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Digital disparities and vulnerability: mobile phone use, information behaviour, and disaster preparedness in Southeast Asia

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english
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Disasters
DOI:
10.1111/disa.12279
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March, 2018
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doi:10.1111/disa.12279

Digital disparities and vulnerability:
mobile phone use, information
behaviour, and disaster preparedness
in Southeast Asia
Chih-Hui Lai Assistant Professor, Department of Communication and Technology,
National Chiao Tung University, Taiwan, Arul Chib Associate Professor, Wee Kim
Wee School of Communication and Information, Nanyang Technological University,
Singapore, and Rich Ling Professor, Wee Kim Wee School of Communication and
Information, Nanyang Technological University, Singapore

This paper proposes an ecological view to investigate how disparities in mobile technology use
reflect vulnerabilities in communities vis-à-vis disaster preparedness. Data (n=1,603) were collected through a multi-country survey conducted equally in rural and urban areas of Indonesia,
Myanmar, Philippines, and Vietnam, where mobile technology has become a dominant and
ubiquitous communication and information medium. The findings show that smartphone users’
routinised use of mobile technology and their risk perception are significantly associated with
disaster preparedness behaviour indirectly through disaster-related information sharing. In addition to disaster-specific social support, smartphone users’ disaster-related information repertoires
are another strong influencing factor. In contrast, non-smartphone users are likely to rely solely on
receipt of disaster-specific social support as the motivator of disaster preparedness. The results also
reveal demographic and rural–urban differences in disaster information behaviour and preparedness. Given the increasing shift from basic mobile phone models to smartphones, the theoretical
and policy-oriented implications of digital disparities and vulnerability are discussed.
Keywords: digital disparities, disaster preparedness, ecological view, information
seeking, information sharing, mobile technology, social support

Introduction
Warning responses are seen as complex social processes that involve the steps of
receiving and sharing information, engaging i; n dialogue, and making decisions under
the constraints posed by physical and social environments (Drabek, 1999). Current
research on disaster preparedness is often guided by the assumption that upon receiving warning messages and understanding the risks, people are more likely to engage
in protective behaviour (Shaw et al., 2004). However, this assumption has been
challenged (Paton and Johnston, 2001), with extant evidence thus far inconclusive
(Shaw et al., 2004; Grothmann and Reusswig, 2006; Kapucu, 2008; Scolobig, De
Marchi, and Borga 2012; Wachinger et al., 2013). Knowledge of a risk is not necessarily equated with the capacity or responsiveness to it (Eiser et al., 2012). Moreover,
© 2018 The Author(s). Disasters © Overseas Development Institute, 2018
Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford, OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA

Chih-Hui Lai, Arul Chib, and Rich Ling

knowledge of risks may be acquired unevenly, reflecting existing social and structural inequalities (Viswanath and Finnegan, Jr., 1996). Yet, there appears to be a
lack of empirical and theoretical consideration of the broader ecology within which
disaster communication and information behaviour occurs (Perreault, Jouston, and
Wilkins, 2014). Particularly acute is the limited applicability of existing models to
understand the relationship between disaster communication and information behaviour and preparedness in developing countries.
The increasingly variable effects of climate change are leading to natural disasters
with deleterious impacts for populous countries in Asia (CRED, 2016a), especially on
the most vulnerable people in low-income nations (CRED, 2016b). In India, for
instance, evacuation decisions are frequently linked to the community, whereas they
are connected to the individual or household in Western nations (Sharma, Patwardhan,
and Parthasarathy, 2009). Likewise, family and community leaders in the Philippines
play an important role in community-based preparedness (Allen, 2006). In rural areas,
economic resource vulnerabilities are further complicated by accompanying sociocultural conditions and informational constraints (Chib and Ale, 2009).
This ecology of geographical and socio-cultural variables increasingly includes
individuals’ use (or lack thereof ) of available and familiar technology in everyday life.
In developed countries, a well-established line of research has evidenced citizens’
active use of advanced media technologies, such as the internet, social media, and
mobile technology to obtain information about disasters (Boyle et al., 2004), to
reconnect with others with similar concerns about the local community (Shklovski,
Palen, and Sutton, 2008), or to pass received information on to other people via
Twitter (Sutton et al., 2014). Mobile technology has emerged as a major tool in
developing nations (Donner, 2008). One should note that the earlier critique of scant
evidence applies similarly here—with electronic channels for delivering warning
messages having low applicability (Ahsan et al., 2016).
Given the disparities between regions, intra-nationally and internationally, this
study contributes to the scholarly debate on mobilisation and normalisation of the
effects of information and communication technologies (ICTs) on human action.
Mobilisation refers to a situation where new technologies afford disadvantaged groups
opportunities for social participation, whereas normalisation suggests that people
who are socially advantaged benefit even more from technological progress (Chen,
2015). Of particular interest is whether digital disparities, or differentials in access to
and use of mobile technology, exacerbate or mitigate existing differentials among
individuals, which in turn affect their disaster preparedness capacities.
To be specific, the paper proposes an ecological view to investigate whether and
how the use of smartphones (versus non-smartphones), coupled with demographic
and geographical differences, reflect disparities in disaster information behaviour
and preparedness. It evaluates and contrasts rural and urban areas of Southeast Asia,
namely in Indonesia, Myanmar, Philippines, and Vietnam. These countries were
selected because of their greater incidence of and vulnerability to natural disasters,
their regional (that is, rural–urban) disparities, and the fast-growing penetration of
mobile technology (see the method section for detailed statistics).

Digital disparities and vulnerability

An ecological view of disaster preparedness
The ecological view emphasises the human–environment relationship (Hawley, 1950;
Bronfenbrenner, 1979), taking the form of multiple levels of influence (such as environmental, interpersonal, personal, or social) on individual behaviour and vice versa
(Sallis, Owen, and Fisher, 2008). The research on the emergency warning–response
process suggests that individuals engage in a series of actions, including following,
searching, and sharing received warning, before deciding whether or not to take
protective action against an impending disaster (Mileti and Sorensen, 1990). Various
factors may come into play to account for variations in this process, including the
characteristics of the warning system (such as channels, content, style, and sources)
and of the public (such as culture, demographics, proximity, resources, and social
network) (Sharma, Patwardhan, and Parthasarathy, 2009).
This work tends to focus on information supply rather than on information demand.
An uncommon exception is the study by Choo and Nadarajah (2014), which investigated the factors that influence early warning information seeking, including cognitive (such as mental structures), affective (such as feelings), and situational (such as
cultural and social context) factors, essentially echoing those posited by risk communication models.
The risk communication literature frequently references ecologically-oriented
theories such as the model of communicating actionable risk (Wood et al., 2012).
Here, information seeking is a function of information received through a diverse
range of channels, knowledge, observation of preparedness, and perceived effectiveness of the action. In particular, this line of inquiry found that actionable information
motivated people’s preparedness action, while information seeking mediated information variables and preparedness action. After receiving information and seeing
others taking action, people were more likely to discuss the matter with others
(milling), and to act. Similarly, Spialek, Czlapinski, and Houston (2016) examined
the ecology of disaster communication, composed of a multitude of communication
resources, and found that connections established helped to provide information on,
and prepare people for, the disaster, and offer post-disaster recovery support.
Although there is an established tradition of ecological perspectives in the field of
disaster research, a systematic assessment of certain fundamental points is missing:
whether and in what way individuals have access to and use available technology
to seek and share risk information and how this affects disaster preparedness in different geographical and socio-cultural settings. Building on the aforementioned
literature on disaster warning, risk communication, and technology use, this paper
presents an ecological view of disaster preparedness by considering personal (information repertoires, mobile technology use, risk perception) and social (information
sharing, social support) factors, in relation to specific vulnerabilities (demographic,
geographical-international, geographical-intra-national) (see Figure 1), before setting out the research questions and associated hypotheses. It goes on to present the
method and the findings, and concludes with a discussion of the policy and theoretical implications.

Chih-Hui Lai, Arul Chib, and Rich Ling

Figure 1. An ecological view of digital disparities and vulnerability in relation to
disaster preparedness

Community and societal factors
Geographical environmental infrastructure (country, urban/rural region)

Interpersonal factors
Disaster-specific social capacity (information sharing, social support)

Individual factors
Disaster-specific information and communication capacity (information repertoires)
Information and communication capacity (routinised mobile technology use)
Human capacity (risk perception, demographic differences)

Source: authors.

Information repertoires and disaster preparedness
Natural disasters pose personal risks that affect individuals directly. Hence they are
likely to engage in goal-directed acquisition of knowledge of impending events. The
literature on the warning–response process recommends disseminating warning messages through multiple channels to aid preparedness behaviour (Sharma et al., 2009;
Sheppard, Janoske, and Liu, 2012). Little is known, though, about whether the tendency to utilise multiple channels also applies to voluntary information seeking. The
model of information repertoires proposes that individuals use different sources (such
as interpersonal communication, newspapers, or television) to obtain information on
a topic of interest (Reagan, 1996). This model of information repertoires has been
applied to environmental crises and post-disaster contexts (see, for example, O’Keefe,
Ward, and Shepard, 2002). The work of Sommerfeldt (2015) revealed that people
variously used the internet, newspapers, radio, short message service (SMS), television, and word of mouth to gather information after the devastating earthquake in
Haiti on 12 January 2010.
Central to information repertoires research are the influences of various motivational factors on media choice behaviours (Reagan, 1996). In the risk communication
literature, risk perception is often considered to be an important factor that influences individuals’ information seeking and processing via different channels, that is,
engagement in information repertoires (Kahlor et al., 2006; Bourque et al., 2012).
Integrating the conceptualisation of risk perception into the model of information
repertoires leads to the first hypothesis (H1) (see Figure 2 for the conceptual model):

Digital disparities and vulnerability

A higher level of risk perception is positively associated with engagement in a wider range
of disaster information repertoires.
In the existing literature on warning–response processes, individuals’ possession of
resources to gather information on potential risk influences how they respond to the
danger and ultimately their protective behaviour (see a review in Dash and Gladwin,
2007). Under the ecological framework, technology use can be treated as part of
individuals’ information and communication capacity to gather information related
to risks, laying the foundation for adaptive behaviour (Stokols et al., 2003). The use
of increasingly ubiquitous mobile phones to access related information is a case in
point. Yet, technology use frequently is intertwined with demographic factors such
as age, income, and sex to reflect social vulnerability and inequality among certain
groups and their susceptibility to environmental hazards (Cutter, Boruff, and Shirley,
2003; Chib, Baghudana, and Kasdani, 2010).
The knowledge gap hypothesis suggests that demographic differences may exacerbate inequitable knowledge acquisition among certain demographic groups, such
as those with lower education or socioeconomic status (Viswanath and Finnegan,
Jr., 1996). For instance, women in Indonesia and Myanmar were less likely to own
mobile phones, reflecting their less privileged socioeconomic situation and traditional gendered roles regarding access to technology (Zainudeen and Galpaya, 2015;
Sylvester, 2016). In contrast, in Vietnam, well-educated and young people were more
likely to use the internet (Pew Research Center, 2015).
Before testing the impact of mobile phones on information repertoires, we argue
that the situation is more complex, and requires a nuanced examination. The first
research question (RQ1) asks:
How do demographic and geographical differences (such as age, education, income, location
of residence, sex) reflect informational disparities, that is, differences in individuals’ range
of disaster information repertoires?
The discussion of disparities in technology use and demographic differences invokes
the debate between the mobilisation and normalisation theses, which propose opposing views as to whether or not existing social gaps between advantaged and disadvantaged groups can be minimised or magnified by technological opportunities
(Hirzalla, van Zoonen, and de Ridder, 2011). Research related to mobilisation shows
that use of mobile technology affords disadvantaged groups opportunities for cultural
participation on their mobile devices (Chen, 2015). However, research supporting
normalisation highlights that people with existing resource advantages (knowledge,
material, networking, skills, or social, for example) tend to use the Internet or new
technologies to enhance their network and social capacities, heightening the differences between themselves and with people who do not have either networking or
technological competence (Ruppel and Burke, 2014).
Accordingly, the mobilisation thesis would call for a stronger relationship between
mobile technology use and disaster information repertoires among those who have

Chih-Hui Lai, Arul Chib, and Rich Ling

better access to mobile phones, whereas the normalisation thesis would suggest a
weaker relationship. Moreover, digital disparities may exist not only because of
access (have/have not) but also due to usage characteristics (Hargittai, 2002). It is
necessary, therefore, to differentiate between routinised use of basic and advanced features of mobile technology when constructing information repertoires about disasters. Given our interest in informational vulnerabilities (Chib and Ale, 2009), or
availability, access, and capacity to use information and communication resources,
two hypotheses (H2a and H2b) are proposed to examine the relationship between
routinised use of mobile technology and disaster-related information repertoires:
Routinised basic use of mobile technology is positively associated with engagement in a
wider range of disaster information repertoires.
Routinised advanced use of mobile technology is positively associated with engagement in
a wider range of disaster information repertoires.
Informational vulnerability may further interact with other socio-structural conditions, particularly geographical location. The study by Choo and Nadarajah (2014)
revealed that, compared to their rural counterparts, suburban dwellers were more
likely to report giving thought or intending to respond to early warning on bushfires,
or to have emergency plans in place. People who do not have sufficient access, capacity,
or motivation to use smartphone features (Van Dijk, 2006), especially those living in
rural areas, might use the basic functions of mobile technology to look for and share
information on disasters. In rural areas of the four countries under review, especially
remote poor areas, feature phones are still the norm, even though the number of smartphone users has increased, particularly in urban parts of Indonesia and Vietnam (Akhtar
and Arinto, 2009; Heimerl et al., 2015). This reasoning about rural–urban divides
with regard to mobile technology use leads to the following hypotheses (H3a and H3b):
The relationship between routinised basic use of mobile technology and engagement in a
wider range of disaster information repertoires differs by geographical region.
The relationship between routinised advanced use of mobile technology and engagement in
a wider range of disaster information repertoires differs by geographical region.

Social processes and preparedness behaviour
Disaster preparedness behaviour is the result of a series of information and communication actions, which involve individuals receiving and sharing information, and
making decisions on adopting preparedness measures (Mileti and Sorensen, 1990;
Drabek, 1999). Advancing the model of information repertoires, we argue that
seeking information from multiple sources and sharing the information received on
impending disaster risks with one’s social networks can enhance knowledge, skills,
and motivation with respect to preparedness behaviour (Levac, Toal-Sullivan, and

Digital disparities and vulnerability

O’Sullivan, 2012). Indeed, proactive seeking of information on particular environmental risks has been found to be a motivator of preparedness (Bourque et al., 2012;
Kirschenbaum, Rapaport, and Canetti, 2017). Moreover, one is likely to discuss the
matter with others after they have received the information (Becker et al., 2012).
Such information sharing facilitates the social construction of risk and helps individuals to develop skills in preparing and responding to disasters (Becker et al., 2012;
Wood et al., 2012). This leads to the following hypotheses (H4 and H5):
A wider range of disaster-related information repertoires is positively associated with preparedness behaviour.
A higher level of disaster-related information sharing with one’s core network is positively
associated with preparedness behaviour.
Seeking disaster-related information and sharing it with personal social networks
creates a social environment in which attitudes and perceptions about impending
disasters are formulated or changed. According to the ecological framework, the
social environment is an important site in which multiple dimensions of influence
converge (McLeroy et al., 1988). For instance, routinised use of mobile technology
may serve as a foundation for building disaster-related information repertoires and
sharing information with one’s social network, which in turn facilitates preparedness behaviour. Risk perception can motivate individuals to engage in preparedness
actions by seeking information on the hazards (Bourque et al., 2012). Yet, no explicit
distinction is made between general and disaster-specific social environments in existing research that applies ecological models in disaster contexts (Kim and Kang, 2010).
What is more, there is a lack of systematic research on the mediating roles of information repertoires and information sharing with respect to technology use and
disaster preparedness. As a result, the extent to which general and disaster-specific
information repertoires and information sharing exhibit differential mediating effects
on individual factors and disaster preparedness is unclear. It is possible that smartphone and non-smartphone users may be influenced by the disaster-specific social
environment differently. Hence, a research question (RQ2) is posed to assess the
mediating roles of disaster information repertoires and disaster information sharing
vis-à-vis the three predictors and preparedness behaviour, and to determine whether
or not the mediating relationships are similar or different among smartphone and
non-smartphone users:
How do disaster-related information repertoires and information sharing mediate the relationships between the predictors—routinised use of mobile technology and risk perception—
and preparedness behaviour, and what similarities or differences are there among smartphone
and non-smartphone users, taking into account demographic and geographical factors?
Ecological frameworks assume that social networks serve as resources and/or vehicles to acquire resources that motivate individual behaviour (McLeroy et al., 1988).

Chih-Hui Lai, Arul Chib, and Rich Ling

Numerous studies have revealed that individuals’ social networks within a community (such as ties to family, friends, and local organisations) play an important supportive role in the building of their capacities to respond to, and recover after,
disasters (Dynes, 2002; Nakagawa and Shaw, 2004; Murphy, 2007; Aldrich and
Meyer, 2014;). For example, the study by Hawkins and Maurer (2010) showed that
the close and weak ties of low-income survivors provided immediate and longer-term
support, respectively, after Hurricane Katrina struck the Gulf Coast of the United
States on 29 August 2005. Much less discussed, though, is the role of these social
networks in pre-disaster preparedness. Notable exceptions include the study by
Hausman, Hanlon, and Seals (2007), which found that individuals with greater social
contacts within the local community were more likely to engage in disaster preparedness, and the study by Heller et al. (2005), which found that receipt of instrumental social support and discussion with social contacts were strong predictors of
disaster preparedness. Yet again, however, there is little differentiation between
general and disaster-specific social support. As mentioned, this study advances the
importance of creating topic-specific social environments in motivating individual behaviour. This leads to the following hypothesis (H6):
Receipt of disaster-specific social support is positively associated with preparedness behaviour.
Finally, experience of interpersonal communication can influence perceptions of
the availability of social support related to disaster situations (Hurlbert, Haines, and
Beggs, 2000). In other words, individuals’ engagement in disaster-related information
Figure 2. The conceptual model of the study*

Notes: * The hypotheses in bold were supported.
Source: authors.

Digital disparities and vulnerability

repertoires and activation of social ties for sharing information on impending disasters can be strengthened by receipt of disaster-specific social support. Moreover, the
ways in which social support is acted upon might differ among people who use basic
mobile phones and those who use advanced smartphones. This leads to the final
research question (RQ3):
How does disaster-specific social support mediate the relationships between disaster-related
information repertoires and information sharing and preparedness behaviour? Are these
relationships similar or different among smartphone and non-smartphone users, taking into
account demographic and geographical factors?

Method
This work was conducted for the Global Disaster Preparedness Center (GDPC) 1 to
aid its plan to implement mobile phone-based weather alert systems in four countries
in Southeast Asia. Indonesia, Myanmar, Philippines, and Vietnam were selected
because of their susceptibility to weather-related disasters and their rapid adoption
of mobile technology.2 They rank among the top 10 countries by disaster subgroup
occurrences; Indonesia and Vietnam further ranked similarly in terms of human
impacts, measured by the number of deaths and affected inhabitants (CRED, 2016c).
Mobile subscriptions per 100 inhabitants in Indonesia, Vietnam, and the Philippines
reached 132, 130.6, and 118, respectively, in 2015, while the rate in Myanmar was 76,
a significant improvement on the 13 of two years earlier (The World Bank, 2016).
Purposive sampling was performed because the objective of the research was to
investigate the implications of differential access to and use of mobile technology for
disaster preparedness by considering not only individual (demographic and psychological, inter alia) but also social and situational (geographical) factors. First, regions
prone to weather-related disasters were identified in each country to elicit responses
from individuals who had disaster-related experience, an important motivator of
citizens’ preparedness for disasters owing to an enhanced sense of risk (Helslott and
Ruitenberg, 2004). Next, urban and rural areas were identified as the sites in which
to recruit survey respondents within the selected regions, based on the preliminary
understanding that adoption and use of mobile technology were different among
rural and urban inhabitants in these four countries (Akhtar and Arinto, 2009; Heimerl
et al., 2015). The World Bank’s general definition of rural–urban areas was referenced: it suggests that rural areas are characterised by low population density and
remoteness, and usually face developmental challenges (Chomitz, Buys, and Thomas,
2005). Local adjustments were made by following each country’s definition of rural–
urban areas, mostly determined by population density, inhabitant characteristics
such as sources of employment (agriculture or not), and level of remoteness. An
approximately equal percentage of the sample came from urban and rural areas in
each country.3

Chih-Hui Lai, Arul Chib, and Rich Ling

Procedures and instrument
The survey was translated into local languages (that is, Burmese, Filipino/Tagalog,
Indonesian, and Vietnamese) and was administered in a face-to-face format between
December 2014 and February 2015. The total size of the sample was 1,603 (n=402 in
Indonesia; n=401 in the Philippines; n=400 in Myanmar; and n=400 in Vietnam)
and was composed of an equal percentage of urban (n=802) and rural respondents
(n=801). The approximately same sample size in each country allows for the making
of more valid comparisons.
The average respondent was 39.28 years old and female (54.9 per cent). As a result
of purposive sampling, 93.8 per cent of the respondents reported using mobile phones,
and more than half (55.8 per cent) adopted smartphones (n=833). As a point of comparison regarding technology use, less than half of the respondents used computers
(36.1 per cent) and the internet (48.4 per cent). Moreover, an overwhelming majority
(97.0 per cent) of the respondents reported personal experience of cyclones/typhoons
or floods.
Table 1 contains measurements of all 15 variables, including seven demographic
and geographical variables.
The following section describes the development of measurement scales for the
dependent and independent variables analysed.
Disaster information repertoires
The variable of disaster information repertories was adapted from existing scales
(Perreault, Jouston, and Wilkins, 2014; Lai and Tang, 2015), with 12 items covering
traditional media, interpersonal channels, and new media used to measure respondents’ information repertoires for impending disasters.4 Respondents were asked whether
they had received information on impending cyclones/typhoons or floods from the
following: the print version of a local newspaper; a local television news broadcast;
a local radio broadcast; a local government website; a website or a blog dedicated to
the local community; a person or an organisation on a social networking website; an
e-mail listserv or a newsletter about the local community; a print newsletter about
the local community; word of mouth from close relatives; word of mouth from close
friends; an internet search using a search engine; and a call to the local government
office (0 = no, 1= yes). A summed scale of disaster information repertoires was created,
ranging from 0–12 (M (mean)=4.67, SD (standard deviation) =2.61).
Preparedness behaviour
Participants were asked whether they had engaged in any particular preparatory
activities for the disaster experienced. Referencing existing scales (see, for example,
Kim and Kang, 2010) and official education materials, such as the Ready Campaign,5
items included: built an emergency kit; made a family communication plan; made
plans to secure the property; learned community evacuation routes; learned the
elevation level of the property and whether the land was flood-prone; kept the radio

Age

Income

Education

Length of
residence

Region (rural)

Risk perception

Basic mobile

Advanced mobile

Information
repertoires

Information
sharing

Practical support

Emotional and
informational
support

Preparedness

2

3

4

5

6

7

8

9

10

11

12

13

14

1

–

0.023

-0.032

0.151***

0.063*

-0.023

0.361***

0.062*

0.129***

-0.167*** 0.152***

-0.184*** 0.132***

-0.213*** 0.080**

0.445***

0.243***

0.480***

-0.248*** -0.059*

-0.227*** 0.109**

0.153***

0.026

–

–

–

–

5

-0.143*** 1

1

–

–

–

4

1

–

–

–

–

–

6

1

–

–

–

–

–

–

7

-0.135**
0.017

0.116**

-0.147*** 0.038

-0.245*** 0.056*

-0.231*** 0.028

0.407***

0.380***

0.335***

-0.136*** -0.096*** 0.207***

-0.143*** -0.053*

-0.136**

-0.164*** -0.098*** 0.095***

-0.108*** 0.061*

-0.094*** -0.104*** 0.041

-0.488*** -0.012

0.092***

0.029

0.112***

-0.091*** -0.209*** 0.307***

0.017

0.012

0.216***

0.007

1

–

–

3

-0.331*** 0.209***

-0.146*** 0.122***

-0.045

-0.260*** -0.059*

-0.036

1

2

0.174***

0.236***

0.220***

0.406***

0.350***

0.431***

1

–

–

–

–

–

–

–

8

0.408***

0.300***

0.265***

0.328***

0.454***

1

–

–

–

–

–

–

–

–

9

Source: authors.

Notes: The country variable was not included owing to its multiple categories. * p<0.05, ** p<0.01, *** p<0.001.

Sex (woman)

1

1

Table 1. Zero correlations among the study variables

0.394***

0.303***

0.232***

0.436***

1

–

–

–

–

–

–

–

–

–

10

0.331***

0.380***

0.331***

1

–

–

–

–

–

–

–

–

–

–

11

0.494***

0.756***

1

–

–

–

–

–

–

–

–

–

–

–

12

0.541***

1

–

–

–

–

–

–

–

–

–

–

–

–

13

1

–

–

–

–

–

–

–

–

–

–

–

–

14

Digital disparities and vulnerability

Chih-Hui Lai, Arul Chib, and Rich Ling

or television on for the latest weather advisories; stored drinking water and food;
checked on the availability of flashlights, batteries, or candles in the household;
checked the function of portable radios; and were aware of the surroundings (0 =no,
1=yes). A scale summing the scores of these 10 items was created, ranging from 0–10
(M=5.99, SD =3.61).
Risk perception
The study adapted the scale of McComas and Trumbo (2001) that measures respondents’ level of risk perception through four questions requiring responses that are
rated on semantic differential scales. Items included: how much cyclone/typhoon or
flooding hazards they had personally faced by living in the area, rated on a sevenpoint scale (1=risk, 7 =high risk); whether living near the area at risk of cyclone/
typhoon or flooding hazards was something that they could think calmly about (1)
or was something they constantly worried about (7); if the area caused cyclone/
typhoon or flooding hazards, whether those risks might extend to future generations?
(1=no, 7 =yes); and whether the risks of cyclone/typhoon or flooding hazards that
might be posed by living near the community were decreasing (1) or increasing (7).
The scale was validated through confirmatory factor analysis using Varimax rotation
and retaining Eigenvalues of one or more, and reliability was achieved (Cronbach’s
α= 0.76, M=4.65, SD=1.56).
Routinised use of mobile technology
The concept of routinised use of mobile technology was operationalised by identifying whether mobile technology had been used in a versatile way, in other words,
multidimensional use (Lai, 2014). Respondents first selected five basic features of
mobile telephones: making/receiving calls; sending/receiving text messages (SMS);
listening to radio/music; taking/sending pictures or videos; and using the alarm
clock (0 =no, 1= yes). A summed scale of routinised basic use of mobile technology was
created, ranging from 0–5 (M =3.63, SD =1.35). Next, smartphone users indicated
usage of eight advanced mobile phone features: sending/reading e-mail; using mobile
messaging (such as WhatsApp or LINE); using location maps; accessing social networking websites; listening to web radios; watching/downloading videos; playing/
downloading games; and playing/downloading music. A summed scale of routinised
advanced use of mobile technology was created, ranging from 0–8 (M=4.83, SD =2.27).
Disaster information sharing
Respondents indicated whether they shared information received on impending
disasters with their close friends and relatives through four means of communication:
face-to-face conversations; telephone calls; text messaging; and online media (0 =no,
1= yes). The concept of media multiplexity was harnessed to tap into the level of
information sharing with one’s social network in the form of multimodal communication. Media multiplexity suggests that the more frequently two people communicate,

Digital disparities and vulnerability

the stronger the tie, and the more types of media they utilise (such as e-mail and
face-to-face conversation) (Haythornthwaite, 2005). Two items were created by
aggregating the scores for information sharing with close relatives (M=2.10, SD =1.14)
and close friends (M=1.97, SD =1.24). The disaster information sharing variable was then
generated by averaging the values of these two items (M=2.03, SD=1.14).
Disaster social support
The scales of disaster-specific social support were adapted from the social support
scale of the Medical Outcomes Study (Sherbourne and Stewart, 1991) and from Vaux,
Riedel, and Stewart (1987). Four items measured receipt of disaster-specific practical
support (sample item: ‘someone to help take them to an evacuation site/shelter when
a natural disaster happens’) and five items measured receipt of disaster-specific
emotional/informational support (sample item: ‘someone to help them look for
information on what to do during a natural disaster’). These nine items were rated
on a five-point scale where 1=never, 2=rarely, 3= sometimes, 4=often, and 5=always.
The scales were validated through confirmatory factor analysis using Varimax rotation and retaining Eigenvalues of one or more, and reliability was achieved for
disaster-specific practical support (Cronbach’s α= 0.90, M=2.78, SD =1.36) and disasterspecific emotional/informational support (Cronbach’s α= 0.96, M=2.89, SD =1.40).
Demographic and geographical factors
Seven demographic and geographical variables were measured: age; country; education; geographical location of residence; income; length of residence; and sex (1=man,
2=woman). Research has shown that demographic factors such as age, education,
and sex are predictors of risk information seeking in routine and non-routine situations, as well as of disaster preparedness (see, for example, O’Keefe, Ward, and Shepard,
2002; Spence, Lachlan, and Burke, 2008; Perreault, Houston, and Wilkins, 2014;
Sommerfeldt, 2015; Kirschenbaum, Rapaport, and Canetti, 2017). The level of
monthly income was measured, where 1=less than USD 20, 2= USD 20 to less than
25, 3=USD 25 to less than 32, 4=USD 32 to less than 50, 5=USD 50 to less than 100,
6 = USD 100 to less than 150, 7 = USD 150 to less than 250, 8 = USD 250 to less than
500, 9 = USD 500 to less than 1,000, 10 =more than USD 1,000 (M=4.32, SD =2.17).
The level of education was measured, where 1=illiterate, 2=primary education, 3=
lower secondary, 4=upper secondary, 5=tertiary or post-tertiary (M=3.66, SD=1.15).
Income and education were tailored to the specific standards in each country.66
Data analysis adjusted local currency to US dollars and transformed country-specific
educational levels to produce comparable scales. Length of residence was measured,
where 1=less than 1 year, 2 =1–5 years, 3= 6–10 years, 4=11–20 years, 5=more than
20 years, and 6 =whole of life (M=4.73, SD =1.43). Dummy coding was performed
as country was a categorical variable, with Myanmar used as the reference category.
The two dependent variables (information repertoires and preparedness behaviour)
were count variables (0–12, 0–10), but since they approximated a normal distribution,

Chih-Hui Lai, Arul Chib, and Rich Ling

linear regression modelling was employed.7 Two interaction terms were created (routinised basic use of mobile technology × region, routinised advanced use of mobile
technology × region) and both routinised use of mobile technology variables were
mean centred to avoid multicollinearity. Tests for indirect effects were conducted using
the PROCESS macro (Hayes, 2013), which uses bootstrapping to generate confidence intervals for estimating indirect effects and thus does not impose the assumption of normality of the responses (Preacher and Hayes, 2008). The significance of
indirect effects is determined by examining bias corrected 95 per cent confidence
intervals. The effect is considered significant if the intervals do not contain zero.

Results
The results of hierarchical linear regression showed that risk perception was not significantly associated with disaster-related information repertoires ( β = 0.028, p > 0.10),
meaning, therefore, that H1 was not supported. Routinised advanced use of mobile
technology did have a significant effect on disaster-related information repertoires
(β = 0.202, p < 0.001), but the same was not true of basic use (β = 0.043, p > 0.10), hence
H2a was not supported and H2b was supported (see Model 2 in Table 2). Although
the relationship between routinised advance use of mobile technology and disasterrelated information repertoires was not significantly moderated by region ( β = -0.055,
p > 0.10), the effect of routinised basic use of mobile technology on disaster-related
information repertoires was moderated by region ( β = 0.243, p < 0.05) (see Model 3
in Table 2), so H3a was supported and H3b was not supported. In comparison to
urban respondents, a clear upward association was discerned between rural respondents’ use of a greater number of basic features of mobile technology and their expanded
range of disaster-related information repertoires.
Apropos RQ1 on demographic and geographical differences reflected in informational disparities, the results showed that respondents who were older and had higher
levels of education and income were likely to have a wider range of disaster information repertoires (β = 0.104, p < 0.01; β = 0.086, p < 0.05; β = 0.132, p < 0.001) (see Model 2
in Table 2). Country differences in disaster-related information repertoires were also
revealed. Compared to respondents in Myanmar, those in the Philippines ( β = 0.535,
p < 0.001) and Vietnam (β= 0.138, p < 0.001) were likely to engage in expanded disasterrelated information repertoires. The results suggest more matured adoption of a variety of advanced media technologies for seeking disaster-related information among
respondents in the Philippines and Vietnam. In contrast, the limited range of information repertoires among respondents in Indonesia and Myanmar echoes the statistics,
highlighting slower adoption of the internet and mobile technologies in these two
countries (International Telecommunications Union, 2016; The World Bank, 2016).
The results also showed that the range of disaster-related information repertoires
was significantly related to preparedness behaviour among the group of smartphone
users ( β = 0.106, p < 0.001; β = 0.085, p < 0.01), even after controlling for the social

Digital disparities and vulnerability

Table 2. Results of linear regression on disaster information repertoires
Model 1
Demographic and
geographical factors

Model 2
Predictors

Model 3
Predictors and
interaction terms

Sex (woman)

-0.045 (-0.260)

-0.032 (-0.187)

-0.033 (-0.193)

Age

0.054 (0.014)

0.104 (0.028)**

0.099 (0.026)**

Income

0.100 (0.138)**

0.086 (0.119)*

0.090 (0.125)*

Education

0.169 (0.607)***

0.132 (0.473)***

0.131 (0.470)***

Length of residence

-0.032 (-0.059)

-0.040 (-0.074)

-0.040 (-0.074)

Region (rural)

0.026 (0.156)

0.049 (0.290)

-0.044 (-0.356)

Country (Philippines)

0.609 (2.383)***

0.535 (2.095) ***

0.531 (2.080)***

Country (Vietnam)

0.198 (0.899)***

0.138 (0.629)**

0.132 (0.601)**

Country (Indonesia)

-0.286 (-1.230)***

-0.316 (-1.363)***

-0.312 (-1.346)***

Risk perception

–

0.028 (0.055)

0.025 (0.048)

Routinised basic mobile use

–

0.043 (0.168)

-0.176 (-0.694)

Routinised advanced mobile use

–

0.202 (0.264)***

0.264 (0.345)*

Routinised basic mobile use × region

–

–

0.243 (0.629)*

Routinised advanced mobile use × region

–

–

-0.055 (-0.048)

F-test

65.386***

55.187***

48.083***

0.502

0.530

0.534

∆ R2

–

0.031

0.005

∆F

–

12.576***

3.052*

Adjusted R

2

Notes: n=577. The coefficients in parentheses are unstandardised regression coefficients. Myanmar was
used as the reference category for the variable of country. * p<0.05, ** p<0.01, ***p<0.001.
Source: authors.

support variables (see Models 5 and 6 in Table 3), meaning that H4 was supported.
Disaster information sharing significantly predicted preparedness ( β = 0.082, p < 0.001),
but the effect disappeared after the inclusion of the social support variables ( β = 0.043,
p > 0.10), hence H5 was partially supported. Disaster-specific practical and emotional
support were significantly related to preparedness behaviour ( β = 0.116, p < 0.001;
β = 0.125, p < 0.001) (see Model 6 in Table 3), so H6 was supported.
Older respondents tended to prepare themselves more for disasters than their
younger counterparts (β= 0.112, p<0.001) (see Model 6 in Table 3). Similarly, respondents who had lived in the area for a long time were more likely to engage in preparedness behaviour than those who had resided there for a shorter period ( β= 0.047, p<0.05).
Regional and country differences were also revealed. Rural residents exhibited a
higher likelihood of preparing themselves for disasters than their urban counterparts
( β = 0.050, p < 0.01). This finding is consistent with prior research on the resilience

Chih-Hui Lai, Arul Chib, and Rich Ling

of rural communities in contrast to their urban or suburban counterparts (Andrew
et al., 2016). Respondents in Vietnam (β= 0.519, p<0.001) and the Philippines (β= 0.171,
p < 0.001) were more likely to engage in preparedness behaviour than those in the
other two countries.
The same regression model was run on the non-smartphone subsample. The
results showed that compared to smartphone users, disaster information repertoires
were not a significant predictor of preparedness behaviour in the non-smartphone
group ( β = 0.019, p> 0.10; β = -0.001, p> 0.10) (see Models 8 and 9 in Table 4). Similar
to what was observed in the smartphone group, disaster information sharing significantly predicted preparedness among non-smartphone respondents ( β= 0.073, p < 0.01),
but the effect disappeared after including the social support variables (β= 0.024, p>0.10).
Nonetheless, the effect of disaster-related social support on preparedness behaviour
was still observed ( β = 0.131, p < 0.001; β = 0.128, p < 0.001) (see Model 9 in Table 4).
Table 3. Results of linear regression on preparedness behaviour for smartphone users
Model 4
Demographic and
geographical factors

Model 5
Predictors

Model 6
Predictors

Sex (woman)

0.030 (0.225)

0.027 (0.204)

0.024 (0.178)

Age

0.117 (0.037)***

0.115 (0.037)***

0.112 (0.036)***

Income

0.046 (0.083)

0.026 (0.047)

0.025 (0.045)

Education

0.009 (0.032)

-0.023 (-0.083)

-0.006 (-0.021)

Length of residence

0.044 (0.111)*

0.046 (0.115)*

0.047 (0.117)*

Region (rural)

0.059 (0.446)**

0.061(0.461)**

0.050 (0.373)**

Country (Philippines)

0.354 (1.691)***

0.268 (1.280)***

0.171 (0.815)***

Country (Vietnam)

0.506 (2.796)***

0.487 (2.692)***

0.519 (2.866)***

Country (Indonesia)

0.110 (0.571)**

0.082 (0.255)**

0.034 (0.177)

Disaster information repertoires

–

0.106 (0.140)***

0.085 (0.113)**

Disaster information sharing

–

0.082 (0.255)***

0.043 (0.132)

Disaster practical social support

–

–

0.116 (0.314)***

Disaster emotional/informational social
support

–

–

0.125 (0.326)***

F-test

202.843***

175.854***

166.770***

Adjusted R2

0.700

0.712

0.734

∆ R2

–

0.012

0.023

∆F

–

16.843***

33.912***

Notes: n=780. The coefficients in parentheses are unstandardised regression coefficients. Myanmar was
used as the reference category for the variable of country. * p<0.05, ** p<0.01, ***p<0.001.
Source: authors.

Digital disparities and vulnerability

Table 4. Results of linear regression on preparedness behaviour for non-smartphone users
Model 7
Demographic and
geographical factors

Model 8
Predictors

Model 9
Predictors

Sex (woman)

0.000 (0.002)

0.008 (0.060)

-0.010 (-0.070)

Age

0.042 (0.012)

0.048 (0.014)*

0.037 (0.011)

Income

0.062 (0.101)*

0.051 (0.082)

0.044 (0.071)

Education

0.038 (0.122)

0.023 (0.073)

0.029 (0.093)

Length of residence

-0.001 (-0.001)

-0.005 (-0.014)

-0.007 (-0.018)

Region (rural)

0.005 (0.036)

0.006 (0.046)

-0.007 (-0.051)

Country (Philippines)

0.437 (2.420)***

0.417 (2.308)***

0.313 (1.731)***

Country (Vietnam)

0.524 (2.524)***

0.526 (2.533)***

0.569 (2.738)***

Country (Indonesia)

-0.017 (-0.084)

-0.033 (-0.165)

-0.107 (-0.534)**

Disaster information repertoires

–

0.019 (0.033)

-0.001 (-0.003)

Disaster information sharing

–

0.073 (0.274)**

0.024 (0.090)

Disaster practical social support

–

–

0.131 (0.347)***

Disaster emotional/informational
social support

–

–

0.128 (0.326)***

F-test

157.030***

131.274***

129.359***

Adjusted R2

0.690

0.694

0.725

∆ R2

–

0.005

0.032

∆F

–

5.398**

36.432***

Notes: n=633. The coefficients in parentheses are unstandardised regression coefficients. Myanmar was
used as the reference category for the variable of country. * p<0.05, ** p<0.01, ***p<0.001.
Source: authors.

The effects of demographic and geographical variables on preparedness behaviour
were strikingly different in the non-smartphone group. Age, length of residence,
and region were not significant predictors of preparedness behaviour ( β = 0.037,
p> 0.10; β = -0.007, p> 0.10; β = -0.007, p> 0.10) (see Model 9 in Table 3), yet country
differences were still observed. Respondents in Vietnam ( β = 0.569, p < 0.001) and the
Philippines (β= 0.313, p < 0.001) were more likely to engage in preparedness behaviour
than those in the other two countries.
RQ2 asked whether the relationships between routinised use of mobile technology
and risk perception and preparedness behaviour were mediated by disaster-related
information repertoires and information sharing. The results demonstrate that smartphone users’ advanced use of mobile technology helped to motivate their preparedness
behaviour through disaster-related information repertoires and information sharing
(bias corrected 95 per cent confidence intervals: 0.0069–0.0589; 0.0033–0.0392; see

Chih-Hui Lai, Arul Chib, and Rich Ling

Table 5. Results of indirect effect tests on disaster preparedness behaviour via
information repertoires and information sharing
Disaster information repertoires

Disaster information sharing

Bias corrected 95 per cent
confidence intervals

Bias corrected 95 per cent
confidence intervals

Point
estimate

Lower

Upper

Point
estimate

Lower

Upper

Risk perception

0.0089

-0.0037

0.0319

0.0292

0.0080

0.0664

Routinised basic mobile use

0.0223

-0.0006

0.0683

0.0502

0.0112

0.1117

Routinised advanced mobile use*

0.0282

0.0069

0.0589

0.0158

0.0033

0.0392

Risk perception

0.0020

-0.0021

0.0154

0.0173

-0.0016

0.0445

Routinised basic mobile use

0.0063

-0.0035

0.0269

0.0155

-0.0005

0.0429

Smartphone users

Non-smartphone users

Notes: * Advanced mobile use was only applicable for the smartphone group. n=555 for smartphone
users and n=606 for non-smartphone users. In each of the tests, the demographic and geographical
variables and other predictors were included as covariates. The two mediators were entered as parallel
mediators in the model. The numbers in bold represent significant indirect effects.
Source: authors.

Table 5). Their routinised basic use of mobile technology also influenced preparedness behaviour, but only indirectly through information sharing (bias corrected 95
per cent confidence interval: 0.0112–0.1117). Similarly, the relationship between risk
perception and preparedness behaviour was mediated by information sharing (bias
corrected 95 per cent confidence interval: 0.0080–0.0664), but not by information
repertoires. In other words, individuals’ risk perceptions were accentuated by sharing
disaster information with their core network, which in turn facilitated preparedness
behaviour for impending disasters. In the non-smartphone group, routinised use of
mobile technology and risk reception were not significantly related to preparedness
behaviour through either information repertoires or information sharing, as shown
by the confidence intervals containing zeros.
RQ3 investigated the indirect effects of disaster-related information repertoires
and information sharing on preparedness behaviour through disaster-specific social
support. Receipt of disaster-specific emotional/informational social support served
as an important mediator that enhanced the effects of disaster information repertoires and information sharing on preparedness behaviour in both smartphone (bias
corrected 95 per cent confidence intervals: 0.0074–0.0472; 0.0266–0.1271; see Table 6)
and non-smartphone (bias corrected 95 per cent confidence intervals: 0.055–0.0588;
0.0403–0.1872) groups. However, receipt of disaster-specific practical support was
only salient as a mediator between information sharing and preparedness in the
smartphone (bias corrected 95 per cent confidence interval: 0.0225–0.1030) and the
non-smartphone (bias corrected 95 per cent confidence interval: 0.0377– 0.1504)

Digital disparities and vulnerability

Table 6. Results of indirect effect tests on disaster preparedness behaviour via social support
Disaster practical
social support

Disaster emotional/informational
social support

Bias corrected 95 per cent
confidence intervals

Bias corrected 95 per cent
confidence intervals

Point
estimate

Lower

Upper

Point
estimate

Lower

Upper

Disaster information repertoires

0.0050

-0.0066

0.0204

0.0224

0.0074

0.0472

Disaster information sharing

0.0561

0.0225

0.1030

0.0668

0.0266

0.1271

Disaster information repertoires

0.0120

-0.0058

0.0369

0.0240

0.0055

0.0588

Disaster information sharing

0.0820

0.0377

0.1504

0.1019

0.0403

0.1872

Smartphone users

Non-smartphone users

Notes: n=780 for smartphone users and n=633 for non-smartphone users. In each of the tests, the demographic and geographical variables and other predictors were included as covariates. The two mediators
were entered as parallel mediators in the model. The numbers in bold represent significant indirect effects.
Source: authors.

group. That is, sharing disaster-related information with one’s close relatives and
friends facilitated individuals’ preparedness behaviour if they had received disasterspecific practical social support from their social contacts.
Taken together, these results revealed a similarity among smartphone and nonsmartphone users in terms of the importance of receipt of disaster-specific social
support in aiding disaster preparedness. Compared to non-smartphone users, though,
smartphone users’ disaster-related information repertoires had direct effects on disaster preparedness. That is, information repertoires appeared to be equally as important as receipt of social support in motivating smartphone users to engage in disaster
preparedness. In contrast, after taking into account the demographic and geographical factors, non-smartphone users’ reliance on social support appeared to be a major
source of influence on their engagement in disaster preparedness.

Discussion
This study proposes an ecological view to understand whether and how digital disparities vis-à-vis access to and use of mobile technology mitigate or exacerbate existing differentials among individuals in terms of their disaster preparedness capacities.
Overall, the data provide strong evidence to support the normalisation hypothesis.
Two patterns emerged with regard to how digital disparities are intertwined with
social and structural vulnerabilities, which, in turn, are reflected in variations in disaster information and preparedness behaviour.
First, digital disparities may exist even within the seemingly more resourceful group
(smartphone users). The level of routinised use of advanced mobile technology features

Chih-Hui Lai, Arul Chib, and Rich Ling

was positively associated with engagement in disaster information repertoires. In
other words, the level of user sophistication in using smartphones could be a source
of disparity (Hargittai, 2002), reflected here in gaps in acquiring disaster information.
Moreover, even within the group of smartphone users, socioeconomic differentials
are still salient as individuals who were older and had higher levels of education and
income were more likely to engage in disaster information repertoires.
Second, digital disparities are manifested differently when associated with personal and socio-structural factors, which in turn are linked to disaster vulnerabilities.
The results of this study showed that non-smartphone users’ employment of a variety
of channels to receive information on impending disasters or to share the information with social contacts was not as powerful a driver of preparedness as it was for
smartphone users. This is indicated by the lack of significant direct effects from nonsmartphone users’ disaster information repertoires and information sharing on preparedness. In addition, there is a lack of indirect effects of non-smartphone users’
risk perceptions and routinised use of mobile technology on preparedness through
disaster information repertoires and information sharing. Moreover, smartphone users
who were older, lived in rural areas, resided in the same place for longer periods, and
had a wider range of disaster information repertoires were more likely to engage in
preparedness behaviour. Yet, these effects are less salient for non-smartphone users.
Indeed, the results echo to some extent the global trend towards rapid urbanisation
and smartphone substitution, particularly in Asia, thus creating problems for young
rural migrants who move to cities and are more exposed to natural hazards (Economic
and Social Commission for Asia and the Pacific, 2016).
Despite the prevailing evidence supporting the normalisation hypothesis, this
study highlights the possible ways in which the impacts of digital disparities may
be mitigated in the context of disaster preparedness. Specifically, disaster vulnerabilities owing to digital disparities may be addressed by building social support
systems. While smartphone users had the advantage of engaging in a wide range of
disaster information repertoires, non-smartphone users relied on their social networks for disaster-specific social support as a possible motivator of disaster preparedness. From the standpoint of preparedness programmes, making use of the social
environment in order to elicit conversations about disasters among community members would be a feasible approach to motivate non-smartphone users to prepare for
impending disasters.
Parsing the demographic variables, using different outlets and sources to disseminate risk-related information would be a more effective way of reaching young urban
migrant smartphone users, and facilitating their preparedness behaviour. As these
smartphone users also engage in information sharing with their social contacts, preparedness programmes should go through them to reach out to other non-smartphone
users in a community where smartphone adoption is not prevalent among residents.
Rural respondents’ use of a greater number of basic features of mobile technology facilitated their engagement in information seeking through multiple sources on
impending disasters. Given the moderate to high proportions of rural populations

Digital disparities and vulnerability

in these four countries (46–66 per cent), and the projected rapid decline in rural
populations by 2050 in all but the Philippines (United Nations, 2015), this finding
is especially informative.
The study suggests ways to address rural populations’ needs and to use available
resources to engage in disaster preparedness, such as through the use of basic mobile
technology. Future research should build on its findings and investigate how urban
and rural users appropriate specific features and functions of mobile technology for
routine and non-routine information seeking and sharing. The inquiry provides
practical insights for the humanitarian sector and community members into the ways
of incorporating mobile technology in initiatives to develop long-term resilience to
different kinds of environmental threats, such as chronic climate change.
The findings revealed differences in disaster preparedness in the four countries
under review. Compared to respondents in Indonesia and Myanmar, those in the
Philippines and Vietnam had a more diverse palette of channels for seeking information on disasters, as well as a higher level of preparedness behaviour, probably
reflecting the greater rate of adoption of smartphones and the shift from traditional
media to mobile internet services for the procurement of news and information in
these two countries (Stryjak, Sharma, and Hatt, 2014; Gallup, 2015). Certainly, more
granular research is needed to pinpoint further regional and country differences in
terms of people’s media habits, as they are likely to affect the ways in which individuals seek and share disaster-related information and prepare for disasters.

Limitations
This study has three key limitations that need to be addressed in future research.
First, the ecological view points to co-evolving and reciprocal influences between
human behaviours and environments. Due to the cross-sectional design, the insights
are limited to explicating how environmental influences shape individual behaviour,
but not the other way around.
Second, the use of purposive sampling rendered an overrepresented picture of
smartphone users. The challenge of identifying precisely urban and rural samples in
the four countries with limited statistical data led to underrepresentation of rural
populations in Vietnam and Myanmar. Future research needs to employ more systematic ways of obtaining representative urban and rural samples in order to refine
the findings of this study. Furthermore, resource and time constraints resulted in a
moderate sample size from each country and a small sample size for the higher-level
grouping variables (county, region), posing a challenge for a statistical examination
of different levels of influence.
Third, despite conceptual reasoning, most of the measurements in this study are
aggregated indices of dichotomised items, which in some ways reflect the problems
encountered in field data collection. There is a fine balance between reducing the
cognitive burden on the respondents and ensuring the validity of the instrument.

Chih-Hui Lai, Arul Chib, and Rich Ling

These limitations could be addressed by using an expanded sample size, refined
measurements, and a longitudinal design. Specifically, longitudinal observation will
produce a more solid understanding of the reciprocal linkage between information
seeking, information sharing, and adaptive preparedness behaviour.

Conclusion
The imperative of comprehending the implications of digital disparities in disaster
preparedness led to this study taking an ecological view. It examined how variations
in the use of mobile technology are connected to social and structural disparities,
which in turn reflect differentials in disaster information behaviour and preparedness.
In support of the normalisation hypothesis, data analysis reveals that digital disparities
exacerbate existing inequalities in light of the trends pertaining to urbanisation and
the increasing uptake of smartphones in the four developing countries (Indonesia,
Myanmar, Philippines, and Vietnam). Even among smartphone users, potential digital disparities exist as individuals’ disaster information acquisition varies depending
on user sophistication and demographic factors, such as age, education, and income.
The study also provides evidence of how digital disparities manifest differently when
associated with personal and socio-cultural factors. For example, smartphone users’
age, engagement in disaster information repertoires, length and location of residence,
risk perception, and routinised use of mobile technology were associated with preparedness behaviour. These effects, though, are less salient for non-smartphone users.
This study presents an empirical and theoretical endeavour to address the issues of
digital disparities and disaster vulnerability in developing nations by delving into the
environmental, individual, and social determinants of disaster preparedness. Case
control and prospective and comparative studies could be the next step to verify
further the ecological model evaluated here and to enhance knowledge of digital disparities and disaster preparedness. Regardless, more research is needed to augment
understanding of how vulnerable communities can build resilience over time in
response to short-term natural disasters and long-term environmental hazards.

Acknowledgements
This project was supported by USAID (United States Agency for International
Development) Grant Award Number AID-OFDA-G-13-00038 and by the Global
Disaster Preparedness Center.

Correspondence
Chih-Hui Lai, Department of Communication and Technology, National Chiao
Tung University, No. 1, Sec. 1, Liujia 5th Rd., Zhubei City, Hsinchu County 302,
Taiwan. Telephone: +886 3 5712121; e-mail: chlai@nctu.edu.tw

Digital disparities and vulnerability

Endnotes
1
2

3

4

5
6

7

For more information see http://www.preparecenter.org/ (last accessed on 23 February 2018).
Indonesia, Myanmar, Philippines, and Vietnam are defined as developing countries and belong
to the category of lower middle-income (USD 1,046–4,125). For the latest classifications see The
World Bank (n.d.).
According to the database of the United Nations Educational, Scientific and Cultural Organization
(UNESCO)—see http://uis.unesco.org/en/country/mm, http://uis.unesco.org/en/country/VN,
http://uis.unesco.org/en/country/PH, and http://uis.unesco.org/en/country/ID (last accessed on
8 March 2018)—rural populations account for 65, 66, 56, and 46 per cent of the total in Myanmar,
Vietnam, Philippines, and Indonesia, respectively. Hence, the purposive sample resulted in under­
representation of rural populations in Vietnam and Myanmar. Please refer to the technical report
for details of sampling and the locations of the fieldwork (Lai, Chib, and Ling, 2015).
The measures of disaster information repertoires, preparedness behaviour, and routinised usage of
mobile technology are formative (to indicate different facets of these constructs) and indices were
used to accumulate scores. Consequently, the value of Cronbach’s alpha associated with these variables is not reported (Petter, Straub, and Rai, 2007).
See https://www.ready.gov/ (last accessed on 27 February 2018).
Owing to the lack of a central and official system that classifies education and income in all four
countries, the categories referenced multiple sources, including governmental and/or statistical
authorities, or existing academic research and nationwide surveys.
As a result of the small sample size of the grouping variable (four countries), which would render
estimations of parameters less precise and less powerful (Garson, 2012), multi-level modelling was
not employed for analysis. However, to confirm the results derived from ordinary least squares
regression reported in the paper, one-way ANCOVA (analysis of covariance) with random effects
modelling was used. The results of both approaches are mostly identical except for country differences, probably due to the small grouping sample size.

References
Ahsan, M.N., K. Takeuchi, K. Vink, and J. Warner (2016) ‘Factors affecting the evacuation decisions
of coastal households during Cyclone Aila in Bangladesh’. Environmental Hazards. 15(1). pp. 16–42.
Akhtar, S. and P.B. Arinto (2009) Digital Review of Asia Pacific 2009–2010. SAGE Publications Inc.,
Thousand Oaks, CA.
Aldrich, D.P. and M.A. Meyer (2014) ‘Social capital and community resilience’. American Behavioral
Scientist. 59(2). pp. 254–269.
Allen, K.M. (2006) ‘Community-based disaster preparedness and climate adaptation: local capacitybuilding in the Philippines’. Disasters. 30(1). pp. 81–101.
Andrew, S., S. Arlikatti, L. Siebeneck, K. Pongponrat, and K. Jaikampan (2016) ‘Sources of organisational resiliency during the Thailand floods of 2011: a test of the bonding and bridging hypotheses’.
Disasters. 40(1). pp. 65–84.
Becker, J. S., D. Paton, D.M. Johnston, and K.R. Ronan (2012) ‘A model of household preparedness
for earthquakes: how individuals make meaning of earthquake information and how this influences
preparedness’. Natural Hazards. 64(1). pp. 107–137.
Bourque, L.B. et al. (2012) ‘An examination of the effect of perceived risk on preparedness behavior’.
Environment and Behavior. 45(5). pp. 615–649.
Boyle, M., M. Schmierbach, C. Armstrong, and D. McLeod (2004) ‘Information seeking and emotional reactions to the September 11 terrorist attacks’. Journalism and Mass Communication Quarterly.
81(1). pp. 155–167.

Chih-Hui Lai, Arul Chib, and Rich Ling

Bronfenbrenner, U. (1979) The Ecology of Human Development. Harvard University Press, Cambridge, MA.
Chen, W. (2015) ‘A moveable feast: do mobile media technologies mobilize or normalize cultural
participation?’. Human Communication Research. 41(1). pp. 82–101.
Chib, A. and K. Ale (2009) ‘Extending the technology–community–management model to disaster
recovery in Asia’. Proceedings of the Third International Conference on Information and Communication
Technologies and Development. http://ieeexplore.ieee.org/abstract/document/5426694/ (last accessed
on 6 March 2018).
Chib, A., A. Baghudana, and S. Kasdani (2010) ‘Rebuilding Bukit Lawang: The role of ICTs for
sustainable development in a post-disaster Indonesian village’. In D.S. Miller and J.D. Rivera (eds.)
Community Disaster Recovery and Resiliency: Exploring Global Opportunities and Challenges. Taylor and
Francis, Boca Raton, FL. pp. 223–247.
Choo, C.W. and I. Nadarajah (2014) ‘Early warning information seeking in the 2009 Victorian
bushfires’. Journal of the Association for Information Science and Technology. 65(1). pp. 84–97.
CRED (Centre for Research on the Epidemiology of Disasters) (2016a) 2015 Disasters in Numbers.
Press release. http://cred.be/sites/default/files/2015_DisastersInNumbers.pdf (last accessed on
1 March 2018).
CRED (2016b) ‘Poverty & death: disaster mortality, 1996–2015’. CRED CRUNCH. 44 (November).
http://cred.be/sites/default/files/CredCrunch44.pdf (last accessed on 1 March 2018).
CRED (2016c) ‘2016 preliminary data: human impact of natural disasters’. CRED CRUNCH. 45
(December). http://reliefweb.int/sites/reliefweb.int/files/resources/CredCrunch45.pdf (last accessed
on 1 March 2018).
Chomitz, K.M, P. Buys, and T. Thomas (2005) Quantifying the Rural-Urban Gradient in Latin America
and the Caribbean. Policy Research Working Paper. 3634 (June). The World Bank, Washington, DC.
Cutter, S.L., B.J. Boruff, and W.L. Shirley (2003) ‘Social vulnerability to environmental hazards’.
Social Science Quarterly. 84(2). pp. 242–261.
Dash, N. and H. Gladwin (2007) ‘Evacuation decision making and behavioral responses: individual
and household’. Natural Hazards Review. 8(3). pp. 69–77.
Donner, J. (2008) ‘Research approaches to mobile use in the developing world: a review of the literature’. The Information Society. 24(3). pp. 140–159.
Drabek, T.E. (1999) ‘Understanding disaster warning responses’. The Social Science Journal. 36(3).
pp. 515–523.
Dynes, R.R. (2002) The Importance of Social Capital in Disaster Response. Preliminary Paper No. 327.
Disaster Research Center, University of Delaware, Newark, DE.
Economic and Social Commission for Asia and the Pacific (2016) Economic and Social Survey of Asia
and the Pacific 2016. United Nations, Bangkok.
Eiser, J.R. et al. (2012) ‘Risk interpretation and action: a conceptual framework for responses to
natural hazards’. International Journal of Disaster Risk Reduction. 1 (October). pp. 5–16.
Gallup (2015) ‘Young Vietnamese increasingly turning to online news sources over state TV’.
11 June. http://aib.org.uk/young-vietnamese-increasingly-turning-to-the-internet-for-news/
(last accessed on 1 March 2018).
Garson, G.D. (2012) Hierarchical Linear Modeling: Guide and Applications. Sage Publications Inc.,
Thousand Oaks, California.
Grothmann, T. and F. Reusswig (2006) ‘People at risk of flooding: why some residents take precautionary action while others do not’. Natural Hazards. 38(101). pp. 101–120.
Hargittai, E. (2002) ‘Second-level digital divide: differences in people’s online skills’. First Monday. 7(4).
http://www.firstmonday.org/ojs/index.php/fm/article/view/942 (last accessed on 1 March 2018).
Hausman, A.J., A. Hanlon, and B. Seals (2007) ‘Social capital as a mediating factor in emergency
preparedness and concerns about terrorism’. Journal of Community Psychology. 35(8). pp. 1073–1083.
Hawkins, R.L. and K. Maurer (2009) ‘Bonding, bridging and linking: how social capital operated
in New Orleans following Hurricane Katrina’. British Journal of Social Work. 40(6). pp. 1777–1793.

Digital disparities and vulnerability

Hawley, A.H. (1950) Human Ecology: A Theory of Community Structure. Ronald Press Company, New
York, NY.
Hayes, A. (2013) Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-based
Approach. Guilford Press, New York, NY.
Haythornthwaite, C. (2005) ‘Social networks and internet connectivity effects’. Information, Communication and Society. 8(2). pp. 125–147.
Heimerl, K. et al. (2015) ‘Analysis of smartphone adoption and usage in a rural community cellular
network’. Proceedings of the Seventh International Conference on Information and Communication Technologies and Development. Article No. 40. https://dl.acm.org/citation.cfm?id =2737880 (last accessed
on 1 March 2018).
Heller, K., D.B. Alexander, M. Gatz, B.G. Knight, and T. Rose (2005) ‘Social and personal factors
as predictors of earthquake preparation: the role of support provision, network discussion, negative
affect, age, and education’. Journal of Applied Social Psychology. 35(2). pp. 399–422.
Helslott, I. and A. Ruitenberg (2004) ‘Citizen response to disasters: a survey of literature and some
practical implications’. Journal of Contingencies and Crisis Management. 12(3). pp. 98–111.
Hirzalla, F., L. van Zoonen, and J. de Ridder (2011) ‘Internet use and political participation: reflections
on the mobilization/normalization controversy’. The Information Society. 27(1). pp. 1–15.
Hurlbert, J.S., V.A. Haines, and J.J. Beggs (2000) ‘Core networks and tie activation: what kinds of
routine networks allocate resources in nonroutine situations?’. American Sociological Review. 65(4).
pp. 598–618.
International Telecommunications Union (2016) ‘ICTEYE’. http://www.itu.int/net4/itu-d/icteye/
(last accessed on 1 March 2018).
Kahlor, L., S. Dunwoody, R.J. Griffin, and K. Neuwirth (2006) ‘Selecting and processing information about impersonal risk’. Science Communication. 28(2). pp. 163–194.
Kapucu, N. (2008) ‘Culture of preparedness: household disaster preparedness’. Disaster Prevention and
Management. 17(4). pp. 526–535.
Kim, Y.-C. and J. Kang (2010) ‘Communication, neighbourhood belonging and household hurricane
preparedness’. Disasters. 34(2). pp. 470–488.
Kirschenbaum, A.A., C. Rapaport, and D. Canetti (2017) ‘The impact of information sources on
earthquake preparedness’. International Journal of Disaster Risk Reduction. 21 (March). pp. 99–109.
Lai, C.-H. (2014) ‘An integrated approach to untangling mediated connectedness with online and
mobile media’. Computers in Human Behavior. 31 (February). pp. 20–26.
Lai, C.-H. and T. Tang (2015) ‘Understanding local news consumption and community participation
via the lens of information repertoires and media multiplexity.’ Mass Communication and Society.
18(3). pp. 325–349.
Lai, C.-H., A. Chib, and R. Ling (2015) State of the Use of Mobile Technologies for Disaster Preparedness in
South East Asia. March. http://preparecenter.org/resources/state-use-mobile-technologies-disasterpreparedness-south-east-asia (last accessed on 1 March 2018).
Levac, J., D. Toal-Sullivan, and T.L. O’Sullivan (2012) ‘Household emergency preparedness: a literature review’. Journal of Community Health. 37(3). pp. 725–733.
McComas, K.A. and C.W. Trumbo (2001) ‘Source credibility in environmental health–risk controversies: application of Meyer’s credibility index’. Risk Analysis. 21(3). pp. 467–480.
McLeroy, K.R., D. Bibeau, A. Steckler, and K. Glanz (1988) ‘An ecological perspective on health
promotion programs’. Health Education and Behavior. 15(4). pp. 351–377.
Mileti, D.S. and J.H. Sorensen (1990) Communication of Emergency Public Warnings: A Social Science Perspective and State of the Art Assessment. Federal Emergency Management Agency, Washington, DC.
Murphy, B.L. (2007) ‘Locating social capital in resilient community-level emergency management’.
Natural Hazards. 41(2). pp. 297–315.
Nakagawa, Y. and R. Shaw (2004) ‘Social capital: a missing link to disaster recovery’. International
Journal of Mass Emergencies and Disasters. 22(1). pp. 5–34.

Chih-Hui Lai, Arul Chib, and Rich Ling

O’Keefe, G.J., H.J. Ward, and R. Shepard (2002) ‘A repertoire approach to environmental information channels’. Science Communication. 23(4). pp. 392–409.
Paton, D. and D. Johnston (2001) ‘Disasters and communities: vulnerability, resilience and preparedness’.
Disaster Prevention and Management. 10(4). pp. 270–277.
Perreault, M.F., J.B. Houston, and L. Wilkins (2014) ‘Does scary matter?: testing the effectiveness of
new National Weather Service tornado warning messages’. Communication Studies. 65(5). pp. 484–499.
Petter, S., D. Straub, and A. Rai (2007) ‘Specifying formative constructs in information systems research’.
MIS Quarterly. 31(4). pp. 623–656.
Pew Research Center (2015) ‘Internet seen as positive influence on education but negative on morality
in emerging and developing nations’. 19 March. http://www.pewglobal.org/2015/03/19/internetseen-as-positive-influence-on-education-but-negative-influence-on-morality-in-emerging-anddeveloping-nations/ (last accessed on 6 March 2018).
Preacher, K.J. and A.F. Hayes (2008) ‘Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models’. Behavior Research Methods. 40(3). pp. 879–891.
Reagan, J. (1996) ‘The “repertoire”; of information sources’. Journal of Broadcasting and Electronic Media.
40(1). pp. 112–121.
Ruppel, E.K. and T.J. Burke (2014) ‘Complementary channel use and the role of social competence’.
Journal of Computer-mediated Communication. 20(1). pp. 37–51.
Sallis, J.F., N. Owen, and E.B. Fisher (2008) ‘Ecological models of health behavior’. In K. Glanz,
B.J. Rimer, and K. Viswanath (eds.) Health Behavior and Health Education: Theory, Research, and
Practice. John Wiley and Sons, San Francisco, CA. pp. 465–486.
Scolobig, A., B. De Marchi, and M. Borga (2012) ‘The missing link between flood risk awareness and
preparedness: findings from case studies in an Alpine region’. Natural Hazards. 63(2). pp. 499–520.
Sharma, U., A. Patwardhan, and D. Parthasarathy (2009) ‘Assessing adaptive capacity to tropical
cyclones in the east coast of India: a pilot study of public response to cyclone warning information’.
Climatic Change. 94(1–2). pp. 189–209.
Shaw, R., K.S.H. Kobayashi, and M. Kobayashi (2004) ‘Linking experience, education, perception
and earthquake preparedness’. Disaster Prevention and Management. 13(1). pp. 39–49.
Sheppard, B., M. Janoske, and B. Liu (2012) Understanding Risk Communication Theory: A Guide for
Emergency Managers and Communicators. May. Department of Homeland Security Science and Technology Centers of Excellence, University of Maryland, College Park, MD.
Sherbourne, C.D. and A.L. Stewart (1991) ‘The MOS social support survey’. Social Science and
Medicine. 32(6). pp. 705–714.
Shklovski, I., L. Palen, and J. Sutton (2008) ‘Finding community through information and communication technology in disaster response’. Proceedings of the 2008 ACM Conference on Computer Supported
Cooperative Work. http://dl.acm.org/citation.cfm?id=1460584 (last accessed on 1 March 2018).
Sommerfeldt, E.J. (2015) ‘Disasters and information source repertoires: information seeking and
information sufficiency in postearthquake Haiti’. Journal of Applied Communication Research. 43(1).
pp. 1–22.
Spence, P.R., K.A. Lachlan, and J.A. Burke (2008) ‘Crisis preparation, media use, and information
seeking: patterns across Katrina evacuees and lessons learned for crisis communication’. Journal of
Emergency Management. 6(1). pp. 11–23.
Spialek, M. L., H.M., Czlapinski, and J.B. Houston (2016) ‘Disaster communication ecology and
community resilience perceptions following the 2013 Central Illinois Tornadoes’. International Journal
of Disaster Risk Reduction. 17 (August). pp. 154–160.
Stokols, D., J.G. Grzywacz, S. McMahan, and K. Phillips (2003) ‘Increasing the health promotive
capacity of human environments’. American Journal of Health Promotion. 18(1). pp. 4–13.
Stryjak, J., A. Sharma, and T. Hatt (2014) Country Overview: Philippines Growth through Innovation.
GSMA Intelligence, London.

Digital disparities and vulnerability

Sutton, J. et al. (2014) ‘Warning tweets: serial transmission of messages during the warning phase of
a disaster event’. Information, Communication and Society. 17(6). pp. 765–787.
Sylvester, G. (2016) Use of Mobile Phones by the Rural Poor. Gender Perspectives from Selected Asian Countries.
Food and Agriculture Organization of the United Nations, LIRNEasia, and International Development Research Centre, Bangkok.
The World Bank (2016) ‘Data: fixed broadband subscriptions (per 100 people)’. http://data.worldbank.org/indicator/IT.NET.BBND.P2?locations=ID-PH-VN-MM&name_desc=false (last accessed
on 23 February 2018).
The World Bank (n.d.) ‘Data: World Bank country and lending groups’. https://datahelpdesk.world
bank.org/knowledgebase/articles/906519 (last accessed on 23 February 2018).
United Nations (2015) World Urbanization Prospects: 2014 Revision. https://esa.un.org/unpd/wup/
Publications/Files/WUP2014-Report.pdf (last accessed on 1 March 2018).
Van Dijk, J. (2006) ‘Digital divide research, achievements and shortcomings’. Poetics. 34(4–5). pp. 221–235.
Vaux, A., S. Riedel, and D. Stewart (1987) ‘Modes of social support: the social support behaviors
(SS-B) scale’. American Journal of Community Psychology. 15(2). pp. 209–232.
Viswanath, K. and J.R. Finnegan Jr (1996) ‘The knowledge gap hypothesis: twenty-five years later’.
Annals of the International Communication Association. 19(1). pp. 187–228.
Wachinger, G., O. Renn, C. Begg, and C. Kuhlicke (2013) ‘The risk perception paradox—implications
for governance and communication of natural hazards’. Risk Analysis. 33(6). pp. 1049–1065.
Wood, M.M. et al. (2012) ‘Communicating actionable risk for terrorism and other hazards’. Risk
Analysis. 32(4). pp. 601–615.
Zainudeen, A. and H. Galpaya (2015) Mobile Phones, Internet, and Gender in Myanmar. GSMA, London.