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AIS Electronic Library (AISeL)
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DEEP STRUCTURE USE OF MHEALTH: A
SOCIAL COGNITIVE THEORY
PERSPECTIVE
Monica Fallon
University of Mannheim3.88;:@:69.::526912
Kai Spohrer
University of Mannheim><;5=2=@:69.::526912
Armin Heinzl
University of Mannheim526:C8@:69.::526912
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Fallon et al./ Deep Structure Use of mHealth
Twenty-Seventh European Conference on Information Systems (ECIS2019), Stockholm-Uppsala, Sweden
1
DEEP STRUCTURE USE OF MHEALTH: A SOCIAL
COGNITIVE THEORY PERSPECTIVE
Research in Progress
Fallon, Monica, University of Mannheim, Mannheim, Germany, fallon@uni-mannheim.de
Spohrer, Kai, University of Mannheim, Mannheim, Germany, spohrer@uni-mannheim.de
Heinzl, Armin, University of Mannheim, Mannheim, Germany, heinzl@uni-mannheim.de
Abstract
Consumer health information technology, such as mobile health applications (mHealth), enable
consumers adopt healthy behaviours and improve health outcomes. We take a closer look at use
concepts to understand how mHealth use facilitates behaviour change. We review the mHealth
literature in information systems (IS) and health IS journals and find that superficial mHealth use
concepts (e.g. binary and duration of use) dominate this literature stream. In line with contemporary
IS research, we suggest that rich and theoretically-driven concepts of mHealth use can help to better
understand what users do with mHealth and how this affects relevant outcomes. We take a social
cognitive theory (SCT) perspective to conceptualize mHealth deep structure use, a rich concept of use
centred on the extent to which tasks represented in mHealth facilitate behaviour change. This paper
contributes to IS research in three key ways. First, we review mHealth literature and identify use
concepts that have been employed to explain effects on outcomes. Second, we provide a theoretically-
driven mHealth deep structure use concept from a SCT perspective. Third, we offer a conceptual lens
that captures how mHealth deep structure use facilitates behaviour change. Future research will
empirically evaluate aspects developed in this mHealth deep structure use concept.
Keywords: mHealth; deep structure use; social cognitive theory; health behaviours; behaviour change
1 Introduction
Consumer health information technology has tremendous potential for enabling individuals to take an
active role in the management of their own health (Chiasson and Davidson, 2004; Agarwal, Gao,
DesRoches and Jha, 2010; Kohli and Tan, 2016). With the rapid development and diffusion of mobile
technologies, mobile health applications (mHealth) have become a prevalent type of health information
technology among consumers. This is evident in both the number of mHealth apps on the market and
the increasing downloads (Research2Guidance, 2017, 2018). Consumers download mHealth to enable
them to change behaviours and improve health outcomes (Krebs and Duncan, 2015). Behaviour change,
such as quitting smoking, eating a balanced diet, and increasing physical activity, is essential for
preventing the onset of adverse health outcomes, disease and premature death (WHO, 2014). However,
research results regarding the effects of mHealth use on changing behaviours and improving health
outcomes remains largely inconsistent (Sawesi et al., 2016; Zhao, Freeman and Li, 2016).
Superficial concepts of mHealth use, such as binary measures of use or the duration of use, provide
little understanding into how the desired outcome is actually achieved. Information Systems (IS)
researchers have recently started to criticize superficial use concepts, and instead conceptualize use as
context-specific interactions between the IS and the tasks it represents (Burton-Jones and Straub, 2006;
Robert and Sykes, 2017; Sykes and Venkatesh, 2017; Bala and Bhagwatwar, 2018). Deep structure use
is a rich use concept that explains how the deep structure of an IS can represent real-world tasks and
how these tasks can be used to facilitate the desired outcome (Burton-Jones & Straub, 2006). However,
Fallon et al./ Deep Structure Use of mHealth
Twenty-Seventh European Conference on Information Systems (ECIS2019), Stockholm-Uppsala, Sweden
2
different IS are used to achieve different outcomes. For instance, mHealth is used to change behaviours
and improve health outcomes (Krebs and Duncan, 2015), whereas provider order entry systems are used
to improve coordination with patient care teams (Harle et al., 2012). Accordingly, IS researchers have
called for contextualizing measures of use, especially in the health domain (Burton-Jones and Volkoff,
2017; Romanow, Rai and Keil, 2018). We follow this call and propose that superficial concepts of
mHealth use are hindering a deeper understanding of how mHealth use facilitates behaviour change.
Against this backdrop, the overarching goal of this paper is to acquire knowledge on the key
mechanisms that facilitate behaviour change through mHealth use. We answer the following research
questions: (1) What concepts of mHealth use are currently researched in IS literature? (2) How can
mHealth deep structure use be theoretically conceptualized? And (3) Which mHealth deep structure
use interactions facilitate behaviour change? To do so, we review literature on mHealth use and identify
weaknesses in current use concepts. Then, we take a closer look at mHealth deep structure use from a
social cognitive theory (SCT) perspective. SCT is one of the most extensively used theoretical
foundations for enabling individuals to change health behaviours (Painter et al., 2008) and specifies
determinants of behaviour change (Bandura, 1991). An SCT perspective allows us to develop a context-
specific and theory-driven mHealth deep structure use concept. The research approach of the paper is
conceptual in nature by envisioning a new way to conceptualize mHealth use (MacInnis, 2011). This
paper adds to existing IS literature by (1) reviewing the literature to identify which current mHealth use
concepts are employed to explain effects on outcomes; (2) providing a theoretically-driven concept of
mHealth deep structure use from an SCT perspective; and (3) offering a conceptual lens that captures
how mHealth deep structure use facilitates behaviour change. In order to achieve these goals, we adapt
previously used guidelines for conceptualizing deep structure use (Burton-Jones & Straub, 2006) and
relate SCT constructs with mHealth capabilities. For our future work, we will further develop a theory
of mHealth effective use by integrating aspects of the user and empirically validating a model of
mHealth deep structure use for behaviour change.
2 Literature Review of mHealth Use
Given our focus on how consumers effectively use mHealth to achieve desired outcomes, we were
interested in how previous research conceptualizes mHealth use and whether use facilitates behaviour
change. We reviewed the IS basket of eight journals (European Journal of Information Systems,
Information Systems Journal, Information Systems Research, Journal of AIS, Journal of Information
Technology, Journal of MIS, Journal of Strategic Information Systems, and MIS Quarterly) and the top
three health IS journals from the AIS SIG Health list (Journal of American Medical Informatics
Association, International Journal of Medical Informatics, and Journal of Medical Internet Research).
Health IS journals were added given the limited amount of mHealth research published in the basket of
eight. We included studies that specifically explored mHealth, focused on the consumer as the user, and
examined behaviour change or health outcomes as a dependent variable. These restrictions allowed us
to limit the scope of our review. Research that focused on tools for health information seeking or health
information archival (e.g. health records) were excluded to avoid blurring boundaries with related health
technologies with different desired outcomes (Agarwal and Khuntia, 2009). We also excluded studies
that focus on physicians’ use of mHealth because they use mHealth to achieve different outcomes (e.g.
patient-physician communication) (Gagnon, Ngangue, Payne-Gagnon and Desmartis, 2015). The
selected studies were reviewed, measures of use were classified, and outcomes were assessed. We
classified use consistent with the conceptualization from Burton-Jones & Straub (2006). Superficial and
lean use concepts were classified as binary use (use/no use), the extent of use (time in app/days used/
app login), or the breadth of use (number of features used). Rich use concepts were classified as
including the user context and/or task context by measuring the degree to which a user employs a system
(e.g. cognitive absorption) or the degree to which the system is employed in a task (e.g. deep structure
use). Table 1 shows the primary contributions of our review. Several things can be observed.
First, we find that a vast majority of mHealth research uses superficial use concepts, such as lean or a
combination of different lean use concepts. These include binary measures of use (e.g. Kirwan, Duncan,
Vandelanotte and Mummery, 2012), the extent of use (e.g. Mohr et al., 2017), and breadth of use (e.g.
Helander, Kaipainen, Korhonen and Wansink, 2014). We did not find any studies that used rich use
Fallon et al./ Deep Structure Use of mHealth
Twenty-Seventh European Conference on Information Systems (ECIS2019), Stockholm-Uppsala, Sweden
3
concepts or examined the extent to which mHealth use facilitates tasks for behaviour change. These
superficial mHealth use concepts, reveal little insight into how users effectively use mHealth to achieve
the desired outcomes.
Use
Study
Use Evaluation
Main findings
mHealth use facilitates behaviour change
Binary
Kirwan et
al., 2012
mHealth vs. internet
mHealth use resulted in increased likelihood to log
greater than 10k steps.
Carter, et
al., 2013
mHealth vs. website vs.
paper diary
mHealth users lost more weight, decreased BMI, and
had less body fat (compared to diary and web).
Turner-
McGrievy
et al., 2013
mHealth vs. no mHealth
(physical activity);
mHealth vs. website vs.
diary (diet)
mHealth users had more frequent self-monitoring of
physical activity, lower BMI, and consumed less
energy.
Binary /
Extent of
Use
Litman et
al., 2015
use of mHealth vs. use of
mHealth and discontinued
vs. never used mHealth;
time since using app
App users exercise more. BMI is negatively
correlated with time since using app.
Extent of
Use
Hales et al.,
2016
social app vs.standard app
(control); use per week
Participants with social mHealth app lost more
weight and had greater BMI reduction than
participants with standard app (control). Use per
week was greater for social app.
Unclear if mHealth use facilitates behavior change
Binary
Turner-
McGrievy
& Tate,
2014
mobile device (mHealth,
mp3 player) vs. stationary
technology (desktop)
Trend towards greater weight loss in app users vs.
website users, but non-significant.
Binary/
Breadth
of Use
Ernsting et
al., 2017
use vs. no use; features
used
20.53% of smartphone users use an mHealth app.
Apps with planning impact physical activity. Apps
with feedback or monitoring impact physical
activity. Apps with feedback or monitoring impact
adherence to doctor advice.
Extent of
use
Ribeiro et
al., 2017
use per day
Participants increased frequency of some cancer
prevention behaviors. Unclear if use of app impacted
cancer prevention behaviors.
Plaza et al.,
2017
use per day
Changes in mindfulness awareness with app use
were insignificant.
Mohr et al.,
2017
number of treatment app
sessions; time in app
Participants showed a reduction in depression and
anxiety. The extent of use was not analyzed in
connection with changes in depression/ anxiety.
Pratap et
al., 2018
problem solving app vs.
cognitive training app vs.
information app (control);
number of days in app
Depressive symptoms improved, but no differences
between app groups. Problem solving app and
information app had more active days of use.
Breadth
of use
Helander et
al., 2014
number of picture
uploads, number of
ratings, pictures per day
Only 9% of active users had a positive trend in their
average healthiness ratings.
Binary /
Extent of
Use/
Breadth
of Use
Direito, et
al., 2015
immersive app vs. non-
immersive app vs. no app;
time spent in app; number
of features used
Both app groups improved time to complete fitness
test, but no significant differences between
immersive/ non-immersive/ control groups. Only
31% of participants used the app more than three
times per week. The features of the immersive app
received the most positive feedback.
Table 1. mHealth Literature Review
Second, our review reveals inconsistent findings on the effects of mHealth use on behaviour change.
Some studies show that the mHealth use has positive effects on behaviour change (Kirwan et al., 2012;
Litman et al., 2015) and health (Hales et al., 2016) while others show non-significant effects on
Fallon et al./ Deep Structure Use of mHealth
Twenty-Seventh European Conference on Information Systems (ECIS2019), Stockholm-Uppsala, Sweden
4
behaviour change (Plaza et al., 2017) and health (Turner-McGrievy and Tate, 2014). These
contradictory findings illustrate that superficial use concepts do not provide a powerful explanatory lens
into how users interact with mHealth. Thus, in an mHealth context, lean measures of use are hindering
a sufficient understanding of the true effects of the technology (Burton-Jones & Straub, 2006).
Third, our literature review indicates that the diversity in research on mHealth use hinders a cumulative
research stream (Keen 1980). The mHealth literature relies on various use concepts for assessing
mHealth effectiveness. For example, Hales et al. (2016) employed two different mHealth applications
with slightly different features and examined the extent to which the applications were used per week.
Other studies used even more simplistic measures of use, such as comparing behaviours of users
provided with an mHealth app to a control condition without an mHealth app (e.g. Kirwan et al., 2012;
Carter, Burley, Nykjaer and Cade, 2013). Although one way of measuring use is not necessarily better
than the other, a systematic conceptualization of mHealth use provides more insights into user
interactions and builds cumulative knowledge on the true effects of mHealth use.
To address the lack of theoretical and methodological clarity of mHealth use and its effects on behaviour
change, the current paper works towards a concept of effective mHealth use. To enable effective use,
the internal structure of an IS should faithfully represent tasks the user needs to carry-out to achieve the
desired outcome (Wand and Weber, 1995; Burton-Jones and Straub, 2006; Burton-Jones and Grange,
2013). To achieve this, the deep structure of an IS should represent the real-world domain it is intended
to model (Wand and Weber, 1995). Thus, in an mHealth context, the deep structure should represent
behaviour change theory and tasks for changing behaviour. Deep structure use represents how features
of the system that relate to core aspects of focal tasks are used to achieve the desired outcome (Desanctis
and Poole, 1994) and captures the interaction of the IS and the tasks it represents (Burton-Jones &
Straub, 2006). Using this view, our mHealth deep structure use concept represents how mHealth
faithfully represents real-world tasks through features and how use facilitates behaviour change.
To overcome the lack of theoretical understanding and methodological weaknesses of prior mHealth
use concepts, we leverage a SCT perspective to create a context-specific notion of mHealth deep
structure use. We use SCT as a basis for identifying important tasks for behaviour change and identify
how these tasks are achieved through mHealth use.
3 Social Cognitive Theory
There are numerous theories in the behavioural sciences that study behaviour change including the
health belief model (Rosenstock, 1960), the theory of planned behaviour (Ajzen, 1991), and social
cognitive theory (Bandura, 1977, 1986). Constructs in SCT have overlap with various other accepted
behaviour change theories (Bandura, 2004) and due to the comprehensive set of determinants specified
in SCT, it has emerged as one of the most extensively used theoretical foundations in health behaviour
research (Painter et al., 2008). Moreover, SCT takes a human agency perspective, in which individuals
have the capability to change their behaviours (Bandura, 2001). In this view, mHealth supports the
agentic user in facilitating tasks necessary for behaviour change.
We follow a two-step method for conceptualizing deep structure use (Burton-Jones & Straub, 2006)
based on SCT. First, we define elements of usage in an mHealth context as the interaction of a user
employing the system to carry out specific tasks for behaviour change. Second, we select measures that
relate theoretically to constructs in the SCT nomological network. There are two constructs in our case:
system usage and tasks to facilitate behaviour change. Therefore, we chain backwards from
theoretically derived SCT constructs for behaviour change to tasks that can be employed during use.
The concept of mHealth deep structure use in this paper is divided into four parts (Figure 1 and Table
2). The first involves a stimulus to facilitate forethought about specific behaviours. The second involves
tasks for self-regulating behaviours. The third aspect involves tasks for incentivizing behaviours. And
the fourth involves tasks for social interactions. The following section describes these aspects and their
interdependence in detail and provides examples for how their use in mHealth can facilitate tasks for
behaviour change.
Fallon et al./ Deep Structure Use of mHealth
Twenty-Seventh European Conference on Information Systems (ECIS2019), Stockholm-Uppsala, Sweden
5
SCT
Determinant
Task
mHealth Deep Structure Use Definition
Stimulus
Forethought
the extent to which users employ features to obtain information about desired
behaviours or to guide their actions towards performing a behaviour
Self-regulating
behaviours
Monitor
the extent to which users employ features to attend to behaviours for self-
observation and self-motivation
Goal-
adjustment
the extent to which users employ features to judge their past behaviours to
set realistic and attainable goals
Feedback
the extent to which users employ features to evaluate behaviours by viewing
current behaviours in relation to their goals
Incentivizing
Self-
incentivizing
the extent to which users employ features to activate personal goals and attain
self-satisfaction from personal accomplishments
Extrinsically
incentivizing
the extent to which users employ features to gain motivation through tangible
rewards
Social
interactions
Social
comparison
the extent to which users employ features to evaluate and appraise personal
standards in relation others
Social
support
the extent to which users employ features to adhere to personal standards and
behaviours through praise, encouragement, and recognition
Table 2. mHealth Deep Structure Use Conceptualization
Figure 1. mHealth Deep Structure Use Conceptualization
3.1 Stimulus
3.1.1 Forethought
A stimulus with information about a behaviour or cues to perform a behaviour are important for forming
intentions to change behaviour. This is regulated by forethought, in which people guide their actions by
considering anticipatory future behaviours and their effects (Bandura, 1991). Over time forethought
generates knowledge about the behaviour and contributes to self-efficacy or one’s belief that they have
the ability to change their behaviour (Bandura, 1989). We conceptualize forethought as the extent to
which users employ features to obtain information about desired behaviours or to guide their actions
towards performing a behaviour. Features of mHealth can provide stimuli with information on health
behaviours, detailed descriptions of how to change behaviours, and cues to perform the behaviour. For
example, a step-counting application can push information to the user about the benefits of taking more
daily steps or cue individuals that it is a nice day for a walk. Moreover, given the mobility of mHealth,
these stimuli can be sent directly to users anytime and anyplace. Attending to and using the information
provided to a user through an mHealth stimulus allows conceptions to be formed about how to practice
the desired behaviour and facilitates intentions to change behaviour. We propose that leveraging the
capability of the information provided in such stimuli will facilitate forethought, increase knowledge
about how to practice the desired behaviour, and contribute to self-efficacy.
However, SCT suggests that forethought alone is not enough to change behaviour. Providing a stimulus
facilitates intentions to change behaviour, but executing the behaviour involves self-regulating
Fallon et al./ Deep Structure Use of mHealth
Twenty-Seventh European Conference on Information Systems (ECIS2019), Stockholm-Uppsala, Sweden
6
mechanisms for transforming forethought into action (Bandura, 1989; Carroll and Bandura, 1990;
Bandura, 1991).
3.2 Self-Regulating Behaviours
3.2.1 Monitoring
SCT of self-regulation explains that people cannot influence their motivations and actions if they do
not monitor their behaviour (Bandura, 1991). Monitoring involves the deliberate attention to an aspect
of one’s behaviour and facilitates self-observation and self-motivation (Bandura, 1991). Individuals
self-observe by monitoring the frequency, duration or intensity of a behaviour. This contributes to
increased awareness of the behaviour and enables one to compare their current behaviour to their
standards. In this way, monitoring behaviour also has a self-motivating effect, in which judging one’s
behaviour motivates individuals to set standards.
We conceptualize monitoring as the extent to which users employ features to attend to behaviours for
self-observation and self-motivation. Advancements in mHealth technology allow users to improve the
continuity and accuracy of monitoring behaviour. mHealth has automatic tracking and search
functionalities that enable individuals to continually and more accurately record and monitor behaviours
(Rusin, Årsand and Hartvigsen, 2013). For example, the integration of GPS in mHealth allows
individuals to more accurately monitor their step-count. Thus, consistent with SCT, mHealth can enable
the fidelity and consistency of monitoring behaviours (Bandura, 1991). Moreover, individuals can use
mHealth to monitor the behaviour, the conditions under which the behaviour occurs (e.g. weather,
context), and the immediate and distal effects the behaviour produces. We propose that leveraging the
capability of monitoring features will facilitate these processes and, thus, with increasing use will
facilitate behaviour change.
3.2.2 Goal-Adjustment
When people monitor behaviours, they are inclined to set goals for progressive improvement, even
though they have not been encouraged to do so (Bandura, 1991). Goal-adjustment results from
evaluative self-reactions that mobilize efforts toward goal attainment. For example, individuals who do
not set goals for themselves achieve no change in effort. These individuals are surpassed by those who
aim to match their previous level of effort who, in turn, are outperformed by those who set themselves
the more challenging goal of improving their past endeavour (Bandura and Cervone, 1983). A judgment
process facilitates goal-adjustment where new goals are set by judging past behaviours (Bandura, 1991).
We conceptualize goal-adjustment as the extent to which users employ features to judge their past
behaviours to set realistic and attainable goals. Features of mHealth allow goal-adjustment through the
technology (e.g. using records of past behaviours to set new goals) or through the user (e.g. further
adjusting goals to make them more attainable or more challenging). For example, mHealth
functionalities can assess users’ past behaviours to automatically set step-count goals and users can
further adjust their step-count goal if they perceive it as unattainable or want more of a challenge.
3.2.3 Feedback
Monitoring behaviours and goal-adjustment have little value without informative performance feedback
and these aspects are interconnected (Bandura, 1998). Ambiguity about the effects of one’s actions
lessens the perception that one has improved (Bandura, 1991). Feedback allows the opportunity to
evaluate one’s progress and takes away this ambiguity. Monitoring behaviours and goal-adjustment act
as proactive and primary methods of motivation, while feedback encourages further adjustments needed
to accomplish desired goals through self-reactive mechanisms (Bandura & Cervone, 1983). Thus, the
motivational effects do not stem from goals themselves, but from responding evaluatively to one’s own
behaviour. Change in motivation is best under conditions combining goals with feedback and decreases
with goals alone and feedback alone (Bandura & Cervone, 1983; Bandura 1991).
We conceptualize feedback as the extent to which users employ features to evaluate behaviours by
viewing current behaviours in relation to their goals. Features of mHealth allow feedback to be
Fallon et al./ Deep Structure Use of mHealth
Twenty-Seventh European Conference on Information Systems (ECIS2019), Stockholm-Uppsala, Sweden
7
integrated into the application by graphics that represent monitoring activities and goals, thus
illustrating how current behaviours compare to goals. For example, a user can evaluate their progress
towards their goals by comparing their current step-count to their goal step-count. This feedback can
be accumulated for days, weeks, months, or years to show the user how they are improving over time.
Moreover, the user can continuously access this feedback and adjust goals as necessary.
3.3 Incentivizing
3.3.1 Self-Incentivizing
Self-regulatory control is achieved by creating incentives for one’s own actions (Bandura, 1998). Self-
incentives affect behaviour through motivation and by activating personal goals for progressive
improvement (Locke, Bryan and Kendall, 1968; Bandura, 1989, 1991). Through this process, people
anticipate self-satisfaction from progressive mastery of a behaviour and are motivated to continue to
pursue that course of action (Bandura, 1991). We conceptualize self-incentivizing as the extent to which
users employ features to activate personal goals and attain self-satisfaction from personal
accomplishments. For example, trivial self-incentives, such as trophies and badges can motivate people
to set their personal goals higher. Moreover, these self-incentives can encourage users to take a course
of action towards this personal accomplishment. For example, the opportunity to earn virtual rewards,
such as a badge or points, for meeting or exceeding a daily step-count goal seven days in a row, can
drive individuals towards improvement through anticipated self-satisfaction of the achievement.
3.3.2 Extrinsically Incentivizing
Extrinsic incentives can further enhance motivation, especially once self-satisfaction is invested in the
activity (Bandura, 1991). Thus, self-satisfaction stems from self-incentives, but extrinsic incentives
further augment motivation towards progressive improvement. Such extrinsic incentives have even
more value when they are combined with feedback performance because reward is linked with progress
(Bandura & Cervone, 1983; Bandura 1991). We conceptualize extrinsically incentivizing as the extent
to which users employ features to gain motivation through tangible rewards. For example, the Nike Run
Club app rewards users with the possibility to win new shoes the more miles they run (Nike, 2019).
3.4 Social Interactions
SCT considers that people differ in the extent to which their actions are guided by personal standards
or social standards (Bandura, 1991). While stimuli for forethought, self-regulating behaviours, and
incentivizing represent ways in which personal standards influence behaviours, social standards also
play an important role. SCT specifies multiple mechanisms in which social interactions impact
behaviour change, which occurs in three ways: First, social interactions contribute to self-regulating
behaviours. Second, social interactions provide partial support for adhering to personal standards.
Third, social interactions facilitate selective activation and disengagement of moral self-regulation
(Bandura, 1986; 1991). According to SCT there are two forms of social interactions, which include
reacting to social situations (social comparison) and directly communicating in social situations (social
support).
3.4.1 Social comparison
Social comparison involves comparing one’s own performance to the achievement of others (Bandura,
1998, 1991, 1986). This offers people a more distal source of motivation for holding to a moral system
or standard in addition to the more proximal motivators for behaviour described in self-regulation
(Bandura, 1991). Social comparison facilitates a judgement process, which begins by people comparing
their performances to others or to standard norms based on representative groups. The information on
others’ behaviours initiates self-comparisons, where one’s attainments are a measure of adequacy in
comparison to others (Bandura, 1998). In this way, social comparison influences a judgement process
by shaping the rules of moral judgement on personal standards and self-appraisal (Bandura, 1991). For
example, people try to surpass their past accomplishments as well as the accomplishments of others and
thus, strive for progressive improvements and higher goals. Comparative evaluations are an ongoing
Fallon et al./ Deep Structure Use of mHealth
Twenty-Seventh European Conference on Information Systems (ECIS2019), Stockholm-Uppsala, Sweden
8
process involving variations in the level, rate, and direction of social comparisons (Bandura, 1991). For
example, people can compare upwards to people performing better or downwards to people performing
worse (Buunk et al., 1990) or to individuals similar to the referent or dissimilar to the referent (Bandura,
1991). Nonetheless, the opportunity to make social comparisons has been shown to be an important
aspect for behaviour change (Wu, Kankanhalli and Huang, 2015).
We conceptualize social comparison as the extent to which users employ features to evaluate and
appraise personal standards in relation others. mHealth has features that allow social comparison
opportunities. Some use leader boards or score boards to show individuals their ranking among other
users (Wu et al., 2015; Zhou, Kankanhalli and Huang, 2016) while others use standard norms to
illustrate how users rank compared to the average user (Helander et al., 2014). mHealth thus, allows a
mobile platform for users to compare their behaviours with a social collection of other users.
3.4.2 Social support
Social support includes both structural support from the quantitative existence of relationships and
functional support from the quality of the relationships and the encouragement provided (Cohen and
Wills, 1985). Social support influences behaviour change through at least two mechanisms. First, social
support provides collective support for adherence to moral standards (Bandura 1991). For example,
people with larger social support networks will receive more collective support and be more likely to
adhere to their moral standards for behaviour change. Second, social support facilitates selective
activation and disengagement from self-regulatory mechanisms, such as goal-setting and self-
monitoring (Bandura, 1991; Anderson, Winett and Wojcik, 2007). Thus, social support encourages
individuals to adhere to and maintain self-regulating behaviours through praise and social recognition
(Bandura, 1986).
We conceptualize social support as the extent to which users employ features to adhere to personal
standards and behaviours through praise, encouragement, and recognition. mHealth enables a platform
for social support. For example, individuals can receive likes on their physical activity (Hamari and
Koivisto, 2015) or words of encouragement and social recognition (Helander et al., 2014). Such types
of social support in mHealth have been shown to increase health outcomes above and beyond apps
without such social support features (Hales et al., 2016). mHealth thus allows a mobile platform for
users to come together to reciprocally support and encourage others.
4 Conclusion and Future Research
In our literature review, we identified that current mHealth research uses superficial use concepts to
explain effects on behaviour change. Following calls for richer and more context-specific technology
use concepts in the health domain (Burton-Jones and Volkoff, 2017; Romanow et al., 2018), this paper
used an SCT perspective to conceptualize an important aspect of effective use, deep structure use. The
theoretically-driven concept developed in this paper provides a lens to understand how tasks represented
in mHealth can be used to facilitate behaviour change. Future research can use this mHealth use concept
to develop a more comprehensive model of how different aspects of deep structure use facilitate
behaviour change. For example, information about health behaviours can be pushed to users through
notifications and messages. This information can facilitate forethought and thus, the use of self-
regulatory features. The use of social interaction features and incentives can moderate self-regulatory
processes and further facilitate behaviour change. In future work, we will further develop a theory of
mHealth effective use by empirically validating this use concept and examining how tasks interact to
facilitate behaviour change. Moreover, we will integrate aspects of the user to better understand how
different users effectively use mHealth. This can be accomplished through using a sequential multiple
assignment randomized trial (SMART) (Lei et al., 2012). Such research may also provide valuable
insights for mHealth app developers as well as physicians and other practitioners who currently have
little guidance regarding what to base their recommendations of mHealth applications on (Riley et al.,
2011; Conroy, Yang and Maher, 2014). Whereas our concept has been developed for behavior change
through mHealth use, it is unclear if it is also useful in other contexts, such as online review systems.
Future research could explore this further.
Fallon et al./ Deep Structure Use of mHealth
Twenty-Seventh European Conference on Information Systems (ECIS2019), Stockholm-Uppsala, Sweden
9
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