# Linking Phubbing Behavior to Self-Reported Attentional Failures and Media Multitasking
## 4 Discussion  
The primary aim of the current study was to use self-report data to explore the relationships between phubbing behavior and everyday attentional abilities. To accomplish this, we first examined the associations between phubbing behavior, as measured by the GSP, and self-reported everyday attentional failures, as measured with the AC-S, AC-D, MW-D, MW-S, MAAS-LO, and ARCES. Having a robust set of validated measures allowed \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Predictor** & **Estimate** & **SE** & \(t\) & \(p\) & **Stand. Estimate** \\ \hline Intercept & 4.07 & 13.24 & 0.31 & 0.759 & - \\ ARCES & 0.67 & 0.14 & 4.67 & \textless{}0.001 & 0.28 \\ AC-S & 3.89 & 1.27 & 3.07 & 0.002 & 0.16 \\ MW-D & 2.63 & 0.94 & 2.80 & 0.005 & 0.15 \\ MW-S & 2.70 & 1.12 & 2.41 & 0.016 & 0.16 \\ SSRQ & \(-0.13\) & 0.07 & \(-1.86\) & 0.064 & \(-0.11\) \\ AC-D & \(-1.93\) & 1.08 & \(-1.79\) & 0.074 & \(-0.08\) \\ Age & \(-0.14\) & 0.08 & \(-1.79\) & 0.074 & \(-0.06\) \\ BIS-11 & 0.14 & 0.11 & 1.33 & 0.185 & 0.08 \\ SCS & 0.10 & 0.10 & 1.03 & 0.304 & 0.05 \\ MAAS-LO & 0.11 & 0.11 & 1.03 & 0.306 & 0.06 \\ \hline \hline \end{tabular} Note. \(R=0.79\); \(R^{2}=0.63\); Adjusted \(R^{2}=0.62\); \(F(10,362)=61.30\), \(p<0.001\). Estimates represent unstandardized regression coefficients; ARCES = Attention-Related Cognitive Errors Scale; AC-S = Attentional Control: Shifting: MW-D = Mind Wandering: Deliberate; MW-S = Mind Wandering: Spontaneous; SSRQ = Short Self-Regulation Questionnaire; AC-D = Attentional Control: Distraction; BIS-11 = Barrat Impulsiveness Scale; SCS = Social Connectedness Scale; MAAS-LO = Mindful Attention Awareness Scale—Lapses Only.  
\end{table} 
Table 4: Summary of expanded standard multiple regression analysis for attentional, psychosocial, personality, and demographic predictors of phubbing behavior (\(N=373\)). us to explore and pin down which of the most common everyday attentional abilities were implicated in phubbing behavior. Attention-related cognitive errors, spontaneous and deliberate mind wandering, difficulties with attentional shifting and distractibility, and lapses in attention were all positively and significantly correlated with phubbing behavior. In other words, individuals who reported more engagement in phubbing behavior also reported a greater frequency of difficulties across a variety of everyday attentional abilities. Intuitively, it makes sense that these various domains of attention were associated with phubbing behavior, as this technoference behavior implies that individuals are shifting or dividing their attention across various competing sources of information. Yet, our study provided some of the first empirical evidence to support this purported connection. Using a cognitive framework to explore the attentional correlates of phubbing behavior complements prior research that has primarily focused on social and personality constructs, and ultimately creates a more wholistic profile of the type of person who is most likely to engage in this behavior. To better understand the relationship between phubbing and individual differences more broadly, we also explored if other select dispositional characteristics, including demographics and psychosocial and personality variables, were associated with phubbing behavior. Aligning with past research [8; 9; 14], we found a significant negative association between phubbing and age, suggesting that younger adults tend to phub more than older adults. However, we did not find gender differences in phubbing behavior. This conflicts with past research [6; 8; 10; 13], which found that women report engagement in phubbing behavior more than men. Most of the studies that found a gender effect for phubbing behavior had recruited more women than men [6; 8], though one study recruited more women and found no gender effect [14]. Approximately 38% of the 373 participants in our study identified as women.