-Organism-Response (S-O-R) framework, we proposed a model which was then tested with
Fig. 1. Global search interest in Coronavirus adopted from Google (dated 18 April 2020).
S.H. Soroya et al.
Information Processing and Management 58 (2021) 102440
the help of N =321 responses collected during the peak time of the COVID-19 pandemic in Finland (March 2020). Structural equation
modeling (SEM) in SmartPLS was used for testing the hypotheses.
2. Background
2.1. Information overload
Information overload is a state in which an individual cannot process incoming information and communication, making the in-
formation ineffective, and nally, towards the termination of information processing (Beaudoin, 2008). In general, "too much in-
formation at hand, exacerbated by the multiple formats and channels available for its communication" leads to information overload
(Bawden & Robinson, 2009, p.3). The concept of information overload is not new. In 1970, Tofer dened information overload as "the
excessive ows and amounts of data or information that can lead to detrimental computational, physical, psychological, and social
effects" (1970, p.311315). However, recently, it has received a lot of attention, especially in social media and virtual collaborations
(Roetzel, 2019). During 20002018 thirty one empirical studies have been conducted in the area of health information overload
(Khaleel et al., 2020).
Literature reports several negative consequences of information overload. Phillips-Wren and Adya (2020) identied information
overload as one of the decision stressors. Information overload also found to have adverse implications for psychological well-being,
such as stress, information anxiety, depressive symptoms, exhaustion, fatigue, and similar others (Bawden & Robinson, 2009; Fu et al.,
2020; Guo et al., 2020; Matthews et al., 2020; Swar et al., 2017)), and adversely affect the well-being of the people (Matthews et al.,
2020). Further, information overload has been found related to the discontinuation of social media networks (Fu et al., 2020) and
information avoidance behavior (Guo et al., 2020). Information overload can also reduce decision quality, impacting information
behavior (Speier, Valacich, & Vessey, 1999).
2.2. Information anxiety
Wurman (1989) originated the idea of information anxiety by dening it as "information anxiety is produced by the ever-widening
gap between what we understand and what we think we should understand. It is the black hole between data and knowledge" (p.34).
However, later in Wurman (2001) , Sheddroff added that "Information anxiety can have many forms, only the rst of which is the
frustration with the inability to keep up with the amount of data present in our life. What makes this worse is that the data is not just
passive, but actively inserting itself into our environment, our attention" (p.16.).
Information anxiety is generally related to technological and library anxiety (Hartog, 2017). Technology and the library both are
mediums to disseminate information. If people feel anxiety regarding these mediums, they may be reluctant to visit these channels and
avoid active information seeking. Library anxiety is generally related to library settings, whereas information anxiety goes beyond the
library space (Eklof, 2013). Information anxiety could be a result of many factors, such as low familiarity with the information
channels, less technical knowledge and expertise, technostress (Ahmad & Amin, 2012), and, of course, information overload (Feng and
Agosta, 2017; Lee, Son, & Kim, 2016a; Matthews et al., 2020 ; Swar et al., 2017). Information anxiety adversely affects
decision-making processes and induces information avoidance (Bawden & Robinson, 2020; Golman, Hagmann, & Loewenstein, 2017 ;
Swar et al., 2017).
2.3. Information avoidance
Information avoidance is ignoring relevant information and useful information sources because there is too much to deal with
(Case, Andrews, Johnson, & Allard, 2005). While information avoidance minimizes the chances of interaction with unnecessary in-
formation, at the same time, it diminishes the chances to receive relevant information. From a cognitive viewpoint, individuals have
limited capacity to process information, and if not adequately addressed, the outcome will be information overload (Sharit & Czaia,
2018). They avoid information acquiring and make decisions based on limited information (Dai, Ali, & Wang, 2020 ). As a result, the
errors in the processing of information and messages they receive begin to increase. They overlook things, make mistakes, misun-
derstand messages, and so forth. Thus, people deliberately avoid information that threatens their happiness and well-being (Carnegie
Mellon University, 2017; Golman et al., 2017).
Information avoidance can be of different types: inattention, physical avoidance, biased interpretation of information, and
forgetting, for example (Dai et al., 2020 ; Golman et al., 2017). In health contexts, information avoidance can occur in several ways, for
example, avoiding healthcare staff (Shepperd, Emanuel, Howell, & Logan, 2015 ), risk information (Deline & Kahlor, 2019), and even
the prognosis (Derry, Epstein, Lichtenthal, & Prigerson, 2019). Health information avoidance behavior is affected by four types of
factors, physiological, psychological, personal cognition, and external environmental (Chuang & Chiu, 2019). In the current situation,
when the whole world is facing the COVID-19 pandemic, an excessive amount of information is available on different information
sources/channels. The available information on COVID-19 while one end can be conicting, on other, it can trigger stress and anxiety,
adversely affecting psychological well-being (Bawden & Robinson, 2009; Swar et al., 2017 ). In this situation, people may start
avoiding information on COVID-19.
S.H. Soroya et al.
Information Processing and Management 58 (2021) 102440
3. Theoretical foundation
3.1. Theoretical framework
The current study investigates the individualshealth information behavior during COVID-19, underpinning the Stimulus-
Organism-Response (S-O-R) framework, shown in Fig. 2 (Mehrabian & Russell, 1974). The S-O-R framework conceptualizes that
environmental stimulus affects an individuals internal state called an organism, which leads to a behavioral response. Mehrabian and
Russell (1974) further explained that our behaviors are the responses that are outcomes of the organism, a cognitive and affective
process, whereas a stimulus triggers the organism. The conceptualization of the S-O-R framework allows us to understand the rela-
tionship of stimuli with the response, enabling the formulation of models containing affective and cognitive intermediary layers (Xu,
Benbasat, & Cenfetelli, 2014).
The S-O-R framework has been largely applied to understand human behaviors, particularly for understanding consumers be-
haviors (Chopdar & Balakrishnan, 2020; Gao & Bai, 2014; Xu et al., 2014). However, lately, this framework has been successfully
applied to examine human behaviors during the COVID-19 pandemic (Laato et al., 2020a; Zheng et al., 2020). Concerning out-
breaks/epidemics, it has been conrmed from the literature (based on S-O-R framework) that human behaviors change due to
environmental factors, for example, consumer purchasing behaviors (Laato et al., 2020a), health management behavior (Farooq et al.,
2020), social behavior and individuals psychological state (Zheng et al., 2020). Therefore, it is considered equally important to
investigate the health information behavior during the COVID-19 pandemic from the lens of the S-O-R framework. Furthermore, the
construct, that is, information overload, information anxiety, and information avoidance, has already been successfully examined by
applying the S-O-R framework in the context of information behaviors on social media (Cao & Sun, 2018; Fu et al., 2020).
For the current study, it is hypothesized that both environmental stimulus and internal stimulus of an individual affect individual
behaviors. Therefore, to investigate health information behavior, two types of stimuli were included in the study: environmental
stimuli and internal stimulus. Environmental stimuli are imposed by the external environment and are somehow not in control of the
person, whereas internal stimuli are related to an individuals own preference/interest and motivation. Since a wide range of infor-
mation sources have been used during the COVID-19 pandemic, we focus particularly on the information sources exposure as an
external stimulus, rather than the knowledge or awareness accumulated from the sources. In line with previous research (Cao & Sun,
2018; Fu et al., 2020) who studied the effect of information overload on the discontinuation of social media use, we used information
overload as another external stimulus. The COVID-19 brought uncertainty at both individual and societal levels. People want to know
about COVID-19 to minimize uncertainty. Therefore, we propose the information-seeking behavior as an internal stimulus in our
study. As the organism is an individuals inner state of mind (cognitive and affective) therefore, we propose information anxiety
(measuring psychological well-being) as an organism and information avoidance as a response.
3.2. Research hypotheses and model
3.2.1. Information seeking and information sources
Internet search data showed that people started actively searching about COVID-19 symptoms and hand sanitizers as their states
announced the rst COVID-19 case (Bento et al., 2020). Musarezaie, Samouei, Shahrzadi, and Ashra-Rizi (2019) argued that in-
dividualsexposure to stress and concerns about their health status develop health information needs, and they search more frequently
and from various information sources. It is already conrmed that active information seekers do not trust a single source (Huff et al.,
2014; Statista, 2020) and tend to consult several information sources (Nelson, 2018). In the context of health information, the con-
sumers use multiple information resources several times (Zhang, 2012) - perhaps due to their concern and sensitivity to the phe-
nomena. Clarke et al. (2016) conducted a literature review and identied that health information seekers consult various information
sources, including technology-based sources, print sources, and human sources such as close social ties and traditional mass media. In
the context of COVID-19, people have been found to use different information sources such as the internet, traditional media, family
members, as well as peers (Wang et al., 2020). Based on the above-mentioned evidence from the health information behavior liter-
ature, we propose the following hypotheses in the context of COVID-19:
H1a: Information seeking is positively related to the frequency of Personal Network exposure
H1b: Information seeking is positively related to the frequency of Mass Media exposure
H1c: Information seeking is positively related to the frequency of Print Media exposure
H1d: Information seeking is positively related to the frequency of Social Media exposure
H1e: Information seeking is positively related to the frequency of Other Internet Sources exposure
Fig. 2. S-O-R framework (Mehrabian & Russell, 1974).
S.H. Soroya et al.
Information Processing and Management 58 (2021) 102440
3.2.2. Information sources exposure and its impact
Literature reports that exposure to different types of information sources positively correlates to the individuals sense of infor-
mation overload and information anxiety.
Individuals consult a variety of information sources for seeking knowledge and advice in different contexts. (Clarke et al., 2016 ;
Farooq et al., 2016; Ramsey, Corsini, Peters, & Eckert, 2017). Previous studies show a relationship between information source
exposure and information overload. For example, Youtube exposure has been proved a signicant predictor of perceived information
overload, either it is used actively or passively (Cao & Sun, 2018; Matthews et al., 2020). Eppler and Mengis (2004) articulated in-
formation technology, including e-mails, the internet, and the Intranet (Bawden, 2001), rise in the number of television channels, and
various distribution channels for the same content (Edmunds & Morris, 2000) as some of the causes of information overload. The
individualswho pay greater attention to news through social media, smartphones, and tablets signicantly perceive more information
overload (Lee, Kim & Koh, 2016b). Excessive health-related internet use signicantly predicts a higher perceived health information
overload (Jiang & Beaudoin, 2016).
Similarly, Serçekus¸, Gencer, and ¨
Ozkan (2020) reported that the frequency of information source exposure is positively correlated
with cancer information overload. Farooq et al. (2020) further established a positive association between information overload and
social media use in the COVID-19 context. Considering that different information sources have been found sources of information
overload in a different context and that social media use is associated with information overload in the COVID-19 context, we propose
the following hypotheses:
H2a: Individuals who report more frequent Personal Networksources exposure will perceive a higher level of information
H2b: Individuals who report more frequent Mass Mediasources exposure will perceive a higher level of information overload.
H2c: Individuals who report more frequent Print Mediainformation sources exposure will perceive a higher level of information
H2d: Individuals who report more frequent Social Mediainformation sources exposure will perceive a higher level of information
H2e: Individuals who report more frequent Other Internet Sourcesexposure will perceive a higher level of information overload.
Information sources exposure/engagement has also been positively associated with the individuals state of information anxiety.
This association has been proved in different contexts. For example, (Lee, Kim & Koh, 2016b) found that the level of attention to news
through social media was signicantly associated with the perceived news information overload which further was related to psy-
chological stress and negative emotion (anxiety). In the context of health information, consumers engagement with multiple infor-
mation sources increases the likelihood of information overload and information anxiety (Bapat, Patel, & Sansgiry, 2017). Therefore, it
is assumed that the frequency of different information sources exposure is a stimulus that directly affects the individuals state of
information anxiety in the COVID-19 context as well, and the following possible relationships are proposed:
H3a: Individuals who report more frequent Personal Networksources exposure will perceive a higher level of information anxiety
H3b: Individuals who report more frequent Mass Mediasources exposure will perceive a higher level of information anxiety
H3c: Individuals who report more frequent Print Mediasources exposure will perceive a higher level of information anxiety
H3d: Individuals who report more frequent Social Mediasources exposure will perceive a higher level of information anxiety
H3e: Individuals who report more frequent Other Internet Sourcesexposure will perceive a higher level of information anxiety
3.2.3. Information overload and its impact
Swar et al. (2017) conrmed that perceived information overload has a signicant positive relationship with psychological
ill-being. The feelings of cognitive strain (Jones, 1997; Schick, Gorden, & Haka, 1990) , stress (Lee, Son & Kim, 2016a), confusion,
depressive symptoms (Matthews et al., 2020), anxiety (Bawden & Robinson, 2009), and low motivation (Baldacchino, Armistead, &
Parker, 2002) are the outcome of information overload. The following hypothesis is proposed to study the relationship between in-
formation overload and information anxiety:
H4: The greater feeling of information overload will result in greater information anxiety.
3.2.4. Information anxiety and information avoidance
Information anxiety, being a cluster of negative emotions, may result in information avoidance. Information avoidance is also
known as non-seeking behavior. Over the last fty years, information avoidance behavior has been primarily studied in the context of
health information, as it tends to be conceptualized as a coping mechanism for dealing with potentially unwanted information
(Manheim, 2014). It has already been proved that individuals ignore information and try to be selective in consulting information
(Bawden, 2001; Sairanen & Savolainen, 2010). Golman et al., 2017) considered anxiety as one of the seven distinct psychological
mechanisms that can produce information avoidance. Similarly, Swar et al. (2017) reported that psychological ill-being constructs
(negative affect, depressive symptoms, and trait anger) created by online health-related information overload negatively impact in-
dividualsonline health information search behavior. Lately, Dai et al. (2020) reported that fatigue (tiredness, disappointment, loss of
interest, or decreased need/ motivation) has a positive relationship with individualsinactive social networking websites usage
intention. Hence, literature provides evidence of an association between information anxiety and information avoidance behavior, and
therefore, the following hypothesis is proposed in the context of COVID-19:
H5: Information anxiety is associated with an individuals state of information avoidance behavior.
S.H. Soroya et al.
Information Processing and Management 58 (2021) 102440
Based on the above discussion, we propose the research model shown in Fig. 3.
4. Methodology
4.1. The context
Data for the study was collected from students, staff, and faculty members of three universities during April 2020 using a web-based
questionnaire in Webropol, an online platform. Collecting data from both the aforementioned population allowed us to collect data
from a diverse population, in terms of age, gender, and household. Moreover, reaching out to the general population for data collection
would have been challenging, given the pandemic situation. Data was collected for about four weeks (from April 4th to April 29th). The
rst case in Finland was reported on January 29th
and the government imposed restrictions on March 16th by closing all educational
institutions, public places, public gatherings, and similar others (Muhonen and Nalbantoglu, 2020). On the day we started data
collection, 2261 conrmed cases were reported in Finland, and the numbers were rising, and by April 28th, there were 5056 conrmed
cases Terveyden ja hyvinvoinnin laitos (2020). We stopped data collection as on April 29th Government decided to open the schools,
giving the perception that the situation was under control.
4.2. Survey design and sample
As mentioned in the previous section, a web-based questionnaire was used to collect the data. The items for the questionnaire were
mainly adapted from earlier literature, however, ve items were included according to the contextual requirements. To ensure content
validity, three subject experts (one from the eld of information systems and two from the eld of library and information science)
examined the statements. They suggested minor changes to make the statements clearer. After that, the questionnaire was pilot tested
using fty responses (who were not part of the actual study) through a web-based questionnaire. The Cronbachs alpha value for all the
constructs remained between 0.70.9, which was satisfactory and showed the reliability of the constructs used in the study. After that,
a web-based questionnaire was prepared, and the link was shared among the potential respondents using e-mail lists, which include all
the students, staff, and faculty members of the respective universities.
In the nal questionnaire, after the introductory paragraph and informed consent, the participants were requested to respond to
items measuring the constructs constituting the research model shown in Fig. 2. The demographic information was asked at the end. A
nonprobability self-selecting sampling technique was used. A total of 321 responses were collected in four weeks. All the questions
were mandatory, so missing data was not an issue. Furthermore, the examination of the dataset revealed no random responses. The
demographic information of the study participants is shown in Table 1.
Fig. 3. Proposed Research Model based on the S-O-R Framework.
S.H. Soroya et al.
Information Processing and Management 58 (2021) 102440
4.3. Measures
The survey questionnaire employed in this study included both single and multi-item constructs. The four primary constructs of the
study, information seeking, information overload, information anxiety, and information avoidance, were measured using multiple
items on a ve-point Likert-type scale ranging from 1 ("strongly disagree") to 5 ("strongly agree"). Information seeking was measured
using four items, two were adapted from Yang and Kahlor (2013) , and the other two were self-developed. Information overloaded was
measured using ve items, three adapted from Williamson, Eaker, and Lounsbury (2012) , and two adopted from Farooq et al. (2020) .
Information anxiety was measured using six items, out of which ve were adapted from L´
opez-Bonilla & L´
opez-Bonilla (2011), and one
was self-developed. Information avoidance was also measured using six items, out of which three were adapted from Guo et al. (2020) ,
one from Hmielowski, Donaway, and Wang (2019) , and one was self-developed. The frequency of use of twelve information sources
was measured on a self-developed six-point continuous scale, that is, Never =0 to More than 6 h =5. Details of items and their sources
are given in the Appendix. The questionnaire also included questions about gender, age, educational level, and living status.
4.4. Data analysis
The data was downloaded from the survey platforms in CSV les. An initial screening was conducted to remove any missing
response and to check data normality. The frequency of information sources used was divided into ve groups to measure exposure to
information sources. Exposure to the personal network, mass media, and print media was measured using single items, whereas
exposure to social media and other internet sources was measured with the help of multiple items (detail in Table 5). Several items had
skewness and kurtosis values higher than the threshold value (0.3) (Kline, 2005; Hair et al., 2014). We used partial least square
structural equation modeling (PLS-SEM) in SmartPLS v3.2 to test our hypothesis. PLS-SEM, a multivariate technique, is particularly
useful when data has normality issues, models with medium to high complexity, and hypotheses are exploratory (Hair, Hult, Ringle &
Sarstedt, 2016).
PLS-SEM analysis takes place in two steps. In the rst, the measurement model is tested to ensure the quality of constructs used in
the model through reliability and validity testing (detail is given in Section 5.1. Measurement Model Testing). In the second step,
structural model assessment is carried out to examine the relationship between the constructs using the path coefcient (β)and co-
efcient of determination (R
). For signicance testing, the complete bootstrapping procedure was run with 5000 samples, and no sign
changes at a signicance level of 0.05. We followed the guidelines by Hair et al. (2016) for evaluation and reporting results.
5. Results
5.1. Measurement model testing
5.1.1. Reective constructs
The reliability of reective measures was assessed through internal consistency, items reliability, convergent, and discriminant
validity (validity). Cronbachs alpha (ɑ) is used traditionally as a measure of internal consistency, composite reliability (CR) has been
found as a better measure of internal consistency (Henseler, Ringle, & Sinkovics, 2009). Although we examined both ɑ and CR, we are
reporting only CR values in this paper. Item reliability was assessed with the help of item loadings. Average variance explained (AVE)
Table 1
Sample Characteristics.
Socio-demographic characteristics % (N =321)
Male 41.4
Female 55.8
Prefer not to tell 3.7
Less than 20 9.0
2125 41.4
2634 21.2
3544 13.4
4554 9.0
55 and higher 5.9
Educational Level
High School or Equivalent 26.8
Bachelor 37.1
Masters 19.6
Licentiate/PhD 16.5
Living Status
Living alone 31.8
Living with Family 68.2
S.H. Soroya et al.
Information Processing and Management 58 (2021) 102440
was used to assess convergent validity, whereas the HTMT
ratio (Henseler, Ringle, & Sarstedt, 2015) was used for determining
discriminant validity. The results of reective measurement model testing are shown in Table 2.
The CR values for all the constructs were above the threshold of 0.70, and item loadings were above the recommended value of 0.70
(Hair et al., 2016). AVE of the reective constructs was also higher than the threshold value (0.5) (Hair et al., 2016). Together, Tables 2
to 4 conrms the reliability and validity of the reective constructs involved in the study. In the nal model, Information seeking was
measured with four items, information overload with ve items, information anxiety, and information avoidance with six items each.
5.1.2. Formative constructs
Unlike reective measures, the quality of formative constructs is assessed by examining collinearity, the signicance of both outer
weights, and item loadings on the given constructs (Hair et al., 2016). The variance ination vector (VIF) should be between 0.2 and 5
(Hair et al., 2016). The signicance of formative items was assessed by examining the signicance (p-value) of outer weights rst, then
checking the item loadings, and lastly signicance of item loadings, if required (Hair et al., 2016). If the outer weights are insignicant,
then item loadings are examined. If items loadings are less than 0.5, then the signicance (p-value) of the item loadings is checked. If
the item loading is/are signicant, the items are retained otherwise removed from further analysis. Table 5 shows the reliability and
validity of formative variables.
As shown in Table 5, the VIF for all the formative items was between 1.223 and 2.156, showing no issue of collinearity. Out of nine
formative items for information source exposure, only one item (OIS2) from other internet sources could not fulll the quality criteria
for formative constructs. Item loadings of OIS2 (p =0.52) were insignicant. After the measurement model assessment, social media
sources exposure was measured with ve items, whereas other internet source exposures were measured with three items.
5.2. Structural model testing
As mentioned in the data analysis section, the structural model was tested by examining the path coefcients (β) and the co-
efcients of the determination (R
). In this way, we examined the relationship of information seeking as an antecedent of information
sources exposure and information load; the impact of information source exposure on information overload and information anxiety;
the effect of information overload on information; and nally, the relationship between information anxiety and information
avoidance. Fig. 4 shows the path coefcients (at p <0.05) and coefcients of determination for Information overload, information
anxiety, and information avoidance. For complete statistics, consult Table 5.
The results in Fig. 4 and Table 5 show that information seeking has a signicant positive relationship with three out of ve sources:
Mass media exposure (β =0.18, p <0.05), print media (β =0.18, p <0.05), and other internet sources (β =0.33, p <0.05) sub-
stantiating hypotheses H
, H
and H
. Information seeking does not have a signicant relationship with personal networks
Table 2
Measurement model for reective measures.
Constructs M SD Loadings alpha CR AVE
Information Seeking 3.76 0.79
IS1 3.75 1.01 0.77 0.80 0.87 0.63
IS2 3.85 0.97 0.77
IS3 3.83 0.91 0.79
IS4 3.60 1.09 0.84
Information Overload 2.84 0.89
OV1 2.90 1.19 0.79 0.85 0.89 0.62
OV2 2.87 1.15 0.80
OV3 2.77 1.14 0.82
OV4 3.07 1.09 0.74
OV5 2.58 1.08 0.80
Information Anxiety 2.46 0.90
IA1 2.71 1.17 0.85 0.89 0.92 0.65
IA2 2.66 1.07 0.72
IA3 2.42 1.11 0.87
IA4 2.13 1.09 0.74
IA5 2.38 1.14 0.85
IA6 2.48 1.16 0.78
Information Avoidance 2.31 0.84
AV1 2.76 1.28 0.70 0.86 0.89 0.58
AV2 2.24 1.09 0.83
AV3 2.31 1.04 0.78
AV4 2.26 1.13 0.75
AV5 2.22 1.11 0.77
AV6 2.05 0.93 0.75
Personal Network Exposure 2.37 1.05 1
Mass Media Exposure 2.25 1.08 1
Print Media Exposure 1.62 0.88 1
The discriminant validity of the reective constructs was assessed using the HTMT ratio, and results are shown in Table 3.
S.H. Soroya et al.
Information Processing and Management 58 (2021) 102440
(β =0.002, p =0.97) and social media (β = − 0.04, p =0.58). Thus, hypotheses H
and H
could not be supported.
Further, while examining the relationship between information sources exposure and information overload, only social media
exposure had a signicant relationship with information overload (β =0.27, p <0.05). All other information sources had an
Table 3
Discriminant validity of the reective constructs using HTMT
Constructs Information anxiety Information avoidance Information overload
Information Anxiety
Information Avoidance 0.64
Information Overload 0.84 0.59
Information Seeking 0.19 0.43 0.13
Additionally, we also examined the Fornell-Larker criterion for discriminant validity. The square root of AVE of all four reective constructs was
found higher than its correlation with other constructs (Wong, 2013) (Table 4).
Table 4
Intercorrelations of the reective constructs.
Constructs Information anxiety Information avoidance Information overload Information seeking
Information Anxiety 0.80
Information Avoidance 0.57 0.76
Information Overload 0.74 0.51 0.79
Information Seeking 0.17 0.36 0.10 0.79
Table 5
Measurement model statistics for formative constructs.
VIF Weights p Loadings p
Social Media Sources Exposure
SM1 1.344 0.25 0.19 0.61 0.00
SM2 2.156 0.47 0.02 0.87 0.00
SM3 1.263 0.27 0.12 0.62 0.00
SM4 1.873 0.42 0.03 0.81 0.00
SM5 1.553 0.13 0.44 0.49 0.00
Other Internet Sources Exposure
OIS1 1.223 0.39 0.01 0.08 0.56
OIS2 1.463 0.06 0.70 0.09 0.52
OIS3 1.253 0.71 0.00 0.81 0.00
OIS4 1.251 0.62 0.00 0.75 0.00
Note: Insignicant loadings are shown in italic.
Table 6
Structural model test results.
Hypotheses Relationship B t P Results
H1a IS>PN 0.002 0.036 0.972 Not Supported
H1b IS>MM 0.177 3.542 0 Supported
H1c IS>PM 0.18 3.858 0 Supported
H1d IS>SM 0.044 0.55 0.582 Not Supported
H1e IS>OIS 0.333 6.753 0 Supported
H2a PN>IO 0.091 1.495 0.136 Not Supported
H2b MM>IO 0.048 0.837 0.403 Not Supported
H2c PM>IO 0.111 1.545 0.123 Not Supported
H2d SM>IO 0.273 4.572 0 Supported
H2e OIS>IO 0.013 0.21 0.833 Not Supported
H3a PN>AXE 0.062 1.487 0.138 Not Supported
H3b MM>AXE 0.002 0.055 0.956 Not Supported
H3c PM>AXE 0.014 0.386 0.7 Not Supported
H3d SM>AXE 0.114 2.133 0.033 Supported
H3e OIS>AXE 0.024 0.583 0.56 Not Supported
H4 IO>AXE 0.692 20.589 0 Supported
H5 AXE>AV 0.564 13.828 0 Supported
Note: IS =Information Seeking, PN =Personal Network Exposure, MM =Mass Media Exposure, PM =Print Media Exposure, SM =Social Media
Exposure, OIS =Other Internet Sources Exposure, IO =Information Overload, AXE =Information Anxiety, AV =Information Avoidance.
S.H. Soroya et al.
Information Processing and Management 58 (2021) 102440
insignicant relationship with information overload (p >0.05). Social media exposure accounted for a 14% variance in information
overload. Thus, out of ve hypotheses between information source exposure and information overload, only one could be substan-
tiated (H
The same results were found when examining the relationship of information source exposure to information anxiety. Out of ve,
only social media exposure has a signicant positive relationship with information anxiety (β =0.11, p <0.05) given evidence for
supporting hypothesis H
only. Other hypotheses, H
, H
, H
and H
could not be substantiated. Besides, information overload
has a signicant positive relationship with information anxiety (β =0.69, p <0.05), providing evidence for supporting hypothesis H
Social media exposure, together with information overload, accounts for 57% variance in information anxiety.
Lastly, information anxiety signicantly predicts information avoidance (β =0.56 p <0.05), substantiating hypothesis H
. Infor-
mation anxiety accounted for 31% of the variance in information avoidance.
6. Discussion
6.1. Key ndings
This study provides several interesting insights related to the information behavior of Finnish people during the Pandemic situ-
ation. However, while interpreting the results, we should keep in mind that the respondents were part of a university with some
educational qualications. The results may not be generalized to the whole population.
Firstly, people who wish to learn about Coronavirus (COVID-19) selected traditional information sources such as mass media
(including television and radio) and print media (newspapers and magazines). Moreover, when seeking information online, they
consulted ofcial websites, such as the government or agencies such as WHO and websites of newspapers. This may be because people
in Finland have been found to trust internet-based sources, such as websites, discussion forums, news forums, online newspapers/
magazines, and health portals, for health-related information (Ek, Eriksson-Backa, & Niemel¨
a, 2013). Surprisingly, friends and family
(personal network) was not a favorite source for COVID-19 related information. This may be because the COVID-19 situation is new for
us, and no one had enough information in a household. It is pertinent to mention here that the COVID-19 situation was developing in
Finland at the time of data collection. Further, the information seeker did not prefer social media (Facebook, WhatsApp, Twitter,
Instagram, or YouTube) as a source of information. It means traditional media sources and ofcial sources of information were popular
among the respondents. Previous research shows that mobile technology and social media usage have become the norm across Finland
(more popular among youngsters below age 25) for various purposes such as socialization, hobbies, and information seeking (Koir-
anen, Keipi, Koivula, & R¨
anen, 2019). In this study, we nd that social media as a source of health-related information (especially
COVID-19) is not a popular medium in Finland. One possible reason for not selecting social media as a source of information for
COVID-19 could be that people have concerns about information quality on social media (Zhao & Zhang, 2017), however, this
Fig. 4.. Structural Model results showing path coefcients and coefcients of determination for information overload, information anxiety, and
information avoidance. All signicant results, shown with an asterisk at p <0.05.
S.H. Soroya et al.
Information Processing and Management 58 (2021) 102440
corroboration requires empirical support through a study. The other possible reason is that the current study was conducted during a
’health emergency, therefore, individuals may behave differently compared to a normal situation. They might be more sensitive and
cautious in information utilization. Furthermore, health organizations, such as WHO and state information channels, also sensitized
people about information credibility.
Secondly, our study identied the exact source that is creating a sense of information overload and information anxiety among the
educated people in Finland. We found that social media exposure for information seeking resulted in information overload and in-
formation anxiety among the study participants. Previous studies (Bawden, 2001; Cao & Sun, 2018; Edmunds & Morris, 2000; Lee, Kim
& Koh, 2016b; Matthews et al., 2020) found that exposure to various information sources creates information overload among people.
Moreover, our ndings corroborate the results of Farooq et al. (2020) , who found that information overload was higher in the people
who used social media as a source of information for COVID-19. Matthews et al. (2020) conrmed that WhatsApp and Youtube are the
predictors of perceived information overload. Previously, Balakrishnan and Grifths (2017) found that YouTube has a positive as-
sociation with excessive and problematic usage patterns in contexts other than COVID-19. The ndings are also in line with another
study conducted in the Chinese context, and it proved that information overload is a strong predictor of exhaustion (a psychological
state of regret) and as a response, the university students intend to quit the use of social media (Cao & Sun, 2018).
Thirdly, we found that information overload resulted in information anxiety in the context of COVID-19. The ndings are consistent
with the previous research. Swar et al. (2017) established a negative correlation between perceived information overload and psy-
chological well-being. During the span of the last three decades, the researchers proved that information overload results in cognitive
strain Jones (1997); Schick et al. (1990) , stress (Lee, Son & Kim, 2016a), confusion, depressive symptoms (Matthews et al., 2020),
anxiety (Bawden & Robinson, 2009), and low motivation (Baldacchino et al., 2002). The current study corroborates the previous
ndings in an emergency context, that is, COVID-19.
Lastly, we found that the people who felt information anxiety avoided further information related to COVID-19. The ndings
conrmed the hypothesis that was developed based on the available literature. Case et al. (2005) suggested that individuals avoid the
information if there is too much to deal with. Other researchers who worked in the same area also concluded that information overload
results in information avoidance (Bawden, 2001; Edmunds & Morris, 2000; Golman et al., 2017). Swar et al. (2017) also reported that
online health-related information overload negatively impacts individualsonline health information search behavior.
6.2. Theoretical contributions and practical implications
The study has twofold implications, theoretical and practical. The existing information behavior theories and models explain that
several factors shape an individuals information behavior, and the context is one of those factors (Wilson, 1999). The current study
explored individualsinformation behavior in a particular context, that is, health crisis and the data were collected when the virus was
spreading, and the uncertainty among the people was at a peak.
Theoretically, to the best of our knowledge, it is the very rst study chalking the extent of information anxiety and information
avoidance underpinning the S-O-R framework in the health crisis. The COVID-19 brought uncertainty at the individual as well as the
societal level, and people want to know about it to minimize the uncertainty level. Therefore, we proposed the information-seeking
behavior as an internal stimulus in our study. Since a wide range of information sources have been used during the COVID-19
pandemic, we focus particularly on the information sources exposure as an external stimulus, rather than the knowledge or aware-
ness accumulated from the sources. The current study expanded the previously proven (Cao & Sun, 2018) effect of information
overload on the discontinuation of information source use, along with information overload. We used information seeking as internal
and source exposure as external stimuli. The model helped identify the stimuli affecting the cognitive and affective state and in-
dividuals actions during a health emergency. By employing the S-O-R framework, the study provides a theoretical lens for model
construction, keeping in mind the external environment factors in health information behavior, technostress literature, information
seeking discontinuous, and crisis management. The framework will signicantly help in understanding the relationship between the
aforementioned factors. The study has examined the individuals information behavior to propose a viable set of actions to manage
health crisis information behavior.
The study has practical implications as well. The study conrmed that active information seekers were using the sources which
were not causing information overload and information anxiety. Therefore, it is suggested that if we want to reduce the information
overload, information anxiety, and information avoidance, which are consequences of information overload and information anxiety,
there is a need to adopt the strategies to make people active information seekers.
There is a need for public training, helping them learn the criteria to examine the information credibility of social media or any
other platform. Mainly three already proven factors, which are, medium credibility (Cooley & Parks-Yancy, 2019 ; Li & Suh, 2015),
source credibility (Westerman, Spence, & Van Der Heide, 2014), and message credibility (Li & Suh, 2015) should be a part of these
kinds of training. This training could be a part of information literacy programs/health literacy programs at the university level for the
student, whereas, for employees, continuous professional development workshops could be arranged to improve information literacy
skills, particularly health literacy skills. Health literacy is "the degree to which individuals have the capacity to obtain, process, and
understand basic health information needed to make appropriate health decisions" (U.S Health Resources and Services Administra-
tion, 2020). It is argued that health literacy is important to develop among all health system stakeholders to cope with the information
overload so that they may be able to lter the required information (Klerings, Weinhandl, & Thaler, 2015). Health literacy not only
helps individuals to make appropriate health-related decisions but also it has been negatively associated with health-related infor-
mation overload (Jiang & Beaudoin, 2016) and information avoidance (St. Jean, Jindal, & Liao, 2017). Therefore, it is recommended
that health literacy training should be a regular part of public training. Particularly regarding crisis management, extensive training
S.H. Soroya et al.
Information Processing and Management 58 (2021) 102440
and guidelines should be provided to the public to prepare them for any crises, otherwise, they will have cognitive and affective
pressures and most likely will make wrong decisions. Furthermore, social networking sites administrators should develop policies
regarding the volume of information sharing (Nawaz et al., 2018), particularly during crises. In the recent past, social bots and cyborg
have been used to develop and steer public opinion in a particular direction (Alsmadi & OBrien, 2020 ; Zhang & Ghorbani, 2020), their
use during a pandemic for causing panic cannot be diminished (Paletz, Auxier, & Golonka, 2019). This area also needs special attention
from the administrators of social networking sites.
6.3. Limitations and future recommendations
The study is not without signicant limitations. For example, we measured single items to measure exposure to information sources
such as personal networks, print media, and mass media, which may have limited the predictive validity of the constructs (Dia-
mantopoulos, Sarstedt, Fuchs, Wilczynski, & Kaiser, 2012). Furthermore, we did not use physicians, pharmacists, and nurses as a
primary source of information.
To further verify and identify any difference, the model should be tested on different social groups and different contexts.
Furthermore, the stimuli other than information source exposure and information overload may also be added as predictors of the
’organism, that is, information anxiety, and similarly for the response, that is, information avoidance. The role of moderators, that is,
the existing state of knowledge, education level, gender, and context, may also be considered to expand further the model for a
comprehensive understanding of information behavior during a health crisis.
One of the study ndings that Finnish people used social media less frequently for seeking COVID-19 related information opens the
direction to future research. It is imperative to conduct explanatory research to understand the reasons for the low use of social media
for health information seeking by Finnish people. Based on the current study, it can be predicted that they may feel overloaded due to
the information explosion on social media channels; the other reason may be the credibility and trust issues. However, future research
can help to explore the actual causes.
Another future avenue that can be looked into is how the users can be trained to make good use of information sources so that they
do not feel overwhelmed. Improving information literacy skills can help in critical evaluation of pieces of information and balanced
decision-making, which may reduce information overload and its consequences (Lee, Lee, & Lee-Geiller, 2020). Information literacy
skills may involve information acquisition, information evaluation, and information use (Ahmad, Wid´
en, & Huvila, 2020).
7. Conclusion
The purpose of this study was to understand the information behavior (information seeking and information avoidance) during the
COVID-19 pandemic in Finland. The study empirically validated the Stimulus, Organism, and Response (S-O-R) framework by
identifying that individuals who have more exposure to social media sources were more likely to feel information overload and in-
formation anxiety during health crises. The frequency of social media sources exposure and the feelings of information overload affect
individuals cognitive and affective state and create information anxiety-causing 57% variance. The cognitive and affective state of
people ultimately leads to action, and based on the study ndings, it is conrmed that an individuals level of information anxiety has a
signicant positive impact on the level of information avoidance. We also found that print media, mass media, and other Internet
sources such as ofcial websites and the websites of newspapers and magazines were primary sources of information during the
COVID-19 pandemic. The study highlights the need that during a situation of uncertainty, particularly a health crisis, individuals
should be trained to control the factors that may create information overload. Further, they should be trained to manage information
anxiety so that they do not avoid information, as avoiding information during a pandemic may be counter-productive for the pre-
ventive measures.
CRediT authorship contribution statement
Saira Hanif Soroya: Conceptualization, Resources, Writing - original draft, Writing - review & editing, Project administration. Ali
Farooq: Methodology, Formal analysis, Investigation, Writing - original draft, Writing - review & editing. Khalid Mahmood: Su-
pervision. Jouni Isoaho: Supervision. Shan-e Zara: Conceptualization.
Following reective constructs used in the study were measured on a 5-point scale (1=strongly disagree, 2=disagree, 3=neither
agree nor disagree, 4=agree, 5=strongly agree)
Constructs Item Description Sources
IS1 - I have sought out COVID-19 related information. Yang and Kahlor (2013)
IS2- I have looked at different information sources to obtain information about COVID-19 Yang and Kahlor (2013)
(continued on next page)
S.H. Soroya et al.
Information Processing and Management 58 (2021) 102440
IS3 - I have paid close attention to COVID-19 related information. Self-Developed
IS4 - I have actively searched for COVID-19 related information. Self-Developed
OV1 - I am overwhelmed by the amount of information that I process daily from multiple channels/
sources about COVID-19.
Williamson et al. (2012)
OV2 - I am often distracted by the amount of information on multiple channels/sources about COVID-19. Farooq et al. (2020)
OV3 - There is so much information available to me on the subject of COVID-19 that I have trouble
choosing what is important and whats not.
Williamson et al. (2012)
OV4 - When I search for information on COVID-19, I usually get too much rather than too little
Williamson et al. (2012)
OV5 - I receive too much information regarding the COVID-19 pandemic to form a coherent picture of
whats happening.
Farooq et al. (2020)
IA1 - I feel apprehensive (anxious) due to too much information on COVID-19 around me. L´
opez-Bonilla & L´
IA2 - Information overload about COVID-19 does not scare me at all. L´
opez-Bonilla & L´
IA3 - Working with too much information related to COVID-19 make me very nervous. L´
opez-Bonilla & L´
IA4 - I feel aggressive and hostile towards too much of available information on COVID-19. L´
opez-Bonilla & L´
IA5 - I get a sinking(unpleasant) feeling when I think of searching for information related to COVID-19 L´
opez-Bonilla & L´
IA6 - I feel stressed about making decisions or choosing the right information on COVID-19. Self-Developed
AV1 - I intentionally ignore some information related to COVID-19. Guo et al. (2020)
AV2 - I scroll down web pages to avoid COVID-19 related information. Guo et al. (2020)
AV3 - I tune out of information about COVID-19. Hmielowski et al., 2019
AV4 - I use different means to avoid information related to COVID-19. Guo et al. (2020)
AV5 - I unsubscribe/leave the information sharing platforms due to excessive information on COVID-19. Self-Developed
AV6 - When it comes to COVID-19, I dont want to know more. Self-Developed
Information Sources:Information sources constructs were formative measured using a 6-point scale [1 =never, 2 =less than one
hour, 3 =12 h, 4 =34 h, 5 =56 h, 6 =more than 6 h]. Each source was presented after the following statement:
"Tell us your daily usage using the following information sources regarding COVID-19 information".
Personal Network Family, friends, and relatives
Mass Media Mass media (Television and/or radio)
Print Media print media (magazine, newspaper, pamphlets, etc.)
Other Internet Sources
OIS1 University E-mail/communications
OIS2 University Intranet
OIS3 Internet searches, online newspapers, websites
OIS4 Governmental/ofcial websites
Social Media Sources
SM1 Facebook
SM2 WhatsApp
SM3 Twitter
SM4 Instagram
SM5 YouTube
Ahmad, F., Wid´
en, G., & Huvila, I. (2020). The impact of workplace information literacy on organizational innovation: An empirical study. International Journal of
Information Management, 51, Article 102041.
Ahmad, U. N. U., & Amin, S. M. (2012). The dimensions of technostress among academic librarians. Procedia-Social and Behavioral Sciences, 65, 266271.
Alsmadi, I., & OBrien, M. J. (2020). How many bots in Russian troll tweets? Information Processing & Management, 57(6), Article 102303.
Balakrishnan, J., & Grifths, M. D. (2017). Social media addiction: What is the role of content in YouTube? Journal of Behavioral Addictions, 6(3), 364377.
Baldacchino, C., Armistead &, C.&, & Parker, D. (2002). Information overload: Its time to face the problem. Management Services, 46(1), 1819.
Bapat, S. S., Patel, H. K., & Sansgiry, S. S. (2017). Role of information anxiety and information load on processing of prescription drug information leaets. Pharmacy,
5(4), 57.
Bawden, D. (2001). Information overload. Library & Information Briengs, (92), 115.
S.H. Soroya et al.
Information Processing and Management 58 (2021) 102440
Bawden, D., & Robinson, L. (2009). The dark side of information: Overload, anxiety and other paradoxes and pathologies. Journal of Information Science, 35(2),
Bawden, D., & Robinson, L. (2020). Information overload: An introduction. Oxford Research Encyclopedia of Politics.
Beaudoin, C. E. (2008). Explaining the relationship between internet use and interpersonal trust: Taking into account motivation and information overload. Journal of
Computer-Mediated Communication, 13(3), 550568.
Bento, A. I., Nguyen, T., Wing, C., Lozano-Rojas, F., Ahn, Y. Y., & Simon, K. (2020). Information seeking responses to news of local COVID-19 cases: Evidence from
Internet search data. arXiv preprint arXiv:2004.04591.
Cao, X., & Sun, J. (2018). Exploring the effect of overload on the discontinuous intention of social media users: An SOR perspective. Computers in Human Behavior, 81,
Carnegie Mellon University. (2017, March 10). Information avoidance: From health to politics, people select their own reality. ScienceDaily. Retrieved June 15, 2020
Case, D. O., Andrews, J. E., Johnson, J. D., & Allard, S. L. (2005). Avoiding versus seeking: The relationship of information seeking to avoidance, blunting, coping,
dissonance, and related concepts. Journal of the Medical Library Association, 93(3), 353362.
Chae, J. (2016). Who avoids cancer information? Examining a psychological process leading to cancer information avoidance. Journal of Health Communication, 21(7),
Chao, M., Xue, D., Liu, T., Yang, H., & Hall, B. J. (2020). Media use and acute psychological outcomes during COVID-19 outbreak in China. Journal of Anxiety
Disorders, Article 102248.
Chopdar, P. K., & Balakrishnan, J. (2020). Consumers response towards mobile commerce applications: SOR approach. International Journal of Information
Management, 53, Article 102106.
Chuang, W. H., & Chiu, M. H. P. (2019). Health information avoidance behavior of patients with type 2 diabetes mellitus. 圖書資訊學刊, 17(2), 71102.
Clarke, M. A., Moore, J. L., Steege, L. M., Koopman, R. J., Belden, J. L., & Caneld, S. M., & Kim, M. S. (2016). Health information needs, sources, and barriers of
primary care patients to achieve patient-centered care: A literature review. Health informatics journal, 22(4), 9921016.
Cooley, D., & Parks-Yancy, R. (2019). The effect of social media on perceived information credibility and decision making. Journal of Internet Commerce, 18(3),
Dai, B., Ali, A., & Wang, H. (2020). Exploring information avoidance intention of social media users: A cognitionaffectconation perspective. Internet Research. https://doi.
org/10.1108/INTR-06-2019-0225. Vol. ahead-of-print No. ahead-of-print.
Deline, M. B., & Kahlor, L. A. (2019). Planned risk information avoidance: A proposed theoretical model. Communication Theory, 29(3), 360382.
Derry, H. M., Epstein, A. S., Lichtenthal, W. G., & Prigerson, H. G. (2019). Emotions in the room: Common emotional reactions to discussions of poor prognosis and
tools to address them. Expert Review of Anticancer Therapy, 19(8), 689696.
Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012). Guidelines for choosing between multi-item and single-item scales for construct
measurement: A predictive validity perspective. Journal of the Academy of Marketing Science, 40(3), 434449.
Edmunds, A., & Morris, A. (2000). The problem of information overload in business organizations: A review on the literature. International Journal of Information
Management, 20, 1728.
Ek, S., Eriksson-Backa, K., & Niemel¨
a, R. (2013). Use of and trust in health information on the Internet: A nationwide eight-year follow-up survey. Informatics for
Health and Social Care, 38(3), 236245.
Eklof, A. (2013). Understanding information anxiety and how academic librarians can minimize its effects. Public Services Quarterly, 9(3), 246258.
Eppler, M. J. (2015). Information quality and information overload: The promises and perils of the information age. Communication and Technology, 5, 215232.
Eppler, M. J., & Mengis, J. (2004). The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related
disciplines. The Information Society, 20(5), 325344.
Fang, Y., Nie, Y., & Penny, M. (2020). Transmission dynamics of the COVID 19 outbreak and - effectiveness of government interventions: A data driven analysis.
Journal of Medical Virology. Retrieved from
Farooq, A., Laato, S., & Islam, A. K. M. N. (2020). Impact of online information on self-isolation intention during the COVID-19 pandemic: cross-sectional study. J Med
Internet Res, 22(5), e19128.
Farooq, A., Balakrishnan, L., Phadung, M., Virtanen, S., Isoaho, J., & Poudel, D. P. (2016, August). Dimensions of Internet use and threat sensitivity: An exploratory
study among students of higher education. In 2016 IEEE intl conference on computational science and engineering (CSE) and IEEE intl conference on embedded and
ubiquitous computing (EUC) and 15th intl symposium on distributed computing and applications for business engineering (DCABES) (pp. 534541). IEEE.
Feng, Y., & Agosta, D. E. (2017). The experience of mobile information overload: Struggling between needs and constraints. Information Research, 22(2), 574.
paperRetrieved from
Fu, S., Li, H., Liu, Y., Pirkkalainen, H., & Salo, M. (2020). Social media overload, exhaustion, and use discontinuance: Examining the effects of information overload,
system feature overload, and social overload. Information Processing & Management, 57(6), Article 102307.
Gao, L., & Bai, X. (2014). Online consumer behaviour and its relationship to website atmospheric induced ow: Insights into online travel agencies in China. Journal of
Retailing and Consumer Services, 21(4), 653665.
Golman, R., Hagmann, D., & Loewenstein, G. (2017). Information avoidance. Journal of Economic Literature, 55(1), 96135.
Guo, Y., Lu, Z., Kuang, H., & Wang, C. (2020). Information avoidance behavior on social network sites: Information irrelevance, overload, and the moderating role of
time pressure. International Journal of Information Management, Article 102067.
Hartog, P. (2017). A generation of information anxiety: Renements and recommendations. The Christian Librarian, 60(1), Article 8.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. New challenges to international
marketing. Emerald Group Publishing Limited.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. The Journal of the
Academy of Marketing Science, 43, 115135.
Hmielowski, J. D., Donaway, R., & Wang, M. Y. (2019). Environmental risk information seeking: The differential roles of anxiety and hopelessness. Environmental
Communication, 13(7), 894908.
Huff, R. M., Kline, M. V., & Peterson, D. V. (Eds.). (2014). Health promotion in multicultural populations: A handbook for practitioners and students (Eds.). SAGE
Jensen, J. D., Pokharel, M., Scherr, C. L., King, A. J., Brown, N., & Jones, C. (2017). Communicating uncertain science to the public: How amount and source of
uncertainty impact fatalism, backlash, and overload. Risk Analysis, 37(1), 4051.
Jiang, S., & Beaudoin, C. E. (2016). Health literacy and the internet: An exploratory study on the 2013 HINTS survey. Computers in Human Behavior, 58, 240248.
Jones, Q. (1997). Virtual-communities, virtual-settlements and cyberarchaeology: A theoretical outline. The Journal of Computer-Mediated Communication, 3(3).
Retrieved from
Hair, J. F., Jr, Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
Hair, J. F., Jr, Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2),
Khaleel, I., Wimmer, B. C., Peterson, G. M., Zaidi, S. T. R., Roehrer, E., & Cummings, E. (2020). Health information overload among health consumers: A scoping
review. Patient Education and Counseling, 103(1), 1532.
Klerings, I., Weinhandl, S. A., & Thaler, K. J. (2015). Information overload in healthcare: Too much of a good thing. Evidenz-informierte Entscheidungen in ¨
109(45), 285290.
Kline, R. B. (2005). Principles and practice of structural equation modeling. Guilford publications.
Koiranen, I., Keipi, T., Koivula, A., & R¨
anen, P. (2019). Changing patterns of social media use?. A population-level study of nland (pp. 115). Universal Access in the
Information Society.
S.H. Soroya et al.
Information Processing and Management 58 (2021) 102440
Laato, S., Islam, A. N., Islam, M. N., & Whelan, E. (2020a). What drives unveried information sharing and cyberchondria during the COVID-19 pandemic? European
Journal of Information Systems, 118.
Laato, S., Islam, A. N., Farooq, A., & Dhir, A. (2020b). Unusual purchasing behavior during the early stages of the COVID-19 pandemic: The stimulus-organism-
response approach. Journal of Retailing and Consumer Services, 57, Article 102224.
Lee, A. R., Son, S. M., & Kim, K. K. (2016a). Information and communication technology overload and social networking service fatigue: A stress perspective.
Computers in Human Behavior, 55, 5161.
Lee, S. K., Kim, K. S., & Koh, J. (2016b). Antecedents of news consumersperceived information overload and news consumption pattern in the USA. International
Journal of Contents, 12(3), 111.
Lee, T., Lee, B. K., & Lee-Geiller, S. (2020). The effects of information literacy on trust in government websites: Evidence from an online experiment. International
Journal of Information Management, 52, Article 102098.
Li, R., & Suh, A. (2015). Factors inuencing information credibility on social media platforms: Evidence from Facebook pages. Procedia Computer Science, 72, 314328.
opez-Bonilla, J. M., & L´
opez-Bonilla, L. M. (2011). Validation of an information technology anxiety scale in undergraduates. British Journal of Educational Technology,
43(2), 5658.
Manheim, L. (2014). Information non-seeking behavior. Information Research, 19(4). Retrieved from
Matthews, J., Karsay, K., Schmuck, D., & Stevic, A. (2020). Too much to handle: Impact of mobile social networking sites on information overload, depressive
symptoms, and well-being. Computers in Human Behavior, 105, Article 106217.
Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. Cambridge: MA: MIT Press.
Muhonen, T., & Nalbantoglu, M. (2020). T¨
a ovat kaikki hallituksen poikkeukselliset toimet koronaviruksen hillitsemiseksi, vaikuttavat l¨
ahes jokaisen kansalaisen
arkeen tiedotustilaisuus katsottavissa kokonaisuudessaan. Helsingin Sanomat (in Finnish). Retrieved 20 April 2020.
Musarezaie, N., Samouei, R., Shahrzadi, L., & Ashra-Rizi, H. (2019). Prediction of health information-seeking behavior components based on health anxiety among
users of public libraries. Journal of Education and Health Promotion, 8, 227.
Nawaz, M. A., Shah, Z., Nawaz, A., Asmi, F., Hassan, Z., & Raza, J. (2018). Overload and exhaustion: Classifying SNS discontinuance intentions. Cogent Psychology, 5
(1), Article 1515584.
Nelson, K. (2018). 50 Social Media Healthcare Statistics to Watch. Retrieved from
watch/ on 20 May 2020.
Paletz, S. B. F., Auxier, B. E., & Golonka, E. M. (2019). Non-genuine actors. In S. B. F. Paletz, B. E. Auxier, & E. M. Golonka (Eds.), A multidisciplinary framework of
information propagation online (pp. 5763). Switzerland: Springer, Cham.
Phillips-Wren, G., & Adya, M. (2020). Decision making under stress: The role of information overload, time pressure, complexity, and uncertainty. Journal of Decision
Systems, 113.
Pian, W., Song, S., & Zhang, Y. (2020). Consumer health information needs: A systematic review of measures. Information Processing & Management, 57(2), Article
Ramsey, I., Corsini, N., Peters, M. D., & Eckert, M. (2017). A rapid review of consumer health information needs and preferences. Patient Education and Counseling, 100
(9), 16341642.
Roetzel, P. G. (2019). Information overload in the information age: A review of the literature from business administration, business psychology, and related
disciplines with a bibliometric approach and framework development. Business Research, 12(2), 479522.
Sairanen, A., & Savolainen, R. (2010). Avoiding health information in the context of uncertainty management. Information Research, 15(4). Retrieved from https://
Schick, A. G., Gorden, L. A., & Haka, S. (1990). Information overload: A temporal approach. Accounting Organizations and Society, 15, 199220.
¸, P., Gencer, H., & ¨
Ozkan, S. (2020). Finding useful cancer information may reduce cancer information overload for Internet users. Health Information &
Libraries Journal. Retrieved from
Sharit, J., & Czaja, S. J. (2018). Overcoming older adult barriers to learning through an understanding of perspectives on human information processing. Journal of
Applied Gerontology, 39(3), 233241.
Shepperd, J. A., Emanuel, A. S., Howell, J. L., & Logan, H. L. (2015). Predicting scheduling and attending for an oral cancer examination. Annals of Behavioral
Medicine, 49(6), 828838.
Speier, C., Valacich, J., & Vessey, I. (1999). The inuence of task interruption on individual decision making: An information overload perspective. Decision Sciences,
30(.2), 337360.
St. Jean, B., Jindal, G., & Liao, Y. (2017). Is ignorance really bliss?: Exploring the interrelationships among information avoidance, health literacy and health justice.
Proceedings of the Association for Information Science and Technology, 54(1), 394404.
Statista. (2020). What sources do you actively use to keep informed about the COVID-19 / coronavirus pandemic? Retrieved from
&sa=D&ust=1590047993235000&usg=AFQjCNGQRG6zJmUbO_sEY7_UemcyrYLxnw on 19 May 2020.
Swar, B., Hameed, T., & Reychav, I. (2017). Information overload, psychological ill-being, and behavioral intention to continue online healthcare information search.
Computers in Human Behavior, 70, 416425.
Terveyden ja hyvinvoinnin laitos (2020)., THL:n kotisivut ja koronainfo [ONLINE] available at, https://thl.//, last checked 20th of May 2020.
Tofer, A. (1970). Future shock. New York: Random House.
U.S. Health resources and Services Administration. (2020). Health literacy. Retrieved from
Wang, P. W., Lu, W. H., Ko, N. Y., Chen, Y. L., Li, D. J., & Chang, Y. P., & Yen, C. F. (2020). COVID-19-related information sources and the relationship with condence
in people coping with COVID-19: Facebook survey study in Taiwan. Journal of Medical Internet Research, 22(6), e20021.
Westerman, D., Spence, P. R., & Van Der Heide, B. (2014). Social media as information source: recency of updates and credibility of information. Journal of Computer-
Mediated Communication, 19(2), 171183.
Wilder-Smith, A., & Freedman, D. O. (2020). Isolation, quarantine, social distancing and community containment: Pivotal role for old-style public health measures in
the novel coronavirus (2019-nCoV) outbreak. Journal of Travel Medicine, 27(2).
Williamson, J., Eaker, P. E., & Lounsbury, J. (2012). The information overload scale. Asist, 2012.
Wilson, T. D. (1999). Models in information behaviour research. Journal of Documentation, 55(3), 249270.
Wurman, R. S. (1989). Information anxiety. New York: Doubleday.
Wurman, R.S. (.2001). Information anxiety (No. 302.234 WUR. CIMMYT.).
Xu, J., Benbasat, I., & Cenfetelli, R. T. (2014). The nature and consequences of trade-off transparency in the context of recommendation agents. MIS Quarterly, 38(2),
Yang, Z. J., & Kahlor, L. (2013). What, me worry? The role of affect in information seeking and avoidance. Science Communication, 35(2), 189212.
Zhang, X., & Ghorbani, A. A. (2020). An overview of online fake news: Characterization, detection, and discussion. Information Processing & Management, 57(2), Article
Zhang, X., Ma, L., Zhang, G., & Wang, G. S. (2020). An integrated model of the antecedents and consequences of perceived information overload using WeChat as an
example. International Journal of Mobile Communications, 18(1), 1940.
S.H. Soroya et al.
Information Processing and Management 58 (2021) 102440
Zhang, Y. (2012). Consumer health information searching process in real life settings. Proceedings of the American Society for Information Science and Technology, 49(1),
Zhao, Y., & Zhang, J. (2017). Consumer health information seeking in social media: A literature review. Health Information & Libraries Journal, 34(4), 268283.
Zheng, L., Miao, M., Lim, J., Li, M., Nie, S., & Zhang, X. (2020). Is lockdown bad for social anxiety in COVID-19 regions?: A national study in the SOR perspective.
International Journal of Environmental Research and Public Health, 17(12), 4561.
S.H. Soroya et al.
... Instead, the existing literature has rather followed the footsteps of Ganguly and Tasoff (2017) ; Oster et al. (2013) and, broadly speaking, has focused on the relation between stress, health-relevant information and its avoidance. In particular, this type of information avoidance seems to arise if people experience stress ( Soroya et al., 2021 ), information overload ( Soroya et al., 2021;Qu et al., 2023 ), and anxiety ( Siebenhaar et al., 2020 ) towards . While this type of information avoidance could also be the result of motivated reasoning, it lacks the pro-social dimension which is the focus of our paper. ...
... Instead, the existing literature has rather followed the footsteps of Ganguly and Tasoff (2017) ; Oster et al. (2013) and, broadly speaking, has focused on the relation between stress, health-relevant information and its avoidance. In particular, this type of information avoidance seems to arise if people experience stress ( Soroya et al., 2021 ), information overload ( Soroya et al., 2021;Qu et al., 2023 ), and anxiety ( Siebenhaar et al., 2020 ) towards . While this type of information avoidance could also be the result of motivated reasoning, it lacks the pro-social dimension which is the focus of our paper. ...
Full-text available
We report the results of an experiment on willful information avoidance regarding measures to address Covid-19. In the experiment, participants choose between two options, each associated with a contribution to the Corona Fund of the Red Cross USA and a payment to the participant. Depending on the treatment, either the participants’ payoff, the donation, both or none of these pieces of information were hidden, but revealable. With this design, we can separate motivated reasons for ignorance from non-motivated reasons, both of which are present in our data. Furthermore, we find evidence of both self-serving and pro-social information avoidance. These behavioral patterns correlate with the subjects’ political attitudes: while voters of the Democratic Party are prone to exhibit pro-social information avoidance, Republican voters rather engage in self-serving information avoidance.
... Akses informasi kesehatan sangat penting untuk peningkatan kesehatan individu dan masyarakat, dimana akan membantu meningkatkan pengetahuan, penggunaan layanan kesehatan, dan adopsi pola perilaku yang lebih sehat (Chen et al., 2019). Saat ini masyarakat lebih terbuka mencari informasi untuk pengambilan keputusan dan berkonsultasi dengan berbagai sumber informasi (Soroya et al., 2021). Penduduk pedesaan lebih cenderung mengandalkan perawat sebagai sumber perawatan biasa. ...
Eliminasi kusta masih terus diupayakan oleh pemerintah dan masyarakat. Media informasi dan keluarg merupakan salah satu unsur yang memiliki peranan penting dalam mencegah penularan kusta. Tujuan penelitian ini adalah menjelaskan hubungan peran media informasi dan efikasi diri keluarga dengan perilaku keluarga dalam mencegah penularan kusta. Jenis penelitian ini adalah penelitian kuantitatif dengan desain korelasional, yang bertujuan mengkaji hubungan antar variabel. Subjek pada penelitian ini adalah keluarga penderita kusta di wilayah kerja UPT Puskesmas Pragaan (n=42), didapatkan melaui simple random sampling (n=38). Pengumpulan data menggunakan kuesioner peran media informasi, efikasi diri, dan perilaku keluarga dalam mencegah penularan kusta. Hasil penelitian menunjukkan adanya hubungan antara peran media informasi dan efikasi diri keluarga dengan perilaku pencegahan penularan penyakit kusta (p-value=0,000; p-value=0,000). Penelitian tentang strategi peningkatan kualitas hidup penderita kusta melalui optimalisasi peran keluarga diperlukan untuk meningkatkan derajat kesehatan penderita kusta dan eliminasi kusta.
Objectives: To examine college students' conflicting COVID-19 information exposure, information-seeking, concern, and cognitive functioning. Participants: 179 undergraduates were recruited in March-April 2020, and 220 in September 2020 (Samples 1 and 2, respectively). Methods: Students completed the Attention Network Test, NASA Task Load Index, and COVID-related questions. Results: In Sample 1, exposure to conflicting information predicted poorer attentional performance and greater COVID-related information-seeking and concern; concern was correlated with workload. In Sample 2, conflicting information was associated with information-seeking. In Sample 1, but not Sample 2, cognitive effects of conflicting information were mediated by information-seeking and virus-related concern. Conclusions: Conflicting COVID-19 information may undermine students' cognitive functions, bearing implications for health, academic performance, and stress. Strategies for countering these effects include enhancing the clarity of institutional messaging, and tailoring course curricula and offering workshops to students, faculty, administrators, and counseling staff to augment students' capacity to comprehend and utilize COVID-related communications.
With the profound transformation of Chinese journalism under the commercialized trend of thoughts, there is a debate on whether Chinese journalists still maintain the public service ideal or just regard it simply as a job. In other words, is journalism a “vocation” or a “profession”? Therefore, it is necessary to conduct an empirical study on journalistic roles. Drawing on the self-determination theory, this study constructs a framework of “career motivation–news attitude” to build a bridge between role orientations and performance, in order to comprehensively understand the Chinese journalistic roles. In this study, a quota sampling survey ( n = 1000), multiple linear regression (MLR), and structural equation modeling (SEM) are used to examine the influence of advocacy and utilitarian motivations on news efficacy and news avoidance, corresponding to news production and consumption attitudes. Furthermore, the study divides the journalistic roles in China into four categories: purposive advocate professionals, dedicated advocate professionals, workaday journalists, and adrift journalists. The results show that advocacy motivation, or the public service ideal, is more significant than utilitarian motivation in the journalistic roles. The former is associated with higher news efficacy and lower news avoidance, while the latter is only associated with higher news avoidance. The study also discusses the demographic factors of journalistic career motivations.
This paper explored the information anxiety of community residents in different prevention and control states from the perspective of information tasks, and provided suggestions on information service for future public health emergency. Through in-depth interviews, the information anxiety scale of community residents in five prevention and control states was constructed. The information tasks and information anxiety of residents in each prevention and control state were investigated through questionnaires, and the differences and causes were analyzed. Our research found that information anxiety was highest when residents were isolated at home, and lowest when they were in centralized isolation. The anxiety of information environment, information quality and quantity were the highest in the case of home isolation and community isolation. The highest degree of information anxiety due to the heaviest burden of information tasks in home isolation, and there is a positive correlation between the two. Residents’ information anxiety is positively correlated with the proportion of material/life tasks, and is negatively correlated with the proportion of daily epidemic notification tasks.KeywordsInformation AnxietyPrevention and Control StatesPublic Health EmergencyInformation Task
Background: During covid-19 period not only general public was victim of anxiety besides all medical professional also face anxiety and change their Information seeking behaviour according their personality. Curiosity is in human nature with the easy access to internet the new horizon to information has been opened. People searching trends have shown that they are interested in health risk to health treatment for their health related problems. Aim: In this study examined the influences of anxiety (ISA) and Personality traits (PT) on health information seeking behaviour (HISB) among the Doctor, paraprofessional and final year medical students who are frontline worker during pandemic situation. Methodology: The study adopted survey method with non-probability convenience sampling to collect statistical. Questionnaires werefiled from 313 participants by utilizing convenient sampling and analyzing the data through SPSS. Results: The result showed that significant relation between personality traits, information seeking anxiety and health information seeking behaviour. In medical library user PT has significant impact on HISB (p<.05), (AVG_PT=.002) and ISA has impact on HISB but it is not significant in medical professional (β -.070) value shows ISA has negative impact on HISB. Practical implication: This study will be beneficial for information professionals, health care workers, policy makers and administrators to access of information resources in hybrid format. Conclusion: Medical professional’s plays an important role in our society. They work hard and served the nation during pandemic situation. Anxiety is natural phenomena to every person. So medical professional also feel anxietybut the medical profession demands its professionals to stay cool, calm and free of anxiety by having analytical and cognitive skills, in order to fulfill the needs of their profession. This research helps to understand that ISA has no significant impact on HISB while PT has significant impact on HISB. Keywords: Information Anxiety, Health information seeking behaviour, Personality Traits
Full-text available
This chapter explores the role of information overload in infodemic management, with particular emphasis on overload in the context of COVID-19. We begin with a brief discussion of how information overload has been considered in fields such as emergency response (where well-timed, data-supported response is advocated) and risk communication (which adds that for messages to be effective, people must first trust the messengers). We propose an expanded conceptualisation that sees information overload as the product of specific techno-social ecosystems. In this model, the timing and content of messages still matters – as does community trust in messengers – but just as important are the emotional states of users at the time of information of exposure, and amplification of those states online via platform algorithms (e.g. trending, aggregation of likes or dislikes) and/or social dynamics (e.g. “piling on.”). Regarding the desire of health authorities to intervene in digital platform governance to combat information overload, we stress that a government’s desire to protect the public should not eclipse human rights to individual free speech and expression. We find a more promising approach in activist movements such as participatory design, where community members organically become more discerning consumers and more effective advocates for information equity.KeywordsInformation overloadInformation avoidanceInfodemicSocial mediaInformation ecosystem
Full-text available
During the COVID-19 pandemic, unusual consumer behavior, such as hoarding toilet paper, was reported globally. We investigated this behavior when fears of consumer market disruptions started circulating, to capture human behavior in this unique situation. Based on the stimulus-organism-response (S-O-R) framework, we propose a structural model connecting exposure to online information sources (environmental stimuli) to two behavioral responses: unusual purchases and voluntary self-isolation. To test the proposed model, we collected data from 211 Finnish respondents via an online survey, and carried out analysis using PLS-SEM. We found a strong link between self-intention to self-isolate and intention to make unusual purchases, providing empirical evidence that the reported consumer behavior was directly linked to anticipated time spent in self-isolation. The results further revealed exposure to online information sources led to increased information overload and cyberchondria. Information overload was also a strong predictor of cyberchondria. Perceived severity of the situation and cyberchondria had significant impacts on people's intention to make unusual purchases and voluntarily self-isolate. Future research is needed to confirm the long-term effects of the pandemic on consumer and retail services.
Full-text available
Lockdown measures have been widely used to control and prevent virus transmission in pandemic regions. However, the psychological effects of lockdown measures have been neglected, and the related theoretical research lags behind the practice. The present study aimed to better understand the mechanism of social anxiety in pandemic regions where the lockdown measures were imposed, based on the conceptual framework of the Stimulus-Organism-Response (SOR). For that, this research investigated how lockdown measures and psychological distance influenced social anxiety in the pandemic region. The Chinese national data was analyzed for the outcome. The results showed that (1) psychological distance mediated the relationship between pandemic COVID-19 severity and social anxiety, (2) lockdown measures buffered the detrimental effect of the COVID-19 pandemic severity on social anxiety, (3) lockdown measures moderated the mediation effect of psychological distancing on social anxiety caused by the COVID-19 pandemic. In conclusion, under the SOR framework, the lockdown measures had a buffer effect on social anxiety in pandemic regions, with the mediating role of psychological distancing.
Full-text available
The World Health Organisation has emphasised that misinformation – spreading rapidly through social media – poses a serious threat to the COVID-19 response. Drawing from theories of health perception and cognitive load, we develop and test a research model hypothesising why people share unverified COVID-19 information through social media. Our findings suggest a person’s trust in online information and perceived information overload are strong predictors of unverified information sharing. Furthermore, these factors, along with a person’s perceived COVID-19 severity and vulnerability influence cyberchondria. Females were significantly more likely to suffer from cyberchondria, with males more likely to share news without verifying its reliability. Our findings suggest that to mitigate the spread of COVID-19 misinformation and cyberchondria, measures should be taken to enhance a healthy scepticism of health news while simultaneously guarding against information overload.
Background An excessive overload of information causes an ineffective management of information, stress and indefiniteness. Furthermore, this situation can prevent persons from learning and making conscious decisions. Objective This study aims to determine the cancer information overload (CIO) and the factors related to it in adults who are Internet users. Methods A cross‐sectional study with 482 Internet users was conducted. The data were collected by using an Introductory Information Form and t he Cancer Information Overload Scale. Results It was found that the Internet was the most used information source (62.2%). The CIO of those with a university level education was found to be high (P = 0.012). It was found that the CIO of individuals who used the Internet (P = 0.031) and newspapers/magazines (P = 0.004) as sources of information was high compared with those who did not use these sources. It was determined from the information obtained that those who found the information to be beneficial and enough had a low CIO (P = 0.004, P = 0.00). Conclusion Health literacy around cancer information is challenging for frequent Internet users. Health professionals, information specialists and librarians should orient people to reliable sources.
Purpose Grounded on the cognition–affect–conation (C–A–C) framework, this study aims to explore how perceived information overload affects the information avoidance intention of social media users through fatigue, frustration and dissatisfaction. Design/methodology/approach/methodology/approach A quantitative research design is adopted. The data collected from 254 respondents in China are analyzed via structural equation modeling (SEM). Findings Perceived information overload directly affects fatigue, frustration and dissatisfaction among social media users, thereby affecting their information avoidance intention. In addition, frustration significantly affects social media fatigue and dissatisfaction. Consequently, social media fatigue influences dissatisfaction among users. Originality/value The literature review indicates that social media overload and fatigue yield negative behavioral outcomes, including discontinuance. However, rather than completely abstaining or escaping, social media users adopt moderate strategies, including information avoidance, to cope with overload and fatigue owing to their high dependence on social media. Unfortunately, merely few studies are available on the information avoidance behavior of social media users. Focusing on this line of research, the current study develops a model to investigate the antecedents of information avoidance in social media.
While users’ discontinuance of use has posed a challenge for social media in recent years, there is a paucity of knowledge on the relationships between different dimensions of overload and how overload adversely affects users’ social media discontinuance behaviors. To address this knowledge gap, this study employed the stressor–strain–outcome (SSO) framework to explain social media discontinuance behaviors from an overload perspective. It also conceptualized social media overload as a multidimensional construct consisting of system feature overload, information overload, and social overload. The proposed research model was empirically validated via 412 valid questionnaire responses collected from Facebook users. Our results indicated that the three types of overload are interconnected through system feature overload. System feature overload, information overload, and social overload engender user exhaustion, which in turn leads to users’ discontinued usage of social media. This study extends current technostress research by demonstrating the value of the SSO perspective in explaining users’ social media discontinuance.
Increased usage of bots through the Internet in general, and social networks in particular, has many implications related to influencing public opinion. Mechanisms to distinguish humans from machines span a broad spectrum of applications and hence vary in their nature and complexity. Here we use several public Twitter datasets to build a model that can predict whether or not an account is a bot account based on features extracted at the tweet or the account level. We then apply the model to Twitter's Russian Troll Tweets dataset. At the account level, we evaluate features related to how often Twitter accounts are tweeting, as previous research has shown that bots are very active at some account levels and very low at others. At the tweet level, we noticed that bot accounts tend to sound more formal or structured, whereas real user accounts tend to be more informal in that they contain more slang, slurs, cursing, and the like. We also noted that bots can be created for a range of different goals (e.g., marketing and politics) and that their behaviors vary based on those distinct goals. Ultimately, for high bot-prediction accuracy, models should consider and distinguish among the different goals for which bots are created.