Phubbing: Age and Online Presence as Necessary
Conditions
Phubbing: edad y presencia en línea
como condiciones necesarias
Dr. Antonio Matas-Terrón. Profesor
Titular de Universidad. Universidad de Málaga. España
Recibido:
2024/07/09 Revisado 2024/09/15 Aceptado: :2024/12/02 Online First: 2024/12/04 Publicado: 2025/01/07
Cómo citar este artículo:
Matas-Terrón,
A. (2025). Phubbing: edad y presencia en línea como condiciones necesarias
[Phubbing: Age and Online Presence as Necessary Conditions]. Pixel-Bit.
Revista De Medios Y Educación, 72, 103–118. https://doi.org/10.12795/pixelbit.111146
ABSTRACT
Phubbing, the act of ignoring someone in favor of a
mobile phone, has become a prevalent social issue in today’s digital era. This
study aimed to identify necessary conditions for phubbing behavior, focusing on
correlates established by Schneider and Hitsfeld (2021). Utilizing data from
their survey, the study analyzed a sample of 278 participants, mainly female
(74%), with an average age of 26.78 years. The methodology employed Necessary
Condition Analysis (NCA). The results revealed that younger age is a significant
factor in higher levels of self-phubbing. Specifically, the study found that in
contexts with high levels of self-phubbing, youth was a critical determinant.
Furthermore, being Permanently Online and Connected emerged as a critical
factor in self-phubbing, especially at higher levels, suggesting an increasing
dependency on being constantly connected. The study concludes that other
variables such as Fear of Missing Out and Mobile Phone Use Norms do not appear
to be necessary conditions for phubbing. These findings highlight the
multifaceted nature of phubbing and underscore the importance of NCA in
revealing the essential conditions contributing to this behavior.
El phubbing, el acto de ignorar a alguien en favor del teléfono móvil,
se ha convertido en un problema social prevalente en la era digital actual.
Este estudio tiene como objetivo identificar las condiciones necesarias para el
comportamiento de phubbing, centrándose en los factores asociados establecidos
por Schneider y Hitsfeld (2021). Utilizando datos de su encuesta, se analizó
una muestra de 278 participantes, en su mayoría mujeres (74%), con una edad
promedio de 26.78 años. La metodología empleada fue el Análisis de Condición
Necesaria (NCA). Los resultados revelaron que una edad menor es un factor
significativo en los niveles más altos de auto-phubbing. En concreto, el
estudio encontró que en contextos de elevado auto-phubbing, la juventud era un
determinante crítico. Además, estar Permanentemente Conectado en Línea surgió
como un factor clave en el auto-phubbing, especialmente en niveles altos, lo
que sugiere una dependencia creciente de estar constantemente conectado. El
estudio concluye que otras variables, como el Miedo a Perderse Algo y las
Normas de Uso del Teléfono Móvil, no parecen ser condiciones necesarias para el
phubbing. Estos hallazgos subrayan la naturaleza multifacética del phubbing y
destacan la importancia del NCA en la identificación de las condiciones
esenciales que contribuyen a este comportamiento.
PALABRAS CLAVES· KEYWORDS
Phubbing; Age; Necesary Condition Analysis (NCA);
Social behavior; Digital Dependency; Fear of Missing Out (FoMO); Smartphone
Addiction
Phubbing; Edad; Análisis de Condiciones Necesarias
(NCA); Comportamiento social; Dependencia digital; Miedo a Perderse Algo (FoMO);
Adicción a los Teléfonos Inteligentes
1. Introduction
The term "phubbing" was first coined in 2012
by Chotpitayasunondh and Douglas, describing it as the act of slighting someone
in a social setting by focusing their attention on the phone rather than the
other person (Garrido et al., 2021). This behavior, characterized by Koc and
Caliskan (2023) as an interruption of the face-to-face communication through
the use of or directing the gaze at a smartphone, essentially involves ignoring
people present in favor of interaction with the phone. However, before the emergence
of the term "phubbing," research had already addressed concepts and
sociocultural trends that helped lay the foundation for understanding this
phenomenon. In its early stages, studies on mobile phones use primarily focused
on addiction and problematic usage patterns (Karadağ et al., 2015). These
studies examined the addictive potential associated with activities such as
texting, Internet access, and playing games on mobile devices (e.g., Warden et
al., 2004; Sánchez-Carbonell & Beranuy, 2007). The findings of these
studies concluded that excessive technology use could have significant negative
consequences, such as deteriorating social relationships or the onset of
anxiety.
In general, the study of phubbing encompasses both
contextual factors and personal characteristics. Authors such as Yuzhanin
(2022) interpret it as a contemporary social and communicative issue, which
could lead to gradual social detachment. In contrast, Karadağ et al.
(2015), and later Chi et al. (2022), suggest that phubbing could stem from the
constant need to stay online, indicating a potential underlying addiction to
technology. This perspective views phubbing as an inability to focus on in-person
interactions due to the constant allure of digital connectivity.
The reviewed literature indicates that phubbing
behavior is influenced by a wide range of factors, including the fear of
missing out (FoMO), social exclusion, anxiety, depression, negative
self-perception, somatiszation, hostility, loneliness, life satisfaction, and
Internet and smartphone-related addictions (Chotpitayasunondh & Douglas,
2016; Do & Nguyen, 2022) which will be discussed in more detail below.
Other factors contributing to phubbing include attentional skills, the habit of
multitasking with media, and the way people perceive understanding and
validation from their partners (Han et al., 2022), along with tendencies toward
withdrawal, compulsion, and euphoria, which significantly influence phubbing
behaviors (Sansevere & Ward, 2021; Frackowiak et al., 2023).
Latifa, Mumtaz, and Subchi (2019) concluded that
factors such as smartphone addiction, empathy, and self-control are key
elements in shaping phubbing behavior, noting that withdrawal and tolerance
tendencies positively affect phubbing, while self-control has a negative
impact. Additionally, it has been recorded that higher rates of phubbing toward
friends are associated with higher levels of depression, social anxiety, and
neuroticism, while a tendency toward amiability seems to correlate negatively
with this behavior (Sun & Samp, 2021). Overall, these studies demonstrate
the complex interaction of individual, psychological, and environmental factors
in the development of phubbing behavior.
As Li et al. (2023) point out, phubbing may be
perceived by those who experience it (usually the companions of the person
engaging in phubbing) as an insult and a lack of respect toward others in
social settings as they prioritize phone use over direct engagement. This
implies phubbing can have negative effects on social relationships, affecting
the communication dynamics, the formation of impressions, the quality of
relationships, and, ultimately, mental health. In particular, it has been
linked to an increase in depressive symptoms and conflicts in relationships due
to smartphone use (Capilla Garrido et al., 2024).
Research on phubbing has revealed its relationship
with various psychological, social, and technological factors. In the
psychological realm, it has been found that individuals with higher levels of
boredom tend to use their phones as an escape, suggesting that boredom may act
as a trigger for phubbing (Lv et al., 2023). On the other hand, the fear of
missing out (FoMO) is an anxiety caused by the feeling that one is missing
important experiences or events by not being connected (Przybylski & Weinstein,
2013), and has been widely identified as a significant correlate of phubbing
(Chi et al., 2022; Gao et al., 2023; Joshi, 2023).
Another important psychological factor is online
vigilance (OV), understood as the tendency to constantly monitor activity and
notifications on social media (Maftei & Măirean, 2023). Other
psychological factors have also been highlighted, such as mental health
(Parmaksiz, 2021), internet addiction, the anxiety trait, loneliness, and
self-esteem (Barbed-Castrejón et al., 2024).
In the social realm, problematic social media use
appears as a factor closely related to phubbing (Chu et al., 2021). At the same
time, it has been observed that individuals with symptoms of smartphone
addiction (e.g., compulsive use, withdrawal symptoms or neglecting
responsibilities) are more likely to engage in phubbing behavior (Safdar et
al., 2023).
The study by Schneider and Hitzfeld (2021) aimed to
identify the dynamics between mobile phone usage norms and phubbing behavior. A
critical aspect of their study was trying to explain the boundary conditions of
the relationship between normative or social norms regarding phone use (Mobile
Phone Norms or MPN) and phubbing, particularly investigating the moderating
effects of FoMO and online vigilance (OV).
The main conclusion of the study, derived from
moderation-mediation regression models, is that mobile phone usage norms (MPN)
have a negative association with phubbing behavior. Additionally, they
concluded that individuals with strong mobile phone usage norms are less likely
to engage in phubbing, which highlights the influential role of social norms in
phubbing practices. In the same study, these authors found a significant
positive correlation between factors such as FoMO and the concept of being permanently
online and connected (POPC) with phubbing.
Regarding the methodological approach to phubbing
research, descriptive studies or explanatory ones based on linear regression
have predominated to date. However, these approaches have not fully captured
the complexity of the phenomenon. Nowadays, there is a notable gap in research
specifically addressing the essential conditions leading to phubbing, as well
as the factors that could prevent this behavior. Addressing these specific
aspects will provide a deeper understanding of phubbing compared to more general
approaches.
In this regard, the Necessary Condition Analysis (NCA)
is a widely used method in organizational sciences and it is crucial for
identifying essential conditions in datasets. This approach, initiated by Dul
(2016), focuses on determining the necessary factors for specific outcomes,
contrasting with traditional logic based on sufficiency. Unlike regression
analysis or qualitative comparative analysis of fuzzy sets (QCA), which
emphasize sufficiency, NCA focuses on the necessity of conditions for the occurrence
of an outcome. While QCA, as described by Bingham et al. (2019), is a
case-oriented method which identifies causal relationships by using qualitative
data, NCA uses variable scores and linear algebra to formulate statements of
necessity. Thiem (2017) emphasizes the importance of best practices in QCA, and
Dul's comparison between NCA and QCA reveals NCA's ability to identify more
necessary conditions.
Based on the knowledge gathered from previous
research, the aim of this study is to determine which correlates of phubbing
can be identified as necessary conditions for the development of phubbing
behavior. This research seeks to deepen the understanding of the essential
factors contributing to phubbing, using Necessary Condition Analysis to discern
these critical elements.
2. Methodology
This study reexamined the data from Schneider and
Hitzfeld (2021), whose methodological approach included an online survey. The
target population of the study conducted by these authors consisted of young
adults active on social media, specifically involving university students. To
this end, they used a convenience sample of 278 participants, predominantly
women (74%). The participants had an average age of 26.78 years (SD = 10), and
more than half had higher education qualifications (51%) or university degrees
(28%). The sample was recruited through social media platforms such as
Facebook, in line with the study's requirements.
Schneider and Hitzfeld (2021) collected data through a
54-item ad-hoc online questionnaire. To avoid missing data, they requested that
all questions be answered. The data collection process began by asking
participants to recall their last meal with friends and estimate both the
frequency and duration of phubbing during that interaction. This allowed for
the calculation of two distinct indices: the Self-Phubbing Index (PIS) and the
Phubbing Toward Others Index (PIO), which measure phubbing directed at oneself
and toward others, respectively. The frequency of phubbing was rated on a scale
from 1 (never) to 7 (very frequent), while the duration was rated from 1
(extremely short) to 7 (extremely long). A multiplicative approach was used to
calculate these indices, combining frequency and duration to ensure that a high
value in one variable would not compensate for a low value in another, in
contrast to the additive approach used by Chotpitayasunondh and Douglas (2016).
In addition to phubbing, questions about other
variables were included: Mobile Phone Usage Norms (MPN), Fear of Missing Out
(FoMO), and the tendency to be Permanently Online and Connected (POPC).
·
Mobile Phone Usage Norms (MPN). This dimension gathers
the participants' perception of the appropriateness of phone use in private
conversation contexts through seven items. The scale used was Hall et al‘s
(2014).
·
Fear of Missing Out (FoMO). Evaluated with the FoMO
Scale by Przybylski et al. (2013), this instrument measures the extent to which
participants experience the fear of being excluded from rewarding social
experiences. Participants responded to statements such as "I worry my friends have more rewarding experiences
than I do" on an appreciation scale ranging from 1 (not applicable at all)
to 7 (fully applicable).
·
Permanently Online and Connected (POPC). To measure
this dimension, the German Online Vigilance Scale (OVS) by Reinecke et al.
(2018) was used, consisting of 12 items divided into three subscales: Salience
(e.g., "I often find myself thinking about online content"),
Monitoring (e.g., "I constantly keep track of what is happening
online"), and Reactivity (e.g., "When I receive an online message, I
immediately pay all my attention to it").
For the present study, a comprehensive review of the
data from Schneider and Hitzfeld was conducted, accessible through the Open
Science Framework repository (https://osf.io/dgm7). Subsequently, preprocessing
was implemented to focus exclusively on the MPN, FoMO, and Permanently Online
and Connected (POPC) scores, as these were the dimensions identified by the
authors as being involved in their explanatory models.
An analytical approach was then developed by using
Necessary but Not Sufficient Condition Analysis (NCA) by Dul (2016), which
complemented the regression analysis. Within NCA, and to enhance its
reliability, a statistical significance test was developed to assess whether
the observed effect was a genuine necessary condition or a spurious result (Dul
et al., 2020). The analysis was carried out in three stages:
·
Formulation of hypotheses based on specific types of
necessary variables. It should be noted that NCA requires formalizing its own
specific hypotheses. These should not be confused with the research hypotheses
or the statistical testing hypotheses. To avoid any confusion, these hypotheses
were written in the results section and not beforehand.
·
Evaluation of the effect size using CE-FDH
(Conditional Effect Fishbone Diagram) for dichotomous and discrete conditions,
and CR-FDH (Contribution Ratio Fishbone Diagram) for discrete conditions with
many values and continuous conditions. Statistical significance was calculated
using bootstrapping (10,000 cases).
·
Dul's criteria were used to interpret effect sizes,
categorizing them as insignificant, medium, large, or very large. The analysis
was carried out with a statistical significance level set at 0.01 or lower (99%
confidence level).
For data analysis, the statistical software R from the
Comprehensive R Archive Network (CRAN) project (R Core Team, 2023) was used.
The NCA package developed by Dul (2023) was employed for the analysis.
3. Analysis and
results
To establish the hypotheses for the Necessary
Condition Analysis (NCA), it is essential to have prior causal hypotheses,
whether theoretical or evidence-based. In this context, the regression model by
Schneider and Hitzfeld (2021) serves as a reference point, particularly because
their data and measurements provide empirical evidence. They concluded that a
higher adherence to Mobile Phone Usage Norms (MPN) is associated with less
phubbing, while the FoMO and being permanently online and connected (POPC) were
positively related to phubbing. Additionally, they observed a significant
negative link between age and phubbing. A classical correlation matrix, which
includes descriptive statistics for the Self-Phubbing Index (PIS) and the
Phubbing Toward Others Index (PIO), is presented in Table 1.
Table 1
Correlation
and descriptive statistics
|
Variable / statistic |
Average |
SD |
Range |
MPN |
FOMO |
POPC |
PIS |
PIO |
SD02_01 (age) |
|
MPN |
5.49 |
0.81 |
3.22-7.00 |
1.00 |
|
|
|
|
|
|
FOMO |
3.27 |
0.98 |
1.10-6.00 |
-0.14** |
1.00 |
|
|
|
|
|
POPC |
3.46 |
1.05 |
1.00-6.50 |
-0.13** |
0.54*** |
1.00 |
|
|
|
|
PIS |
4.27 |
4.69 |
0.00-20.00 |
-0.19** |
0.31*** |
0.28*** |
1.00 |
|
|
|
PIO |
5.79 |
8.29 |
0.00-49.00 |
-0.04 |
0.08 |
0.00 |
0.36*** |
1.00 |
|
|
SD02_01 (age) |
26.78 |
10.00 |
16.00- 66.0 |
0.19** |
-0.41*** |
-0.26*** |
-0.26*** |
-0.01 |
1.00 |
Note: Significancy: *(p<.1);
**(p<.05); ***(p<.001)
Table 1 shows the correlation between each pair of
variables in each cell, with asterisks indicating the strength of the
statistical evidence supporting each correlation. As seen in Table 1, MPN
presents a negative, moderately significant correlation with PIS, suggesting
that a higher adherence to mobile phone usage norms is associated with a
reduction in self-phubbing (PIS). However, its correlation with PIO is weak and
not statistically significant, indicating a less relevant relationship. On the
other hand, FoMO shows a positive and moderately significant correlation with
PIS, indicating that higher levels of FoMO correspond to an increase in
self-phubbing. The correlation with PIO is positive but weak and
non-significant, pointing to a less defined relationship with the perception of
phubbing in others. POPC also shows a positive, moderate, and statistically
significant correlation with PIS, aligning higher POPC scores with greater
self-phubbing. However, its correlation with PIO is again imeaningless and
non-significant.
Overall, the lack of significant correlations with PIO
for most of the variables indicates that the perception of phubbing in others
may be influenced by different factors not represented in this dataset. It is
worth noting that no significant differences were found based on sex, so sex
was not included in the NCA (Necessary Condition Analysis) study.
The correlation matrix also shows patterns that are
worth noting, particularly in how FoMO and POPC relate to PIS. The results
indicate that both variables play a significant role in the stress related to
phubbing. On the other hand, the lack of statistically significant
relationships stands out, especially regarding the variable of phubbing
perception in others (PIO).
Based on all these results, the specific hypotheses
for the NCA analysis are as follows:
·
NCA Hypothesis 1: It is hypothesized that the
adherence to mobile phone usage norms (MPN) is a necessary condition for
reducing the Self-Phubbing Index (PIS).
·
NCA Hypothesis 2: It is proposed that the fear of
missing out (FoMO) is a necessary condition for increasing PIS.
·
NCA Hypothesis 3: It is postulated that being
permanently online and connected (POPC) is a necessary condition for higher
levels of PIS.
·
NCA Hypothesis 4: It is considered that belonging to a
younger demographic group is a necessary condition for higher levels of PIS.
These hypotheses aim solely to explore the essential
factors that contribute to the onset and the intensity of self-phubbing, as
indicated by the PIS index. Table 2 presents the results of the Necessary
Condition Analysis (NCA), including the accuracy levels and effect sizes for
each variable as a predictor of the Self-Phubbing Index (PIS).
Table 2
Effect sizes of the necessary conditions and significance tests for the
independent variables as predictor of the Phubing Index Self
|
Variable |
CR-FDH ES |
A |
p value |
CI |
OI |
CE-FDH ES |
A |
p value |
CI |
OI |
|
MPN |
0.10 |
98.9% |
.016 |
57.51 |
49.32 |
0.11 |
100% |
.089 |
58.82 |
30.00 |
|
FOMO |
0.13 |
99.3% |
.039 |
60.46 |
30.27 |
0.18 |
100% |
.007 |
59.18 |
10.00 |
|
POPC |
0.24 |
98.6% |
.000 |
48.78 |
4.63 |
0.27 |
100% |
.000 |
48.48 |
0.00 |
|
SD02_01 (age) |
0.504 |
97.8% |
.000 |
4.57 |
0.00 |
0.60 |
100% |
.000 |
18.00 |
0.000 |
Note:
ES: Effect Size, A= Accuracy, CI= Condition Inefficiency, OI= Outcome
Inefficiency, CE-FDH= Ceiling Envelopment-Free Disposal Hull, CR-FDH= Ceiling
Regresssion-Free Disposal Hull, p value were estimates with 10000 permutations
and are treated as significant if p<.05. Accuracy refers to the percentate
of values that are below the ceiling line.
NCA provides revealing interpretations for the
variables in the study. Both the CR-FDH and CE-FDH approaches yield similar
results, although a slightly lower precision is observed in all cases when
using CR-FDH, which is more suitable for continuous variables or those with a
large number of values. Notably, both MPN and FoMO show significant effects as
necessary conditions, with an effect size of 0.11 for MPN and 0.18 for FoMO,
both achieving a C-Precision of 100%. On the other hand, the high inefficiency
of the condition for MPN (CI: 58.82) and the lower inefficiency of the outcome
(OI: 30.00 for MPN and 10.00 for FoMO) suggest a significant potential for
improvement in these areas.
In contrast, age (SD02_01) shows a relevant effect as
a necessary condition with an effect size (ES) of 0.60 and a C-Precision of
100%. Therefore, age can be considered a key factor, characterized by low
condition inefficiency (CI: 18) and no outcome inefficiency, emphasizing its
constant and significant role. Meanwhile, POPC also shows solid results under
the CE-FDH approach (ES: 0.27, A: 100%), indicating its significant influence
in this context.
Finally, Table 3 shows the graphs and bottlenecks for
the predictors that demonstrated significant effects. The table describes the
thresholds required for SD02_01 (age) and POPC in relation to the Self-Phubbing
Index (PIS).
Table 3
Bottlenecks for SD02_01 (age) and POPC
|
|
NCA plot
SD02_01 x PIS |
NCA plot POPC x PIS |
|
|
|
|
|
|
|
|
|
PIS |
SD02_01 (age) |
POPC |
|
0 |
66.000 |
NN |
|
10 |
59.000 |
1.083 |
|
20 |
53.000 |
1.667 |
|
30 |
50.000 |
2.083 |
|
40 |
35.000 |
2.083 |
|
50 |
27.000 |
2.083 |
|
60 |
27.000 |
2.083 |
|
70 |
27.000 |
3.000 |
|
80 |
27.000 |
3.417 |
|
90 |
25.000 |
3.833 |
|
100 |
25.000 |
3.833 |
Note: Bottleneck CE-FDH (cut-off point = 0); Conditioned variable=PIS
(percentage.range); Condition variables (actual score)= SD02_01, POPC
Regarding the Self-Phubbing Index (PIS), the results
show an inverse relationship between PIS and the age required to avoid being a
bottleneck. For low PIS scores (0-10), a significantly higher age is needed,
specifically 66 and 59 years, to avoid being a limiting factor. As PIS
increases, this age requirement gradually decreases, dropping to 25 years at
the highest levels of PIS. This highlights a greater tendency for self-phubbing
among younger individuals.
In contrast, the findings regarding POPC indicate that
at low levels of PIS, the tendency to be permanently online and connected is
not a significant factor, as shown by the 'NN' (not necessary) value for a PIS
of 0. However, this changes when PIS reaches 10, at which point being
constantly online reappears as a critical element. It is observed that the
required POPC score increases progressively with PIS, starting at 1.083 and
rising to 3.833 at the highest levels of PIS (90 and 100). This pattern implies
that, for higher degrees of self-phubbing, the condition of being permanently
online and connected becomes increasingly essential.
4. Discussion
The main objective of this study was to assess which
correlates of phubbing, identified in Schneider and Hitzfeld‘s (2021) research,
constitute necessary conditions for the development of phubbing behavior.
Before analyzing this aspect in detail, the article initially proposed an
expansion and refinement of the concept of phubbing. Specifically, it is
suggested that the concept of phubbing should not only apply to a lack of
attention to others in in-person meetings where the same physical space is
shared, but should also extend to virtual spaces. In this sense, phubbing would
also refer to behavior that involves a lack of attention to social interaction,
whether mediated by technological elements or not and regardless of whether the
same physical space is shared (e.g., in video calls or group chats). In other
words, the distinguishing factor to identify phubbing would be the lack of
attention to the social interaction of direct participants in favor of
"consuming network stimuli." This would likely require a nuanced
interpretation of the results of this and other studies.
Regarding the analyzed variables, age has shown to be
a necessary, indispensable condition (although not sufficient) for the
occurrence of phubbing. This is consistent with the studies reviewed, which
indicate that age plays a significant role in phubbing behavior, particularly
among adolescents (Hong et al., 2019; Xie & Xie, 2020) and during puberty
(Michaud et al., 2006).
However, this issue needs further study, as some
studies have found that phubbing seems to increase among teenagers aged 14 to
16 in comparison with their younger peers aged 12 to 13 (Cebollero-Salinas et
al., 2022).
Leist (2019) emphasizes the importance of viewing age
not just as a sociodemographic variable, but as a dynamic factor that interacts
with other variables. This perspective aligns with traditional proposals
(Wohlwill, 1969; Freund & Isaacowitz, 2013), where age is managed as a
dimension of behavioral change rather than as a causal variable. However,
authors such as Ratnasari & Oktaviani (2020) and Winkelmann & Geber
(2022) assert that age is a causal factor in phubbing. In this interaction, both
the developmental growth of self-esteem and emotional independence are
prominently involved, so older adolescents would be more vulnerable to being
affected by online social recognition (Valkenburg et al., 2017; Błachnio
et al., 2019).
From a sociocultural perspective, this study also
acknowledges phubbing is not only a
social phenomenon but is deeply embedded in the reality of a Western culture.
Social norms, connectivity expectations, and mass access to mobile devices in
these cultural contexts shape the conditions under which phubbing emerges and
becomes normalized. It is important to consider how this behavior may vary in
cultures with different levels of access to technology or in those that
prioritize more traditional social dynamics over constant connectivity.
Therefore, phubbing must be understood within a cultural framework that allows
us to interpret how technology and cultural values interact to shape this
phenomenon.
In this discussion, it is important to note that most
of the research on phubbing, including the studies we consulted, is
cross-sectional rather than longitudinal. This is partly because phubbing is a
relatively new concept. Therefore, the differences observed based on age could
reflect generational gaps rather than individual evolution over a lifetime.
Schneider's (2019) study found a significant positive
relationship between the tendency to be permanently online and connected (POPC)
and phubbing behavior. The findings of our study extend this conclusion by
suggesting that POPC is not only positively related to phubbing but also a
necessary condition for its occurrence. However, it is important to consider
that the connection between POPC and phubbing, as highlighted in the
literature, is mediated by factors such as FoMO (Grieve et al., 2021) and emotional
expression (Cebollero-Salina et al., 2022; Guazzini et al., 2021), among
others. Although Schneider and Hitzfeld (2021) did not find moderating effects
between these variables, they pointed out that "a possible reason could be
that participants reported only moderate levels of POPC and FoMO. Therefore, a
sample with higher levels and greater variability in POPC and FoMO might have
yielded different results" (p. 1083).
Regarding POPC, the bottleneck analysis indicates that
a slight decrease of one point on the POPC scale could significantly reduce a
person's level of phubbing (e.g., from selecting "very often" to
"often"). This result suggests that phubbing is more closely related
to technology addiction issues than to mere social disinterest.
These results are in line with the findings of Arenz
and Schnauber-Stockmann (2023), who identified a set of correlates including
norms and experiences related to technology, technical equipment, the use of
phones (smartphones) and the Internet , and problematic usage patterns.
According to these authors, the strongest predictors of phubbing behavior were
problematic usage patterns such as smartphone addiction, Internet addiction,
and addiction to social networking services.
Regarding sex and gender, the reviewed articles
suggest that men, women, and transgender individuals are equally at risk of the
negative influences of social media (Rajesh & Draper, 2022). This
conclusion, consistent with the results of the present study, suggests that
dysfunctional behaviors linked to technology, including phubbing, do not seem
to develop differentially between genders.
Finally, it is important to note the absence of
certain findings in this study. Specifically, aspects related to the assessment
of the presence of phubbing in others (PIO) did not emerge as significant. This
absence requires further investigation to explore why individuals may focus
more on their own phubbing behavior than on that of others. Additionally, the
lack of sufficient evidence to consider other variables as indispensable raises
questions about the suitability of the current methodology and the validity of
the construct of the measures used. Therefore, further exploration of this
phenomenon is needed, employing diverse methodological approaches and working
toward an operational consensus on the concept of phubbing itself.
5. Conclusions
In conclusion, the results obtained, when contrasted
with the existing research on the subject, lead to certain conclusions of
interest, among which the following stand out:
1. Differences in
phubbing levels in relation to age highlight intergenerational differences.
However, given the relatively recent emergence of the phubbing phenomenon, it
is difficult to state that phubbing evolves with age or how it will manifest in
current generations as they age.
2. The tendency to be
constantly connected and online (POPC) is a fundamental factor in self-phubbing
(PIS), especially at high levels. It also emerges as an increasingly essential
condition as PIS increases.
3. However, it is
noteworthy that the FoMO and mobile phone usage norms (MPN) do not manifest as
indispensable conditions for the occurrence of phubbing, although it is clear
that these factors influence phubbing.
On the other hand, the study has shown that Necessary
Condition Analysis (NCA) is a complement to traditional correlational and
regression analyses, allowing the exploration of the indispensable conditions
for the occurrence of a psychosocial phenomenon such as phubbing.
It should also be noted that this study has certain
limitations that condition its conclusions. Among others, it is important to
consider that the sample is predominantly female. Although throughout the study
the results show that there are no differences between sexes, and there are
evidences in the same direction in the consulted literature (Rajesh &
Draper, 2022), it should not be overlooked that future studies should examine
the potential existence of differential patterns based on sex and gender.
Other areas to investigate in future studies include
exploring the causal relationships between phubbing and its correlates as
individuals age, through longitudinal studies. Similarly, the impact of social
norms in different cultures over time should be assessed. Another area is the
study of neurobiological mechanisms and how changes across age may influence
behaviors such as phubbing. In general, future studies should be longitudinal
in nature to analyze the evolution of phubbing as well as the impact of its
direct correlates.
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