Understanding Knowledgeable Workers' Behavior Toward COVID-19 Information Sharing Through WhatsApp in Pakistan

Abstract

Using social media through mobile has become a major source of disseminating information; however, the motivations that impact social media users' intention and actual information-sharing behavior need further examination. To this backdrop, drawing on the uses and gratifications theory, theory of prosocial behavior, and theory of planned behavior, we aim to examine various motivations toward information-sharing behaviors in a specific context [coronavirus disease 2019 (COVID-19)]. We collected data from 388 knowledgeable workers through Google Forms and applied structural equation modeling to test the hypotheses. We noted that individuals behave seriously toward crisis-related information, as they share COVID-19 information on WhatsApp not only to be entertained and seek status or information but also to help others. Further, we noted norms of reciprocation, habitual diversion, and socialization as motivators that augment WhatsApp users' positive attitude toward COVID-19 information-sharing behavior.

Full text

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ORIGINAL RESEARCH
published: 07 October 2020
doi: 10.3389/fpsyg.2020.572526
Edited by:
Pilar Lacasa,
University of Alcalá, Spain
Reviewed by:
Barbara Caci,
University of Palermo, Italy
Anja Podlesek,
University of Ljubljana, Slovenia
Esther Cuadrado,
University of Córdoba, Spain
*Correspondence:
Talat Islam
talatislam@yahoo.com
Specialty section:
This article was submitted to
Human-Media Interaction,
a section of the journal
Frontiers in Psychology
Received: 14 June 2020
Accepted: 15 September 2020
Published: 07 October 2020
Citation:
Islam T, Mahmood K, Sadiq M,
Usman B and Yousaf SU (2020)
Understanding Knowledgeable
Workers’ Behavior Toward COVID-19
Information Sharing Through
WhatsApp in Pakistan.
Front. Psychol. 11:572526.
doi: 10.3389/fpsyg.2020.572526
Understanding Knowledgeable
Workers’ Behavior Toward COVID-19
Information Sharing Through
WhatsApp in Pakistan
Talat Islam1*, Khalid Mahmood2, Misbah Sadiq3, Bushra Usman4and
Sheikh Usman Yousaf5
1Institute of Business Administration, University of the Punjab, Lahore, Pakistan, 2Department of Information Management,
Faculty of Economics and Management Sciences, University of the Punjab, Lahore, Pakistan, 3Department of Economics
and Finance, College of Economics and Management, Al Qasimia University, Sharjah, United Arab Emirates, 4School
of Management, Forman Christian College, Lahore, Pakistan, 5Hailey College of Commerce, University of the Punjab,
Lahore, Pakistan
Using social media through mobile has become a major source of disseminating
information; however, the motivations that impact social media users’ intention and
actual information-sharing behavior need further examination. To this backdrop, drawing
on the uses and gratifications theory, theory of prosocial behavior, and theory of planned
behavior, we aim to examine various motivations toward information-sharing behaviors
in a specific context [coronavirus disease 2019 (COVID-19)]. We collected data from
388 knowledgeable workers through Google Forms and applied structural equation
modeling to test the hypotheses. We noted that individuals behave seriously toward
crisis-related information, as they share COVID-19 information on WhatsApp not only to
be entertained and seek status or information but also to help others. Further, we noted
norms of reciprocation, habitual diversion, and socialization as motivators that augment
WhatsApp users’ positive attitude toward COVID-19 information-sharing behavior.
Keywords: theory of planned behavior, COVID-19, information sharing behavior, social media, developing country,
theory of prosocial behavior, theory of use and gratification
INTRODUCTION
A decade ago, information about crises was first informed by the affected ones through mobile
phones, then were reported on social media (Oh et al., 2011). Nowadays, social media has become
a major and rapid source of improvising, communicating, and distributing information during
crises (Zhao et al., 2016). This is because social media has shown a great potential to respond to
affected people during crises. However, there remained a criticism on the accuracy and quality of
the information provided through social media by the volunteers (Alexander, 2014). For instance,
at the early stage of tragedy or crisis, complete information about crises may not be available, and
if in such situations social media users keep on posting and reposting inaccurate information,
these could result in serious damages. Indeed, social media is a quick source of distributing
information or rumors compared with traditional media (Tripathy et al., 2013). In fact, while
searching “false news about earthquake,” one can find millions of fake news about the incident
posted by citizens, and most of the news is there to create more panic about another imminent
earthquake (Tanaka et al., 2013).
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It does not necessarily mean that social media is only a
source to spread false information during crises; in fact, it
can be used as a channel to combat rumors. Zhao et al.
(2016) noted that social media users first authenticate and
then broadcast crises-related information. Similarly, Bird et al.
(2012) also noted social media users’ positive attitudes toward
crises-related information sharing. In March 2011, when Japan
was hit by an earthquake tsunami, social media (Twitter) was
actively involved, as Stirratt (2011) noted 49% of the circulated
information was either positive or somewhat positive, whereas
only 7% of the information was negative or somewhat negative
about the emergency response. The world is facing a similar kind
of problem because of the new pandemic [coronavirus disease
2019 (COVID-19)]. The issue (COVID-19) is still new with lots
of rumors on social media.
In December 2019, Hubei province in China captured the
world’s attention when pneumonia (lung disease) caused by a
coronavirus emerged in Wuhan. The city of Wuhan is located in
central China and is a key industrial and transportation hub with
over 11 million population. At the beginning, it was believed that
severe acute respiratory syndrome coronavirus 2 (SARS-CoV-
2) is typically not transmissible to humans, as it has its origin
rooted back to animals. However, in case of SARS, this virus
is transmissible from animals to humans and humans to other
humans. The severity of virus can be estimated by the fact that it
took 3 months to reach the first 100,000 cases and only 12 days
for another 100,000 cases (WHO, 2020).
Numerous misinformation is reported on several social media
platforms regarding cure, prevention, outcomes, and etiology of
the disease (Sandhya, 2020). Although these rumors are masking
health behaviors, however, promoting erroneous practices is not
only spreading the virus but also causing poor mental and
physical health. For example, in India, a father of three kids
was diagnosed with COVID-19 who then committed suicide
(Joe, 2020). Similarly, after hearing about chloroquine (a drug
primarily used to treat malaria), as a powerful drug to treat
COVID-19 on media, several Nigerians were reported overdosed
by their health minister (Busari and Adebyo, 2020). Similarly,
the news of lockdown created panic regarding stationeries and
groceries, which unbalanced demand–supply gaps and disrupted
the supply chain in many countries (Spencer, 2020). These
rumors largely affected individuals’ psychological and physical
health, thereby generating the need to study what motivates social
media users to share such information.
Ji et al. (2014) used rumor dynamic theory and developed
an anti-rumor model. Similarly, Tripathy et al. (2011) and
Tripathy et al. (2013) also developed anti-rumor models (i.e.,
neighborhood, beacon, and delayed start models). These models
were developed for social media through a technological
perspective and thus are very complex to understand for a
layman. However, studies suggesting anti-rumor models from a
social–psychological perspective are scarce. For example, Zhao
et al. (2016) developed a norm activation model based on
the theory of planned behavior to understand social media
users’ information-sharing behavior, while Chen et al. (2018)
extended this model by examining motivational factors toward
such behaviors and suggested future researchers to identify more
factors. In addition, past studies have highlighted the role of social
media (mostly Facebook or Twitter) toward dissemination of
crisis-related information (Tanaka et al., 2013;Zhao et al., 2016;
Chen et al., 2018). However, studies on the factors that motivate
social media users (WhatsApp) to share such information are
limited. To fill in this gap, we selected WhatsApp users because
statistics show that 29 million WhatsApp messages are sent every
minute in Pakistan (Khan, 2020).
Moreover, past studies have identified entertainment,
“individual’s desire to experience emotions through online
participation” (Park et al., 2009), information seeking, “seeking
for information as a consequence of a need to satisfy some goal”
(Lee and Ma, 2012), socialization, “talking with others to achieve
a sense of community and peer support on the particular topic
of the group” (Karnik et al., 2013), status seeking, “maintaining
personal status, as well as of their friends, through the online
group participation” (Malik et al., 2016), habitual diversion,
“entertaining activity as an escape from reality or routine”
(Krause et al., 2014), and norms of reciprocity, “repaying in kind
what others have done for us” (Chen et al., 2018), as motivators
for information-sharing behavior on social media. However, how
these motivators work holistically during crises (COVID-19 in
this study) and the benefits associated with these need to be shed
light. Therefore, we aim at extending past studies by examining
the roles of socialization, status seeking, norms of reciprocity,
habitual diversion, information seeking, and entertainment
(motivational factors) of WhatsApp users’ attitudes toward
COVID-19 information-sharing behavior. We used Batson’s
(1990) theory of prosocial behavior (TPSB), Ajzen’s (1991) theory
of planned behavior (TPB), and Katz et al.’s (1974) uses and
gratifications theory (U&G) to develop a novel model toward
social media sharing behavior of knowledgeable workers. In
simple words, our study aims to examine:
(1) The role of socialization, status seeking, habitual
diversion, information seeking, norms of reciprocity,
and entertainment toward COVID-19 information-sharing
behavior through WhatsApp (supporting from TPSB
and U&G).
(2) How these factors affect the actual behaviors (TPB)
Uses and Gratifications Theory
According to U&G, individuals fulfill their gratifications by
selecting specific media over alternatives. Literature has suggested
U&G as the utmost significant theory that explains the
determinants and meaning of social media users’ behavior in the
field of communication studies (Malik et al., 2016). Researchers
started using U&G in explaining and identifying the motivations
behind the use of traditional media. However, with the passage of
time, traditional media was replaced by internet, which changed
individuals’ behavior of using social media. Few of the studies
have used U&G to examine the users’ motivations of using social
media, such as Twitch, Snapchat, Instagram, Twitter, WeChat,
and Facebook (Phua et al., 2017;Sjöblom et al., 2017;Chen et al.,
2018). Kim and Yang (2017) argued that social media users use
“share,” “comments,” “care,” and “like/dislike” as communication
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behaviors. Among these, “like/dislike” and “care” are driven by
affect, whereas “comment” is driven by cognition. However,
“share” is driven by both cognition and affection.
Krause et al. (2014) suggested that individuals highly motivate,
involve, and devote when contributing something via social
media, and their sharing depends upon communal incentive
and self-interest (Fu et al., 2017). Indeed, content such as
music (Krause et al., 2014), links (Baek et al., 2011), pictures
(Malik et al., 2016), information regarding health (AlQarni
et al., 2016), news (Lee and Ma, 2012), and crises-related
information (Chen et al., 2018) matters while sharing on
social media. Malik et al. (2016) identified information seeking,
status seeking, and habitual diversion as gratification among
368 Facebook users while posting photos. Chen et al. (2018)
identified norm of reciprocity, habitual diversion, and status
seeking motivators for sharing crises-related information on
WeChat. AlQarni et al. (2016) analyzed 1,551 Facebook posts
on diabetes mellitus from the Arabic world to understand
users’ gratification. They concluded that most of the users
post their personal experiences to create awareness as norms
of reciprocity. Park et al. (2009) noted that most of the
information-sharing activities on social media (Facebook) take
place through group applications. They noted that most of
the students use social media to seek information about civic
activities, status seeking, and socializing, instead of political
activities. Lee and Ma (2012) studied 203 students and identified
that socialization and status seeking positively influence while
entertainment and information seeking insignificantly associated
with their intention to share information. According to Chen
et al. (2018), factors that motivate social media users to share
crises-related information need further attention. Therefore, we
aim to examine how previously examined motivations (getting
entertainment, seeking information, habitual diversion, status
seeking, socialization, and norms of reciprocity) for information-
sharing behaviors on social media can make a difference
during COVID-19 outbreak with the assumption that getting
entertainment may negatively affect said behaviors. According to
Zhao et al. (2016), social media users may behave with maturity
regarding sharing crises-related information. More specifically,
Chen et al. (2018) studied 365 WeChat users and noted a
negative influence of entertainment on attitudes toward behavior
for crises-related information. We extend existing literature in
two ways. First, past studies have examined these motivators
with information-sharing intention (Park et al., 2009;Lee and
Ma, 2012); we extend these studies and attempt to understand
these motivators through TPB. Therefore, we examined these
motivators’ influence on attitudes toward behavior, subjective
norms (SN), and perceived behavioral control (PBC). The
motivators, i.e., getting entertainment, seeking information,
habitual diversion, status seeking, socialization, and norms of
reciprocity, help individuals to evaluate their favorable or non-
favorable behaviors (PBC) (Park et al., 2009;Malik et al., 2016;
Chen et al., 2018). Whereas status seeking and socialization can
also affect individuals PBC (an individual’s perception about ease
or difficulty to perform a behavior) and SNs [an individual’s
perception about whether his/her near ones (e.g., teachers, friends,
peers, spouse, and parents) want him/her to behave in a specific
manner], given that individuals around us impact our beliefs
about favorable situations. Specifically, we aim to examine
whether the gratification of sharing COVID-19 information on
social media identified by literature (in isolation) can impact
WhatsApp users’ attitudes toward information-sharing behavior,
SNs, and PBC. Thus, we may hypothesize:
H1: Getting entertainment (a) has a negative impact, whereas
seeking information (b), habitual diversion (c), status seeking (d),
and socialization (e) have a positive impact, on WhatsApp users’
attitudes toward COVID-19 information-sharing behavior.
H2: Seeking status (a) and socialization (b) have a positive
impact on WhatsApp users’ subjective norms about COVID-19
information-sharing behavior.
H3: Seeking status (a) and socialization (b) have a positive impact
on WhatsApp users’ perceived behavioral control toward COVID-
19 information-sharing behavior.
Theory of Prosocial Behavior
We extend the literature by arguing that, in addition to
gratification, individuals may voluntarily share COVID-19
information on WhatsApp for prosocial purposes (i.e., TPSB).
According to Sanstock and Topical (2007), prosocial behavior
includes obeying rules, cooperating, donating, sharing, helping,
and complying to socially acceptable behaviors. However, social
psychologists posit a different perspective behind individuals’
prosocial behavior on social media. Morozov (2010) argued
that social media users lack strong bonding, therefore, it may
not be an essential platform for prosocial behavior, called
“Slacktivism.” In contrast, while studying Facebook and Twitter,
Fatkin and Lansdown (2015) noted a significant association
between exposure to social media and prosocial behavior. For
example, Michelle Sollicito created a page “Snowed Out Atlanta”
on Facebook to help people after sensing traffic gridlock, as a
result, many open pages and groups were created to help people
in snow disasters. Likewise, a page “blood donations of Hailey
College of Banking and Finance” created many other open groups
to help those who need blood in the country. Similarly, the
concept of Black Friday by Americans was adopted by many
other countries in the world (Fatkin and Lansdown, 2015). These
findings show that, despite weak ties among users, social media
can be a source of prosocial helping behaviors.
While exploring prosocial behavior, Eisenberg et al. (1998)
identified social status, egoistic concerns, perceived fairness
system, empathy toward others’ welfare, and reciprocity as
the motivations behind such behaviors. Pai and Tsai (2016)
argued that norms of reciprocity may be the key motivating
factor that impact individuals’ information-sharing behavior.
Norms of reciprocity is a universal norm that individuals must
pay back to the one who helped them at the time of need
(Gouldner, 1960). Literature is mixed while applying the concept
of reciprocity on information-sharing behavior on social media.
For example, Wasko and Faraj (2000) noted a negative, Wiertz
and De Ruyter (2007) noted an insignificant, while Chang
and Chuang (2011) noted a positive and significant association
of norms of reciprocity with individuals’ information-sharing
behavior on social media. It can be inferred that the association
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between norms of reciprocity and information-sharing behavior
may depend on the context and conditions (Pai and Tsai,
2016). Following the same, we argue that in case of crises,
WhatsApp users consider it their responsibility to share accurate
and updated information to benefit sufferers, thus we may
hypothesize:
H4: Norms of reciprocity have a positive impact on WhatsApp users’
attitudes (a), subjective norms (b), and perceived behavioral control
(c) toward COVID-19 information-sharing behavior.
Theory of Planned Behavior
According to Ajzen (2011), individuals’ behavior is dependent
upon their belief about controlling their behavior, perception
about their near ones that they want them to perform a certain
behavior, and/or they have a favorable attitude toward that
behavior. Thus, TPB elucidates the inspiring and informational
influence on individuals’ behaviors. Researchers have been using
TPB in the field of computers since the 1980s. However, few of
the researchers have used this theory in explaining users’ online
behaviors, such as service and shipping usage (Lu et al., 2007),
watching video (Cha, 2013), and shopping (Cheng and Huang,
2013). Later, researchers start using TPB in exploring individuals’
behavior using social media such as privacy protection (Taneja
et al., 2014), combating rumor (Zhao et al., 2016), crises
information sharing (Chen et al., 2018), and location disclosure
(Chang and Chen, 2014). Zhao et al. (2016) inculcates that
social media-related behaviors can best be explained with the
help of TPB. Given that, we aim to extend these studies by
examining WhatsApp user’s behavior during the COVID-19
pandemic through TPB. As discussed earlier, TPB explains an
individual’s behavioral intention (BI) through three aspects,
i.e., “attitude toward behavior, subjective norms, and perceived
behavioral control.” According to Ajzen (2011), individuals first
evaluate their behavior (favorable or not favorable) to develop
their BI, called attitude toward behavior (ATB). We argue that
COVID-19 information-sharing attitude impacts social media
users’ sharing intention. Following the same, we hypothesize:
H5: A positive attitude toward COVID-19 information sharing has
a positive impact on WhatsApp users’ intention to share COVID-19
information.
As per TPB, the second aspect that influences BI is SNs.
SN refers to an individual’s perception about whether his/her
near ones (e.g., teachers, friends, peers, spouse, parents, etc.)
want him/her to behave in a specific manner (Ajzen, 2011). In
simple words, SN is an individual’s perception about consent
or condemnation of his behaviors by the majority (Amjad and
Wood, 2009). Chang et al. (2014) noted that an individual’s
63% of the variance of gameplay intentions was explained by
SNs. Similarly, Bai et al. (2014) noted an individual’s 57%
of the intentions to continue hygienic food-handling behavior
is explained by SNs. Particular to social media, Chen et al.
(2018) also found SNs positively impacting on individuals’
intention of sharing crises-related information. Therefore, we
may hypothesize:
H6: Subjective norms have an impact on WhatsApp users’ intention
to share COVID-19 information.
According to TPB, PBC is the third aspect that impacts BI.
This aspect varies across situations because it is an individual’s
perception about ease or difficulty to perform a behavior.
Therefore, individuals when perceiving favorable situations
would behave accordingly. Ajzen (2011) inculcates that PBC
also has a tendency to impact individual’s actual behaviors
(AB). In particular, individuals have multiple sources to share
information; however, we aim to examine how PBC predict
WhatsApp users’ actual and BI to share COVID-19 information.
Thus, we hypothesize:
H7: Perceived behavioral control has a positive impact on
WhatsApp users’ behavioral intention (a) and actual behavior (b)
of sharing COVID-19 information.
Literature has suggested that individual’s BI positively affects
their ABs in microblogging (Jiang et al., 2016) and transportation
(Bamberg et al., 2007). Whereas others have identified a mixed
result studying solar energy usage (Hai et al., 2017) and
combating rumor (Zhao et al., 2016) and electronic waste
(Echegaray and Hansstein, 2017). Thus, there is a need to
further examine the association between BI and AB. We aim
to examine whether intention to share COVID-19 information
affects individuals’ AB toward information sharing or not by
hypothesizing:
H8: Behavioral intention has a positive impact on WhatsApp users’
behavior of sharing COVID-19 information.
MATERIALS AND METHODS
Sample and Procedure
We collected data from the students of MBA executive because
of the following reasons. First, we wanted to understand the
behaviors of well-educated people toward COVID-19. Higher
Education Commission of Pakistan has authorized universities
that an applicant must have 16 years of education with a
minimum of 2 years of work experience to be enrolled in MBA
executive (which served the purpose). Second, although English
is considered as the official language, still many of the employees
remained unable to understand English (Raja et al., 2004;Islam
et al., 2019, 2020a,b), thus educated people were selected as
they can better respond to the questionnaires in English. Finally,
during the lockdown, data collection was difficult in real settings.
We conducted an online survey where a link was shared on the
WhatsApp groups of executive students. The students were noted
to disseminate COVID-19-related information on these groups
on a frequent basis. Further, few of the students or their family
members were COVID-19 positive. The respondents were well
explained about the purpose of this study and were ensured about
the anonymity of their responses. Within 15 days, we received
394 responses out from 420 students. The data on all variables
were collected from the same respondent; therefore, we followed
the instructions of Podsakoff et al. (2012) to cope with the issue of
common method variance (CMV). In addition, we also examined
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Harman’s single-factor test, and a single factor was found to have
no more than 50% variance. The test supported the conclusion
that CMV is absent.
We consider age, gender, qualification, and sector as control
variables as these can have effects on respondents’ attitudes and
behaviors (Davis et al., 2019;Ahmad et al., 2020). According
to gender, 84.5% (n= 328) of the respondents were male and
15.5% (n= 60) of the respondents were female, which represent
the male-dominant culture of the country (Islam et al., 2020b).
According to age, 46.4% (n= 180) of the respondents were
between 31 and 40 years, 35.6% (n= 138) were less than 30 years,
12.4% (n= 48) were between 41 and 50 years, and only 5.7%
(n= 22) were above the age of 50 years. Based on sector, 64.7%
(n= 251) of the respondents were from the manufacturing sector,
while 35.3% (n= 137) were from the service sector. Interestingly,
33.2% (n= 129) of the respondents were habitual WhatsApp users
for at least 2 h/day, 30.4% (n= 118) use WhatsApp for 3 h/day,
19.3% (n= 75) use WhatsApp for 1 h/day, and 17.0% (n= 66)
were using WhatsApp for more than 3 h/day.
Measures
We adapted questionnaires from the past studies and modified
them according to the situation (COVID-19). Respondents
responded using a five-point Likert scale (see Appendix A).
We used six factors (i.e., norms of reciprocity, socialization,
status seeking, habitual diversion, information seeking, and
entertainment) about the reasons of participating in online
discussions. Among these, questionnaires on four factors
(i.e., self-status seeking, socialization, information seeking, and
entertainment) comprised of three items for each factor were
adapted from the study of Park et al. (2009), who reported their
reliability ranges between 0.81 and 0.87. These factors were also
validated by Chen et al. (2018) in the Southeast Asian context.
Using the same factors, we noted its values of Cronbach’s alpha
ranges between 0.70 and 0.82. We adapted another three-item
scale of habitual diversion from the study of Malik et al. (2016)
and noted 0.71 as the value of its reliability. Finally, norms of
reciprocity were measured through a three-item scale of Pai and
Tsai (2016), and we noted 0.73 as the value of its reliability.
Information about (SN, ATB, PBC, AB, and BI was obtained
through Ajzen’s (1991) three-item scale for each. These scales
have been validated by Oh et al. (2013),Han (2015),Zhao et al.
(2016), and Chen et al. (2018). We noted 0.70, 0.79, 0.83, 0.86, and
0.83 as the values of its reliability, respectively (see Appendix A).
Statistical Analyses
We applied structural equation modeling (SEM) to test
the hypotheses. The data were examined for the basis
assumptions of SEM (e.g., missing values, outliers, normality,
and multicollinearity). First, we conducted a confirmatory
factor analysis (CFA) as we used validated scales. CFA
was performed using AMOS version 24, applying maximum
likelihood estimation. According to Mîndrilã (2010), in case of
an ordinary scale, weighted least squares (WLS) parameter is
best but only when data are asymmetric or show a high level
of heteroskedasticity. The data for the study were examined
for heteroskedasticity and found to be normally distributed;
therefore, maximum likelihood method was used (Li, 2016). We
followed Williams et al. (2009) for mode fit indices, Hair et al.
(2018) for the values of factor loading, composite reliability,
and average variance extracted, and Cronbach’s alpha. We
then examined Pearson correlation to examine the strength of
bivariate relationships among variables. Finally, we examined the
structural model to test the hypotheses.
RESULTS
We examined the hypotheses through SEM using AMOS.
Therefore, first, the data were examined to fulfill their
basic assumptions (i.e., missing values, outliers, normality,
and collinearity).
Preliminary Analyses
The data (394 responses) were found to be free from missing
values because they were collected through Google Forms and
a condition of compulsory answer was applied. We applied
Mahalanobis distance test at P<0.01 to identify outliers;
therefore, six were excluded (Hair et al., 2018). Regarding
normality, the values of skewness and kurtosis (i.e., ±1 and
±3, respectively) were noted to be within range (Byrne, 2016).
Finally, none of the correlation was found to be more than 0.85
(Table 1), which identifies the absence of collinearity in the data
(Tabachnick and Fidell, 2019).
Descriptive Statistics
The results of descriptive statistics are presented in Table 1. The
mean values show that the respondents agreed about five factors
[i.e., norms of reciprocity (3.82), socialization (3.56), status
seeking (3.60), habitual diversion (3.59), and information seeking
(3.63)], whereas respondents disagreed about entertainment
(1.68) as the reason for participating in online discussions during
the COVID-19 pandemic. Further, they also agreed on SNs (3.83),
ATB (3.56), perceived behavioral control (3.47), actual behavior
(3.72), and behavioral intention (3.82). Moreover, the values of
Cronbach’s alpha of all the variables were also noted well above
the standard value of 0.70 (Hair et al., 2018) (Appendix A).
Further, we noted positive and significant correlations among
variables used (rranging between 0.32 and 0.67, P<0.05), except
entertainment as it was noted to have a negative correlation with
other variables (rranging between −0.30 and −0.59, P<0.01).
Structural Equation Modeling
We followed Anderson and Gerbing (1998), and SEM was
applied in two stages where, first, CFA was conducted to
examine the measurement model (11-factor model as all the
factors were included while examining the measurement model)
because scales used by us were adapted; second, the structural
model was examined. We used “chi-square/degree of freedom
(x2/df ≤3.0), Tucker–Lewis index (TLI ≥0.90), comparative
fit index (CFI ≥0.90), goodness-of-fit index (GFI ≥0.90), root
mean residual (RMR ≤0.10), and root mean square error of
approximation (RMSEA ≤0.08)” for model fit, as suggested
by Williams et al. (2009) and found our model fit, i.e., x2/df
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TABLE 1 | Results of correlation, mean, and standard deviation.
Variables 1 2 3 4 5 6 7 8 9 10 11
(1) Norms of Reciprocity (NOR) 1
(2) Socialization 0.59** 1
(3) Status Seeking (SS) 0.55** 0.67** 1
(4) Habitual Diversion (HD) 0.63** 0.60** 0.58** 1
(5) Information Seeking (IS) 0.54** 0.58** 0.53** 0.51** 1
(6) Entertainment −0.44** −0.30** −0.38** −0.40** −0.42** 1
(7) Subjective Norms (SN) 0.45** 0.33** 0.35** 0.43** 0.38** −0.47** 1
(8) Attitude Toward Behavior (ATB) 0.58** 0.43** 0.37** 0.48** 0.40** −0.38** 0.31** 1
(9) Perceived Behavioral Control (PBC) 0.55** 0.53** 0.50** 0.50** 0.49** −0.55** 0.41** 0.53** 1
(10) Actual Behavior (AB) 0.46** 0.33** 0.38** 0.40** 0.32** −0.53** 0.49** 0.43** 0.53** 1
(11) Behavioral Intention (BI) 0.56** 0.45** 0.47** 0.49** 0.44** −0.59** 0.49** 0.47** 0.61** 0.63** 1
Mean 3.82 3.56 3.60 3.59 3.63 1.98 3.83 3.56 3.47 3.72 3.82
Standard Deviation 0.71 0.70 0.74 0.75 0.71 0.68 0.65 0.79 0.82 0.84 0.81
(981.248/440) = 2.23, TLI = 0.90, CFI = 0.91, GFI = 0.90,
RMR = 0.039, RMSEA = 0.056, and P-Close = 0.014. Further,
we followed Hair et al. (2018) to examine loading (i.e., ≥0.50),
average variance extracted (i.e., ≥0.50), and composite reliability
(i.e., ≥0.60) and noted that our scales fulfilled the criteria (see
Appendix A).
Hypotheses Testing
Results generated through the structural model (maximum
likelihood parameter estimation) are presented in Figure 1 and
Table 2. The structural model was found to be fit, i.e., x2/df
(1,077.551/465) = 2.31, TLI = 0.92, CFI = 0.93, GFI = 0.92,
RMR = 0.044, RMSEA = 0.058, and P-Close = 0.001. The
values revealed that entertainment negatively impacts (β=−0.14,
CR = −3.190, P= 0.001), habitual diversion (β= 0.15, CR = 3.447,
P= 0.000), socialization (β= 0.11, CR = 2.497, P= 0.013), and
norms of reciprocity (β= 0.42, CR = 9.563, P= 0.000) positively
impact, whereas seeking information (β=−0.04, CR = 0.901,
P= 0.368) and status seeking (β=−0.07, CR = −1.548,
P= 0.122) insignificantly impact on WhatsApp users’ attitudes
toward COVID-19 information-sharing behavior. These findings
support H1a, H1c, H1e, and H4a and rejects H1b and H1d,
respectively. The values further show that seeking status (β= 0.14,
CR = 3.011, P= 0.003), socialization (β= 0.13, CR = 2.786,
P= 0.000), and norms of reciprocity (β= 0.37, CR = 7.899,
P= 0.000) were also noted to have a positive impact on WhatsApp
users’ SNs about COVID-19 information-sharing behavior. These
results support H2a, H2b, and H4b, respectively. Similarly,
seeking status (β= 0.19, CR = 4.144, P= 0.000), socialization
(β= 0.25, CR = 5.609, P= 0.000), and norms of reciprocity
(β= 0.36, CR = 8.131, P= 0.000) were also noted to have a positive
impact on WhatsApp users’ perceived behavioral control toward
COVID-19 information-sharing behavior. These results support
H3a, H3b, and H4c, respectively.
The results further revealed that ATB (β= 0.17, CR = 2.646,
P= 0.008), SNs (β= 0.24, CR = 6.425, P= 0.000), and
perceived behavioral control (β= 0.55, CR = 14.506, P= 0.000)
positively impact WhatsApp users’ intention to share COVID-19
information. Finally, perceived behavioral control (β= 0.16,
CR = 3.103, P= 0.002) and behavioral intention (β= 0.49,
CR = 9.180, P= 0.000) were also found to predict WhatsApp
users’ actual behavior toward COVID-19 information. These
results support H5, H6, H7a, H7b, and H8, respectively.
DISCUSSION
The aim of this study was to develop and understand a
model about the motivations toward WhatsApp users’ COVID-
19 information-sharing behavior in a developing country.
We consider the framework of TPB and extend with the
help of TPSB and U&G. We examined hypotheses on 388
responses collected during the COVID-19 pandemic through
an online survey. Unlike past studies, the findings of this
study are interesting. For example, past studies confirmed that
most of the social media users (especially mobile) consider
social media a source of entertainment (Leggatt, 2011). Tsang
et al. (2014) noted that entertainment positively associated
with users’ ATB. On the other hand, Lee and Ma (2012)
identified an insignificant association between entertainment
and information-sharing behavior. Further, Chen et al.’s (2018)
findings revealed a negative association between entertainment
and attitude toward information sharing. Similarly, information
seeking and status seeking also show a mixed result. For
example, Malik et al. (2016) identified that social media users
share information (photos) for information seeking and status
seeking, while Chen et al. (2018) identified a non-significant
association of status seeking and information seeking with ATB.
It can be inferred that motivating factors impact individuals’
information-sharing behavior differently in different contexts,
i.e., situation, culture, etc. (Fu et al., 2017). Considering the
situational factor (i.e., COVID-19), we noted that individuals
do not share COVID-19 information on WhatsApp to be
entertained. Precisely, individuals respond to crises with a
serious attitude and try to disseminate authentic information
(Chen et al., 2018). Contradicting previous studies, we further
noted that information and status seeking does not motivate
individuals toward information sharing during the COVID-19
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Islam et al. COVID-19 Information Sharing Behavior
Entertainment
Information Seeking
Norms of
Reciprocity
Habitual Diversion
Status seeking
Socialization
Subjective Norms
Perceived Behavioral
Control
Attitude towards
Behavior
Actual Behavior
Behavioral Intention
FIGURE 1 | Structural model.
TABLE 2 | Results of hypotheses testing.
Hypotheses Standardized βCR SE PResult
Entertainment→Attitude Toward Behavior −0.14 −3.190 0.046 0.001 H1a is accepted
Information Seeking→Attitude Toward Behavior −0.04 0.901 0.045 0.368 H1b is rejected
Habitual Diversion→Attitude Toward Behavior 0.15 3.447 0.043 0.000 H1c is accepted
Status Seeking→Attitude Toward Behavior −0.07 −1.548 0.043 0.122 H1d is rejected
Socialization→Attitude Toward Behavior 0.11 2.497 0.045 0.013 H1e is accepted
Norms of Reciprocity→Attitude Toward Behavior 0.42 9.563 0.043 0.000 H4a is accepted
Status Seeking→Subjective Norms 0.14 3.011 0.040 0.003 H2a is accepted
Socialization→Subjective Norms 0.13 2.786 0.042 0.000 H2b is accepted
Norms of Reciprocity→Subjective Norms 0.37 7.899 0.042 0.000 H4b is accepted
Status Seeking→Perceived Behavioral Control 0.19 4.144 0.044 0.000 H3a is accepted
Socialization→Perceived Behavioral Control 0.25 5.609 0.047 0.000 H3b is accepted
Norms of Reciprocity→Perceived Behavioral Control 0.36 8.131 0.046 0.000 H4c is accepted
→Attitude toward BehaviorBehavioral Intention 0.17 2.646 0.039 0.008 H5 is accepted
Subjective Norms→Behavioral Intention 0.24 6.425 0.042 0.000 H6 is accepted
Perceived Behavioral Control→Behavioral Intention 0.55 14.506 0.038 0.000 H7a is accepted
Behavioral intention→Actual Behavior 0.49 9.180 0.058 0.000 H8 is accepted
Perceived Behavioral Control→Actual Behavior 0.16 3.103 0.057 0.002 H7b is accepted
CR represents t-value. SE, standard error; P, significance.
pandemic. This may be because individuals primarily focus
on the pandemic (crisis) and want to be assured before
sharing the same information on social media as they prefer
to combat rumors (Zhao et al., 2016). In line with literature,
we also noted socialization, habitual diversion, and norms
of reciprocity as motivating factors for “attitudes toward
information-sharing behavior.”
Past studies have identified socialization, status seeking, and
norms of reciprocity as the motivational factors for SNs; we
identified the same for perceived behavioral control as well. This
finding suggests that WhatsApp users use prosocial behaviors
regarding sharing information, rather than being just rumor
mills (Hjorth and Kim, 2011). This finding can be justified by
arguing, although status seeking, and socializing is considered
bad during the pandemic; still, the desire to connect with others
to get helpful information overcomes the fear of information
sharing. According to Kim (2014), individuals are prone to
anxiety when they feel isolated or find themselves with lack
of sufficient information. However, having themselves equipped
with timely information may help them in getting out of the state
of anxiety. Regarding norms of reciprocity, individuals consider
it their responsibility to pay back to the society by sharing
pandemic-related information on WhatsApp.
Finally, consistent with the TPB, we noted that ATB, SNs, and
perceived behavioral control positively predict WhatsApp
users’ behavioral intention and actual behavior toward
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Islam et al. COVID-19 Information Sharing Behavior
COVID-19-related information. This finding suggests that
WhatsApp users who feel an obligation have a positive attitude
toward others and are more confident about sharing information
and are more likely to be involved in sharing COVID-19
information with others. Replying to the contradictory results
(Zhao et al., 2016), we identified behavioral intention as the
predictor of actual behavior. Thus, TPB, U&G, and TPSB fit
to understand WhatsApp users’ information-sharing behavior
during the COVID-19 pandemic.
Implications and Limitations
The findings of our study contribute theoretically and practically.
First, most of the previous studies regarding information-sharing
behavior have been conducted in western countries where
Twitter or Facebook remained their prime focus. However, the
prime focus of our study was to understand the individuals’
information-sharing behavior during COVID-19 in a non-
western context. Second, past studies mostly have studied generic
information-sharing behaviors (e.g., Zhao et al., 2016;Chen
et al., 2018), whereas we examined the same in a specific
context (COVID-19) and found contradictory results, which
generated the need to further understand social media users’
information-sharing behavior along with their motivations.
Third, as we investigated the relationship between motivations
and information sharing, therefore, the findings of our study may
likely benefit academicians, policy makers, and all other related
stakeholders. Finally, our study extends the existing literature
about information sharing in the field of behavioral research by
combining TPSB, U&G, and TPB.
This study suggests practitioners to handle crises by
understanding that educated individuals in developing
countries are very serious regarding disseminating crises-related
information. They do not share information to be entertained
or seek status, but to be socialized as to alleviate their anxiety
and tension by sharing crisis-related information (COVID-
19 here). Further, educated social media users feel that it is
their responsibility to share crisis-related information with
others for their betterment and to combat rumors. Given that,
healthcare professionals should release relevant and sufficient
information on social media through different channels, such as
WhatsApp, Twitter, Facebook, or Snapchat, etc. While doing so,
disseminating misleading information may be prevented.
Despite implications, the study has few limitations. First, we
collected data from highly educated individuals using WhatsApp,
which may raise a question on its generalizability to other
populations as the results might be different considering less
educated individuals and other social media channels. Second,
most of the respondents of this study were male, which may raise
a question of gender bias results. Therefore, future researchers are
suggested to have equal representation of both male and female
participants. Third, the data on independent and dependent
variables were collected from the same source, which may
generate biased results; therefore, future researchers are suggested
to collect data as dyads (i.e., user and his/her colleague). In
addition, such data restrict the researchers to identify the exact
direction. Fourth, there exists a gap in the measures used as some
of the questions are about sharing COVID information, while
others are about sharing authentic COVID information. Finally,
we used motivations based on U&G and TPSB; future researchers
are suggested to identify other unexplored motivations toward
information-sharing behavior.
CONCLUSION
Drawing upon the U&G, TPSB, and TPB, we examined a
model to understand the motivations that impact social media
(WhatsApp) users while sharing COVID-19 information. We
noted that social media users do not share crises-related
information to be entertained or for information seeking and
status seeking. They behave with maturity and consider their
responsibility to share authentic information during crises. The
findings of this study suggest that healthcare professionals share
relevant information on social media for further dissemination.
Such policies would not only help victims in adopting accurate
precautionary measures but also help to combat rumors.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by Institute of Business Administration, University of
the Punjab. Written informed consent for participation was not
required for this study in accordance with the national legislation
and the institutional requirements.
AUTHOR CONTRIBUTIONS
TI developed the manuscript, collected the data, and conducted
the analysis. KM initiated the idea. MS helped in incorporating
suggested changes, while BU and SY gave the manuscript a final
proofread. All the authors contributed to the article and approved
the submitted version.
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2020 Islam, Mahmood, Sadiq, Usman and Yousaf. This is an open-
access article distributed under the terms of the Creative Commons Attribution
License (CC BY). The use, distribution or reproduction in other forums is permitted,
provided the original author(s) and the copyright owner(s) are credited and that the
original publication in this journal is cited, in accordance with accepted academic
practice. No use, distribution or reproduction is permitted which does not comply
with these terms.
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Islam et al. COVID-19 Information Sharing Behavior
APPENDIX
APPENDIX A | Questionnaire.
Variables of the study λCR AVE α
Entertainment (Park et al., 2009) [1- strongly disagree to 5- strongly agree]
E1: Sharing COVID-19 information through WhatsApp is entertaining for me. 0.76 0.82 0.61 0.82
E2: Sharing COVID-19 information through WhatsApp is fun for me. 0.84
E3: Sharing COVID-19 information through WhatsApp is exciting for me. 0.72
Information seeking (Park et al., 2009) [1- strongly disagree to 5- strongly agree]
IS1: I share COVID-19 information through WhatsApp to get useful information through other’s feedback. 0.72 0.81 0.59 0.72
IS2: I share COVID-19 information through WhatsApp to get other’s opinion through their feedback. 0.82
IS3: I share COVID-19 information through WhatsApp to learn more about pandemic. 0.76
Status seeking (Park et al., 2009) [1- strongly disagree to 5- strongly agree]
SS1: I share COVID-19 information through WhatsApp, because I want others to perceive me as sociable. 0.71 0.74 0.50 0.70
SS2: I share COVID-19 information through WhatsApp, because I want others to perceive me as knowledgeable. 0.65
SS3: I share COVID-19 information through WhatsApp, because I want others to perceive me as valuable. 0.74
Socializing (Park et al., 2009) [1- strongly disagree to 5- strongly agree]
SO1: I share COVID-19 information through WhatsApp to share something with others. 0.73 0.77 0.53 0.76
SO2: I share COVID-19 information through WhatsApp to stay in touch with people I know. 0.77
SO3: I share COVID-19 information through WhatsApp to feel like I belong to a community. 0.69
Habitual Diversion (Malik et al., 2016) [1- strongly disagree to 5- strongly agree]
HD1: I share information through WhatsApp as it is a part of my routine. 0.67 0.76 0.54 0.71
HD2: I share information through WhatsApp as it is one of my habits. 0.72
HD3: I cannot stop myself sharing information through WhatsApp. 0.79
Attitude Toward Behavior (Chen et al., 2018)
ATB1: For me, sharing information about COVID-19 through WhatsApp is: (1-Bad to 5-Good) 0.69 0.80 0.57 0.79
ATB2: For me, sharing information about COVID-19 through WhatsApp is: (1-Foolish to 5-Wise) 0.80
ATB3: For me, sharing information about COVID-19 through WhatsApp is: (1-Harmful to 5-Beneficial) 0.77
Perceived Behavioral Control (Zhao et al., 2016) [1- strongly disagree to 5- strongly agree]
PBC1: I think it’s easy for me to share COVID-19 information though WhatsApp. 0.75 0.83 0.62 0.83
PBC2: I am confident enough, if I want to share COVID-19 information though WhatsApp, I can. 0.80
PBC3: I have time, resources and knowledge to share COVID-19 information though WhatsApp. 0.81
Subjective Norms (Cheung and To, 2016) [1- strongly disagree to 5- strongly agree]
SN1: My friends would think I should share information about COVID-19 through WhatsApp. 0.76 0.81 0.58 0.70
SN2: My family would think I should share information about COVID-19 through WhatsApp. 0.73
SN3: My colleagues would think I should share information about COVID-19 through WhatsApp. 0.81
Behavioral Intention (Zhao et al., 2016) [1- strongly disagree to 5- strongly agree]
BI1: I will verify the authenticity of information about COVID-19 before sharing through WhatsApp. 0.80 0.83 0.63 0.83
BI2: I am willing to refute rumors about COVID-19 on WhatsApp. 0.83
BI3: I will make efforts to refute rumors about COVID-19 on WhatsApp. 0.73
Actual Behavior (Oh et al., 2013) [1- strongly disagree to 5- strongly agree]
AB1: During pandemic (COVID-19), I transmitted information through authentic institutions. 0.78 0.87 0.68 0.86
AB2: During pandemic (COVID-19), I had only transmitted information with external source interlinkage. 0.84
AB3: During pandemic (COVID-19), I had confirmed the authenticity of information before sharing through WhatsApp. 0.85
Norms of Reciprocity (Pai and Tsai, 2016) [1- strongly disagree to 5- strongly agree]
NOR1: I would feel an obligation to share COVID-19 information with others to help them be informed. 0.76 0.83 0.62 0.71
NOR2: When I receive COVID-19 information from others through WhatsApp, feel it right to share out to help others. 0.79
NOR3: I would feel an obligation to spare time from my schedule to share COVID-19 information within WhatsApp
community, if it needed that information.
0.81
Frontiers in Psychology | www.frontiersin.org 11 October 2020 | Volume 11 | Article 572526