How “phubbing” becomes the norm: The antecedents and consequences of snubbing via smartphone

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DOI: 10.1016/j.chb.2016.05.018
Cite this publication
Abstract
Smartphones allow people to connect with others from almost anywhere at any time. However, there is growing concern that smartphones may actually sometimes detract, rather than complement, social interactions. The term “phubbing” represents the act of snubbing someone in a social setting by concentrating on one’s phone instead of talking to the person directly. The current study was designed to examine some of the psychological antecedents and consequences of phubbing behavior. We examined the contributing roles of Internet addiction, fear of missing out, self-control, and smartphone addiction, and how the frequency of phubbing behavior and of being phubbed may both lead to the perception that phubbing is normative. The results revealed that Internet addiction, fear of missing out, and self-control predicted smartphone addiction, which in turn predicted the extent to which people phub. This path also predicted the extent to which people feel that phubbing is normative, both via (a) the extent to which people are phubbed themselves, and (b) independently. Further, gender moderated the relationship between the extent to which people are phubbed and their perception that phubbing is normative. The present findings suggest that phubbing is an important factor in modern communication that warrants further investigation.
Full length article
How phubbingbecomes the norm: The antecedents and
consequences of snubbing via smartphone
Varoth Chotpitayasunondh
*
, Karen M. Douglas
School of Psychology, Keynes College, University of Kent, Canterbury, CT2 7NP, United Kingdom
article info
Article history:
Received 19 December 2015
Received in revised form
22 March 2016
Accepted 7 May 2016
Keywords:
Phubbing
Smartphones
Internet addiction
Smartphone addiction
abstract
Smartphones allow people to connect with others from almost anywhere at any time. However, there is
growing concern that smartphones may actually sometimes detract, rather than complement, social
interactions. The term phubbingrepresents the act of snubbing someone in a social setting by
concentrating on ones phone instead of talking to the person directly. The current study was designed to
examine some of the psychological antecedents and consequences of phubbing behavior. We examined
the contributing roles of Internet addiction, fear of missing out, self-control, and smartphone addiction,
and how the frequency of phubbing behavior and of being phubbed may both lead to the perception that
phubbing is normative. The results revealed that Internet addiction, fear of missing out, and self-control
predicted smartphone addiction, which in turn predicted the extent to which people phub. This path also
predicted the extent to which people feel that phubbing is normative, both via (a) the extent to which
people are phubbed themselves, and (b) independently. Further, gender moderated the relationship
between the extent to which people are phubbed and their perception that phubbing is normative. The
present ndings suggest that phubbing is an important factor in modern communication that warrants
further investigation.
©2016 Elsevier Ltd. All rights reserved.
1. Introduction
Recent years have seen an explosion in communication tech-
nology, creating devices and systems that support one-to-one, one-
to-many, and many-to-many human interactions (Gummesson,
2004; Huang, Lee, &Hwang, 2009; Tews, Sukhatme, &Matari
c,
2002). Sales of smartphones (cellular phones that function much
like computers) dominate the global share of communication de-
vices, and it is projected that more than 50% of active communi-
cation handsets in use worldwide will be smartphones by mid-
2016 (Kemp, 2015). People tend to prefer smartphones to com-
puters when going online (Ofcom, 2015), and smartphones have
become an integral part of peoplesdaily lives (Jones, 2014;
Oulasvirta, Rattenbury, Ma, &Raita, 2012; Roberts, Yaya, &
Manolis, 2014). They provide opportunities for users to connect
with friends, family, colleagues and absent others (Andreassen &
Pallesen, 2014; Do &Gatica-Perez, 2013; Echeburua &de Corral,
2010; Kuss &Grifths, 2011; Park, Kee &Valenzuela, 2009), to play
games (Cheok, Sreekumar, Lei, &Thang, 2006), for entertainment
(Zhang, Chen, &Lee, 2014), for education (Cummiskey, 2011), and
for research (Raento, Oulasvirta, &Eagle, 2009).
However, despite the obvious benets of smartphones, in recent
years researchers have become increasingly concerned about their
potential adverse effects on mental and physical health, and the
quality of social interactions (Baron &Campbell, 2012; Campbell &
Kwak, 2010; Choliz, 2010; Ha, Chin, Park, Ryu, &Yu, 2008; Khan,
2008; Lee, Chang, Lin, &Cheng, 2014). Like many people have
become addicted to the Internet, more and more people are
becoming problematic smartphone users, causing concern about
the potential consequences of smartphone overuse (e.g., Beranuy,
Oberst, Carbonell, &Chamarro, 2009). In particular, the concept
of phubbing,dened as the act of snubbing others in social in-
teractions and instead focusing on ones smartphone (Haigh, 2015),
appears to have negative consequences for communication be-
tween partners, detrimentally affecting relationship satisfaction
and feelings of personal wellbeing (Roberts &David, 2016). How-
ever, little is known about what causes phubbing behavior, and
how it has become an acceptable or normative feature of modern
communication. In the current study, we develop and test a model
explaining these factors.
*Corresponding author.
E-mail addresses: vc216@kent.ac.uk (V. Chotpitayasunondh), k.douglas@kent.ac.
uk (K.M. Douglas).
Contents lists available at ScienceDirect
Computers in Human Behavior
journal homepage: www.elsevier.com/locate/comphumbeh
http://dx.doi.org/10.1016/j.chb.2016.05.018
0747-5632/©2016 Elsevier Ltd. All rights reserved.
Computers in Human Behavior 63 (2016) 9e18
2. Background
2.1. Smartphone addiction
Researchers have focused on the effects of excessive smart-
phone use on mental and physical health (Jenaro, Flores, G
omez-
Vela, Gonz
alez-Gil, &Caballo, 2007). Findings suggest that smart-
phone users who show a tendency to be addicted to their phones
appear more likely to experience health problems, in a similar way
to how those who show a tendency toward Internet addiction
(Beranuy et al., 2009) and game addiction (Lee, Ko, &Chou, 2015)
experience health problems. In addition, smartphone addiction and
Internet addiction have been found to be associated with depres-
sion (Beranuy et al., 2009; Thomee, Harenstam, &Hagberg, 2011)
and anxiety (Cheever, Rosen, Carrier, &Chavez, 2014; Dalbudak
et al., 2013; Lepp, Barkley, &Karpinski, 2014). Finally, aggression
and a lack of attention have been found to be associated with
Internet and smartphone addiction in children (Davey &Davey,
2014; Park &Park, 2014). Therefore, there appears to be reason
for concern about the consequences of smartphone overuse for the
individual.
The consequences of smartphone use for the quality of social
interactions between individuals have also caused concern. Spe-
cically, Habuchi (2005) argued that mobile phones can diminish
the quality of interpersonal interactions, producing a tele-
cocooningeffect, where people are diverted from face-to-face
exchanges with others and therefore lose the art of face-to-face
interaction (Habuchi, 2005). In other research, conversations
where smartphones were present reported lower levels of
empathic concern compared to those in the absence of a smart-
phone on the table (Misra, Cheng, Genevie, &Yuan, 2014). Other
researchers have found lower levels of perceived relationship
quality, partner trust, and perceived empathy in the presence of
mobile phones (Przybylski &Weinstein, 2013; Roberts &David,
2016). Many recent media reports have also commented on the
intended and unintended disconnection among people that occurs
when people use smartphones (Barford, 2013; Kelly, 2015; Mount,
2015).
In 2012, a campaign by the Macquarie Dictionary resulted in the
creation of a word to represent this problematic behavior (Pathak,
2013). Specically, the term phubbing(a portmanteau of the
words phoneand snubbing) describes the act of snubbing
someone in a social setting by using ones phone instead of talking
to the person directly in ones company (Haigh, 2015). In other
words, phubbing involves using a smartphone in a social setting of
two or more people, and interacting with the smartphone rather
than the person or people present. For the purposes of the present
research, a phubbermay be dened as a person who starts
snubbing someone in a social situation by paying attention to his/
her smartphone instead, and a phubbeemay be dened as a
person who is ignored by his/her companion(s) in a social situation
because his/her companion(s) uses or check their smartphones
instead. Although researchers have begun to consider some of the
consequences of problematic smartphone use like phubbing, such
as negative consequences for relationship satisfaction and personal
wellbeing (Roberts &David, 2016), very little is known about what
causes phubbing, and how it has become a pervasive feature of
modern communication. We draw upon existing ndings in other
domains of communication (specically Internet communication)
to understand the factors that predict smartphone addiction and
phubbing behavior, and also to understand how phubbing has
become a strong norm of communication.
2.2. Possible predictors of smartphone addiction and phubbing
First, Internet addiction has been dened as a maladaptive
pattern of Internet use leading to clinically signicant impairment
or distress(Goldberg, 1996, p.1). Some researchers argue that
problematic smartphone behavior is closely related to Internet
addition and may have some similar consequences. Specically,
researchers investigating smartphone addiction have shown that
like Internet addiction, problematic smartphone use is associated
with withdrawal, intolerance, compulsive behavior and functional
impairment (Lin et al., 2014; Mok et al., 2014; Takao, Takahashi, &
Kitamura, 2009). Excessive smartphone use and compulsive
smartphone checking is also associated with interpersonal rela-
tionship problems such as inhibition of interpersonal closeness and
trust development (Przybylski &Weinstein, 2013), interference of
other social activities (Walsh, White, &Young, 2008), and insecu-
rity in romantic relationships (Kuss &Grifths, 2011). Moreover, in
a recent study, Internet addiction was positively related to phub-
bing behavior (Karada
g et al., 2015). It is therefore reasonable to
suggest that problematic Internet use would be associated with
problematic smartphone use, which in turn may predict phubbing
behavior.
Second, we investigate the predictive value of fear of missing out
(FoMO), which is described as the fears, worries, and anxieties
people may have in relation to being in (or out of) touch with the
events, experiences, and conversations happening across their
extended social circles(Przybylski, Murayama, DeHaan, &
Gladwell, 2013, p.1842). FoMO debilitates people by arousing
their insecurities and has been found to be associated with
persistent mobile phone overuse (Carbonell, Oberst, &Beranuy,
2013). This anxiety about being left out of the information circuit
also plays a crucial role in seeking out social networking services,
need satisfaction, life satisfaction, and mood (Przybylski et al.,
2013), which have all been connected to levels of smartphone
addiction (Davey &Davey, 2014; Kwon et al., 2013; Salehan &
Negahban, 2013). Recent research has found FoMO to be associ-
ated with problematic mobile phone use (Cheever et al., 2014;
Hong, Chiu &Huang, 2012; Lepp et al., 2014). It is therefore plau-
sible to suggest that FoMO would predict mobile phone addition,
which in turn may predict phubbing behavior. The fear of missing
important information on social media, for example, may be
associated with problematic phone use, meaning that people then
turn to their phones rather than interacting with the people in their
presence.
Third, several studies have shown that self-control is closely
related to addictive behavior (Kim, Namkoong, Ku, &Kim, 2008;
Malouf et al., 2014; Mehroof &Grifths, 2010; Perry &Carroll,
2008; Tangney, Baumeister, &Boone, 2004) and has also been
linked to problematic smartphone use (Billieux, Van der Linden,
dAcremont, Ceschi, &Zermatten, 2007). It is argued that, similar
to substance-dependence related symptoms, people with high ur-
gency or high level of difculty controlling their impulses may be
unable to moderate their mobile phone use (Billieux, Van der
Linden, &Rochat, 2008). Meanwhile, lack of perseverance can
disturb task focusing and increase the incidence of irrelevant cog-
nitions (Bechara &Van Der Linden, 2005), which may also enhance
the frequency of mobile phone use (Billieux et al., 2008). It is
therefore reasonable to suggest that self-control, in predicting
smartphone addiction, may in turn predict problematic smart-
phone behavior in the form of phubbing.
Therefore, smartphone addiction itself should be a proximal
predictor of phubbing behavior. Phubbing and smartphone addic-
tion may share the same properties because they are both related to
inappropriate smartphone uses and behaviors. It seems inevitable
that people who are addicted to their smartphones will use their
V. Chotpitayasunondh, K.M. Douglas / Computers in Human Behavior 63 (2016) 9e1810
device uncontrollably even it is discourteous or a prohibited time
and place to do so (Bianchi &Phillips, 2005; Billieux et al., 2014;
Jones, 2014; Walsh, White, Hyde, &Watson, 2008).
2.3. How has phubbing become the norm?
Phubbing behavior, phubbers and phubbees can be commonly
seen everywhere in todays modern society (Haigh, 2015). Another
question is therefore how this behavior has become acceptable or
normative. Understanding the relationship between the extent to
which people phub and the extent to which they are phubbed is an
important part of answering this question. The concept of reci-
procity in social psychology plays a key role for understanding
human interaction and social exchanges (Berg, Dickhaut, &
McCabe, 1995; Cialdini, 1993; Falk &Fischbacher, 2006). Reci-
procity occurs when someone returns a social action that has
positive consequences for another (Pelaprat &Brown, 2012)or
retaliates with an action, resulting in negative consequences
(Keysar, Converse, Wang, &Epley, 2008). In terms of phubbing,
ignoring companions via smartphone may cause such behaviors to
be reciprocated intentionally or unintentionally. In turn, and with
repeated reciprocity of phubbing behavior, this may inuence the
extent to which phubbing is perceived to be normative or accept-
able. In the past, social norms often took decades or centuries to be
developed or recalibrated (Axelrod, 1986; Miller &Prentice, 1996;
Sherif, 1936). However, societies have always experienced dra-
matic shifts in new social norms and people tend to adopt these
norms rapidly (Sunstein, 1996). Norms are also derived from
observable and personal behavior (Miller &Prentice, 1996). It is
therefore possible to gauge the extent to which observable
behavior (being phubbed) and personal behavior (phubbing) can
predict the extent to which people view phubbing as normative.
2.4. Gender
Gender has been found to play a crucial role in inuencing many
smartphone-associated behaviors such as preference for online
activities (Ha &Hwang, 2014), mobile phone addiction (Baron &
Campbell, 2012; Geser, 2006), internet addiction (Geser, 2006;
Jang &Ji, 2012), self-control (Nakhaie, Silverman, &LaGrange,
2000), and communication etiquette (Forgays, Hyman, &
Schreiber, 2014). However, very little is currently known about
how phubbing behavior, being phubbed, and perceived social
norms of phubbing differ between males and females. Meanwhile,
gender has a moderating effect on the relationship between social
norms and many aspects of human consumption behavior
(Kolyesnikova, Dodd, &Wilcox, 2009) such as alcohol consumption
(Lewis &Neighbors, 2004), internet banking (Karjaluoto, Riquelme,
&Rios, 2010), and online purchasing (Dittmar, Long, &Meek, 2004).
Recently, it was found that gender plays a moderating role on the
relationship between phubbing behavior and both mobile phone
and Internet addiction (Karada
g et al., 2015). It is therefore
reasonable to propose that gender plays an important role in
determining phubbing behavior, is associated with the antecedents
of phubbing, and inuences the extent to which phubbing is
perceived as normative.
2.5. The present study
Although phubbing has become a growing area of interest in
recent years, research on the social antecedents and effects of
phubbing is extremely limited. Further, research on the perceived
normativity of phubbing is, to our knowledge, non-existent.
Knowing more about these factors will extend our understanding
of social behavior within an environment of rapidly shifting
communication technologies. The main aim of our study is there-
fore to examine the factors that predict phubbing behavior, and
explore the ways in which people redene social communication
norms as their own behavior, and the behavior of those around
them, changes. In particular, we studied the contributing roles of
Internet addiction, fear of missing out, and self-control in predict-
ing smartphone addiction, and how smartphone addiction may
lead to phubbing behavior. Moreover, we also examined the po-
tential effects of gender. Participants participated in an online study
where they completed scales to measure each of the above
variables.
2.6. Research model and hypotheses
Drawing on our literature review, we have developed a research
model to explicate the key determinants of phubbing behavior and
the perceived social norms of phubbing. The predicted model is
depicted conceptually in Fig. 1. We hypothesized that Internet
addition and FoMO would positively predict smartphone addiction,
and that self-control would negatively predict smartphone addic-
tion. Next, we predicted that smartphone addiction would posi-
tively predict phubbing behavior. Further, we hypothesized that
phubbing behavior would positively predict the extent to which
people are phubbed. We also predicted that both phubbing and
being phubbed would positively predict the extent to which people
perceive phubbing as normative. Finally, we predicted that gender
would moderate the relationships between each determinant in
our proposed model.
3. Method
3.1. Participants
After giving their informed consent, participants completed an
online questionnaire designed via Qualtrics software. Two hundred
and seventy-six participants (102 men and 174 women) ranging in
age from 18 to 66 (M¼28.09, SD ¼9.64) consisted of 88 under-
graduate students at the University of Kent (who participated for
course credit), 88 participants from Amazons Mechanical Turk
(MTurk), and 100 volunteers from personal contacts on social
networking sites. Eight participants (2.90%) who chose No, I do not
use a smartphoneas a response in any questions within this study,
were excluded. Then, we removed 17 participants (6.16%) who did
not nish the questionnaire. In total, 251 participants (93 men and
158 women) ranging in age from 18 to 66 (M¼27.70, SD ¼9.59)
remained in the study. The demographics of the sample are pre-
sented in Table 1.
3.2. Materials and procedure
The phubbing questionnaire, Smartphone Addiction Scale e
Short Version (SAS-SV), Internet Addiction Test (IAT), Fear of
Missing Out Scale (FoMOs), and Brief Self-Control Scale (BSCS) were
employed in this study.
Phubbing questionnaire. Initially, phubbing frequency and fre-
quency of being phubbed were measured using items scored (1)
never, (2) less often, (3) once weekly, (4) 2 times or more per week, (5)
once daily, (6) 2e3 times per day, (7) 4e5 times per day, (8) 6e9 times
per day, (9) 10 times or more per day. Regarding the small numbers
of participants in some response categories, the nine categories for
phubbing and being phubbed were collapsed into four (less often,
less than once daily, 1e3 times per day, and 4 times or more per
day). Meanwhile, phubbing duration and duration of being phub-
bed (per day) were measured using items scored (1) less than
15 min, (2) 15e30 min, (3) 30e60 min, (4) 60e90 min, (5)
V. Chotpitayasunondh, K.M. Douglas / Computers in Human Behavior 63 (2016) 9e18 11
90e120 min, (6) 2e3h, (7) 4e6h, (8) more than 6 h. Again, because
of low frequency of some choices, we collapsed duration categories
into four (less than 15 min, less than an hour, 1e2 h, and more than
2 h). Phubbing frequency and phubbing duration were summed to
create one score for overall phubbing behavior. Further, scores for
the frequency and duration of being phubbed were summed to
create an overall score of being phubbed. To assess familiarity with
the term phubbing, participants were asked Do you know what
the term phubbingmeans?(yes or no).
Last, we measured perceived social norms of phubbing. Three
items measured descriptive norms which are based on observa-
tions of othersbehavior (Borsari &Carey, 2003). Items were: Are
you familiar with this type of situation?,Do you think that people
recognize phubbing behavior?, and Do you think that phubbing
behavior typical amongst people around you?(1 ¼not at all,2¼a
little,3¼somewhat,4¼quite a bit,5¼very much;M¼10.99,
SD ¼2.36). Two items measured injunctive norms, which are
related to the inference of othersapproval of phubbing (Borsari &
Carey, 2003). These were: Do you think that phubbing behavior is
appropriate?and Do you think that other people view phubbing
behavior as appropriate?using the same response categories as
the previous set of questions (M¼4.06, SD ¼1.38). Although both
were included in the study, we expected no differences in re-
lationships associated with descriptive and injunctive norms and so
in our predicted model, they were combined to a general measure
of perceived social norms of phubbing.
Smartphone Addiction Scale - Short Version (SAS-SV). This scale
was developed from the original 33-item Smartphone Addiction
Scale (SAS). This involved participants rating their agreement with
10 items (1 ¼strongly disagree;6¼strongly agree;
a
¼0.91,
M¼27.00, SD ¼10.11) such as Missing planned work due to
smartphone use,Wont be able to stand not having a
Fig. 1. Proposed conceptual phubbing model using path analysis.
Table 1
General characteristics of participants by gender.
Characteristics Male N¼93% (n) Female N¼158% (n) Total N¼251% (n)
Age (years)
Mean ±SD 30.30 ±10.18 26.17 ±8.90 27.70 ±9.59
Occupation
Attending university Full-time 30.11 (28) 48.73 (77) 41.83 (105)
Working Full-time 47.31 (44) 30.38 (48) 36.65 (92)
Attending university Part-time 7.53 (7) 11.39 (18) 9.96 (25)
Working Part-time 8.60 (8) 3.80 (6) 5.58 (14)
Currently unemployed 6.45 (6) 5.70 (9) 5.98 (15)
Education
No formal education 1.08 (1) 0.63 (1) 0.80 (2)
Primary level education 1.08 (1) 0.63 (1) 0.80 (2)
Secondary level education 25.81 (27) 43.67 (69) 38.25 (96)
College education (Bachelors) 40.86 (38) 34.81 (55) 37.05 (93)
College education (Graduate) 27.96 (26) 20.25 (32) 23.11 (58)
Ethnicity
White/Caucasian 58.06 (54) 56.96 (90) 57.37 (144)
Black British Caribbean 0.00 (0) 0.63 (1) 0.40 (1)
Black British African 1.08 (1) 7.59 (12) 5.18 (13)
Other Black background 0.00 (0) 1.27 (2) 0.80 (2)
Asian British Indian 0.00 (0) 1.27 (2) 0.80 (2)
Asian British Pakistani 0.00 (0) 1.90 (3) 1.20 (3)
Chinese 8.60 (8) 8.23 (13) 8.37 (21)
Other Asian background 24.73 (23) 14.57 (23) 18.33 (46)
African American 2.15 (2) 1.27 (2) 1.59 (4)
Hispanic 1.08 (1) 1.27 (2) 0.40 (1)
Other (including mixed ethnicity) 2.15 (2) 5.06 (8) 3.98 (10)
Rather not say 2.15 (2) 1.27 (2) 1.59 (4)
V. Chotpitayasunondh, K.M. Douglas / Computers in Human Behavior 63 (2016) 9e1812
smartphone, and The people around me tell me that I use my
smartphone too much(Kwon, Kim, Cho, &Yang, 2013). In this
study, 32.3% of female and 29% of male participants scored over the
cut-off value of smartphone addiction (higher than 31 for men and
33 for women).
Internet Addiction Test (IAT). This scale contains 20 items con-
sisting of eight items based on the DSM-IV criteria (Diagnostic and
Statistical Manual of Mental Disorders, 4th Edition) for pathological
gambling and alcoholism such as How often do you nd that you
stay online longer than you intended?and How often do your
grades or school work suffer because of the amount of time you
spend online?, along with 12 new items such as How often do you
form new relationships with fellow online users?and How often
do you lose sleep due to late-night log-ins?(Young, 1998). Par-
ticipants responded on a 5-point scale (1 ¼rarely;5¼always;
a
¼0.89; Frangos, Frangos &Sotiropoulos, 2012) to measure mild,
moderate, and severe Internet addictive behavior. The scores can
range from 20 to 100; the higher the score, the greater the prob-
lems that the Internet causes. Young (2009) suggested that a score
ranging from 20 to 49 points is an average online user who has no
problem in controlling over their Internet usage. A score ranging
from 50 to 79 indicates experiencing in occasional or frequent
problems due to Internet usage, and a score ranging from 80 to 100
signies signicant impacts on a persons life directly caused by
Internet usage. In this study, the mean IAT score was 33.05
(SD ¼14.79). The majority of participants (n¼217, 86.5%) were
categorized as average users. Thirty-three participants (13.1%) were
problematic users and only one male participant was categorized as
an addictive user.
Fear of Missing Out Scale (FoMOs). The Fear of Missing Out scale
(FoMOs), developed by Przybylski et al. (2013) contains 10 items to
assess fear of missing out phenomena such as I fear others have
more rewarding experiences than me,I fear my friends have more
rewarding experiences than me, and I get worried when I nd out
my friends are having funwithout me. Participants responded on a
5-point scale (1 ¼not at all true for me,5¼extremely true of me;
a
¼0.90, M¼2.19, SD ¼0.79).
Brief Self-Control Scale (BSCS). The Brief Self-Control Scale
(Tangney et al., 2004) is a 13-item questionnaire asking participants
to rate how well statements (e.g., I am good at resisting tempta-
tion,I have a hard time breaking bad habits, and I never allow
my self to lose control) describe them on a 5-point scale (1 ¼not
like me at all;5¼very much like me,
a
¼0.85, M¼40.48, SD ¼8.23).
4. Results
All statistical tests were performed using SPSS Statistics version
23.0 and AMOS version 23.0 for Windows. Participantsreported
frequency and duration of phubbing and being phubbed are shown
in Table 2.
4.1. Predictors of phubbing behavior
As shown in Table 3, a Spearmans rank-order correlation was
computed to assess the relationships among variables. All correla-
tions between variables in this study were statisticallysignicant in
the expected directions. Self-control negatively predicted smart-
phone addiction, whereas Internet addiction and FoMO positively
predicted smartphone addition. Further, there was a positive cor-
relation between smartphone addiction and phubbing behavior,
and between phubbing behavior and being phubbed. Moreover,
both phubbing behavior and being phubbed positively correlated
with the extent to which people perceived phubbing as normative.
4.2. Testing the predicted model
Missing data were removed before computing the path analysis
in accordance with requirements set by AMOS. The following hy-
pothesized paths were tested as shown conceptually in Fig. 1: (1)
Internet addiction, fear of missing out, and self-control predict
smartphone addiction (2) smartphone addiction predicts phubbing
behavior (3) phubbing behavior predicts the experience of being
phubbed, and (4) phubbing behavior and experience of being
phubbed predict descriptive and injunctive norms of phubbing.
As seen in Fig. 2 and Table 4, being phubbed signicantly pre-
dicted the perceived social norms of phubbing (
b
¼0.15, p¼0.047).
Phubbing behavior also signicantly predicted and had a divergent
effect on both the social norms of phubbing (
b
¼0.19, p¼0.011) and
being phubbed (
b
¼0.58, p<0.001).
It was found that smartphone addiction signicantly predicted
phubbing behavior (
b
¼0.45, p<0.001). Further, when the effect on
smartphone addiction from each variable was calculated, it was
revealed that Internet addiction (
b
¼0.41, p<0.001) and fear of
missing out (
b
¼0.33, p<0.001) were positive predictors of
smartphone addiction, whereas self-control negatively predicted
smartphone addiction (
b
¼0.12, p¼0.016).
4.3. Moderating effect of gender
Differences in frequency and duration of phubbing and being
phubbed according to gender were determined by running A
Mann-Whitney Utest as seen in Table 5. Results indicated that the
frequency of phubbing for females (mean rank ¼142.03) was
signicantly higher than for males (mean rank ¼98.76),
U¼9880.00, z¼4.73, p<0.001. The result also showed that the
duration of phubbing was signicantly greater for females (mean
rank ¼137.67) than for males (mean rank ¼106.17), U¼9191.50,
z¼3.86, p<0.001.
A Mann-Whitney Utest was also run to determine if there were
differences in frequency and duration of being phubbed according
to gender. Frequency of being phubbed for females (mean
rank ¼142.68) was signicantly greater than for males (mean
rank ¼97.67), U¼9982.00, z¼4.91, p<0.001. The results also
indicated that the duration of phubbing was signicant higher for
females (mean rank ¼136.47) than for males (mean rank ¼108.22),
U¼11,043.00, z¼3.629, p¼0.001. In conclusion, the results
revealed that women (mean rank ¼143.67) phubbed their com-
panions more than men (mean rank ¼95.98; (U¼10,138.50,
z¼5.14, p<0.001), and women (mean rank ¼142.40) were
phubbed by their companions more than men (mean rank ¼98.14)
(U¼9938.00, z¼4.75, p<0.001).
Further, a Mann-Whitney Utest was run to determine if there
were differences in the IAT score, SAS-SV score, and FoMOs score,
which were not normally distributed for both males and females, as
assessed by Shapiro-Wilks test (p<0.05). Meanwhile, regarding a
normally distributed BSCS score, an independent sample t-test was
run to assess BSCS score. The SAS-SV score for females (mean
rank ¼137.67) was signicantly higher than for males (mean
rank ¼106.18), U¼9190.50, z¼3.21, p¼0.001, as seen in Table 6.1.
On the other hand, the BSCS score, computed with independent
sample t-test as in Table 6.2, was greater in males (M¼42.77,
SD ¼8.51) than female participants (M¼39.13, SD ¼7.77),
M¼3.65, 95% CI [1.58, 5.72], t(249) ¼3.47, p¼0.001. A Mann-
Whitney Utest showed no signicant difference between Gender
and IAT score and FoMOs score in our study.
As we found signicant gender differences among many vari-
ables, we checked the model t for both men and women before
conducting multi-group analysis in AMOS. Our proposed model
had acceptable goodness of t for both male participants
V. Chotpitayasunondh, K.M. Douglas / Computers in Human Behavior 63 (2016) 9e18 13
(
c
2
(93) ¼6.87, p¼0.810, CFI ¼1.00, RMSEA ¼0.00) and female
participants (
c
2
(158) ¼19.54, p¼0.052, CFI ¼0.98, RMSEA ¼0.07).
We compared an original unconstrained model to alternative
constrained models, which imposed gender equality constraint of
each path in the model. Standardized estimates, constrained
c
2
,
Dc
2
, and its p-value in the nested model were explored to compare
gender effects in each path of the model.
Due to the signicant chi-square difference (
Dc
2(1)
¼6.38,
p<0.05) as seen in Table 7, gender had a moderating effect on the
relationship between being phubbed and the social norms of
phubbing, which was stronger in men (
g
¼0.36, p<0.01)
compared to the same relationship in women (
g
¼0.00, p>0.05).
Table 2
General characteristics of phubbing behavior and being phubbed as a function of gender.
Characteristics Male N¼93% (n) Female N¼158% (n) Total N¼251% (n)
Phubbing frequency
Less often 46.2 (43) 21.5 (34) 30.7 (77)
Less than once daily 25.8 (24) 25.3 (40) 25.5 (64)
2e3 times per day 21.5 (20) 29.7 (47) 26.7 (67)
4 times per day or more 6.5 (6) 23.4 (37) 17.1 (43)
Phubbing duration
Less than 15 min per day 77.4 (72) 52.5 (83) 61.8 (155)
Less than an hour per day 17.2 (16) 36.7 (58) 29.5 (74)
1e2 h per day 5.4 (5) 4.4 (7) 4.8 (12)
More than 2 h per day 0.0 (0) 6.3 (10) 4.0 (10)
Frequency of being phubbed
Less often 32.3 (30) 15.2 (24) 21.5 (54)
Less than once daily 31.2 (29) 17.7 (28) 22.7 (57)
2e3 times per day 25.8 (24) 35.4 (56) 31.9 (80)
4 times per day or more 10.8 (10) 31.6 (50) 23.9 (60)
Frequency of being phubbed
Less than 15 min per day 67.7 (63) 44.9 (71) 53.4 (134)
Less than an hour per day 24.7 (23) 43.0 (68) 36.3 (91)
1e2 h per day 6.5 (6) 10.8 (17) 9.2 (23)
More than 2 h per day 1.1 (1) 1.3 (2) 1.2 (3)
Table 3
Descriptive statistics and spearman correlations among study variables.
Variables 1 2 3 4 5 6 7 M SD
1 SAS-SV e0.66
**
0.61
**
0.39
**
0.49
**
0.29
**
0.23
**
27.00 10.11
2 IAT e0.58
**
0.40
**
0.39
**
0.28
**
0.26
**
33.05 14.79
3 FoMOs e0.39
**
0.33
**
0.22
**
0.15
*
21.90 7.89
4 BSCS e0.31
**
0.20
**
0.21
**
40.48 8.23
5 Phubbing e0.59
**
0.28
**
3.81 1.61
6 Being phubbed e0.28
**
4.16 1.58
7 Social Norms of phubbing e15.04 2.94
*
p<0.05,
**
p<0.01.
Fig. 2. Phubbing model with standardized coefcients.
V. Chotpitayasunondh, K.M. Douglas / Computers in Human Behavior 63 (2016) 9e1814
As such, a hierarchical multiple regression was run to conrm the
increase in variation. Gender moderated only the effect of being
phubbed on perceived social norms of phubbing, as evidenced by a
statistically signicant increase in total variation explained of 2.4%,
F(1, 245) ¼6.568, p<0.05 and the coefcient of the interaction
term (b¼0.753, SE ¼0.294) which was statistically signicant
(p<0.05). We also went on to compare and found no signicant
moderating role of gender on the path between internet addiction
and smartphone addiction, fear of missing our and smartphone
addiction, self-control and smartphone addiction, smartphone
addiction and phubbing, phubbing and being phubbed, and
phubbing and social norms of phubbing.
In conclusion, the hypothesis suggesting that gender has a
moderating effect was conrmed, but only for the relationship
between being phubbed and the extent to which phubbing feels
like normative behavior for people (see Fig. 2). Overall however, the
predicted model found good support in the current data.
5. Discussion
To our knowledge, this study represents the rst examination of
both the antecedents and consequences of phubbing behavior. We
found that Internet addiction, fear of missing out, and self-control
predicted smartphone addiction, which in turn predicted phub-
bing behavior and the extent to which people are phubbed. Further,
phubbing behavior and the experience of being phubbed predicted
the extent to which phubbing was perceived to be normative.
Finally, gender moderated the effect of being phubbed on the
perceived social norms of phubbing.
5.1. Theoretical contributions
First, these results suggest that the key predictors of problematic
Internet use ederived from theoretical perspectives and empirical
research on Internet addition ealso predict problematic smart-
phone use (Billieux et al., 2014; Lee et al., 2014, 2014; Lin et al.,
2014), and this in turn predicts a behavior that is likely to be
detrimental to everyday social interactions. Indeed, smartphones
have a wider variety of functions and applications than ordinary
cell phones that have less technological capability (Falaki et al.,
2010). This multi-functional improvement may therefore alter the
denition of smartphone addiction from previous conceptualiza-
tions (Takao et al., 2009). In particular, it is now more important to
focus on Internet-based activities rather than on normal cell phone
uses when taking into account the behaviors that people engage in
when using mobile phone technology (Kwon et al., 2013). Ongoing
theoretical developments explaining Internet behavior are also
therefore likely to explain changes in smartphone behavior.
However, this study goes further to develop a theoretical ac-
count of why phubbing has become normative. Specically, our
study suggests that phubbing may have become the norm as a
result of both observed and personal behavior. People are phubbed,
but they are also phubbers. In an environment where people are
constantly switching from being the protagonists and recipients of
this behavior, our data suggests that phubbing becomes seen as the
norm. This may in part occur because personal behaviors, beliefs,
and attitudes can often lead to false-consensus effects such that
individuals assume that others think and do the same as them-
selves (Berkowitz, 2005; Marks &Miller, 1987; Ross, Greene, &
Table 4
Results of standardized structural path estimates.
Dependent Variable Independent Variable BSE
b
t-value p R Square
Social norms of phubbing Phubbing 0.35 0.14 0.19 2.54 0.012 0.09
Being phubbed 0.28 0.14 0.15 1.98 0.049
Being phubbed Phubbing 0.58 0.05 0.60 11.74 0.000 0.36
Phubbing Smartphone addiction 0.07 0.01 0.45 7.90 0.000 0.20
Smartphone addiction Internet addiction 0.28 0.04 0.41 7.08 0.000 0.52
Fear of missing out 0.42 0.07 0.33 5.79 0.000
Self-control 0.14 0.06 0.12 2.40 0.017
B, unstandardized coefcients; SE, standard error;
b
, standardized coefcients.
Table 6.1
Comparison of psychometric measurements (IAT, SAS-SV, and FoMOs) between genders.
Male (n¼93) Female (n¼158) Mann-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed)
Mdn Mean rank Mdn Mean rank
Internet addiction
IAT score 31.00 121.92 33.00 128.40 7726.00 20,287.00 0.68 0.495
Smartphone addiction
SAS-SV score 24.00 106.18 29.00 137.67 9190.50 21,751.50 3.32 0.001
Fear of missing out
FoMOs score 20.00 118.43 21.00 130.46 8051.00 20,612.00 1.27 0.205
Table 5
Non-parametric test of the gender difference in scores of phubbing and being phubbed.
Male (n¼93) Female (n¼158) Mann-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed)
Mdn Mean rank Mdn Mean rank
Phubbing frequency 2.00 98.76 3.00 142.03 9880.00 22,441.00 4.73 <0.001
Phubbing duration 1.00 106.17 1.00 137.67 9191.50 21,752.50 3.86 <0.001
Phubbing sum score 3.00 95.98 4.00 143.67 10,138.50 22,699.50 5.14 <0.001
Being phubbed frequency 2.00 97.67 3.00 142.68 9982.00 22,543.00 4.91 <0.001
Duration of being phubbed 1.00 108.22 2.00 136.47 9000.50 21,561.50 3.33 0.001
Sum score of being phubbed 3.00 98.14 5.00 142.40 9938.00 22,499.00 4.75 <0.001
V. Chotpitayasunondh, K.M. Douglas / Computers in Human Behavior 63 (2016) 9e18 15
House, 1977). People may therefore assume that others phub in the
same way that they do themselves, therefore perpetuating the
behavior. Further, when people experience phubbing and notice
the behavior occurring frequently around them, they may be likely
to conclude that this behavior is socially acceptable (Ross, 1977).
Our study shows a signicant relationship between these two de-
terminants, such that phubbing positively predicts the extent to
which people are phubbed. Further, the rule of reciprocity can be
assumed as a strong determining factor that turns a phubber into a
phubbee. People, in response to discontented actions, tend to
commit retaliatory behavior in response (Falk &Fischbacher, 2006;
Keysar et al., 2008). Snubbing companions by smartphone may
therefore cause phubbing behaviors to be reciprocated.
5.2. Gender as a moderator
Furthermore, we explored the moderating effect of gender on
each part of our model. Unexpectedly, it was found that gender
moderates only the relationship between being phubbed and the
perceived social norms of phubbing. The relationship is stronger for
males than females. Along with the gender-specic model com-
parison in Table 7, the extent to which males are phubbed tends to
be the main predictor of perceived social norms of phubbing in
men, whereas the extent to which females phub their companions
tends to be the main predictor in women. This can perhaps be
explained by subjective motivations and communication differ-
ences between women and men. Research suggests that males see
smartphones as empowering devices with instrumental functions,
while females use smartphones as facilitators of social interaction
(Baron &Campbell, 2012; Geser, 2006). As a social activity, phub-
bing is perhaps therefore more predictive of perceived normative
behavior for males because, since they engage in phubbing less
than women, norms are more informed by observing others
behavior rather than their own.
5.3. Implications
By identifying the factors that predict smartphone addiction,
this study can contribute to the assessment of problematic
smartphone behavior and interventions to deal with this. More
novel, however, is our nding that phubbing is a direct conse-
quence of problematic smartphone use. By identifying phubbing as
a key outcome, practitioners may use phubbing behavior as a
measure of the success of interventions targeted at problematic
smartphone use. The results of this study also allow us to better
understand how problematic smartphone use has become
acceptable or normative. Efforts to address problematic smart-
phone use may therefore benet from considering the role of norm
development and how norms can be both informed by, and at the
same time fuel behavior. These ndings also raise awareness about
the etiquette associated with smartphone use compared to other
domains and the how the expectations of communicators may
change as technology develops further.
5.4. Limitations and future directions
Several limitations of this study need to be considered in future
research. First, the number of participants was relatively small
compared to other online surveys and the ratio of gender was not
1:1. Participants were predominately young females, and of White/
Caucasian or Asian ethnic background. The unequal distribution of
age, gender and ethnicity did not allow us to analyze the potential
effects associated with these variables. In particular, further
research is required to establish what smartphones and phubbing
behavior may mean differently for women and men. Further, in a
sample where gender was more equally distributed, we could have
considered not only our proposed model but also gender-specic
models of how phubbing becomes the norm for each gender.
Age differences are also likely to be important. Age differences
are well established in other communication domains such as
phone manner (Turner, Love, &Howell, 2008) and the use of mobile
phones while driving (Lipscomb, Totten, Cook, &Lesch, 2007). In
addition, older people tend to view otherssmartphone behavior as
more negative compared to their own (Hakoama &Hakoyama,
2012). Further studies should therefore consider the inuence of
age on the phenomena studied in the current research.
Another important extension of this work would be to investi-
gate the real-life effects of phubbing behavior on the quality of
Table 6.2
Comparison of psychometric measurement (BSCS) between genders.
Male (n¼93) Female (n¼158) Independent sample t-test df Sig. (2-tailed)
Mean SD Mean SD
Self-control
BSCS score 42.77 8.51 39.13 7.78 t ¼3.47 249 0.001
Table 7
Comparison of gender differences in the paths of model.
Standardized estimates Subgroup comparison
(unconstrained
c
2(22)
¼26.39)
Results
Male (n¼93) Female (n¼158) Constrained
c
2(23)
Dc
2(1)
Internet addiction /smartphone addiction 0.41
***
0.44
***
26.41 0.02 NS
Fear of missing out /smartphone addiction 0.34
***
0.32
***
27.00 0.61 NS
Self-control /smartphone addiction 0.06 0.10 26.63 0.24 NS
Smartphone addiction /phubbing 0.36
***
0.44
***
28.76 2.37 NS
Phubbing /being phubbed 0.53
***
0.57
***
26.50 0.11 NS
Phubbing /social norms of phubbing 0.01 0.30
**
29.41 3.02 NS
Being phubbed /social norms of phubbing 0.36
**
0.00 32.77 6.38
*
M>F
M¼Males, F ¼Females, NS ¼not signicant.
*
p<0.05.
**
p<0.01.
***
p<0.001.
V. Chotpitayasunondh, K.M. Douglas / Computers in Human Behavior 63 (2016) 9e1816
social interactions. Extending on the survey research of Roberts and
David (2016), experimental work could shed light on the effects of
different degrees of phubbing on factors such as relationship
satisfaction and feelings of social inclusion. Furthermore, longitu-
dinal studies in which the nature of phubbing behavior in routine
communication is tracked over time would further inform re-
searchers about the potential consequences of phubbing.
Further, respondents in the current study were sampled among
adults who participated for course credit, were paid on MTurk, or
were acquaintances of the researchers on social networking sites.
Whilst this provided a diverse sample, it was not entirely random.
Also, because all measures were self-reported, we cannot conrm
responses with the exact frequency and duration of peoples
phubbing experiences. Finally, because there were no established
scales of general phubbing behavior in the literature, we designed
the measures ourselves. Validated tools need to be created to more
fully understand this phenomenon and researchers need to pay
careful attention to sampling and measurement issues in future
research.
6. Conclusions
To the best of our knowledge, this study is the rst to consider
both the antecedents and consequences of phubbing behavior. It is
also the rst to consider how phubbing may have become such a
pervasive norm in modern communication. A signicant portion of
the worlds population use smartphones to conduct their everyday
lives. Many people simply cannot live without them. It is therefore
increasingly important for social scientists to consider the impact
that they have on the quality of social life.
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  • ... Therefore, there are different researches that cover different social interactions in different contexts in different kind of social relationships, which will give understanding of phubbing whether as an acceptable or unacceptable norm. It can be predicted to what extent people can be phubbed by phubbing behavior itself and phubbing can result in a vicious, self-reinforcing cycle that makes the behavior become regularizing (Chotpitayasunondh, 2016).Such kind of self-reinforcing cycle of phubbing behavior induce more phubbing and may make it acceptable norm rather than hindering it. This kind of behavior is becoming very common everywhere and is widespread because of which it is becoming day to day norm and people hinder to complain about it openly. ...
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  • ... Education is in the nature of all humans who are producing and transmitting the culture. This phenomenon, called phubbing, seems to have become normative in everyday communication (Chotpitayasunondh & Douglas, 2016). ...
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    In 21st century, face-to-face social interaction tends to decrease as many people prefer to spend considerable time on their Smart Phones ignoring people around them in social environments. People are exposed to ignorant behaviours by their companions looking at their phones repeatedly in social settings. After an extensive literature review, data are collected from 352 participants via SurveyMonkey software. The statistical analysis like Frequencies, Crosstabs, One-Way ANOVA, Independent t-tests, Chi-Square analysis, Regression analysis with IBM SPSS Statistics 25 were carried out and Models are drawn with IBM SPSS AMOS 26 Graphics. According to the results of this research, it is aimed to create a model which points out the significant factors that lead to phubbing. The obtained Model "Phubber-Phubbee Model", can be used as a guide for minimizing the addictive effects of the phubbing phenomenon that could provide new directions for further studies in this research area.
  • ... Workload context, dependence on smartphone for work ; Li and Lin (2018) Evolving identities Marchant and O'Donohoe (2019) Fear of missing out (FOMO), fear of rejection, abandonment, avoidant attachment, envy Chotpitayasunondh and Douglas (2016); Elhai et al. (2016); E. Kim and Koh (2018) Shyness, social liquidity (i.e., the ease with which one can establish interpersonal relationships), introversion, self-esteem, social assurance, social self-efficacy, interpersonal sensitivity, relational maladjustment, adult attachment, attachment to friends Bian and Leung (2015); Chiu ( ...
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  • ... "Phubbing" is defined at their marketing campaign's website site as "the act of snubbing someone in a social setting by looking at your phone instead of paying attention" [7]. Researchers got on board with the term and phubbing has since been found to reduce communication quality and relationship satisfaction by reducing the feelings of belongingness and positive affect [8], make both phubbers and the phubbed to be more likely to see phubbing as an inevitable social norm [9], and be thought of as 'bad' by young people, even if they are doing it themselves [10]. "Partner phubbing" has further been found to reduce relationship satisfaction by creating conflicts over cell phone use [11] and cause depression in China for couples married more than seven years [12]. ...
  • ... FoMO refers to the "pervasive apprehension that others might be having rewarding experiences from which one is absent" (Przybylski et al., 2013(Przybylski et al., , p. 1841. FoMO has been predominantly explored in digital settings where it has been related to problematic usage of technologies including smartphones, social network sites, and the internet more generally (Chotpitayasunondh & Douglas, 2016;Oberst et al., 2017;Wolniewicz et al., 2018). It is assumed that one of the main characteristics of FoMO is the urge to constantly keep in touch and monitor what other friends are doing (Przybylski et al., 2013). ...
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  • ... FoMO refers to the "pervasive apprehension that others might be having rewarding experiences from which one is absent" (Przybylski et al., 2013(Przybylski et al., , p. 1841. FoMO has been predominantly explored in digital settings where it has been related to problematic usage of technologies including smartphones, social network sites, and the internet more generally (Chotpitayasunondh & Douglas, 2016;Oberst et al., 2017;Wolniewicz et al., 2018). It is assumed that one of the main characteristics of FoMO is the urge to constantly keep in touch and monitor what other friends are doing (Przybylski et al., 2013). ...
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    Partner phubbing (Pphubbing) can be best understood as the extent to which an individual uses or is distracted by his/her cell phone while in the company of his/her relationship partner. The present study is the first to investigate the oft-occurring behavior of Pphubbing and its impact on relationship satisfaction and personal well-being. In Study 1, a nine-item scale was developed to measure Pphubbing. The scale was found to be highly reliable and valid. Study 2 assessed the study's proposed relationships among a sample of 145 adults. Results suggest that Pphubbing's impact on relationship satisfaction is mediated by conflict over cell phone use. One's attachment style was found to moderate the Pphubbing - cell phone conflict relationship. Those with anxious attachment styles reported higher levels of cell phone conflict than those with less anxious attachment styles. Importantly, Pphubbing was found to indirectly impact depression through relationship satisfaction and ultimately life satisfaction. Given the ever-increasing use of cell phones to communicate between romantic partners, the present research offers insight into the process by which such use may impact relationship satisfaction and personal well-being. Directions for future research are discussed.
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    There is a considerable debate on addiction and abuse to Smartphone among adolescents and its consequent impact on their health; not only in a global context, but also specifically in the Indian population; considering that Smartphone's, globally occupy more than 50% of mobile phones market and more precise quantification of the associated problems is important to facilitate understanding in this field. As per PRISMA (2009) guidelines, extensive search of various studies in any form from a global scale to the more narrow Indian context using two key search words: "Smartphone's addiction" and "Indian adolescents" was done using websites of EMBASE, MEDLINE, PubMed, Global Health, Psyc-INFO, Biomed-Central, Web of Science, Cochrane Library, world library - World-Cat, Indian libraries such as National Medical Library of India from 1 January, 1995 to March 31, 2014 first for systematic-review. Finally, meta-analysis on only Indian studies was done using Med-Calc online software capable of doing meta-analysis of proportions. A total of 45 articles were considered in systematic-review from whole world; later on 6 studies out of these 45 related to Smartphone's addiction in India were extracted to perform meta-analysis, in which total 1304 participants (range: 165-335) were enrolled. The smartphone addiction magnitude in India ranged from 39% to 44% as per fixed effects calculated (P < 0.0001). Smartphone addiction among Indian teens can not only damage interpersonal skills, but also it can lead to significant negative health risks and harmful psychological effects on Indian adolescents.
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