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The Psychology of Habit
Corresponding author: Joseph B. Bayer [bayer.66@osu.edu]
Technology Habits:
Progress, Problems, and Prospects
Joseph B. Bayer
The Ohio State University
Robert LaRose
Michigan State University
Keywords
Internet, Online, Smartphone, Digital, Addiction, Components, Cues, Contexts
At the turn of the twentieth century, an early
study on telegraphic habits appeared in
Psychological Review (Bryan & Harter, 1899).
This long-forgotten article demonstrated
how mastery of the telegraph depended on a
hierarchal set of habits. And in some ways,
not all that much has changed. The habits
associated with QWERTY keyboards re-
placed the core processes found among tele-
graphic operators in the twentieth century.
Sure, the physical keys and symbols are
different, the individual goals and man-
oeuvres are different, and the surrounding
contexts and cultures are different. Yet the
habits of grouping automatically selecting
keys to represent letters, combining co-
occurring letters into words, and words into
phrases, endure.
Technology habits have been objects of research for over 100!years and provided heuristic
cases for the study of habits over the last two decades. This chapter traces the history of
research on information and communication technologies in daily life, with an eye toward
measurement and conceptualization problems. Similar to the new technologies of earlier
eras, the prominence of current habitual manifestations has raised challenging questions
for both researchers and societies. These new-er!media habits may exaggerate core habit-
ual mechanisms by providing a wide spectrum of potential cues, possible contexts, and
complex rewards—resulting in dynamic habits that appear to be “special”. We discuss
how research on technology habits serves to uncover the assumptions, boundaries, and
moderators of habit, while calling for a revised approach to address recurring problems!in
the literature. Altogether, the chapter clarifies how technology habit research contributes
to a broader understanding of habitual behaviour.
CITATION
Bayer, J. B., & LaRose, R. (2018). Technology Habits: Progress, Problems, and Prospects. In B. Verplanken
(Ed.), The Psychology of Habit: Theory, Mechanisms, Change, and Contexts (pp. 111-130). Cham: Springer.
Version of Record available at: https://doi.org/10.1007/978-3-319-97529-0_7
Bayer & LaRose Technology Habits
So, what is the contribution of technology
habit research then? This chapter reviews the
history of research on technology habits in
the fields of communication studies and
information systems, while also reflecting on
the role of emergent technologies for habit
research at large. Along the way, we trace
how issues of measurement and conceptual-
ization both challenge and advance the
identification of replicable factors that ex-
plain technology habit mechanisms, ante-
cedents, and consequences. In doing so, we
discuss how technology habits repeatedly
appear special, and often addiction-like, by
modulating core habitual processes. Re-
sponding to the above question, we suggest
that studying technology habits helps to
elucidate the assumptions, boundaries, and
moderators of habitual behaviour more
broadly.
What are Tech Habits?
One of the cyclical challenges in studying
technology habits is the question of how to
define them, as well as how to describe the set
of qualifying behaviours. In the years since
the first study of telegraph habits, research-
ers have directed attention to habits across a
range of technological innovations. Do bi-
cycle habits represent a technology habit?
Probably not in the contemporary sense, but
maybe they should: transportation modes
such as stage coaches were synonymous with
“communication” before the invention of the
telegraph (DeLuca, 2011). Alternatively, bike-
share app usage is likely to be seen as a
technology habit today, exhibiting how
“technology” focuses not only on the physical
object itself but also on the ways in which it is
applied. Nonetheless, from a more historical
perspective, these innovations are no more
technological, or even necessarily social,
than old-fashioned bicycles.
Over the last two decades, a myriad of
keywords have been applied to organize the
everyday habits associated with emergent
media, including Internet, electronic, device,
gaming, virtual, online, interactive, mobile,
digital, network, and information and comm-
unication technologies (ICT) habits (LaRose,
2010a; Limayem & Hirt, 2003). Increasingly,
and owing perhaps to the convergence of
traditional mass interpersonal communica-
tion systems (Walther & Valkenburg, 2017),
“technology” is used as a catchall term (e.g.
Clements & Boyle, 2018; Kuss & Billieux,
2017). Of course, if we understand “tech-
nology” to be literally “the study of tech-
nique”, then transportation mode, health,
and exercise habits that are said to dominate
habit research (Orbell & Verplanken, 2015)
might also be termed technology habits. In
spite of this caveat, we adopt the term “tech
habits” here to avoid further fragmentation
while reflecting on the state of research
progress, with a special emphasis on every-
day innovations examined in the fields of
communication and information systems.
A Short History: Progress in Tech
Habit Research
Even before the popularization of the Inter-
net and the renaissance of habit research in
social psychology in the 1990s, habits were a
topic of interest in information systems
(Limayem & Hirt, 2003) and communication
studies (LaRose, 2010a, 2015). Within comm-
unication research, for instance, habit re-
search can be traced to a single item in
Rubin’s (1984) “ritual gratifications” measure
(“It’s just a habit”) that was a predictor of
television use. However, the gratifications
examined in such work are defined to be
actively and consciously processed, so habits
cannot be gratifications (LaRose, 2010a).
As scholarly attention turned from the
television to the Internet, habits were found
to be significant predictors of diverse pat-
terns of online behaviour, including general
Internet use, online shopping, downloading
media files, social networking, and online
news consumption. Similar to social psych-
Bayer & LaRose Technology Habits
ology research that verified the explanatory
power of habits within the Theory of Planned
Behavior (TPB), habits were pitted against
competing variables emphasizing reflective
thought processes and explained more var-
iance in Internet usage than consciously
processed outcome expectations or grat-
ifications alone (see LaRose, 2010a). Although
most of these studies relied on correlational
designs, some experimental work has offered
evidence of a causal relationship between
habits and tech usage (Tokunaga, 2013).
Along a similar timeline, addiction arose
as a rival explanation for frequent use of
online tech. However, many addiction stud-
ies sampled normal populations, leading to
separate (but clearly overlapping) lines of
inquiry on tech habits. In response, LaRose
(2010a, 2010b) proposed that so-called addict-
ive uses among normal users were the result
of deficient self-regulation. The deficient
self-regulation model of media habits re-
ceived support in a meta-analysis against a
rival model of “problematic” Internet use
(Tokunaga & Rains, 2010) and remains a
viable explanation (Tokunaga, 2017). Despite
the potential misnomer, tech addictions can
also be interpreted through habitual neural
mechanisms (Smith & Graybiel, 2016), and
thereby aid in our understanding of (neg-
ative) habits. Consequently, this chapter re-
engages with the addiction perspective, but
only as applied to normal populations (see
also LaRose, 2010b).
Parallel developments in the information
systems literature, beginning with Limayem
and Hirt (2003), found that habits were more
powerful predictors of technology usage than
reflective influences (e.g. derived from the
Theory of Planned Behavior; TPB). Habits
were later included in what is now a
dominant theory in the field, the Unified
Theory of Acceptance and Utilization of
Technology (UTAUT2; Venkatesh, Thong, &
Xu, 2012). Similar to TPB, UTAUT2 focuses
on a subset of beliefs that are theorized to
determine the acceptance and utilization of
consumer information systems (e.g. perform-
ance, price value, and hedonic outcomes).
Likewise, social norms are addressed
through perceived social support for system
use, and perceived behavioural control is
accounted for through facilitating cond-
itions and ease of use. Notably, habits are
conceptualized on the same level as the TPB-
derived concepts, with past work demon-
strating their capacity to explain both intent-
ions and later information system use.
Together, the value of habit perspectives
has been established in multiple areas of
research on emergent technologies over the
last two decades. Concurrent efforts have
integrated habit research in communication
with developments in social psychology,
information systems, and neuroscience
(LaRose, 2010a, 2010b, 2015). This synthesis
included a variety of psychological studies
that employed media habits as focal
behaviours (e.g. Verplanken & Orbell, 2003)
and demonstrated the pervasiveness of
media habits in daily life (e.g. Wood, Quinn,
& Kashy, 2002). Hence, from the telegraph to
the television to the computer, technology
habits have long operated as key heuristic
cases for the study of habitual behaviour.
Measurement Challenges
As in other literatures, technology research
rewards innovators of novel pursuits, even as
publication delays and overlooked develop-
ments in allied areas lead to redundancy.
This is especially so when researchers re-
spond to the latest technological innovations
and social trends. Accordingly, technology
habit research was already well under way in
both communication and information sys-
tems prior to the publication of the Self-
Report Habit Index (SRHI; Verplanken &
Orbell, 2003). This led to differing, but inter-
secting, approaches to habit measurement
that persist through today. Below, we
document some of the pivotal issues that
complicate tech habit operationalization
before moving on to the implications for
Bayer & LaRose Technology Habits
conceptualization.
Improvements upon Rubin’s (1984) orig-
inal habit question added statements that
further conveyed the meaning of “habit” (e.g.
“part of my routine”) to produce reliable
multiple-item scales. LaRose (2010a, 2010b)
proposed recognizing all dimensions of
automatic behaviour (lack of awareness,
attention, intention, and control). This led to
a two dimensional solution termed deficient
self-observation (connoting a lack of aware-
ness, attention) and deficient self-reaction
(intention, control) (LaRose, Kim, & Peng,
2011; Tokunaga, 2015). Deficient self-
observation parallels automaticity indicators
in the SRHI, as became evident when SRHI
items were integrated with the self-
observation measure (LaRose et al., 2011).
However, the SRHI does not include self-
reaction indicators (e.g. “I would find hard
not to do”, “That would require effort not to
do it”) in sufficient abundance to constitute a
separate dimension.
Moreover, indicators of past behavioural
frequency in the established SRHI are
problematic for tech habit researchers who
aim to predict usage and its consequences.
This issue has led to frequency-independent
measures derived from the SRHI (Bayer &
Campbell, 2012; Bayer, Dal Cin, Campbell, &
Panek, 2016; c.f., Gardner, Abraham, Lally, &
de Bruijn, 2012). Such measures of tech habits
reflect automaticity, but otherwise depart
from the SRHI (see Chap. 3 in this volume, for
a discussion of this issue).
Other tech habit measures emerged that
placed greater emphasis on the lack of
control and intention dimensions of auto-
maticity. In particular, Limayem and Hirt
(2003) produced a reliable multi-item
measure for information systems researchers
that contained statements that parallel some
found in the SRHI. However, the measure
also invoked the term “addiction” and so
combines the two dimensions pro- posed by
LaRose (2010a, 2010b), while once again
blurring the distinction between normal tech
habits and addictions. As noted above,
current information systems research is
informed by UTAUT2, which deploys a
subset of items that emphasizes deficient
self-reaction (vs. self-observation) in two of
its three items.
An array of additional tech measures tap
into habit dimensions indirectly. For
instance, Facebook Intensity (Ellison,
Steinfield, & Lampe, 2007), which is defined
as an intense relationship with Facebook,
nonetheless contains some of the same basic
dimensions as the SRHI. Specifically, the
scale contains items relating to frequency of
use and self-concept (e.g., “Facebook has
become part of my daily routine”),
paralleling the SRHI without using the habit
label. Similar scales focusing on constructs
such as “involvement” and “dependence”
were developed for other online social be-
haviours that can appear obsessive (e.g.
Walsh, White, & Young, 2010).
In addition, a plethora of technology
addiction, problematic use, and compulsive
use scales have been developed and adapted
(see Tokunaga & Rains, 2016). As noted
above, these scales are relevant since much of
the extant research on problematic be-
haviour is focused on normal populations.
Hence, such syndromes may be understood
as habits that include deficient self-reaction
items in their operational definitions (e.g. “try
to cut down the amount of time you spend
and fail?”; “stay online longer than intend-
ed?”) (LaRose, 2010a; Tokunaga, 2015; Young,
1999), and so their scales encompass further
examples of habit measures.
Most recently, researchers have adopted
techniques outside of standard self- report
(see also Habit Research in Action). The
Response Frequency Measure of Media
Habits (RFMMH; Naab & Schnauber, 2016)
asks respondents which medium they would
use to achieve certain goals (e.g. enter-
tainment) under time pressure. Although
moderately correlated with the SRHI, the
relatively long (3–7 s) response intervals allow
for thoughtful deliberation. In turn, the
measure likely reflects goal–behaviour
Bayer & LaRose Technology Habits
associations that are related to habit strength
at moderate levels, but may be less valid than
context–behaviour associations (Neal, Wood,
Labrecque, & Lally, 2012). Separately, early
research on news habits has found evidence
of pupil dilation while individuals view
habitually consulted sources (i.e. Facebook
newsfeed), as compared to a control con-
dition without cues (Chen, Tao, Liu, &
LaRose, 2018).
Conceptualization Challenges:
Jingles, Jangles, Clatters, and
Clamors
Conflicting operational definitions emit con-
ceptual noise. Tech habit research is thus
subject to the jingle problem (Thorndike,
1904); that is, habit measures such as the
SRHI and UTAUT2’s habit scale share the
same variable label, and even come from
similar origins, yet their scales emphasize
distinct dimensions of repetitive behaviour.
By contrast, the SRHI and Facebook Intensity
amount to a jangle problem (Kelley, 1927)
with common measurement elements but
different labels (“habits,” “intensity”). To
those well-known issues, we provisionally
add two new terms to describe the con-
ceptual noise in the field. Clatters are similar
constructs that proceed from different,
incompatible paradigms—but that aim to
explain the same underlying phenomenon.
For example, behavioural theories (e.g.
UTAUT2, TPB) and disease models (e.g.
HABIT RESEARCH IN ACTION
Prospects for Tuning Down the Noise
Given abundant overlap, empirical validation and integration among available
measures is required, with special opportunities coming from less-obtrusive techniques.
The RFMMH allows long reaction times that invite conscious reflection, and so methods
that confine reactions to the millisecond range may be valuable in future work.
Neuroscience studies sometimes employ reward devaluation to measure habit strength,
an approach that could be applied to tech habits; for example, by removing the chroma
cues or reducing the number of apps that are accessible from smartphones. Habit
formation suppresses peripheral physiological responses and pupil dilation, which can be
used to verify that users are responding to cues (Chen et al., 2018). Researchers have drawn
on functional Magnetic Resonance Imaging (fMRI) to examine attention habits in humans
(Anderson, 2016), as well as online media cognition (Meshi et al., 2015), but this approach
has yet to be extended to tech habits directly.
More generally, studies are needed that compare and contrast competing
measurement approaches, along with their structures, head-to-head. The tech addiction
literature has produced a multitude of instruments (e.g. over a dozen forms for measuring
Internet addict-ion). In turn, addiction instruments have spawned offshoots covering
specific devices, applications, and features within applications. In contrast to habit
research, most of these instruments aspire to be diagnostic instruments. Because of this,
addiction measures tend to include both the consequences of use (e.g. neglect of work,
school, and family and social commitments) and the habitual processes that may produce
those consequences, such as deficient self-observation and self-reaction. Separation of the
two components (as in LaRose et al. 2011) might help to converge the two streams of
research; for example, deficient self-reaction may be well correlated with addiction items
that bespeak loss of self-control. Unobtrusive physiological, neurological, and reaction
time measures requiring controlled lab conditions are advancing our knowledge of habits,
but establishing their relationships (if any) to self-reported and digital measures is vital to
understanding technology habits in real-world environments.
Bayer & LaRose Technology Habits
addiction, compulsion) can be said to clatter
with one another. Last, we might designate
clamors: analogous concepts and scales
developed in different fields of study—but
take little notice of one another. For example,
communication research can be said to
clamor with information systems over com-
peting models of tech habits. Despite the
apparent cacophony, the jingles, jangles,
clatters, and clamors nonetheless further our
understanding of the mechanisms, ante-
cedents, and consequences of tech habits.
Causal Mechanisms of Overuse
Rising above the noise, a fundamental
question about the underlying mechanisms
of tech behaviour remains actively debated.
In particular, does highly repetitive tech-
nology use represent a pathology that
originates with chronic dysphoria, per-
sonality traits, or neurological disorders, as
“disease” models imply? Empirical research
suggests that only a small population of
clinically addicted Internet users exists
(Alter, 2017; Griffiths & Kuss, 2015; Tokunaga,
2017). Mental illness is generally marked by
severe life consequences (e.g. losing friends
or jobs), rather than “agree somewhat” with
smartphone use complaints (Kardefelt-
Winther et al., 2017; Van Deursen, Bolle,
Hegner, & Kommers, 2015). Hence, the neg-
ative outcomes of technology addiction
should only be correctable through profess-
ional therapeutic intervention. Yet, “addict-
ive” use of new media is often resolved
through spontaneous remission (LaRose,
2015). Accordingly, among normal popu-
lations at least, the deficient self-regulation
model presents a viable alter-native to the
disease model (Tokunaga, 2017).
A secondary question about causal
ordering also helps to resolve the clamoring
of behavioural and disease models. That is,
do psychosocial problems such as depression
and loneliness precede or follow the
development of tech habits? Examining a
body of research limited to correlational
evidence, Tokunaga (2017) concluded that
either direction is possible for the causal
arrow between problems and habits, with
some evidence for cyclical patterns of
causation. Hence, tech use that results in
negative life consequences may originate in
efforts to alleviate dysphoric states with
rewarding tech behaviour (Kuss & Billieux,
2017). Unfortunately, certain users, these
initial efforts may be hampered by deficient
self-regulation and a spiral of mounting use
(LaRose et al., 2011; van Rooij, Ferguson, van
de Mheen, & Schoenmakers, 2017), especially
when surrounded by encouraging others
(Klimmt & Brand, 2017). Similarly, research
suggests that deficient self-regulation can
lead directly to negative consequences, as
well as indirectly contribute through the
frequency and duration of mobile use (Soror,
Hammer, Steelman, Davis, & Limayem, 2015).
To summarize, though tech behaviours
rarely cross the threshold into problematic
behaviour, habit is likely play a role in those
cases.
Furthermore, more problematic tech
behaviours might eventually be explained
through fundamental habit mechanisms.
Two distinct neural mechanisms (Smith &
Graybiel, 2016), one involving ongoing inter-
actions between automatic and deliberative
processes (action–outcome habits), and
another that acts independently of imme-
diate reinforcement contingencies and defies
self-control (stimulus-response habits), para-
llel the distinction between deficient self-
observation and deficient self- reaction sides
of habit automaticity. Investigations that
separate self-observation (awareness, atten-
tion) from self-reaction (intention, control)
find that the two facets are related (LaRose et
al., 2011; Tokunaga, 2017; Van Deursen et al.,
2015), although the directional arrows shift
between studies and reciprocal causation
remains a possibility. Therefore, future work
is needed to examine whether normal and
extreme users of technologies can be disting-
uished in terms of habitual cognition alone.
Bayer & LaRose Technology Habits
Antecedents and Consequences
The conceptual noise above raises the quest-
ion of whether some individuals are more
susceptible to tech habits than others. A
growing list of personality facets have
received recent attention as antecedents of
tech habits, including trait self-regulation,
impulsiveness, and sensation seeking (Bayer,
Dal Cin, et al., 2016; Wilmer & Chein, 2016).
Demographic, motivational, and lifestyle
variables add to the list of anteced- ents (Van
Deursen et al., 2015). For instance, a seminal
UTAUT2 study found a three-way inter-
action effect among age, gender, and prior
experience on mobile Internet use, as well as
correlations between habit strength and a
range of situational factors (e.g. expected
performance, social influence; Venkatesh et
al., 2012). In general, communication models
have predicted habit strength from the
expected outcomes of behaviour, self-
efficacy, and depression, whereas inform-
ation systems research has focused on user
satisfaction and the various uses as further
antecedents of tech habits (see LaRose, 2015,
for a review).
Habit is also a powerful predictor of
adoption and continuance for a long list of
technologies, usually surpassing the strength
of conscious intentions (LaRose, 2015). The
sheer volume of use may partially account for
both positive and negative effects, but there
is reason to suspect that habit contributes
beyond time commitment (Tokunaga, 2016).
Online safety habits contributed to the
performance of protective behaviours (Tsai
et al., 2016), whereas texting habits predicted
risky behaviour while driving and walking
(Panek, Bayer, Dal Cin, & Campbell, 2015)
and responding to phishing emails
(Vishwanath, Harrison, & Ng, 2016). Studies
have also documented a variety of psycho-
social problems that covary with tech habits,
including depression, anxiety, loneliness,
and neglect of important obligations
(Tokunaga & Rains, 2016). Recent time series
research points to habits (vs. time dis-
placement) as the cause of functional
difficulties involving social and professional
life (Tokunaga, 2016). Overall, research has
introduced a wide range of antecedents and
consequences of tech habits, though
measurement limitations hamper the ability
to disentangle key habitual mechanisms.
What is Special about Tech Habits?
Amid operational and conceptual diversity,
extant research on tech habits has con-
tributed to our understanding of habit
acquisition and performance in daily life.
Primarily, this body of work has focused on
the role of habits—in competition with other
individual factors—in predicting, explaining,
and regulating user behaviour. More recent
perspectives, however, question whether
tech habits may change human cognition at a
more basic level (Barr, Pennycook, Stolz, &
Fugelsang, 2015; Clayton, Leshner, &
Almond, 2015; LaRose, Lin, & Eastin, 2003;
Meshi, Tamir, & Heekeren, 2015; Sparrow &
Chatman, 2013; Wilmer, Sherman, & Chein,
2017). National surveys (Anderson & Perrin,
2017), daily diary (Wood et al., 2002),
experience sampling (Hofmann, Vohs, &
Baumeister, 2012), and digital tracking
(Oulasvirta, Rattenbury, Ma, & Raita, 2012)
studies all suggest that tech usage accounts
for a substantial proportion of complex
habits in daily life. But are these habits
special, or do such societal and academic
reactions to their presence reflect a default
response to encountering the new?
Prior research on tech habits has neither
fully articulated whether they are theo-
retically (in)distinguishable from other
domains of habits nor related them to the
broader literature on habits. The same
neurocognitive mechanisms (e.g. Smith &
Graybiel, 2016) presumably explain habitual
Tinder swiping as well as they do Television
clicking, tooth brushing, and wallet handling.
Nonetheless, new-er tools might provide
novel cues, contexts, and rewards to develop
Bayer & LaRose Technology Habits
habits, and these factors may allow for habits
to manifest in (seemingly) distinctive ways.
From this vantage, the study of tech habits is
the study of moderation effects on habitual
processes; that is, how the cues, contexts, and
outcome contingencies created by emergent
technologies moderate habit formation, per-
formance, and change.
Increasingly, technology research has
questioned the common focus on particular
technologies, rather than conceptualizing or
manipulating their underlying attributes. In
response, some researchers have called for a
greater focus on “affordances” (Evans,
Pearce, Vitak, & Treem, 2017; Fox & McEwan,
2017). At a basic level, affordances represent
the “possibilities for action” separating a
technology (or other objects) from a user
(Evans et al., 2017), typically oriented around
the role of conscious or perceived functions.
Nonetheless, many dimensions of tech-
nologies are “hidden” to the user (Gaver,
1991), and such dimensions may aid in the
explication of tech habits. Rather than
engendering a new form of cognition, tech
habits may highlight how latent action
possibilities influence habit mechanisms.
There are a variety of significant afford-
ances (e.g. Fox & McEwan, 2017; Sundar, Jia,
Waddell, & Huang, 2015) with the potential to
influence habitual processes to some degree.
On a related front, recent work has suggested
that particular affordances may interact with
online behaviours in the context of self-
control (Hofmann, Reinecke, & Meier, 2016).
Hofmann et al. (2016) highlighted four
aspects that may contribute to the high level
affective temptation seen in online media,
including immediate gratifications, ubiq-
uitous availability, attentional demands, and
habitual usage itself. Separately, LaRose
(2015) proposed a series of technological
features that may influence habitual form-
ation and change (e.g. anytime, anywhere,
anonymity, anyhow). Ultimately, a par-
simonious set of dimensions that will
transcend specific technologies may be
required to build an enduring framework—
yet more groundwork is needed first.
Here, we take a sideward step by
discussing how the components of a given
behaviour may moderate habits via their
fundamental elements: repetition, automa-
ticity, and cueing in stable contexts (Orbell &
Verplanken, 2015). The sections below ex-
plicate how the underlying components of a
tech practices may influence habit action
possibilities. In particular, we revisit cue and
context properties of tech behaviours noted
in past work, as well as outcome properties
(Gardner, 2015), with the potential to
moderate habit strength and performance.
Certainly, the elements of repetition and
automaticity are equally significant (and
interwoven with the activation of contextual
cues). However, we underline the latter
elements due to the tendency of tech habits
to challenge the meaning of “cued in stable
contexts” (Orbell & Verplanken, 2015).
Cue Properties
Technologies that can be used more reg-
ularly than others inherently increase the
opportunities for repeat behaviour, and
thereby the likelihood and speed of habit
formation. Therefore, portable technologies
afford more opportunities (Schrock, 2015) for
a given cue to be acquired and activated due
their continual presence. In addition to
allowing for more cue exposure and rapid
cue–behaviour associations, portable objects
(e.g. phones, boom-boxes, newspapers)
inhabit more environments and thus allow
for a greater variety of spatial cues to become
associated with a habit. Further, online
capabilities substantially widen the range of
behaviours that can be performed through a
given tool. Paired together, portability and
connectivity bring about new layers of
potential cues (Wilmer & Chein, 2016). By
providing an ever- present venue, an array of
physical environments, and hyperlinks to
bottomless information, emergent tech-
nologies open up extra opportunities for
different cues to form in conjunction with
Bayer & LaRose Technology Habits
said habit (Bayer, Campbell, & Ling, 2016).
Online technologies are not just
ubiquitous; they also provide abundant
action possibilities within and between
devices, applications, and features. The same
behavioural “chunks” (such as a smartphone
“up swipe”) may become associated with
multiple responses and incorporated as the
starting points in various behavioural scripts.
Cues may be triggered internally or ex-
ternally, including the “technical cues” that
emanate from a technology itself (e.g. no-
tifications, buzzes, sounds). These attention-
demanding triggers may provide for more
salient cues than passive objects that lay in
the background (Carden & Wood, 2018;
Hofmann et al., 2016). The rising influence of
personalized algorithms, in particular, may
hold important implications for cue learning
in the not-too-distant future. Research has
also turned attention inward to delineate the
contribution of different sources of cues to
aggregate tech habits (e.g. smartphone
checking), such as the role of spatial, tech-
nical, and mental cues that compose the
global “habit” (Bayer, Campbell, & Ling, 2016;
Hall, 2017). Following Neal et al. (2012), future
work is needed in the tech domain to
empirically identify fundamental cue pat-
terns across technologies and individuals.
The de facto standardization of particular
action sequences by popular technology
interfaces points to the possibility of iden-
tifying a parsimonious set of cues underlying
tech habits. In total, the same technology is
likely to engender many different cues, and
the same habit is likely to traverse many
different technologies.
Context Properties
Habits are defined to occur in stable contexts,
but what is a context? Within the habit
literature, contexts are most commonly
treated as particular locations, situational
elements, and preceding actions (Wood, 2017;
Wood & Neal, 2007). Tech habits, however,
are noteworthy due to their “anytime, any-
where” nature (LaRose, 2015). Portability may
produce a degree of what appears to be
“context-independence” (Bayer & Campbell,
2012). In many cases, it may be that the
technology itself, or the embedded virtual
environment, is the context. For example, the
notification panel on a smartphone may
operate as a context for interface habits. In
other cases, it may be that the context is a
mental state or frame of mind (in line with
preceding action states), as opposed to a
location or situation. For instance, the mental
state of boredom may provide a context in
which cues (e.g. loneliness) develop for
checking the phone automatically. In this
way, some tech habits are perhaps more
similar to mental habits or attention habits
than physical routines (Anderson, 2016;
Bayer, Campbell, & Ling, 2016; Verplanken,
Friborg, Wang, Trafimow, & Woolf, 2007).
Given the multidimensional nature of mod-
ern tech contexts, future research may
require greater attention to context operat-
ionalization.
The wide spectrum of overlapping spat-
ial, virtual, and mental contexts also create
new opportunities for different habits to
become interwoven with one another. Tech-
nologies that exhibit compatibility with other
habits afford faster cue associations (cf.,
“innovation clusters”, LaRose & Hoag, 1996).
Individuals may “slip” back into old habits
unless new habits are highly compatible with
individual routines (Labrecque, Wood, Neal,
& Harrington, 2017). Entry-level smartphone
habits, such as placing and receiving voice
calls, may become “gateways” (Oulasvirta et
al., 2012) to later habits such as texting and
casual gaming. As a result, technologies that
come with wide functionality, or comprehen-
siveness of use (Limayem, Hirt, & Cheung,
2007), allow for discrete contexts (e.g. Gmail,
Facebook, Snapchat) to appear in successive
bursts or become embedded in scripts (Bayer,
Campbell, & Ling, 2016). Continual access to
related habits lend themselves to rapid
“chunking”, such as swiping and password
entry during habit formation. Altogether,
Bayer & LaRose Technology Habits
tech habits can satisfy a variety of needs
concurrently (Naab & Schnauber, 2016;
Sundar & Limperos, 2013; Wang & Tchernev,
2012), and new habits are likely to develop
faster, and remain stronger, as complements
to old contexts.
Outcome Properties
Habit formation initially depends on the rate
and size of the reward (Gardner, 2015; c.f.,
Wood, 2017), whether the pellets dispensed
by Skinner or tweets emitted by Twitter.
Although online tech often provides imme-
diate gratifications in ways similar to sweets
(Hofmann et al., 2016), such actions are not
always rewarding. Rather, ever-present tech-
nologies offer instant outcomes (vs. rewards).
Technologies that are characterized by cer-
tain reward schedules have the potential to
facilitate stronger habitual conditioning. In
particular, many technologies provide inter-
mittent reward schedules, such as the act of
checking a Twitter newsfeed that may have
variable results (Vishwanath, 2016) that can
increase the pace of activation and ward
against extinction (James & Tunney, 2017).
The contemporary state of being perm-
anently connected (Vorderer & Kohring,
2013) offers numerous sources of intermittent
rewards at semi-random times, ranging from
direct messages to news headlines (van
Koningsbruggen, Hartmann, & Du, 2017).
Beyond primary reinforcement, versatile
tools may produce secondary rewards (and
punishment) associated with each catalytic
cue. A cue (e.g. boredom) to check a smart-
watch (e.g. Fitbit) may produce a reward by
revealing the time, while also inducing
secondary rewards and/or punishments (e.g.
steps, badges)—all synchronously.
Emergent technologies thereby offer an
amalgam of reward types, which can influ-
ence habitual processing in numerous ways.
Since habit formation is especially sensitive
to social rewards (Graybiel, 2008), tech-
nologies that provide social updates may
allow for more powerful effects. Likewise,
strict norms of social availability mean that
individuals are expected to check for social
updates—or face repercussions (Ling, 2012).
Finally, technologies can modulate the level
of delay in behavioural outcomes. Indeed,
new media are defined by their interactivity
(Sundar et al., 2015), producing some
combination of positive, neutral, and neg-
ative rewards with minimal delay in response
to user feedback. By contrast, technologies
that provide locks, passwords, and silencers
act as reward buffers. Depending on the tool
at hand and customized settings, tech-
nologies may tighten or loosen the cue-
outcome loops that facilitate habit formation
(LaRose, 2015). The immediacy (e.g. clicks,
beeps, bubbles, colors, numbers) of inter-
active habits may be established and exting-
uished more quickly than non-technical
habits. However, once behaviours are
codified as stimulus-response habits, they are
relatively insensitive to negative outcomes
(Smith & Graybiel, 2016; Wood, 2017).
From Problems to Prospects
As demonstrated in the above sections, tech
habit research is challenged by the
inherently dynamic nature of technology
itself, as well as what tech habits are
perceived to be. Societal narratives defining
new-er habits as technology habits corre-
spond to the “technology-as-novelty” pers-
pective (McOmber, 1999). Technology habits
reformulate the ever-changing expectations,
predispositions, and practices of a given
society—in line with the sociological notion
of habitus (Bourdieu, 1977; Crossley, 2013;
Papacharissi, Streeter, & Gillespie, 2013).
Once a tech habit becomes part of the taken-
for-granted expectations, newer technologies
inevitably supplant the old in society,
creating a continuing stream of research
within which theories of habits may be
reexamined. In other words, the new habits
of today become the built-in behaviours of
tomorrow. The result is that “tech habits” end
Bayer & LaRose Technology Habits
up with nebulous definitions, as indicated by
the long list of keywords applied to con-
temporary technology behaviour.
The uncertain scope of tech habits is
compounded by overlap with the addiction
label, particularly given the widening pur-
view of addictive behaviour (Alter, 2017;
Wiederhold, 2018). Part of the problem is that
the terms “habit” and “addiction” are often
used loosely outside of their central liter-
atures (and colloquially in broader society).
Although we have focused on habits in this
chapter, the addiction perspective continues
to collide with tech habit research. In re-
sponse to early disease model investigations
of “excessive” usage (now considered average
levels of use), there have been growing calls
to reassess the assignment of the “addiction”
label across disciplines (Billieux et al., 2014;
Griffiths & Kuss, 2015; Tokunaga, 2015). Most
recently, criticisms about technological and
other controversial addictions were fun-
neled into exclusion criterion to limit false
positives (Kardefelt-Winther et al., 2017).
Collectively, the ambiguity surrounding
tech habits has implications for societies and
researchers alike. Regardless of diagnostic
rules, the substantial gap between the num-
ber of problem users and total users results in
a conflicting narrative in society (Klimmt &
Brand, 2017; Ryding & Kaye, 2017). Chun
(2016) argues that, in the age of new media,
habit has become even further pinned to the
notion and lexicon of addiction in society.
The expansive use of the addiction label may
be viewed as part of the “habitus of the new”
(Papacharissi et al., 2013), as a newly virtual
society struggles with new conditions and
potential threats. Tech habits underline the
tendency of humans to fear change (some-
times reasonably; Alter, 2017). That said,
accounts of spontaneous remissions of
seemingly destructive tech habits are often
overlooked in favor of sensational stories, at
least until those habits are taken-for- granted
(Ling, 2012). On the positive side, the
uncertainty forces individuals and societies
to reflect on the benefits and costs of tech
habits (Lim, 2013), including potential tech
solutions to tech problems (Klimmt & Brand,
2017). For example, recent updates include
features that tweak the frequency or attract-
iveness of cues (e.g. greyscale interfaces,
notification blockers), offering possibilities to
enact habit change by changing the virtual
environment (c.f., Carden & Wood, 2018).
Against this backdrop, it becomes clear
that a more reflective and sustainable ap-
proach to researching tech habits is required.
From a practical standpoint, different labels
beget different literatures, splintering the
progress being made and adding further
uncertainty to the underlying mental pro-
cesses. Here, we suggest the core question for
tech habits is not whether basic mechanisms
change as a function of newer tech (they
presumably don’t). Rather, the goal should
be to explicate what components they em-
ploy that moderate the underlying elements
of habit. None of the above components are
unique to particular technologies—whether
comic books or virtual reality—but they are
often salient characteristics of those objects.
In line with more conscious perceptions of
technological affordances (Evans et al., 2017),
the above components should be viewed
along a spectrum. A smartphone is not the
first technology tool to be portable—but it is
more so than a laptop computer or a folding
chair. Taking this perspective, “technology
habits” represent a novel amalgam of behav-
ioural components.
The question of how certain components
affect habit mechanisms, and how various
technologies align with those components,
deserves empirical research. For instance,
there is the potential to perform meta-
analyses that reevaluate observed habit
strength as a function of tech components.
Going forward, a research agenda starts with
research to further conceptualize and dev-
elop support for key components of tech
habits, including how exactly they intersect
with the basic elements of habit (Orbell &
Verplanken, 2015). In line with other areas of
tech research, studies seldom measure or
Bayer & LaRose Technology Habits
manipulate technological attributes directly;
conversely, the moderating components are
typically limited to the discussion section.
This may be partly due to the measurement
challenges associated with extracting par-
ticular components, particularly while main-
taining the real-world validity. By virtue of
their complexity, however, tech habits reveal
the built-in challenges involved in disting-
uishing standalone habits from more global
sets of habits, chunks, and scripts. A century
later, tech habits continue to echo Bryan and
Harter’s (1899) early study on the hierarchal
nature of telegraph habits.
Tech habits thereby help to clarify the
boundary conditions of habit mechanisms—
and offer innovative avenues for future
research (Carden & Wood, 2018). Indeed, the
prominence of tech habits during everyday
life brings about abundant opportunities to
study these components in naturalistic
environments. Hence, emergent methods are
slated to help habit researchers unpack some
of the underlying elements and components
discussed above. For instance, mobile and
digital methodologies (Bayer, Ellison,
Schoenebeck, Brady, & Falk, 2017; Harari et
al., 2016) are well-positioned to untangle the
roles of spatial and virtual contexts in habit
formation, while also allowing for testing
hierarchal interactions of different habits
(e.g. walking habits and swiping habits).
Moreover, research on the moderating
components of tech habits can assist in
clarifying the lines between the normal
habits and clinical problems. As a whole, tech
habits remain well-positioned to explicate
real-world habitual behaviour.
Future tech habit research should also
move beyond predicting personal conse-
quences associated with use to examine how
habits contribute to broader societal con-
cerns about technology. For example, recent
research suggests that Facebook habit
strength moderates the likelihood of indi-
viduals engaging in selective exposure to
attitude consistent political content on the
platform newsfeed (Chen et al., 2018). Among
habitual users of the Facebook newsfeed,
selective exposure was stronger when
presented on a screen that contained familiar
cues (e.g. standard logo, URL, color scheme,
and layout) than comparable neutral cues.
Since initial steps in a sequence of actions
limit deliberations over later steps (Smith &
Graybiel, 2016), this result might be ex-
plained as a weakening of critical reflect-ion
on message content once an news script was
cued. Restricted deliberation following the
initiation of a context cue might also explain
intense online experiences such as flow states
(Tokunaga, 2013) and immersive engagement
(Kuru, Bayer, Pasek, & Campbell, 2017).
When packaged into compact scripts,
seemingly special habits paired with other
unreflective forms of cognition may jointly
contribute to the “addiction-like” aura of
these habits (Bayer, Dal Cin, et al., 2016).
In sum, tech habits often seem “special”,
even when operating through the same basic
elements of past telegraphic operators. For
this reason, the deconstruction of habits into
component parts may help to explain the
societal skepticism, and potential pathologiz-
ing, of new habits. The realization that tech
“addictions” are often just new-er habits that
appear special is not new itself; back in the
1970s, television was described as “the plug-in
drug” (Winn, 1977). One does not need to be
omniscient to presume that tech habits will
continue to emerge that will pose theoretical
and clinical obstacles to our future research-
ers and societies. Yet the way we approach
this perpetual problem can change.
Conclusion
This chapter mapped the trajectory of con-
temporary tech habits, an umbrella term
encompassing a growing array of new media
behaviours. Due to their ubiquitous role in
everyday life, tech behaviours contribute to
our understanding of dynamic habits by
challenging the preconceptions of standard
habitual action—at least at first. Each new
Bayer & LaRose Technology Habits
layer of innovation reinvigorates old con-
cerns and promises related to the impacts of
emergent technology on individual and
societal well-being (Carbonell & Panova,
2017; Ryding & Kaye, 2017; Wilmer et al., 2017).
To be sure, there are other innovations that
are also deserving to represent the “tech
habits” mantle from a mechanical standpoint
(e.g. medical or transportation inventions).
Those pertaining to daily information,
communication, and leisure activities, how-
ever, often receive an outsized share of
concerns compared to their tech brethren.
As a consequence, tech habits offer a
valuable case for considering the positive and
negative outcomes that result from a
perennial research focus on new-er habits.
On the positive side, research has dem-
onstrated the immense role of habit in tech
adoption and usage, as well as key ante-
cedents and consequences—all while
encountering successive waves of trans-
formative inventions. Simultaneously, their
behavioural complexity and real-world
relevance makes them revealing as heuristic
cases, affirming the adaptive power of habits
in societal progress (or lack thereof; James,
1890). In the process, we suggest that research
on tech habits helps to illuminate the hidden
mechanisms and moderators supporting
habitual behaviour at large.
Looking forward, this chapter suggests
that researchers place greater emphasis on
the underlying components of habitual
behaviour, rather than the fleeting features
of the present. Why does deconstructing the
gears of tech habits matter? We propose that
examining how newer technologies rely on
certain components that exaggerate habit-
ual cognition may help to explain, and to
some extent justify, the uncertainty
surrounding them in both societal and
academic dis-course. Novel combinations of
cues, contexts, and outcomes can make a
technology habit look powerfully, and
perhaps deceivingly, special. With this in
mind, future research should examine new-
er habits through more generalizable
paradigms, not limited to particular devices
or applications, and avoid spinning the same
flywheels over and over again.
Bayer & LaRose Technology Habits
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