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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
(http://creativecommons.org/licenses/by/4.0/).
From information seeking to information avoidance:
Understanding the health information behavior during a global
health crisis
Saira Hanif Soroya
a
, Ali Farooq
b
,
*
, Khalid Mahmood
a
, Jouni Isoaho
b
, Shan-e Zara
a
a
Department of Information Management, University of the Punjab, Lahore, Pakistan
b
Department of Future Technologies, Faculty of Science & Engineering, University of Turku. Finland
ARTICLE INFO
Keywords:
Information seeking
Information overload
Information anxiety
Information avoidance
COVID-19
ABSTRACT
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 ofcial 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 signicant relationship with information overload as well as information anxiety. Besides,
information overload also predicted information anxiety, which further resulted in information
avoidance.
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: saira.im@pu.edu.pk (S.H. Soroya), alifar@utu. (A. Farooq), Khalid.im@pu.edu.pk (K. Mahmood), jisoaho@utu. (J. Isoaho),
shanezara47@gmail.com (S.-e. Zara).
Contents lists available at ScienceDirect
Information Processing and Management
journal homepage: www.elsevier.com/locate/infoproman
https://doi.org/10.1016/j.ipm.2020.102440
Received 14 July 2020; Received in revised form 9 November 2020; Accepted 14 November 2020
Information Processing and Management 58 (2021) 102440
2
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 scientic and ofcial websites are prominent. The available statistics from Google trends also conrm 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.,
2017).
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 society’s 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-specic 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
3
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, Tofer dened 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.311–315). However, recently, it has received a lot of attention, especially in social media and virtual collaborations
(Roetzel, 2019). During 2000–2018 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) identied 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 dening 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 conicting, 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.
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3. Theoretical foundation
3.1. Theoretical framework
The current study investigates the individuals’ health 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 individual’s 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 conrmed 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 individual’s 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 individual’s 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 individual’s 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-
dividuals’ exposure 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 conrmed 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 identied 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).
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3.2.2. Information sources exposure and its impact
Literature reports that exposure to different types of information sources positively correlates to the individual’s 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 signicant 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
individuals’ who pay greater attention to news through social media, smartphones, and tablets signicantly perceive more information
overload (Lee, Kim & Koh, 2016b). Excessive health-related internet use signicantly 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 Network’ sources exposure will perceive a higher level of information
overload.
H2b: Individuals who report more frequent ’Mass Media’ sources exposure will perceive a higher level of information overload.
H2c: Individuals who report more frequent ’Print Media’ information sources exposure will perceive a higher level of information
overload.
H2d: Individuals who report more frequent ’Social Media’ information sources exposure will perceive a higher level of information
overload.
H2e: Individuals who report more frequent ’Other Internet Sources’ exposure will perceive a higher level of information overload.
Information sources exposure/engagement has also been positively associated with the individual’s 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 signicantly 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, consumer’s 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 individual’s 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 Network’ sources exposure will perceive a higher level of information anxiety
H3b: Individuals who report more frequent ’Mass Media’ sources exposure will perceive a higher level of information anxiety
H3c: Individuals who report more frequent ’Print Media’ sources exposure will perceive a higher level of information anxiety
H3d: Individuals who report more frequent ’Social Media’ sources exposure will perceive a higher level of information anxiety
H3e: Individuals who report more frequent ’Other Internet Sources’ exposure will perceive a higher level of information anxiety
3.2.3. Information overload and its impact
Swar et al. (2017) conrmed that perceived information overload has a signicant 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-
dividuals’ online 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 individuals’ inactive 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 individual’s state of information avoidance behavior.
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Information Processing and Management 58 (2021) 102440
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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 conrmed cases were reported in Finland, and the numbers were rising, and by April 28th, there were 5056 conrmed
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 Cronbach’s alpha value for all the
constructs remained between 0.7–0.9, 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.
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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 coefcient (β)and co-
efcient of determination (R
2
). For signicance testing, the complete bootstrapping procedure was run with 5000 samples, and no sign
changes at a signicance 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. Reective constructs
The reliability of reective measures was assessed through internal consistency, items reliability, convergent, and discriminant
validity (validity). Cronbach’s 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)
Gender
Male 41.4
Female 55.8
Prefer not to tell 3.7
Age
Less than 20 9.0
21–25 41.4
26–34 21.2
35–44 13.4
45–54 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
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8
was used to assess convergent validity, whereas the HTMT
0.85
ratio (Henseler, Ringle, & Sarstedt, 2015) was used for determining
discriminant validity. The results of reective 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 reective constructs was also higher than the threshold value (0.5) (Hair et al., 2016). Together, Tables 2
to 4 conrms the reliability and validity of the reective 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 reective measures, the quality of formative constructs is assessed by examining collinearity, the signicance of both outer
weights, and item loadings on the given constructs (Hair et al., 2016). The variance ination vector (VIF) should be between 0.2 and 5
(Hair et al., 2016). The signicance of formative items was assessed by examining the signicance (p-value) of outer weights rst, then
checking the item loadings, and lastly signicance of item loadings, if required (Hair et al., 2016). If the outer weights are insignicant,
then item loadings are examined. If items loadings are less than 0.5, then the signicance (p-value) of the item loadings is checked. If
the item loading is/are signicant, 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 fulll the quality criteria
for formative constructs. Item loadings of OIS2 (p =0.52) were insignicant. 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 coefcients (β) and the co-
efcients of the determination (R
2
). 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 coefcients (at p <0.05) and coefcients 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 signicant 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
1b
, H
1c
and H
1e
. Information seeking does not have a signicant relationship with personal networks
Table 2
Measurement model for reective 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 reective 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
9
(β =0.002, p =0.97) and social media (β = − 0.04, p =0.58). Thus, hypotheses H
1a
and H
1d
could not be supported.
Further, while examining the relationship between information sources exposure and information overload, only social media
exposure had a signicant relationship with information overload (β =0.27, p <0.05). All other information sources had an
Table 3
Discriminant validity of the reective constructs using HTMT
0.85
ratio.
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 reective constructs was
found higher than its correlation with other constructs (Wong, 2013) (Table 4).
Table 4
Intercorrelations of the reective 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: Insignicant 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
10
insignicant 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
2d
).
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 signicant positive relationship with information anxiety (β =0.11, p <0.05) given evidence for
supporting hypothesis H
3d
only. Other hypotheses, H
2a
, H
2b
, H
2c,
and H
2e,
could not be substantiated. Besides, information overload
has a signicant positive relationship with information anxiety (β =0.69, p <0.05), providing evidence for supporting hypothesis H
4
.
Social media exposure, together with information overload, accounts for 57% variance in information anxiety.
Lastly, information anxiety signicantly predicts information avoidance (β =0.56 p <0.05), substantiating hypothesis H
5
. 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 qualications. 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 ofcial 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 ofcial 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¨
as¨
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 coefcients and coefcients of determination for information overload, information anxiety, and
information avoidance. All signicant results, shown with an asterisk at p <0.05.
S.H. Soroya et al.
Information Processing and Management 58 (2021) 102440
11
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 identied 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) conrmed that WhatsApp and Youtube are the
predictors of perceived information overload. Previously, Balakrishnan and Grifths (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
conrmed 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 individuals’ online 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 individual’s information behavior, and the context is one of those factors (Wilson, 1999). The current study
explored individuals’ information 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-
dividual’s 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 signicantly help in understanding the relationship between the
aforementioned factors. The study has examined the individual’s information behavior to propose a viable set of actions to manage
health crisis information behavior.
The study has practical implications as well. The study conrmed 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
12
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 & O’Brien, 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 signicant 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
individual’s 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 conrmed that an individual’s level of information anxiety has a
signicant positive impact on the level of information avoidance. We also found that print media, mass media, and other Internet
sources such as ofcial 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.
Appendix
Following reective 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
Information
Seeking
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
13
(continued)
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
Information
Overload
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 what’s not.
Williamson et al. (2012)
OV4 - When I search for information on COVID-19, I usually get too much rather than too little
information.
Williamson et al. (2012)
OV5 - I receive too much information regarding the COVID-19 pandemic to form a coherent picture of
what’s happening.
Farooq et al. (2020)
Information
Anxiety
IA1 - I feel apprehensive (anxious) due to too much information on COVID-19 around me. L´
opez-Bonilla & L´
opez-Bonilla
(2011)
IA2 - Information overload about COVID-19 does not scare me at all. L´
opez-Bonilla & L´
opez-Bonilla
(2011)
IA3 - Working with too much information related to COVID-19 make me very nervous. L´
opez-Bonilla & L´
opez-Bonilla
(2011)
IA4 - I feel aggressive and hostile towards too much of available information on COVID-19. L´
opez-Bonilla & L´
opez-Bonilla
(2011)
IA5 - I get a sinking(unpleasant) feeling when I think of searching for information related to COVID-19 L´
opez-Bonilla & L´
opez-Bonilla
(2011)
IA6 - I feel stressed about making decisions or choosing the right information on COVID-19. Self-Developed
Information
Avoidance
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 don’t 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 =1–2 h, 4 =3–4 h, 5 =5–6 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/ofcial websites
Social Media Sources
SM1 Facebook
SM2 WhatsApp
SM3 Twitter
SM4 Instagram
SM5 YouTube
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