MOBILE PHONE HABITS DURING FACE TO FACE FIRST ENCOUNTERS: AN 

INVESTIGATION OF SELF-DISCLOSURE AND NONVERBAL MIMICRY  

By 

Travis Kadylak 

A DISSERTATION  

Submitted to 

Michigan State University 

in partial fulfillment of the requirements 

for the degree of 

Information and Media - Doctor of Philosophy 

2019 

 

 

ABSTRACT 

Mobile Phone Habits During Face to Face First Encounters: An Investigation of Self-

Disclosure and Nonverbal Mimicry  

 

By 

Travis Kadylak 

 

Mobile phones are widely adopted around the world. In contemporary society, 

mobile phone use is acutely integrated into core social and psychological aspects of 

everyday life, such as verbal and nonverbal interpersonal communication. Though 

mobile phones offer users many affordances for social connection that can facilitate 

interpersonal communication and affiliation formation, previous research on phubbing 

[phone-snubbing] suggests that mobile phone use during face to face (FtF) interactions 

can breach interpersonal expectations, be perceived as ostracizing, lead to unfavorable 

interpersonal evaluations, cause conflict within relationships, and impede affiliation 

formation.  

 

 I aimed to advance expectancy violation theory (EVT) by using the axioms of the 

theory to make predictions involving nonverbal behaviors (e.g., phubbing expectancy 

violations and mobile phone mimicry), interpersonal judgements, and self-disclosure. 

Much like self-disclosure, humans evolved to automatically engage in nonverbal 

mimicry, or synchronous behavioral matching (i.e., automatically or unintentionally 

touching one’s face after their interaction partner engaged in the same behavior), as a 

means of building affiliation and promoting positive interpersonal judgements. Following 

this premise, I examined whether mobile phone checking mimicry, or behavioral 

matching between FtF interaction partners involving how they use their mobile phones, 

may promote positive interpersonal judgements (e.g., increased perceived liking, trust, 

 

 

and empathy) that subsequently may be associated with higher levels of self-disclosure.        

 

Phubbing effects research currently maintains that mobile phone use, during FtF 

interactions, tends to lead to adverse interpersonal outcomes among mobile phone 

users of all ages. However, I used a 2x1 between subject laboratory experiment with 

college students (N = 77) to assess whether phubbing, expectancy violations, and 

mobile phone checking mimicry influenced interpersonal judgements and self-disclosure 

within the context of a face-to-face get-to-know-you activity. The results suggest that 

mobile phone checking, perceptions of negative phubbing expectancy violations, and 

mobile phone checking mimicry, may have limited effects on self-disclosure and 

interpersonal judgements in a get-to-know-you activity. Specifically, phubbing was 

inversely associated with self-disclosure; however, mobile phone checking mimicry was 

positively associated with self-disclosure. Though the external validity of the findings 

may be limited due to the student sample, this study advances expectancy violation 

theory by demonstrating the potential prosocial and antisocial effects of mobile phone 

use during FtF interactions. Additional study limitations, theoretical and practical 

implications, and directions for future research are discussed.  

  

 

 

ACKNOWLEDGEMENTS 

 

This dissertation would not have been possible without support from the Information & 

Media PhD program, my committee members Dr(s) Anastasia Kononova, Robby Ratan, 

and Rick Wash, and most importantly my mentor/Dissertation Chairperson Dr. Shelia 

Cotten whose guidance and encouragement has been fundamental to my academic, 

professional, and personal development. In addition, the support from my close friends 

and family was crucial for my successful completion of this project and the Information & 

Media PhD program. Finally, I had the privilege of working along side many talented 

graduate students that helped me improve my research skills and enrich my time at 

MSU. I look forward to our continued collaboration and friendship.  

 

iv 

 

TABLE OF CONTENT 

LIST OF TABLES ................................................................................................................... vii 
 
LIST OF FIGURES ................................................................................................................ viii 
 
CHAPTER 1: INTRODUCTION .............................................................................................. 1 
 
CHAPTER 2: BACKGROUND LITERATURE AND THEORY ......................................... 9 
Chapter Overview ........................................................................................................ 9 
Mobile Communication Habits………...…………………………………………………….9 
Phubbing: A Brief Conceptual Explication…….……………………………………………....16 
Expectancy Violation Theory and Phubbing….…….…………...……………………...........21 
Research on Phubbing Effects……….………………………………………………………….28 
Phubbing and Perceived Communication Quality……..…………………………………….32 
Phubbing and Self-Disclosure…………….………………………………………………..34 
Mobile Phone Mimicry during FtF Interactions….…….……………………………………...39  
Significance of The Present Study……….……………………………………………………..42 
Hypotheses.…….…..………….…………………………………………………………….44 
 
CHAPTER 3: METHODS .............................................................................................. 46 
Sample ....................................................................................................................... 46 
Study Procedure ........................................................................................................ 48 
Measures……………………………………………………………………………………..53 
Analytical Procedures ………………………………………………………………………59 
 
CHAPTER 4: RESULTS ............................................................................................... 60 
Descriptive Results .................................................................................................... 60 
Regression Results for Perceived Phubbing EV (H1a supported) ............................. 61 
Regression Results for Perceived Liking (H1b partially supported) ........................... 62 
Regression Results for Perceived Trust and Empathy ............................................... 64 
Regression Results for Self Disclosure (H1c partially supported) .............................. 67 
Indirect Effect Between Mobile Checking and Liking (H2 partially supported) ........... 70 
   Mobile Phone Checking Mimicry Results……………………………………………………...71 
 
CHAPTER 5: DISCUSSION .......................................................................................... 74 
Chapter 5 Overview ................................................................................................... 74 
Summary of Results………………………………………………………………………...74                                                                                          
Discussion of Results……………………………………………………………………… 75 
Contributions to EVT Theory……………………………………………………………….78 
Practical Implications………………………………………………………………………..80 
Study Limitations…………………………………………………………………………….82 

 
CHAPTER 6: CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS .................. 85 

 

v 

 
APPENDICES ............................................................................................................... 90 
Appendix A: Confederate Instructions ........................................................................ 91 
Appendix B: Measures ............................................................................................... 93 
 
REFERENCES .......................................................................................................... 95 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

vi 

 

LIST OF TABLES 

Table 1 Descriptive Statistics for Dependent, Independent, 
and Control Variables……………………………………….………………………………...60  
 
Table 2 Regression Results for Perceived Phubbing EV as Dependent Variable……...62 
 
Table 3 Regression Results for Perceived Liking as Dependent Variable………………63 
 
Table 4 Regression Results for Perceived Trust and Empathy as Dependent 
Variables………………………………………………………………………………………..65 
 
Table 5 Regression Results for Self Disclosure Behavioral Measure as Dependent 
Variable………………………………………………………………………………………....67 
 
Table 6 Regression Results for the Self-Reported Self Disclosure Measures as 
Dependent 
Variable…………………………………………………………………………………………70 
 
 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

vii 

 

LIST OF FIGURES 

Figure 1 Phubbing Habits & Technoference………………………………………………..16 

Figure 2 Full Conceptual Model for Determinants of Interpersonal Evaluations………..43 

Figure 3 Full Conceptual Model for Determinants of Self-Disclosure…………………....44 

Figure 4 Python Randomization Procedure………………………………………………...47 

Figure 5 Statistical Power……..……………………………………………………………...48 

Figure 6 Sensitivity Analysis………………………………………………………………….48 

Figure 7 Experimental Design………………………………………………………………..51 

Figure 8 Between Condition Difference in Self-Disclosure………………………………..69 

Figure 9 Mediation Analysis…………………………………………………………………..72 

Figure 10 Phone Mimicry and Behavioral Measure of Self-Disclosure…………………....74 

Figure 11 Phone Mimicry and Self-Reported Measure of Self-Disclosure………………74 

 

viii 

CHAPTER 1: INTRODUCTION 

In today’s digital age, people are more connected to others and have more 

opportunities for affiliation than ever before in recorded history (Chayko, 2007, 2017); 

yet, ostracism is still a common social experience (Nezlek, Wesselmann, Wheeler & 

Williams, 2015; Wesselmann & Williams, 2017; Williams, 2007). The same technologies 

that provide affordances for social connection and affiliation, also afford avenues that 

may lead to the experience of social rejection and ostracism online (Williams & Jarvis, 

2006; Wolf, Levordashka, Ruff, Kraaijeveld, Lueckmann & Williams, 2015) and offline 

(e.g., David & Roberts, 2017; Gergen, 2002; Hales, Dvir, Wesselmann, Kruger & 

Finkenauer, 2018; Turkle, 2017). As a social species, human beings inherently seek to 

fulfil their need for affiliation and belonging, which is achieved through nonverbal and 

verbal interpersonal communication (Argyle & Dean, 1965; Burgoon, 1978; Leary & 

Baumeister, 2017; Leary & Cox, 2008). People also actively strive to avoid affiliative 

threats, such as experiencing ostracism (i.e., feeling ignored or socially rejected from a 

group) (Balcetis, 2016; Williams, 2007, 2009). The experience of ostracism can be 

distressing and physically uncomfortable due to the neurological overlap between the 

brain circuitry that is responsible for physical pain and social pain (Eisenberger & 

Lieberman, 2004). As William James (1890) famously wrote,  

If no one turned around when we entered, answered when we spoke, or minded 

what we did, but if every person we met ‘cut us dead,’ and acted as if we were 

non-existing things, a kind of rage and impotent despair would ere long well up in 

us, from which the cruelest bodily tortures would be a relief; for these would 

make us feel that however bad might be our plight, we had not sunk to such a 

1 

 

 

 

 

 

 

 

 

depth as to be unworthy of attention at all (p. 293–294). 

Since James’ time, philosophers, psychologists, sociologists, and communication 

scholars have studied both the causes and effects of ostracism. For at least the past 

three decades, researchers have studied how using information and communication 

technologies (ICTs) can facilitate affiliation formation and bonding as well as have 

adverse effects on relationships and well-being (i.e., technoference). Mobile phones, for 

example, are the most widely adopted ICT around the world (Poushter, Bishop & Chwe, 

2018). They have become deeply embedded into the social and psychological 

configuration of contemporary daily life (Bayer, Campbell & Ling, 2016; Hampton, 2016; 

Ling, 2012).  

 

A substantial body of mobile communication research, or research on untethered 

mobile ICT use for mediated communication purposes (Campbell, 2013), demonstrates 

the affiliative and connecting affordances of mobile communication (e.g., Vanden 

Abeele, De Wolf & Ling, 2018; Bayer, Campbell & Ling, 2016; Campbell, 2013; Castells, 

Fernandez-Ardevol, Qiu & Sey, 2009; Katz, Katz & Aakhus, 2002; Ling, 2004, 2008). 

Mobile phone use can allow people to develop and maintain close relational bonds with 

others (e.g., Katz, 2011; Licoppe & Smoreda, 2005; Pettigrew, 2009; Campbell & Ling, 

2017), enhance a sense of connected presence across vast distances (Cui, 2016; 

Licoppe, 2004), promote computer mediated self-disclosure (Desjarlais & Joseph, 

2017), micro-coordinate daily activities (Ling & Yttr, 2002; Vanden Abeele, Schouten & 

Antheunis, 2017), and create shared experiences through augmented reality (Hjorth & 

Richardson, 2017).  

 

2 

The anytime and anyplace affordances for social connectivity, that are made 

possible by mobile phone use, have also led to an array of evolving, and at times 

conflicting, social norms (Gergen, 2002; Hall, Baym & Miltner, 2014; Kelly, Miller-Ott & 

Duran, 2017; Ling, 2005; Ling & McEwen, 2010; Srivastava, 2005). Most people in 

contemporary society have developed some degree of internalized connectedness 

(Burchell, 2015; Bayer, Campbell & Ling, 2016), whereby the internalized social norms 

of being accessible and responsive to one’s social network necessitate the development 

of mobile communication habits, or connection habits (Bayer, Campbell & Ling, 2016; 

LaRose, Connolly, Lee, Li, & Hales, 2014). Competing social norms for being attentive 

to one’s FtF interaction partner and to be response to one’s social network through 

mobile communication can come into conflict with each other when mobile 

communication habits are enacted and subsequently perceived as inappropriate by 

one’s FtF communication partner.  

 

Mobile communication habits allow one to efficiently manage social expectations 

for being accessible to remote others and manage one’s limited cognitive resources 

(Bayer, Campbell & Ling, 2016; LaRose et al. 2014; LaRose, 2015). Mobile 

communication, or connection, habits are mobile communication behaviors, such as text 

messaging or checking one’s mobile phone, that involve limited conscious awareness, 

attention, or deliberation (Bayer, Dal Cin, Campbell & Panek, 2016) – for instance, the 

behavior can occur with cognitive automaticity or automatic mental processes (Bargh, 

1994; Bargh & Ferguson, 2000).  

Mobile media and communication habits, such as automatic mobile phone-

checking behaviors, are widely pervasive throughout contemporary society (Bayer, 

 

3 

Campbell & Ling, 2016; Bayer, Dal Cin, Campbell & Panek, 2016; LaRose, 2015). For 

example, on average, adults age 18-24 check their mobile phone around 80 times per 

day (Deloitte, 2016) – which could likely be an underestimate given that self-reported 

frequencies of media habit behaviors are notoriously unreliable because of the potential 

for one to enact the behavior without conscious deliberation or without realizing one 

engaged in the behavior (Ellis, Davidson, Shaw & Geyer, in press; LaRose, 2015). In 

addition, about 85-89% of mobile phone users also engage with their mobile phone 

during recent FtF interactions (Rainie & Zickuhr, 2015; Richter, 2018).  

 

During FtF dyadic interactions, people are expected to appear at least 

moderately attentive and psychologically present (Burgoon, 2015). When excessive 

mobile phone use during FtF interactions is viewed as inappropriate, the behavior is a 

negative expectancy violation (Miller-Ott & Kelly, 2015). Though people can intentionally 

engage in phubbing as an avoidance behavior, mobile phone-checking, which is 

perhaps the most prominent behavior perceived as phubbing, involves a high degree of 

cognitive automaticity. People do not typically actively, and intentionally, try to 

negatively breach interpersonal expectations or make others feel ignored/socially 

rejected by engaging in excessive mobile phone use during FtF interactions. Instead, 

phubbing behaviors usually occur with limited cognitive deliberation (Bayer, Campbell & 

Ling, 2016; LaRose, 2015).  

 

Although phubbing can occur as an intention driven behavior (e.g., averting one’s 

attention to check their mobile phone can occur as a goal-driven avoidance strategy), 

mobile checking and mobile messaging (sending/receiving messages) behaviors 

typically occur with a high degree of cognitive automaticity (Bayer, Dal Cin, Campbell & 

 

4 

Panek, 2016; LaRose, 2010, 2015). As such, the potential effects of inappropriate 

mobile phone use behaviors (i.e., one’s communication partner feels ignored/rejected 

during a FtF interaction because of excessive mobile phone use) are thought to occur 

as unintended consequences of non-deliberative/unintentional (habitual) mobile media 

behaviors (LaRose, 2015).      

 

The widespread embeddedness of mobile communication habits can engender 

competing norms and expectations during face-to-face (FtF) interactions (Campbell, 

2008; Kelly, Miller-Ott & Duran, 2017; Ling & McEwen, 2010). People must balance 

their expectations to be available and responsive to remote others and also to attend to 

one’s physically co-present communication partners, without breaching implicit 

expectations for nonverbal behaviors that signal attention (e.g., gaze or body language). 

For example, excessive mobile phone use during FtF interactions, or phubbing [phone-

snubbing] (Bianchi & Phillips, 2005; Roberts & David, 2016), can breach expectations 

for undivided attention during FtF encounters, such as first dates (Miller-Ott & Kelly, 

2015).           

 

Mobile communication and phubbing effects research has consistently 

documented the potential ostracizing (Hales, Dvir, Wesselmann, Kruger & Finkenauer, 

2018) and adverse effects that phubbing expectancy violations (e.g., mobile phone 

usage behaviors that are perceived as breaching normative demands to appear 

attentive and adhere to expected etiquette during FtF interactions) can have on 

impression formation during first encounters (Vanden Abeele et al., 2016), romantic 

relationships (Roberts & David, 2016), and subjective well-being (e.g., Halpern & Katz, 

2017; Roberts & David, 2016). Fewer studies explicitly investigate how phubbing affects 

 

5 

the act of interpersonal communication or actual verbal and nonverbal communication 

processes.  

 

One key aspect of interpersonal communication that is paramount for developing 

close relational bonds is self-disclosure, defined as communicating information about 

oneself to other people (e.g., Cozby, 1973, Tardy & Smithson, 2018; Utz, 2015). Self-

disclosure works as a social adhesive that promotes bonding by reducing uncertainty 

and enhancing trust. Systematic literature reviews and empirical studies consistently 

demonstrate that self-disclosure helps individuals cultivate affiliation and intimacy within 

all types of interpersonal relationships (Collins & Miller, 1994; Laurenceau, Barrett & 

Pietromonaco, 1998; Wenzel, Sprecher & Harvey, 2018). In large part, self-disclosure 

processes have been overlooked within the phubbing literature. Given that self-

disclosure works as a social glue that acts as a building block for interpersonal 

relationships, it is important to understand whether the widely pervasive behavior of 

phubbing hinders self-disclosure processes. 

Significance of the Study    

 

I use arguments from the disclosure decision model (DDM) (Omarzu, 2000) and 

social penetration theory (Altman & Taylor, 1973) to make a priori predictions about how 

phubbing may influence self-disclosure processes. In addition, phubbing expectancy 

violations may be especially salient if phubbing transgressions (i.e., the enactment of 

phubbing behavior) occur during potentially face-threating situations, such as 

conversations that involve self-disclosure (Przybylski & Weinstein, 2013). Therefore, in 

this study I investigate phubbing effects during meaningful dyadic first encounters, 

where meaningful refers to conversations in which people disclose a subjectively 

 

6 

meaningful event that has occurred in their life during the past year. This type of 

conversation has been shown to be an appropriate get-to-know-you-activity during first 

encounters (Aron, Melinat, Aron, Vallone & Bator, 1997) and it has also been 

successfully applied to the mobile communication domain (e.g., Przybylski & Weinstein, 

2013).   

 

This study may help explain phubbing effects because phubbing expectancy 

violations have been shown to be ostracizing (David & Roberts, 2017; Hales, Dvir, 

Wesselmann, Kruger & Finkenauer, 2018), which ultimately could be detrimental to 

interpersonal communication processes. Furthermore, I synthesize social psychology 

literature on nonverbal behavioral mimicry, which demonstrates that nonverbal mimicry 

is an evolved and cognitively automatic behavior (Chartrand & Bargh, 1999) that also 

builds affiliation and increases favorable interpersonal evaluations, during FtF 

interactions (Lakin & Chartrand, 2003). I examine nonverbal mobile phone mimicry (i.e., 

imitating a FtF communication partner’s mobile phone-checking behaviors) through the 

lens of Bayer, Campbell and Ling’s (2016) socio-cognitive model of connection cues, 

connection norms, connection habits, as well as other mobile communication habit 

studies (e.g., Bayer, Dal Cin, Campbell & Panek, 2016; LaRose et al., 2014; LaRose, 

2015).  

 

The present study attempts to contribute novel insights into how phubbing, a 

pervasive and common mobile communication behavior (Chotpitayasunondh & 

Douglas, 2016, 2018), may influence verbal (e.g., self-disclosure) and nonverbal (e.g., 

mobile phone-checking mimicry) interpersonal communication processes that have 

historically facilitated the satisfaction of one’s need for affiliation. Phubbing can occur 

 

7 

intentionally (e.g., as an avoidance behavior) but also as a habit – involving little to no 

conscious awareness. Mobile phone-checking behaviors are one of the most common 

mobile communication behaviors and also one of the most cognitively automatic types 

of mobile communication (Bayer, Campbell and Ling, 2016; Bayer, Dal Cin, Campbell & 

Panek, 2016; Deloitte, 2016). As such, this study aims to advance interpersonal 

communication theory and potentially observe unintended consequences of mobile 

communication habits (i.e., mobile phone-checking behaviors) that breach interpersonal 

expectations. This study also attempts to contribute to the literature by investigating 

both verbal and nonverbal interpersonal behaviors during phubbing interactions, which 

may yield a unique understanding of how mobile communication habits (e.g., automatic 

mobile phone-checking) influence verbal and nonverbal interpersonal communication 

during FtF dyadic interactions.    

 

 

 

 

 

 

 

 

 

8 

CHAPTER 2: BACKGROUND LITERATURE AND THEORY 

Chapter Overview 

 

In this chapter, I synthesize previous research on mobile communication habits 

and interpersonal communication during FtF interactions. The chapter begins by 

discussing relevant background literature on mobile communication habits. I maintain 

that phubbing is a type of mobile communication habit that breaches interpersonal 

expectations for attention and behavioral etiquette. Consistent with previous mobile 

communication research (e.g., Kelly, Miller-Ott & Duran, 2017; Miller-Ott & Kelly, 2015, 

2017; Vanden Abeele, Antheunis & Schouten, 2016; Vanden Abeele & Postma-

Nilsenova, 2018), I extend expectancy violation theory (Burgoon & Hale, 1988) to 

develop predictions about novel phubbing effects on interpersonal communication 

processes – with a specific focus on self-disclosure and nonverbal behavioral mimicry.     

Mobile Communication Habits    

 

Habit, defined as a type of cognitive automaticity, is a fundamental aspect of 

daily life and it is made possible because the brain has evolved to use cognitive 

resources efficiently to translate sensory input into one’s perceived reality (Bargh, 1994; 

Verplanken & Orbell, 2003). The brain naturally and continuously processes sensory 

input to detect neural network patterns, which serve as the biological foundations that 

allow automatic mental processes to develop (Eagleman, 2015). Mobile communication 

habits are mobile communication behaviors (e.g., texting, checking Twitter, Facebook, 

or Instagram feeds, etc.) that involve limited attention or cognitive resources (Bayer, 

Campbell & Ling, 2016; LaRose, 2015). Though these mobile communication behaviors 

do not necessarily involve limited attention or resources for all who do the behaviors, 

 

9 

mobile communication behaviors likely take place with some degree of cognitive 

automaticity if the behavior is enacted routinely (LaRose, 2015).  

 

Mobile communication behaviors that one does without thinking or deliberation, 

without having to consciously remember that they enacted the behavior, or that one 

starts doing before they even realize they are engaging in the actual mobile 

communication behavior are considered mobile communication habits (LaRose, 2015; 

Verplanken & Orbell, 2003). In general, habits develop out of repeated behaviors in 

stable environments, which over time, allow for the development of mental 

representations that become triggered into action by environmental cues (i.e., cognitive 

automaticity) (Bargh, 1994). This notion of habit predates the 19th century in classic 

psychological studies, such as Bryan and Harter (1899). From neurological and socio-

cognitive perspectives, mobile communication habits are not thought to differ much from 

any other type of habit (LaRose, 2015).  

 

Mobile communication habits, much like all habits, develop because the human 

brain has finite (or limited) cognitive resources (LaRose, 2015). When people frequently 

engage in routine mobile communication behaviors in stable conditions, future 

enactment of the behavior can start to be triggered into action by various types of cues 

(e.g., environmental cues). This process is made possible by the creation and 

maintenance of synapses (i.e., the links between neurons) in the brain.  

 

However, media habits in general and mobile media communication habits 

specifically (e.g., phubbing and mobile phone-checking behaviors) are distinct from 

other types of habits in at least one key way – the cues that trigger the automatic mental 

processes that guide media habits are not confined by time nor space, as opposed to 

 

10 

non-media habits, such as the automatic mental processes involved with cooking or 

bathing habits (Bayer, Campbell & Ling, 2016; LaRose, 2010, 2015; LaRose et al. 

2014). Traditional habits are confined to the stable environments which trigger the 

behavior into action, while interactive media devices, such as mobile phones, can be 

considered stable environments that trigger the automated cognition that can drive 

media use (LaRose, 2015).  

 

This conceptualization of mobile communication habits is complimentary to 

LaRose’s (2015) explication of interactive media habits. Mobile communication habits 

are a type of interactive media habit (i.e. checking the weather by using an application 

on one’s mobile phone). Media habits typically develop out of routines – however, not all 

routines are necessarily habits. For example, one might routinely make a phone call 

home to a friend or relative (e.g., perhaps a grandparent) every couple of weeks. But, 

the routine may not involve a high degree of cognitive automaticity – in other words, the 

behavior involves conscious deliberation.  

 

While on the other hand, routine mobile media behaviors, such as checking one’s 

Instagram feed many times throughout the day, might involve far fewer cognitive 

resources (more cognitive automaticity). In addition, mobile communication habits may 

develop stronger synapses that are deeply ingrained in the brain’s hardwire relative to 

non-social habits, because human beings are an inherently social species and, as such, 

experience visceral pleasure from the release of oxytocin and dopamine that results 

from the satisfaction of affiliative needs (Lakin & Chartrand, 2003; Leary & Baumeister, 

2017).  

 

11 

 

Consistent with this conceptualization of the neurological foundation of cognitive 

automaticity and social habits, Bayer, Campbell and Ling (2016) proposed a socio-

cognitive model of connection cues, connection norms, and connection habits, which 

explicates the social and cognitive mechanisms that explain when and where people 

enact mobile communication or, connection, habits. Specifically, they focused on mobile 

phone-checking behaviors because of the prevalence of the behavior in contemporary 

society and the limited attention required to perform the behavior. The approach is 

premised on the concept of internalized connectedness (e.g., Burchell, 2015; Bayer, 

Campbell and Ling, 2016; Ling & McEwan, 2010), which maintains that given the 

widespread adoption (Poushter, Bishop & Chwe, 2018) and pervasive social 

embeddedness of mobile phones (Ling, 2012), individuals in contemporary society 

commonly internalize the social norm of being accessible to others.  

 

People can adhere to the normative expectation of being accessible to others by 

being responsive to mobile messages and by checking their mobile phone throughout 

the day to avoid breaching interpersonal expectations to be responsive (Burchell, 2015; 

Gray, 2018; Ling, 2012). Bayer, Campbell and Ling (2016) refer to the social norm and 

responsibility to be accessible and responsive to others by using mobile communication 

as a type of connection norm, which through the process of becoming internalized leads 

one to develop cognitive schema, or mental shortcuts, that can guide mobile phone-

checking behaviors. As connection norms become more internalized, the associated 

behaviors (e.g., cue – response link for connection habits) likely become more 

hardwired into one’s brain (LaRose et al., 2014; LaRose, 2015).   

Through the cognitive processes of priming and spreading activation, schema 

12 

 

 

work to make connection norms salient or accessible to the individual (LaRose, 2015). 

As connection norms become more cognitively accessible, they are more likely to 

influence behavior – specifically, by guiding one’s attention to connection cues which 

trigger mobile communication or, connection, habits (Bayer, Campbell and Ling, 2016). 

According to Bayer, Campbell and Ling (2016), connection norms can affect connection 

habits (e.g., automatic mobile phone-checking behaviors) via two distinct cognitive 

routes – direct activation and by spreading activation through selective attention 

processes.  

 

Along the direct activation route, the key prime that makes the connection norm 

salient is a temporal cue (i.e., the longer that one goes without checking their mobile 

phone) the more likely they will be to breach their normative responsibility to be 

accessible and responsive. Therefore, as the time lapse between one’s last checking 

behavior increases, so does the likelihood that one will attend to a connection cue and 

automatically check their mobile phone. Along the indirect socio-cognitive pathway that 

can facilitate the enactment of connection habits, or automatic checking behaviors, 

Bayer, Campbell and Ling (2016) identify three main types of cues that can trigger 

automatic checking behaviors.  

 

Connection cues, as defined by Bayer, Campbell and Ling (2016), consist of 

technical, mental, and spatial cues. Technical cues include notification features of 

mobile phones, such as alert sounds, vibrations, and various types of message and 

alert notifications that can appear on one’s mobile phone screen. In addition, technical 

cues are thought to be the most prominent cue for connection habits because 

notification and attention-grabbing cues are built into the design of mobile phones, but 

 

13 

also because multiple technical cues can trigger an array of associated connection 

habits.  

 

Mental cues consist of mood-states, motivations, and thoughts that prime a 

connection norm that can aid in guiding a connection habit into action. Spatial cues are 

aspects of one’s proximal environment that include physical objects, places, and other 

people. For example, the mere sight of another person using their device can trigger a 

multitude of mobile media and connection habits by observers (e.g., David & Roberts, 

2017; Katz, 2006; Finkel & Kruger, 2012). Much like technical cues, spatial cues, such 

as observing a nearby person using their mobile phone, can trigger connection habits in 

an exponential manner (i.e., mobile checking has been shown to beget mobile checking 

by proximal others).  

 

As connection norms become more cognitively salient, which occurs as a 

function of time-lapse between checking behaviors, in conjunction with one’s 

idiosyncratic social network expectations, and environmental factors, the individual 

becomes primed to selectively attend to connection cues (i.e., technical, spatial, or 

mental) that trigger connection habits (i.e., mobile phone-checking behaviors) (Bayer, 

Campbell and Ling, 2016). Mobile phone-checking behaviors, as opposed to other 

aspects of mobile messaging behavior such as message sending behaviors, require 

less attention and involve more cognitive automaticity; checking behaviors are more 

likely to be initiated out of habit (Bayer, Dal Cin, Campbell & Panek, 2016).      

Mobile communication behaviors can occur with varying degrees of cognitive 

automaticity (Bayer, Dal Cin, Campbell & Panek, 2016; LaRose, 2015). As such, 

phubbing can occur both because of connection habits (cognitive automaticity) and 

 

14 

deliberative behavioral processes that have varying intentions (i.e., turning towards 

one’s mobile phone as an intentional avoidance behavior in a crowded elevator). Taken 

together, Bayer, Campbell and Ling’s (2016) model focused on automatic mobile 

phone-checking behaviors provides both social and cognitive mechanisms that explain 

a range of behaviors that may be classified as mobile phone-checking, such as 

phubbing as well as nonverbal and automatic mobile phone mimicry behaviors (i.e., 

mobile checking imitations).  

 

In subsequent sections I discuss how phubbing can have unintended and 

adverse impacts on interpersonal communication. Though most mobile phone users 

report that they attempt to limit their mobile phone use during FtF interactions, their 

attempts to do so are often unsuccessful (Richter, 2018). Many people are always 

connected through their mobile phones and engage in mobile phone-checking 

frequently throughout the day, which allows for untold possible phubbing scenarios. 

Given that today’s digital society is anticipated to continue to become more 

digitally/virtually connected through mobile technologies, it is important to consider how 

common and pervasive mobile communication habits, such as phubbing, potentially 

influence interpersonal communication processes, such as self-disclosure, that are 

imperative for affiliation formation.  

In the next section, I briefly explicate the concept of phubbing by synthesizing 

how the concept has been defined in previous literature. Subsequently, I conceptualize 

phubbing as mobile phone use during FtF interactions that breaches implicit social 

expectations and etiquette. From this perspective, phubbing can occur as a result of 

automatic checking behaviors, or as a result of intention driven behavior, that violates 

 

15 

behavioral expectations during a given FtF interaction. However, I suspect that most 

phubbing behaviors involve at least some degree of cognitive automaticity. As such I 

conceptualize phubbing as both a mobile communication habit and a type of 

technoference (defined below) (McDaniel & Coyne, 2016).      

Phubbing: A Brief Conceptual Explication 

 

Common phubbing behaviors, such as automatic mobile phone-checking 

behaviors (Bayer, Dal Cin, Campbell & Panek, 2016), can occur out of habit. In addition, 

phubbing is inherently a type of technoference, defined as the interference of 

technology use on central aspects of everyday life (e.g., interpersonal communication 

and relationships) (McDaniel & Coyne, 2016). Therefore, before reviewing previous 

definitions of phubbing, I first maintain that phubbing is both a type of mobile 

communication habit and also a specific form of technoference (Figure 1).  

Figure 1 Phubbing Habits & Technoference  

 

Conceptual ambiguity, or inconsistent definitions for a concept, can be 

detrimental to theory building and the advancement of knowledge within a given field 

(Dance, 1970). Unfortunately, conceptual ambiguity is present in the phubbing literature. 

 

 

16 

Definitions of phubbing differ along several conceptual fault lines. One of the key issues 

in the phubbing literature is that some researchers have defined phubbing very broadly. 

Other definitions of phubbing encompass all mobile phone behaviors that occur around 

physically present others, or in specific relational contexts. For example, a recent and 

novel study by Vanden Abeele and Postma-Nilsenova (2018) defined phubbing as “the 

practice of using one’s phone during FtF social interaction” (p. 304). This 

conceptualization of phubbing is flawed because it is too encompassing, and it 

describes behaviors that are not phubbing. For instance, this definition does not exclude 

mobile phone use during FtF interactions for shared purposes – such as using one’s 

mobile phone to help a physically co-present friend navigate driving directions or take a 

photo together, which should not be considered phubbing because these mobile phone 

use behaviors do not involve snubbing, ignoring, or expectancy violations related to 

desired attention.   

 

Another recent definition of phubbing offered by Krasnova, Abramova, Notter and 

Baumann (2016) defined phubbing as, “excessive use of smartphones in the romantic 

contexts” (p. 1), which is also a potentially imprecise definition for several reasons. First, 

the use of “excessive” is nebulous and requires additional clarification, which is not 

provided. Also, the term “romantic contexts” is ambiguous because this definition could 

describe both FtF and CMC interactions (e.g., excessive text messaging behavior 

directed at one’s romantic partner). In addition, Krasnova et al.’s (2016) definition of 

phubbing could also include actions that are not “snubbing” behaviors, such as 

excessively showing one’s romantic partner photographs on their mobile phone, which 

could be an expectancy violation by being too excessive in a sense but still not qualify 

 

17 

as snubbing or ignoring behaviors.  

 

Others provide slightly more precise definitions, while still leaving room for 

(mis)interpretations. For example, a rigorous longitudinal study by Halpern and Katz 

(2017) defined partner phubbing (Pphubbing) as “A partner is Pphubbed when the 

significant other decides to attend the cellphone instead of communicating with him/her” 

(p.387). This definition specifies that the act of phubbing involves deliberative cognitive 

functions, which is inconsistent with the notion that mobile checking behaviors can 

consist of both deliberative and automatic cognitions (Bayer, Campbell & Ling, 2016; 

Bayer, Dal Cin, Campbell & Panek, 2016). As such, the definition does not allow 

phubbing to be categorized as a type of mobile communication habit. 

 

Moreover, Karadag et al. (2015) defined phubbing as “an individual looking at his 

or her mobile phone during a conversation with other individuals, dealing with the 

mobile phone and escaping from interpersonal communication” (p. 60). Though this 

definition highlights the potential for phubbing to disrupt interpersonal communication, 

the first portion [clause of the sentence] of the definition leaves open the possibility that 

merely looking at one’s mobile phone during an interaction is considered phubbing (i.e., 

the definition does not specify that phubbing is inherently a perceived expectancy 

violation). For instance, this definition does not exclude mobile checking behaviors that 

are enacted to search online for conversation relevant information. If partner A checks 

her phone to look up information about a restaurant that partner B is planning to visit, 

partner A may withdraw momentarily from verbal communication; however, because 

partner B knows partner A is looking up conversationally relevant information, they may 

not perceive Partner A’s verbal withdrawal as conveying a lack of attention or breaching 

 

18 

expected etiquette. Therefore, the behavior would not be considered a snubbing 

behavior.  

 

The definition offered by Karadag et al. (2015) also implies that phubbing is an 

intentional act of “escaping from interpersonal communication.” However, phubbing 

behaviors such as mobile phone-checking can occur with limited awareness and devoid 

of intention (e.g., Bayer, Dal Cin, Campbell & Panek, 2016). Therefore, this definition is 

too narrow in scope to include mobile media habits (automatic cognitive processes), 

which likely drive most mobile media behaviors (Bayer, Campbell & Ling, 2016; LaRose, 

2015), including phubbing.  

 

Another conceptual issue found in the phubbing literature is that some 

researchers have overtly labeled phubbing behaviors as a type of pathological disorder. 

For example, in an article titled “The Virtual World’s Current Addiction: Phubbing,” 

Karadag et al. (2016) defined phubbing as “using a smartphone instead of actively 

participating in an on-going discussion being had by one’s surrounding peers” (p. 250). 

In addition, the authors proposed conclusions that stressed the inherent addictive 

properties of mobile phones and warn that mobile phone addiction could cause “serious 

psychopathological” issues throughout the Turkish population. This conceptualization of 

phubbing is limited.  

 

Karadag et al.’s (2016) definition is devoid of any consideration of behavioral 

self-control, which is a key psychological component of media addiction (LaRose, 

2015). Therefore, if someone uses their mobile phone frequently, while they are 

physically around their peers, it does not necessarily indicate that one is addicted to 

using their mobile phone. As an alternative approach, for example, Bayer, Campbell, 

 

19 

and Ling’s (2015) notions of connection cues, connection norms, and connection habits 

provide socio-cognitive explanations for mobile checking behaviors. In doing so, their 

approach avoids making alarmist claims about widespread mobile pathology and 

instead outlines socio-cognitive mechanisms that help explain when and why people 

engage in mobile phone-checking behaviors.  

 

Other definitions include the portmanteau [phone-snubbing] in their definition of 

phubbing – which could be a step towards conceptual clarity; however, this 

conceptualization might be too broad because it is unclear as to whether the “snubbing” 

is perceived by the sender, the receiver, or both. For example, definitions that include 

the portmanteau may leave open the question as to whether one is referring to 

phubbing as a goal directed avoidant communication strategy or whether snubbing is 

perceived to occur by the phubbee.  

 

An individual can be phubbed (receiver) even if the phubber (sender) is not 

intending on making the other communication partner feel snubbed during the 

interaction. Because the theoretical approaches used to study phubbing tend to 

examine how people perceive others’ behaviors, such as politeness theory and 

expectancy violation theory, the literature could benefit from a refined conceptualization 

of phubbing that emphasizes the interactional nature of phubbing behaviors. More 

precisely, if the phubbee does not perceive the behavior as rude or conveying a lack of 

attention (an expectancy violation), then the communication partner that is using their 

mobile phone is not snubbing the other – they are merely using their mobile phone 

(regardless of their intent).    

Studies by Chotpitayasunondh and Douglas (2016) as well as Vanden Abeele et 

20 

 

 

al. (2016) offer definitions of phubbing which define the behavior as the act of snubbing 

another person during a FtF interaction by focusing one’s attention to their mobile 

phone instead of towards the other. This conceptualization of phubbing requires fewer 

assumptions than some of the others discussed above. In addition, this 

conceptualization is consistent with the notion that phubbing is a negative expectancy 

violation. If mobile phone-checking behaviors are not perceived as an expectancy 

violation (i.e., as inappropriate), then the behavior is likely innocuous and should not be 

labeled, or referred to, as phubbing. More specifically, the phubbing expectancy 

violation must be perceived by the non-phubber (i.e., the person being phubbed).  

Expectancy Violation Theory and Phubbing  

 

Consistent with previous mobile communication research (e.g., Kelly, Miller-Ott & 

Duran, 2017; Miller-Ott & Kelly, 2015, 2017; Vanden Abeele, Antheunis & Schouten, 

2016; Vanden Abeele & Postma-Nilsenova, 2018), I will use expectancy violation theory 

(EVT) (Burgoon & Hale, 1988) to investigate novel phubbing effects. Phubbing is an 

appropriate phenomenon to be studied with EVT because phubbing is a type of 

expectancy violation. If one feels snubbed or ignored by their interaction partner’s 

mobile phone use, then social norms, or anticipated behavioral etiquette, were 

breached, which constitutes an expectancy violation. By conceptualizing phubbing as 

an expectancy violation, EVT can be used to explain why phubbing influences 

interpersonal communication and relationships.           

Background on EVT  

 

All human interactions inherently involve some degree of implicit and/or explicit 

behavioral expectations (i.e., anticipated verbal or nonverbal behaviors) (Burgoon, 

 

21 

1993, 2015). Humans have evolved to strive to make predictions about the behavior of 

others as a survival mechanism (e.g., Eagleman, 2015). Expectancy violation theory 

(EVT) (Burgoon & Hale, 1988) was originally developed to explain how unexpected 

nonverbal behaviors influence interpersonal communication and relationships (Burgoon 

& Hale, 1988; Burgoon, 2015). The primary concepts involved in EVT include 

expectancy violations, arousals, and the rewardingness (i.e., social status or 

attractiveness) of the communication partners (White, 2008). The theory posits that 

when expectancy violations (e.g., not adhering to an implicit social norm or breaching 

idiosyncratic interpersonal expectancies) are perceived to occur, such expectancy 

violations can give rise to positively or negatively valanced arousals (Burgoon, 1993, 

2015).  

 

Expectancy violations occur when anticipated or endorsed (i.e., what one 

perceives as appropriate) behaviors are not met, which is a judgement that is 

dependent upon the context of the interaction, the relationship between the 

communication partners, and indicators of how rewarding or socially attractive the 

communication partners are perceived to be (e.g., status and physical attraction) 

(Burgoon, Guerrero & Floyd, 2016). Due to humans’ evolved need for affiliation with 

rewarding others, more rewarding communication partners are less likely to be 

perceived to enact negative expectancy violations (White, 2008). In addition, 

expectancy violations that are perceived as a welcomed surprise (positive expectancy 

violations) lead to positive arousals, while disappointing expectancy violations (negative 

expectancy violations) lead to negatively valanced arousals (White, 2008).   

Expectancy violations can lead to both positive and negative interpersonal 

22 

 

 

evaluations and relationship outcomes – which can also work as a function of 

uncertainty (Afifi & Burgoon, 2000). More specifically, negative expectancy violations 

can lead to uncertainty that can diminish interpersonal judgements and affinity (White, 

2008). Expectancy violations are more likely to give rise to uncertainty when the 

behavior is incongruent with previous behavior with a specific communication partner 

(Burgoon, 2015). For instance, if one’s coworker is usually friendly and outgoing and 

then suddenly becomes more distant and less friendly. This behavioral change may 

lead to a negatively valanced incongruent expectancy violation. The example consists 

of an incongruent expectancy violation because the coworker’s behavior departed from 

their expected course of action (i.e., friendly to unfriendly), which could give rise to 

uncertainty and potentially lead to unfavorable interpersonal evaluations (Berger, 1993). 

Conversely, if one’s coworker is normally distant and unfriendly but then suddenly starts 

to become friendlier and more outgoing – this could be classified as an incongruent, but 

positively valanced, expectancy violation.  

 

By increasing the amount of information communicated between the coworkers, 

and due to the positive valance of the behavior, they may experience lower levels of 

uncertainty and more favorable interpersonal evaluations (i.e., increased liking), which 

can cultivate affiliation and change the way they interact with one another in the future 

(White, 2008). However, congruent expectancy violations are less arousing and tend to 

lead to less severe violations (i.e., if one’s unfriendly coworker acts slightly more distant 

and unfriendly than normal) his behavior may create a less severe arousal compared to 

when a friendly coworker breaches one’s expectations by being unfriendly (i.e., a 

negative expectancy violation).   

 

23 

 

Moderately negative expectancy violations (e.g., decreases in expected 

affection) can lead to more severely negative interpersonal judgements of the 

transgressor in comparison to moderately positive expectancy violations (e.g., increases 

in expected affection), which can lead to positive interpersonal judgements (e.g., 

increased liking) (Floyd & Voloudakis, 2006). However, positive expectancy violations 

tend to be less arousing and have a smaller overall effect than negative expectancy 

violations. In general, though, negative nonverbal expectancy violations usually result in 

negatively valanced arousals and less favorably interpersonal outcomes (e.g., less 

perceived affinity) (Burgoon, Guerrero & Floyd, 2016).   

 

Research repeatedly demonstrates that the environmental and situational context 

of a given interaction, the relationships between the communication partners, and 

characteristics of the communicators (e.g., status, attractiveness, and liking) all work in 

conjunction to determine whether a given behavior elicits an arousal and is perceived 

and evaluated as an expectancy violation (Burgoon, 1993, 2015). Similarly, more likable 

and rewarding transgressors are evaluated less harshly by the person judging the 

transgressor in comparison to those that are less likable and rewarding (Burgoon, 2015) 

– which can be manipulated by subtle nonverbal behaviors, such as smiling (LaGrance 

& Hecht, 1995). For the past two decades, mobile communication researchers have 

investigated nonverbal expectancy violations, during FtF and computer mediated 

interactions.  

 

Mobile communication research has documented distinct types of mobile 

communication norms and expectancy violations (e.g., Forgays, Hyman & Schreiber, 

2014; Katz, Katz & Aakhus, 2002; Lacohée, Wakeford & Pearson, 2003; Ling, 1998, 

 

24 

2012; Ling & McEwen, 2010; Lipscomb, Totten, Cook & Lesch, 2007; Miller-Ott, Kelly & 

Duran, 2012; Miller-Ott & Kelly, 2015, 2017; Vanden Abeele & Postma-Nilsenova, 

2018). Phubbing expectancy violations are thought to arise from the rivaling normative 

expectancies that people have to be responsive and appear at least moderately 

attentive during FtF interactions (Burgoon, 2015), but also to be responsive to their 

social network through the use of their mobile phone (Ling, 2012; Miller-Ott, Kelly & 

Duran, 2012; Miller-Ott & Kelly, 2015). As a result of these competing norms, negative 

mobile communication expectancy violations often involve a lack of responsiveness 

(Ling, 2012; Sbarra, Briskin & Slatcher, in press).    

 

Much like all other expectancy violations, phubbing expectancy violations are 

contingent on social and environmental contexts, relationship contexts, and 

characteristics of the communication partners. Consistent with other studies in this 

domain (e.g., Rainie & Zickuhr, 2015), people have different mobile etiquette 

expectations for themselves and their relationship partners depending upon the intimacy 

of the social setting (i.e., private vs. public locations) (Hall, Baym and Miltner, 2014). For 

example, in public settings compared to private/intimate settings, breaches to mobile 

etiquette injunctive norms may be perceived as less severe of an expectancy violation 

and less likely to influence interpersonal evaluations.  

 

College students in romantic relationships report that they expect their partner to 

convey higher levels of attention by not excessively using their mobile phone during 

intimate settings, such as when they are on a date, and relatively lower levels of 

attention during less intimate settings, such as when they are relaxing and watching 

television (Miller-Ott & Kelly, 2015). In addition, partners in less established 

 

25 

relationships tend to view direct communication about phubbing behaviors as potentially 

face-threating and inappropriate (Miller-Ott et al., 2012). However, participants in more 

established romantic relationships, relative to partners on a first date, have been shown 

to be more likely to use direct communication strategies to set idiosyncratic rules about 

excessive mobile phone use during intimate FtF interactions with each other and to 

directly address phubbing expectancy violations.   

 

Even within the same social context, not all phubbing behaviors are equally 

arousing (Vanden Abeele et al., 2016). For example, proactive phubbing behaviors (i.e., 

engaging with one’s mobile phone without being prompted by a technical cue or 

message alert) is a more severe expectancy violation in comparison to responsive 

phubbing (i.e., checking one’s mobile phone in response to a message or technical 

prompt, such as vibration or an alert sound). Vanden Abeele et al. (2016) explained this 

discovery by maintaining that because others understand that people have a normative 

responsibility to be responsive via their mobile phone, responsive phubbing is less face-

threatening and ostracizing than proactive phubbing.  

 

Previous research on phubbing expectancy violations also demonstrates that 

norms about mobile phone use during FtF interactions can differ between age-cohorts 

(e.g., Rainie & Zickuhr, 2015) and between different cultures (e.g., normative 

differences in Japan and the United States) (Campbell, 2007; Ito, Okabe & Matsuda, 

2005). For example, older adults (aged 65 and above) tend to be less likely to engage in 

phubbing and they also tend to hold less permissive views about mobile phone use 

during FtF interactions compared to younger adults (e.g., Kadylak et al., 2018; Rainie & 

Zickuhr, 2015; SawChuk & Crow, 2012). As such, older adults may view phubbing as a 

 

26 

more severe expectancy violation relative to younger adults. While, on the other hand, 

phubbing may be a relatively less severe expectancy violation among homogeneous 

samples of young adults (Hall, Baym & Miltner, 2014).             

 

Together, the research focused on phubbing effects suggests that expectancy 

violation theory plays a key role in both defining and describing phubbing expectancy 

violations. EVT provides a framework to explain why phubbing may affect interpersonal 

evaluations (e.g., perceived liking and trust), communication processes (e.g., self-

disclosure), and ultimately affiliation and relationship formation. However, the bulk of the 

research has focused on interpersonal and relationship outcomes, while limited 

attention has been given to important verbal and nonverbal interpersonal 

communication processes that enhance affiliation, such as self-disclosure and 

nonverbal behavioral mimicry. This research provides insights into the mechanisms of 

EVT, thereby extending the theory. I aim to extend EVT by investigating a novel 

communication outcome (actual self-disclosure) in the phubbing context and by 

investigating a novel mechanism (nonverbal mobile phone-checking mimicry) that may 

mitigate phubbing effects. The following section further discusses relevant phubbing 

effects research and considers the findings within an expectancy violation framework.   

Research on Phubbing Effects 

 

Though not directly relevant to this study, a sizable body of research has 

investigated the negative impacts that phubbing can have on interpersonal relationships 

among marital partners (Roberts & David, 2016) and coupled adults (Halpern & Katz, 

2017). More specific to the present study, phubbing can take away from interpersonal 

interactions and potentially disrupt interpersonal communication processes, among 

 

27 

young adults (Misra et al, 2016). In this section, research is classified by specific types 

of phubbing effects that occur during FtF interpersonal interactions among young adults. 

Research consistently shows that phubbing decreases perceived communication quality 

and interpersonal judgements (e.g., perceived liking and trust). In the subsections that 

follow, I synthesize the relevant phubbing literature and discuss directions for future 

research, which I then propose to test in this study.    

 

When people are phubbed, they are more likely to have their perceptions of the 

phubber biased in a negative manner, because phubbing is a negative interpersonal 

expectancy violation (e.g., Vanden Abeele et al., 2016). Phubbing involves nonverbal 

immediacy behaviors that can breach interpersonal expectations (e.g., averting one’s 

eye-contact/attention away from the communication partner and towards one’s mobile 

phone instead) (Vanden Abeele & Postma-Nilsenova, 2018). Communication partners 

that engage in phubbing are perceived as less attentive and less polite compared to 

non-phubbers (Kelly, Miller-Ott & Duran, 2017; Vanden Abeele et al., 2016). However, 

not all phubbing behaviors have equivalent effects.  

 

Responsive phubbing (e.g., checking one’s mobile phone in response to a text 

message alert) can have less severe effects on interpersonal and conversational 

judgements in comparison to proactive phubbing (e.g., checking one’s mobile phone 

without being noticeably prompted) (Vanden Abeele et al., 2016). Proactive phubbing is 

a more severe expectancy violation compared to responsive phubbing because other 

people understand that interaction partners have a normative responsibility to be 

responsive to their social network through mobile communication, which highlights the 

value of expectancy violation theory in phubbing research.  

 

28 

 

Proactive phubbing does not actually need to be an intention driven, proactive 

behavior – though at times it could be. Proactive phubbing may appear to be unsolicited 

and therefore potentially a more severe expectancy violation than reactive phubbing. 

However, a proactive phubber can unknowingly use automatic mental processes which 

guide them to enact the mobile phone-checking behavior (Bayer, Dal Cin, Campbell & 

Panek, 2016) that could be perceived as phubbing. For example, Bayer, Campbell and 

Ling (2016) maintained that spatial connection cues (e.g., perceiving that a nearby 

customer in a restaurant is checking their phone) can activate mobile communication 

norms (e.g., one’s own responsibility to be responsive to their social network) and 

ultimately trigger a connection habit (e.g., automatic mobile phone-checking – which 

could be perceived as an expectancy violation, and therefore categorized as proactive 

phubbing).  

 

Though phubbing effects are contingent on the type of phubbing behavior 

enacted (e.g., proactive vs. reactive), a considerable body of literature shows that 

phubbing effects are also contingent upon conversational contexts (e.g., Misra et al, 

2016; Przybylski & Weinstein, 2013). For example, research repeatedly shows support 

for the mere presence hypotheses (i.e., that the mere physical presence of a mobile 

phone between people in a FtF dyadic interaction can disrupt affiliation formation 

processes, such as decreased perceived trust and empathy) (Dwyer, Kushlev & Dunn, 

2018; Misra et al., 2016; Przybylski & Weinstein, 2013). However, the effects of the 

mere presence of a mobile phone (not even actual phubbing behaviors) have been 

shown to be moderated by the intimacy of the given conversation (i.e., casual 

conversational topic about plastic holiday decorations vs. a meaningful conversation 

 

29 

involving self-disclosure) (Misra et al., 2016; Przybylski & Weinstein, 2013).  

 

Mobile phone use during casual FtF conversations is less likely to be deemed an 

expectancy violation and subsequently hinder interpersonal evaluations (e.g., lower 

perceived trust and empathy), compared to mobile phone use during more intimate FtF 

conversations (e.g., interactions that involve self-disclosure) (Przybylski & Weinstein, 

2013). Therefore, it follows that severe phubbing behaviors (e.g., excessive proactive 

phubbing) during intimate FtF conversations (e.g., interactions that consist of self-

disclosure) can be expected to lead to more negative effects than less severe phubbing 

expectancy violations.  

 

 

In line with Bayer, Campbell, and Ling’s (2016) socio-cognitive approach to 

mobile communication habits, one explanation for the evidence found in support of the 

“mere presence” hypothesis is that interactive media devices, such as a mobile phone, 

can work as the stable environmental conditions, or cues, that make relevant norms and 

attitudes more assessible. As a result, the relevant norms and attitudes related to social 

connection and responsiveness can promote the likelihood that a connection cue will 

trigger a connection habit into action. Therefore, in past studies participants may have 

reported less favorably interpersonal evaluations, as well as lower levels of 

conversational quality, whilst in the presence of a mobile phone because their 

connection norm (e.g., expectation to be responsive to one’s social network through 

mobile communication) and connection habit (e.g., checking behavior to view potential 

messages/alerts) was primed. However, because they did not have their own mobile 

phone with them to enact their mobile phone-checking habit, they could have become 

distracted by the primed attitudes/norms.   

 

 

30 

 

This type of dynamic could be an unusual experience relative to the rest of 

emergent adults daily FtF interactions. For instance, some young people report 

symptoms of separation anxiety when they do not have their mobile phone (Cheever, 

Rosen, Carrier & Chavez, 2014). Participants in ‘mere presence’ studies could be 

distracted by perceiving a mobile phone in the room because they are reminded that 

they themselves are without their own mobile phone and that they are unable to be 

responsive to their social ties.  

 

Though the mere presence of a mobile phone can be disruptive to FtF 

interactions and diminish interpersonal evaluations, research consistently shows that 

phubbing expectancy violations (i.e., engaging in excessive mobile phone-checking or 

messaging behaviors) have more adverse impacts on interpersonal evaluations than 

the mere presence effects (Vanden Abeele et al., 2016). More specifically, phubbing 

expectancy violations signal inattention and a lack of interest in the given 

communication partner. However, aside from highlighting that phubbing negatively 

affects interpersonal judgements, research also shows that phubbing decreases 

perceived communication quality.  

 

In the next section, I synthesize findings on the relationship between phubbing 

and perceived communication quality. Subsequently, a series of hypotheses about the 

relationship between phubbing with verbal and nonverbal aspects of interpersonal 

communication.    

Phubbing and Perceived Communication Quality 

 

Much like phubbing research that focuses on interpersonal judgements, research 

on perceived communication quality and other conversational indicators, such as 

 

31 

conversational enjoyment and flow (Dwyer, Kushlev & Dunn, 2018), are oriented 

towards studying FtF interactions instead of investigating long-term phubbing effects 

(e.g., such as relationship satisfaction or relational conflict) (Roberts & David, 2016). In 

addition, the conversational context (e.g., intimate vs. casual) of an interaction 

moderates phubbing effects on interpersonal communication indicators, such as 

perceived communication quality (Przybylski & Weinstein, 2013).  

 

Research, typically in experimental settings, consistently has shown that 

phubbing can curtail perceived communication quality and closeness (Misra, Cheng, 

Genevie & Yuan, 2016; Przybylski & Weinstein, 2013; Vanden Abeele, et al. 2016) as 

well as undermine affiliative needs, such as a sense of belonging by engendering 

ostracism (Chotpitayasunondh & Douglas, 2018; Hales, Dvir, Wesselmann, Kruger & 

Finkenauer, 2018), among FtF dyadic communication partners. Aside from investigating 

judgements about interpersonal communication during FtF interactions, in which 

phubbing occurs, research has fallen short in terms of generating knowledge about how 

phubbing impacts actual interpersonal communication processes. Previous ‘mere 

presence’ research repeatedly has shown that potential technoference effects (e.g., 

being distracted by the mere sight of a mobile phone) are more severe during 

meaningful conversations involving self-disclosure compared to conversations about 

plastic holiday decorations (e.g., Przybylski & Weinstein, 2013; Misra, Cheng, Genevie 

& Yuan, 2016). Mobile communication research has yet to investigate whether phubbing 

influences self-disclosure processes.  

 

This study expands work in this area by examining nonverbal and verbal aspects 

of interpersonal communication during FtF dyadic interactions – with a specific focus on 

 

32 

self-disclosure. One of the most important aspects of interpersonal communication, 

which has evolved to be a fundamental building block that allows humans to satisfy their 

evolved need for affiliation and cultivate bonding, is self-disclosure (Sbarra, Briskin & 

Slatcher, in press; Sprecher & Treger, 2015).  

 

Though disclosing information about one’s self is one of the most common forms 

of interpersonal communication needed to cultivate relationships (e.g., Collins & Miller, 

1994), this aspect of interpersonal communication has yet to be thoroughly examined as 

an outcome in phubbing research. Specifically, I seek to answer the question: do people 

self-disclose less while their interaction partner engages in phubbing compared to FtF 

interactions in which mobile phones are not physically present? By investigating this 

question, this study goes beyond assessing perceptions of conversational quality and 

instead investigate whether phubbing influences the verbal and nonverbal interpersonal 

communication responses enacted by the communication partner that is not engaging in 

the phubbing behavior.  

 

Previous mobile communication research has recommended that because 

phubbing hinders interpersonal evaluations (e.g., trust, liking, etc), which are 

prerequisites for self-disclosure to occur during interpersonal interactions, future 

phubbing effects research must examine whether phubbing influences actual self-

disclosure processes (Sbarra, Briskin & Slatcher, in press; Vanden Abeele et al., 2016). 

As such, this study builds from previous phubbing effects research to investigate the 

relationship between phubbing and self-disclosure. Because research shows that 

phubbing undermines existing relationships and potentially even subjective well-being, 

among many types of samples and relationship partners (Dwyer, Kushlev & Dunn, 

 

33 

2018; Roberts & David, 2016; Wang, Xie, Wang, Wang & Lei, 2017), studying the 

relationship between phubbing and self-disclosure could help to explain previous 

research that has focused on potential long-term effects of phubbing as well (i.e., on 

relationship satisfaction, conflict, and dissolution).  

Phubbing and Self-Disclosure 

 

Self-disclosure is defined as disclosing information about one’s self (e.g., 

thoughts, feelings, emotions, past experiences, future aspirations) with another person 

(Cozby, 1973; Laurenceau, Barrett & Pietromonaco, 1998). Self-disclosure brings 

people together, helps satisfy fundamental affiliation needs, and initiate rapport during 

FtF first encounters (Laurenceau, Barrett & Pietromonaco, 1998; Sprecher & Treger, 

2015), which needs to occur reciprocally (i.e., both interactions partners utilize similar 

breadth and depth of self-disclosure) to maintain balance in the interaction (Berg, 1987; 

Sprecher, Treger, Wondra, Hilaire & Wallpe, 2013).  

 

There are two different types of reciprocal self-disclosure behaviors, extended 

reciprocity, which occurs over long periods of time over the course of a relationship, and 

reciprocal turn-taking, which occurs during a given interaction (Sprecher et al., 2013). 

Some findings suggest that over long periods of time self-disclosure within relationships 

tends to approach an equilibrium through extended reciprocity processes (Greene, 

Derlega & Mathews, 2006), while turn-taking self-disclosure reciprocity (within a given 

interaction) is thought to be more volatile relative to extended or relational reciprocity 

(i.e., the balance of self-disclosure between partners in a relationship over time) 

(Wenzel, Sprecher & Harvey, 2018).        

Social penetration theory (SPT) (Altman & Taylor, 1973) is used to explain self-

34 

 

 

disclosure in first encounters and throughout relationship development. Consistent with 

much of the previous research on self-disclosure, SPT maintains that relationships 

develop, in large part, as a function of self-disclosure processes. Self-disclosure 

promotes bonding and increases the intimacy of a given relationship (Carpenter & 

Greene, 2016). From this perspective, affiliation formation and relationship development 

are thought to occur along a continuum of intimacy. The more that communication 

partners disclose information about themselves, in a reciprocal manner, the more 

intimate and close the relationship can become (Carpenter & Greene, 2016; Collins & 

Miller, 1994). In addition, as relationships develop, the depth of self-disclosure tends to 

increase (i.e., people disclose more personal and revealing information about 

themselves, while during early relationship stages and first encounters people tend first 

discuss a wider breadth of self-disclosure).  

 

In this study, I plan to investigate depth of disclosure during FtF dyadic first 

encounters because depth of disclosure enhances intimacy more than a wide breadth of 

disclosure about an array of topics. If phubbing disrupts depth of disclosure, it would 

provide evidence for a novel avenue by which phubbing can negatively affect the 

development of interpersonal relationships.    

 

The disclosure decision model (DDM), which is complimentary to SPT, proposes 

that people strategically assess whether disclosing information about oneself is 

advantageous or risky (Omarzu, 2000). According to the DDM, depth of disclosure is 

more likely than breadth of disclosure to be hindered by perceptions of subjective risk – 

for instance, one’s expectation that their disclosure will be met with social rejection or 

ostracism. Self-disclosure is most common in close intimate relationships because 

 

35 

revealing personal information about oneself is most likely to occur when one can 

anticipate how the receiver will respond and when the receiver is perceived as 

trustworthy and caring. However, reciprocal disclosure is especially important during 

initial encounters because it is the most common way to promote the intimacy of the 

relationship. Self-disclosure reduces uncertainty and promotes affiliative bonds that 

helps satisfy one’s innate need for social connection and belonging (Reis, Maniaci, 

Caprariello, Eastwick & Finkel, 2011).   

 

Humans are most likely to self-disclose to other people that they have favorable 

interpersonal evaluations of during FtF first encounters (e.g., people that they like, trust, 

and perceive as caring), because people are more likely to self-disclose to other people 

they expect to respond favorably (Greene, Derlega & Mathews, 2006; Sprecher et al., 

2013). In addition, if a potential disclosure target is perceived to display availability 

signals that may indicate one’s attempt to self-disclose will be met by rejection (e.g., 

nonverbal cues that convey a lack of attention, such as averting one’s gaze), then the 

likelihood that one will self-disclose declines (Omarzu, 2000). Nonverbal immediacy 

cues (e.g., facial expressions, eye contact/gaze, body language, and proximity, etc.) 

can influence disclosure processes during first encounters. Though phubbing research 

indicates that phubbing can disrupt nonverbal immediacy cues that hinder interpersonal 

evaluations, research has yet to examine if phubbing influences depth of self-disclosure, 

which could likely influence affiliation formation and relationship maintenance 

processes.     

 

Phubbing behaviors involve nonverbal immediacy cues (e.g., averting one’s 

gaze, that can signal expectancy violations) (Vanden Abeele & Postma-Nilsenova, 

 

36 

2018), which can directly undermine perceptions of interpersonal trust, empathy, and 

liking (e.g., Vanden Abeele et al., 2016). In addition, researchers have insisted the need 

to examine how phubbing affects specific communication processes (i.e., self-

disclosure) (e.g., Przybylski & Weinstein, 2013; Sbarra, Briskin & Slatcher, in press; 

Vanden Abeele et al., 2016). As such, I not only aim to test Vanden Abeele et al.’s 

(2016) prediction, I also propose to incorporate previous phubbing effects findings with 

arguments formalized in the DDM to test potential explanatory mechanisms between 

phubbing expectancy violations and self-disclosure.  

 

Phubbing has been shown to be a negative expectancy violation that can be 

perceived as ostracizing (e.g., David & Roberts, 2017; Hales, Dvir, Wesselmann, Kruger 

& Finkenauer, 2018). Because normative expectancy violations and perceived 

indicators of social rejection impede self-disclosure (e.g., Burgoon, 1993; Omarzu, 

2000), I expect that phubbing will negatively affect self-disclosure. 

Mediating role of Interpersonal Evaluations - Perceived Trust, Empathy, and Liking

 

In support of SPT and the DDM, research shows that people self-disclose more 

to others that they like, trust, and perceive as empathetic. Phubbers (the person using 

their mobile phone) are more likely to be perceived as less trustworthy and less 

empathic (Vanden Abeele et al., 2016; Przybylski & Weinstein, 2013; Roberts et al., 

2017).  

 

Because the previous literature clearly shows that phubbing expectancy 

violations can be ostracizing for the person being phubbed, it is plausible to consider 

that phubbing expectancy violations impede other fundamental interpersonal 

communication processes. One area that has received limited phubbing research 

 

37 

attention, but that is also affected by nonverbal behavior cues relevant to the phubbing 

context (e.g., everting one’s gaze), is nonverbal behavioral mimicry. Nonverbal 

behavioral mimicry is an evolved and automatic process that promotes affiliative bonds 

and influences interpersonal evaluations (e.g., liking) (Duffy & Chartrand, 2015). This 

study contributes to the literature by assessing self-disclosure as a potentially salient 

outcome in the phubbing context, but also by assessing the interrelations between 

nonverbal (e.g., mimicry) and verbal (self-disclosure) aspects of interpersonal 

communication, during FtF interactions, which could have implications for affiliation 

formation and human bonding.   

Mobile Phone Mimicry during FtF Interactions       

 

In line with EVT’s focus on nonverbal behaviors (Burgoon, 1993, 2015),  

Chartrand & Bargh’s (1999) notion of the chameleon effect maintains that during FtF 

interactions people unconsciously mirror the nonverbal behaviors (e.g., body language, 

proximity, facial expressions, etc.) of their interaction partner and automatically 

coordinate their body movements (Bernieri, 1998). Research has consistently shown 

that when an individual observes a behavior being enacted by an interaction partner 

they are more likely to also engage in the same behavior (Bargh et al.1996; Chartrand & 

Bargh, 1999). Behavioral matching is an evolved and pervasive aspect of everyday FtF 

communication (Duffy & Chartrand, 2015). More precisely, during FtF interactions, 

people tend to engage in nondeliberative imitation of, and coordination with, their 

interaction partners’ gestures (e.g., Bernieri, 1988; Chartrand & Bargh, 1999; Chartrand 

& Lakin, 2013).  

Research indicates that during dyadic interactions, when confederates scratch 

38 

 

 

their nose frequently, in response their interaction partners also scratch their nose more 

times throughout the interaction - compared to participants in baseline experimental 

conditions (e.g., Chartrand & Bargh, 1999). In addition, studies have observed a similar 

pattern for other nonverbal gestures, such as foot shaking, which is especially 

pronounced with socially rewarding or attractive (e.g., high status) communication 

partners (e.g., Chartrand & Bargh, 1999; Duffy & Chartrand, 2015; Lakin & Chartrand, 

2003).    

 

Premised in part on prior findings, I maintain that mobile phone-checking 

behaviors are likely to be mimicked by FtF interaction partners. In addition, Bayer, 

Campbell and Ling (2016) explicated a theoretical model focused on connection cues, 

norms, and habits that incorporates socio-cognitive mechanisms, which can be applied 

to mobile checking mimicry, during FtF interactions. Theoretically, observing one’s 

interaction partner check their mobile phone during a FtF interaction can work as a 

connection cue (specifically, a spatial cue). Connection cues make one’s own 

connection norms more salient (e.g., one’s responsibility to be accessible and 

responsive to their social network through the affordances of mobile communication), 

which can subsequently make one more likely to automatically engage in one’s own 

mobile communication habits (i.e., checking one’s mobile phone for messages).  

 

In this context, I refer to mobile phone-checking mimicry, instead of phubbing, 

because if both communication partners are engaging in the same behavior, then one 

may be less likely to deem the other’s mobile checking behavior as inappropriate (i.e., a 

phubbing expectancy violation). I define mobile checking mimicking as behavioral 

matching of nonverbal mobile phone use during FtF interactions, while mobile 

 

39 

mimicking synchronicity can be described as the amount of time between mobile 

mimicking reciprocation. For example, mobile mimicking synchronicity is the timespan 

between when communication Partner A checks their phone until Partner B 

responsively checks their mobile phone. If the behavioral matching occurs within 5 

seconds of the initial behavior, then the nonverbal behavioral matching is considered 

mimicry (Chartrand & Lakin, 2013).     

 

The nondeliberative mimicry of gestures is an evolutionary process that has both 

survival and social functions – primarily, to build affiliations and ultimately cultivate 

human bonding (Lakin & Chartrand, 2003; Hove & Risen, 2009) – which is consistent 

with the finding that mimicking is positively associated with interpersonal evaluations 

(e.g., liking), during FtF interactions (e.g., Chartrand & Bargh, 1999; Duffy & Chartrand, 

2015). For example, a series of studies by Hove and Risen (2009) showed that during 

FtF dyadic interactions, behavioral mimicking in real-time (synchronicity) had positive 

effects on affiliation and building rapport.  

 

Mimicry consistently increases affiliation and rapport, which can subsequently 

increase empathy, interpersonal evaluations (e.g., liking), prosocial behaviors, and 

social attraction (Duffy & Chartrand, 2015). In addition, recent findings have shown that 

imitation during FtF dyadic interactions increases oxytocin levels (Spengler et al., 2017), 

which is a hormone that is widely accepted as a biological factor that promotes 

relational bonding and affiliation (Feldman, Weller, Zagoory-Sharon & Levine, 2007).  

 

Because axioms of social penetration theory and the disclosure decision model 

suggest that people strategically disclose more information about one’s self to those 

that one has a stronger affiliative bond with and that one likes more (Duffy & Chartrand, 

 

40 

2015), I also expect that higher levels of mobile checking mimicry will be positively 

associated with higher levels of self-disclosure – a relationship that I anticipate will be 

mediated by higher levels of liking (i.e., positive interpersonal evaluations). Research 

consistently shows that mimicry enhances interpersonal evaluations and that people 

self-disclose more to others they like, trust, and perceive as empathetic (Collins & Miller, 

1994; Duffy & Chartrand, 2015).  

 

Although previous mobile communication research has investigated different 

types of mobile communication synchrony and mimicry (i.e., mimicry in response 

times/synchrony of text-messages) (e.g., Ling, 2012; Gray, 2018) and CMC research, 

more generally, has shown that lexical mimicry (e.g., mimicking an interaction partners 

diction) can cultivate trust in conjunction with affiliation and rapport (e.g., Scissors, Gill & 

Gergle, 2008), research has yet to examine mobile checking mimicry within a phubbing 

context. However, as discussed above, the notion of mobile checking mimicry is 

consistent with Bayer, Campbell and Ling’s (2016) socio-cognitive approach to mobile 

communication habits as well as the existing research on nonverbal mimicry (e.g., Duffy 

& Chartrand, 2015).  

 

In this study, I argue that the socio-cognitive mechanisms of mobile 

communication checking behaviors as well as evolved automatic mimicking processes 

will work in a complementary manner to facilitate automatic mobile phone-checking 

mimicry. As such, mobile phone mimicking behavior may counterintuitively be a unique 

avenue by which mobile checking behaviors, during FtF interactions, help to bring 

people together by increasing interpersonal evaluations (e.g., liking) and self-disclosure, 

which have been shown to enhance affiliation and the intimacy of relationships. 

 

41 

Significance of The Present Study 

 

First encounters have been studied by previous phubbing effects researchers 

(e.g., Vanden Abeele et al., 2016; Przybylski & Weinstein, 2013). By examining first 

encounters I will be able to investigate impression formation without having their past 

relationship experience influence interpersonal evaluations or self-disclosure, which is 

likely to occur in more established relationships. In addition, first encounters are 

particularly useful when studying phubbing because it allows one to avoid interfering 

with idiosyncratic interpersonal dynamics that could confound the findings and alert 

study participants as to the purpose of the study. For example, when manipulating 

phubbing behaviors – if participants have a past history with one another to draw from, it 

may become obvious that the behaviors that are being manipulated are out of the 

ordinary and highlight what the researchers are attempting to test, which could influence 

outcomes being examined.  

 

In established relationships, people tend to use direct communication strategies 

to address phubbing expectancy violations, compared to less established relationships 

(Miller Ott and Kelly, 2015). Direct communication strategies (i.e., directly asking one’s 

interaction partner “who keeps texting you?” or “why do you keep looking at your 

phone?”) could put a participant in a precarious position by forcing them to either 

explicitly deceive their friend/relationship partner or, instead, inform their partner that 

they were instructed to use their mobile phone during the interaction (i.e., a phubbing 

induction) – all of which could hinder the naturalness of the study. In addition, according 

to the social penetration theory, societal norms play a more salient role in shaping self-

disclosure processes during first encounters and earlier relationship stages compared to 

 

42 

established relationships, which is another benefit of studying phubbing expectancy 

violations during FtF first encounters in an experimental setting.  

 

Imprudent, or inappropriate, mobile phone use during formal first encounters, 

such as a first date, is typically viewed as an expectancy violation (i.e., viewed as 

phubbing) because people tend to have higher expectations for undivided attention 

during first encounters (Miller-Ott & Kelly, 2015). Therefore, this study takes place within 

a first encounter setting in which same-sex participants will discuss meaningful subject 

matter, as opposed to a completely casual conversational topic. This study assesses 

mobile phone checking behaviors, perceived phubbing, expectancy violations, 

interpersonal judgements, and self-disclosure among previously unacquainted college 

students. The study hypotheses are recapped in the conceptual models below (Figures 

2-3). 

Figure 2 Full Conceptual Model for Determinants of Interpersonal Evaluations  

 

 

 

 

 

43 

Figure 3 Full Conceptual Model for Determinants of Self-Disclosure 

 

Hypotheses:  

H1a. On average, participants in the phubbing condition will report higher levels of 

perceived phubbing expectancy violations than those in the true control condition.  

H1b. On average, participants in the phubbing condition will report lower levels of 

interpersonal evaluations than those in the true control condition.   

H1c. On average, participants in the phubbing condition will report lower levels of self-

disclosure than those in the true control condition.   

H2. Perceived phubbing expectancy violations will mediate the relationship between 

study conditions and interpersonal evaluations (H2a) as well as with self-disclosure 

(H2b) 

H3. Mobile phone mimicry will moderate the association between study conditions and 

perceived phubbing expectancy violation.  

H4. Mobile phone mimicry will be inversely associated with perceived phubbing 

expectancy violations (within both conditions).  

 

44 

H5. Mobile phone mimicry will moderate the association between study conditions and 

interpersonal evaluations.  

H6. Mobile phone mimicry will be positively associated with interpersonal evaluations 

(within both conditions) 

H7. Mobile phone mimicry will moderate the association between study conditions and 

self-disclosure.  

H8. Mobile phone mimicry will be positively associated with self-disclosure (within both 

conditions) 

 

 

 

 

 

 

 

 

 

 

 

45 

CHAPTER 3: METHODS 

 Sample 

 Participants were recruited through the SONA Student Pool, an online research 

platform at a large Midwestern university in the U.S. SONA allows university 

researchers to recruit students and community members to participate in studies that 

have been approved by the Institutional Review Board (IRB). The study participants 

were undergraduate students from a large Midwestern university (aged 18-25). All study 

sessions occurred in a lab space in the Communication building at the university. 

Inclusion criteria also required participants to own a mobile phone (all participants had a 

smartphone). Participants were compensated 1.0 SONA credit for their participation. 

Sixty one percent of the sample was female (39% male). Dyads consisted of same-sex 

strangers to avoid potential confounding effects outlined in previous sections – 

specifically, to help rule out the potential confounding impacts that physical and sexual 

attraction can have on self-disclosure and nonverbal behavioral mimicry (Collins & 

Miller, 1994; Dindia & Allen, 1992; Duffy & Chartrand, 2015). In order to ensure 

participants were assigned a same-sex interaction partner, I created separate SONA 

recruitment pages for males and females that were otherwise identical.  

 

Dyads were randomly assigned to one of two study conditions (phubbing 

condition (PC) vs. control condition (CC)). Dyads were randomized into conditions using 

a simple Python program that I created (Figure 4): 

 

 

 

 

46 

Figure 4 Python Randomization Procedure  

 

Prior to each interaction, participants confirmed that they had not previously met 

their interaction partner. Participants were told that if they happened to accidently or 

randomly sign-up for a timeslot with someone that they were previously acquainted with, 

they were compensated but excluded from the study (which occurred once). Because I 

randomized the dyads into one of two study conditions, there was an equal chance that 

any dyads that successfully hid their past relationship experience ended up in either 

condition – though no dyads raised such suspicions. As such, I do not think participant 

dishonesty skewed results. 

 

A preliminary power analysis was computed using the statistical software 

program G*Power Version 3.0 (Faul, Erdfelder, Lang & Buchner, 2007). Based on 

previous research (e.g., Misra et al., 2016), I expected to observe between condition T-

test results with small to medium effect sizes (i.e., Cohen’s d = 0.2 to 0.6) with 90% 

confidence and an alpha error probability of 0.05. The power analysis indicated that a 

total sample size of 88 participants (Critical t = 1.66, Df = 86) was required in order to 

reach the desired statistical power. Though 18 people that signed up for the study did 

not show up for their study timeslot, data were collected from 180 participants in 90 total 

interactions. With data cleaning (-10) (n = 41 treatment, control = 39, n = 80) and then 

missing cases (-3), 77 total participants were included in the analyses. The total N per 

 

47 

group was: 38 treatment, 39 control. Figures 5 and 6 show the sensitivity and power 

analyses.  

Figure 5 Statistical Power 

 

 

Figure 6 Sensitivity Analysis 

 

 

 

 

48 

Study Procedure 

 

The present study was designed to manipulate mobile phone use during 

meaningful face-to-face first encounters. The study had a 2x1 between-subjects design: 

2 (mobile phone (phubbing) condition vs. true condition) x 1 (meaningful conversation 

topic). Given this study’s interest in automatic processes (e.g., mobile phone 

habits/mimicry) and depth of self-disclosure, an experimental design was advantageous 

compared to other methods. For example, fully observational methods could also 

continue to provide novel insights regarding mobile phone habits and interpersonal 

communication outcomes (i.e., depth of self-disclosure). However, in observational 

settings researchers are likely to assess ad-hoc self-reported accounts regarding the 

conversational and relational context of the people interacting (i.e., one dyad might be 

talking about holiday decorations or the local weather, while other groups could be 

discussing an intimate topic involving self-disclosure), which is important because 

conversational and relational context influence phubbing effects (e.g., Przybylski & 

Weinstein, 2013) as well as nonverbal mimicry behaviors (Duffy & Chartrand, 2015).    

 

Prior to beginning the study, participants completed informed consent forms 

along with pre and post-test pencil and paper surveys immediately preceding and 

following their 10-minute interaction. Participants were recruited to partake in a 10-

minute get-to-know-you interaction activity, which is a common duration for phubbing 

related experiments (e.g., Przybylski & Weinstein, 2013; Vanden Abeele et al., 2016; 

Vanden Abeele et al., in press). In line with similarly designed studies (Misra et al, 2016; 

Vanden Abeele et al., 2016), participants in all study conditions were instructed to 

discuss a personally meaningful event that occurred in their life during the past year 

 

49 

(e.g., Przybylski & Weinstein, 2013). All dyadic interactions were recorded with a 360-

degree video camera (360fly HD). The video recordings were used to assess nonverbal 

mobile phone-checking mimicry behaviors. The recordings were also used to assess 

the behavioral measure of self-disclosure, which will be discussed in the measures 

section below. 

 

In both study conditions, participants were physically positioned a little over four 

feet from one another during the interaction and they had a table positioned between 

them (Figure 7). The diameter of the table was exactly 4 feet. Though other mobile 

communication studies have opted for slightly closer physical proximity (e.g., in an 

experiment by Przybylski and Weinstein (2013) dyads sat 3 feet away from one 

another), I separated participants by an extra 1.5 feet because less than 4 feet of 

distance may be considered an intimate personal space, while proxemic distances 

greater than 4 feet are considered less intimate social zones (Hall, 1966, Walters et al., 

2005). In addition, it was important that non-confederate participants were close enough 

to see that their interaction partner received a message and the subsequent technical 

connection cues (i.e., that their mobile phone screen lit up, vibrated, and received an 

alert message) but that the non-confederate was not close enough to read the content 

of the message and/or sender information.   

 

 

 

 

 

 

 

50 

Figure 7 Experimental Design 

 

 

In each condition, the participant that arrived at the lab second was instructed to 

start the interaction in the speaker role, while the participant that arrived at the lab first 

was instructed to start the interaction in the listener role. The speaker role consisted of 

being the participant that discussed their meaningful event first during the interaction. 

Both participants were informed that they did not have set time-limits as to how long 

each person needed to discuss their meaningful event. In addition, participants in all 

conditions were instructed to otherwise communicate back and forth as they otherwise 

would while getting to know someone.  

 

Within each dyad, the participant that arrived at the lab first was selected as the 

confederate. The confederates were briefly trained prior to the interaction (see Appendix 

A for confederate instructions). They were Instructed to lift their phone from the table 

and read/look at their phone for about 2 to 3 seconds, clear the message off their 

screen by swiping, and then place their phone back on table as naturally as possible. 

 

51 

Confederates were trained to check their phone for 2 to 3 seconds because evidence 

suggests that is the average duration of phubbing behaviors (Vanden Abeele, 

Hendrickson, Pollmann & Ling, 2019). The confederate received 3 text messages 

during the interaction – 1 minute into the interaction, 3 minutes and 30 seconds, and 6 

minutes and 30 seconds into the interaction. Similar responsive messaging techniques 

and frequencies have been demonstrated to induce phubbing effects in previous 

research (e.g., Vanden Abeele et al. 2016).  

 

In addition, confederates were instructed to try to gaze at their mobile phone for 

the same amount of time throughout the interaction. To ensure quality control, data was 

only included for dyads in which all participants, including the confederates, followed all 

instructions accurately. For example, if the confederate missed one of the text-

messages or checked their device for less than 2 seconds, data from the dyad was 

excluded from all analyses. The non-confederates kept their mobile phones with them 

during the interaction as well. The non-confederate participants were also instructed to 

place their mobile phone on the table, which allowed for the possibility of mobile phone 

checking mimicry behaviors to occur. During each interaction, the confederate-

participant always started the interaction activity in the listener role and their survey 

responses were excluded from the analysis.   

 

In the true control condition, the participants also had their mobile phones during 

the interaction. However, within each dyad, neither participant was instructed to use 

their mobile phone by study personnel nor trained as a confederate.  In the true control 

condition, the interaction activity followed the same format as the phubbing condition. I 

also used the same randomization technique to select confederates vs. non-

 

52 

confederates to randomly exclude half of the control group data (N = 50) to maintain 

balance. This exclusion technique has been used in similarly designed phubbing 

experiments (e.g., Vanden Abeele et al., 2016). Specifically, the participants that arrived 

first to the lab were instructed to sit in the same seat as that the confederates sat in 

within the phubbing condition, they started the interaction in the listener role, and their 

survey responses were excluded from the analysis. By having the excluded participants 

start as the listener in each condition, it helped avoid the confounding potential that the 

excluded participants’ depth of self-disclosure could have on the included participants’ 

self-disclosure (i.e., people disclose more to people that disclose more to them) and it 

allowed me to assess the behavioral measure of self-disclosure.   

Measures 

 

The key dependent variable that was assessed immediately following the 

interaction activity is self-disclosure, measured by 3-items adapted from Laurenceau, 

Barrett and Pietromonaco (1998). Example items included “During the interaction I 

disclosed my thoughts” and “During the interaction I disclosed my emotions.” Each item 

was measured by a 5-point Likert scale ranging from 1 (very little) to 5 (a great deal). 

Participant responses were averaged together to compute a composite measure (α = 

0.7). All measures are listed in Appendix B along with previously observed reliability 

findings.    

 

The behavioral measure of self-disclosure was computed by observing the 

number of seconds that the participant initially engaged in self-disclosure from the start 

of the interaction. The measure is consistent with previously validated behavioral 

approaches used to assess self-disclosure during first encounters (Sprecher et al., 

 

53 

2013; Vondracek, 1969). The timer used for this measurement was connected to the 

360-degree camera – both of which started at the exact moment the speaker started to 

engage with their interaction partner. This behavioral measure of self-disclosure may be 

more accurate/valid measure of actual self-disclosure compared to other options, such 

as the total word count. Specifically, each participant was instructed to start the 

conversation by discussing a meaningful event/experience, which forced them to 

engage in self-disclosure. While reviewing the audio/video footage of each interaction, 

study personnel assessed whether the participant followed the instruction to start the 

conversation by discussing their recent meaningful event. If the participant did not start 

the conversation by discussing their meaningful event and, instead, discussed some 

other topic or started by asking their partner a question (e.g., “hey, how’s your day going 

today?”) then data from the interaction was excluded from analysis. Study personnel 

observed the first turn-taking switch, whereby the participant’s interaction partner 

started to speak and/or started discussing their meaningful event – which was guided by 

previous studies on self-disclosure and turn-taking (Sprecher et al., 2013).    

 

In the phubbing condition, the time measure captured participants’ response (i.e., 

they stop speaking) or non-response (they continue speaking) after their confederate-

partner checked their mobile phone at exactly 1-minute into the interaction. The total 

word count measure, on the other hand, would include much more noise (i.e., 

potentially many words that do not involve any self-disclosure and perhaps reflect how 

fast participants speak more than actual self-disclosure) and not necessary observe 

responses (or non-responses) to checking behaviors. Though the time measure could 

also be confounded by how fast participants are speaking, the measure has the added 

 

54 

affordance of assessing mobile phone checking behaviors disrupt self-disclosure in 

response to the first phubbing behavior by the confederate.   

 

Following other recent phubbing studies (e.g., Vanden Abeele et al., 2016), 

interpersonal evaluations (e.g., liking/social attraction) were assessed by using the 

social attraction measure developed by Weisband and Atwater (1999). The scale 

consisted of 3-items to evaluate one’s interaction partner: “I like my interaction partner,” 

“I would like to see my interaction partner again (dropped based on factor analysis 

results),“ and “I dislike my interaction partner (this item was reverse coded).” Response 

options ranged from 1 (strongly disagree) to 5 (strongly agree). Responses were 

averaged into a composite measure of interpersonal liking (α = 0.81).    

 

Perceived partner trust and empathy were assessed by measures used in prior 

experimental phubbing effects research (Misra et al, 2016; Przybylski & Weinstein, 

2013). Trust was assessed by a 5-item measure originally developed by Larzelere and 

Huston (1980). Response options ranged from 1 (strongly disagree) to 5 (strongly 

agree) (α = 0.86). In addition, perceived emotional empathy was assessed by a 6-

itemed scale developed by Reis and Carmichael (2006). Response options ranged from 

1 (not true at all) to 5 (completely true) (α = 0.84).  

 

Perceived phubbing expectancy violation was assessed by a 2-itemed measure 

of mobile etiquette that was compatible with Chotpitayasunondh and Douglas’s (2016) 

measure of injunctive phubbing norm. The items stated, “my interaction partner’s mobile 

phone use was appropriate during our interaction” and “my interaction partner’s mobile 

phone use made me feel ignored during our interaction.” I reverse coded the first item, 

so that a higher score reflected a more severe expectancy violation. Response options 

 

55 

range from 1 (strongly disagree) to 5 (strongly agree) (α = 0.84).  

 

The primary independent variable was the pair-wise comparisons between the 

two experimental study conditions. Specifically, I was interested in comparing the two 

study groups’ depth of self-disclosure and interpersonal evaluations of the confederates 

(perceived liking, trust, empathy). In addition, a key interest was to assess potential 

differences between the two conditions regarding perceptions of phubbing expectancy 

violations as well as mobile phone checking mimicry (which was otherwise considered a 

predicting variable for several hypotheses). After data collection was completed, I 

assessed mobile phone checking mimicry by reviewing the video footage of each of the 

dyadic interactions that occurred.  

 

Consistent with Chartrand & Lakin’s (2013) definition of behavioral mimicry, 

mobile phone checking mimicry was operationally defined as behavioral matching of the 

confederate’s mobile phone checking behavior, within 5 seconds or less of the initial 

behavior. While reviewing the video footage of each interaction, phone checking 

behaviors were classified as phone mimicry if the participant met the following criteria: 

1) the checking behavior occurred with 5 seconds or less of the confederate’s phone 

checking behavior, 2) the checking behavior involved the physical touching of one’s 

device, and 3) the participant glanced at their phone (averted their gaze towards their 

device) while engaging in the checking behavior. If the participant’s checking behavior 

did not meet all three criteria, then the behavior was not classified as phone mimicry. 

The 360-degree video footage allowed for detailed inspection of nonverbal behaviors, 

including one’s gaze and engagement with their phone. The mobile phone checking 

mimicry measure was dummy coded as (1 = the participant engaged in at least 1 mobile 

 

56 

phone mimicry behavior, 0 = the participant did not engage in any mobile phone mimicry 

behaviors). This coding was used because among participants that engaged in mimicry 

all participants only engaged in 1 mobile phone mimicry behavior.    

 

The analysis controlled for sex (1 = female, 0 = male) and pre-interaction positive 

affect, which was assessed by the Diener and Emmons (1984) mood index, because 

prior mobile communication research suggests that it is an appropriate measure of 

positive affect in this experimental domain (Przybylski & Weinstein, 2013). Mood states 

can work as a covariate that affects impression and relationship formation processes in 

the mobile communication domain (Misra et al., 2016; Przybylski & Weinstein, 2013). 

Participant’s mood was assessed following their completion of the informed consent 

form prior to the interaction activity (α = 0.89). In addition, I controlled for personality 

factors, specifically extraversion, using the 10-item version of the Big Five inventory 

(Rammstedt & John, 2007). The extraversion measure consisted of two items “I see 

myself as someone who is reserved (this item was reverse coded)” and “I see myself as 

someone who is outgoing and sociable.“ Response options ranged from 1 (strongly 

disagree) to 5 (strongly agree) (α = 0.63).  I also controlled for participants’ age (ordinal 

measure ranging from 18 - 25) and race (African American, Asian, Caucasian, Other).  

 

Though the sample was homogenous in terms of age (18-25) and education 

(college students), I also collected data on participants’ age and college level 

(freshman, sophomore, etc.); however, age was also used as a control variable 

because it could otherwise confound perceptions of expectancy violations, interpersonal 

evaluations, and self-disclosure. In addition, I controlled for the non-confederate 

participants’ mobile phone checking habit, because it could influence mobile phone 

 

57 

mimicry and perceived phubbing expectancy violations. Mobile phone checking habit 

was assessed by 4-items developed and validated by Verplanken and Orbell (2003). 

Response options ranged from 1 (strongly disagree) to 5 (strongly agree) (α = 0.90). 

However, investigating what influences one to be more likely to engage in mobile phone 

mimicry was not the central focus of this study; future research could examine this topic. 

Analytical Procedures  

 

A series of t-tests and OLS linear regression models were used to assess the 

hypotheses previously outlined in this study, which allowed me to assess potential direct 

effects of the study conditions on the outcomes of interest and allow for sex, race, age, 

mobile phone habit, extraversion, and positive affect (pre-interaction) to be controlled. 

Similar data analysis procedures have been used in previous phubbing effects 

experiments (e.g., Vanden Abeele et al., 2016).  

 

The analysis was performed using IBM SPSS Statistics software version 25, 

which afforded the use of multiple comparisons tests and generate a range of 

descriptive statistics (e.g., means, standard deviations, Cohen’s d, etc.) that helped 

contextualize effect sizes (Lakens, 2013). In addition, Hayes PROCESS macro version 

3.0 was used with multiple regression analysis to test the proposed mediation effects. 

All effect sizes were reported regardless of whether the association was statistically 

significant (i.e., p < .05). For the multiple regression analysis, the variables were entered 

in the model in steps to assess mediation effects. Specifically, step 1 consisted of a 

simple bivariate regression model to assess the association between study conditions 

and each outcome. Step 2 included perceived phubbing expectancy violations, while the 

final step entered in the mobile phone mimicry variable and the control variables.  

 

58 

 

All scales used in the analysis factored together and also had internal 

consistency (α = 0.7 or greater). In addition, the behavioral measure of self-disclosure 

had 3 outliers; however, removal of those cases did not meaningfully change the 

results. Interestingly, within the phubbing condition, 2 of the 3 outliers on the behavioral 

measure of self-disclosure also happened to be in the minority of participants that also 

engaged in mobile phone mimicry – which happened to be consistent with the study 

hypotheses.  

 

 

 

 

 

 

 

 

 

 

 

59 

CHAPTER 4: RESULTS 

Descriptive Results 

 

The analytical sample had a mean age of 20 years old (range = 18-24) and was 

61% female and 39% male. The racial composition of the sample was 17% Asian, 10% 

African American, 8% ”Other,” and 65% White/Caucasian. Participants had strong 

mobile phone checking habits (M = 4.4, SD = 0.80, range = 1 to 5). In addition, all 

descriptive mean differences between the two study conditions were in the expected 

direction. See Table 1 for descriptive statistics. 

 

 

 

  

Table 1 Descriptive Statistics for Dependent, 
Independent and Control Variables 

 
Full 
Sample 
Means 
(n = 77) 

 
 

 
 SD 

 

 

      
     
SD 

 

Phubbing 
Condition  
Means 
(n = 38) 

 

 

Control 
Condition 
Means 
(n = 39) 

 

 
 
 SD 

 

Condition (1 = PC)  

49% 

      

   

 

First Self Discloser Stop 
(in seconds) 

Self-Reported  
Self Disclosure 

105.10 

73.86 

75.24 

60.68 

 134.21 

74.59*** 

3.69 

0.90 

3.55 

0.89 

  3.82 

 0.90 

Perceived Liking 

4.80 

0.41 

Perceived Trust 

Perceived Empathy 

Perceived Phubbing 
Expectancy Violations 

4.18 

4.36 

0.61 

0.49 

1.84 

1.12 

4.70 

4.12 

4.32 

2.61 

0.50 

  4.90 

 0.26*** 

0.70 

  4.25 

0.44 

  4.40 

 0.52 

 0.55 

1.16 

  1.10 

 0.29*** 

Phone Mimic (1 = yes) 

4% 

0.19 

8% 

0.27 

  0.00 

 0.00 

  Note: PC = phubbing condition, CC = control condition. Phone mimic 1 = 1 or 

more mimic, 0 = no mimics, ***p< .001.  

 
 

 

 

 

60 

Age 

Female 

Asian 

African American 

Other 

White 

61% 

17% 

10% 

 8% 

65% 

 

  

  

 

 

63% 

21% 

8% 

5% 

66% 

3.17 

       

   59% 

       

   13% 

       

   13% 

       

   10% 

       

   64%   

1.01 

   3.46 

  

  

  

  

  

 1.00 

 0.62 

 0.95 

 

                   Table 1 (cont’d) 

 

 

  

 
Full 
Sample 
Means 
(n = 77) 

 
 

 
 SD 

 

Phubbing 
Condition  
Means 
(n = 38) 

 

      
     
SD 

 

Control 
Condition 
Means 
(n = 39) 

 

 
 
 SD 

20.32 

1.25 

20.26 

1.27 

  20.38 

 1.25 

 

Extraversion (pre) 

3.32 

1.01 

Positive Affect (pre) 

Mobile Habit (post) 

3.36 

4.37 

0.68       

3.27 

0.73 

   3.46 

0.80 

4.47 

0.61 

   4.27 

  Note: PC = phubbing condition, CC = control condition. Phone mimic 1 = 1 or 

more mimic, 0 = no mimics, ***p< .001.  

 

Regression Results for Perceived Phubbing EV (H1a supported) 

 

H1a was supported by the descriptive findings and regression results (see Table 

2). On average, participants in the phubbing condition were more likely to perceive their 

interaction partner’s mobile phone use as inappropriate during the interaction (β = 1.59, 

SE = 0.21, p < .001) – net of all controls. Simple bivariate regression analysis showed 

that the between condition difference explained about 45% of the variance in perceived 

phubbing expectancy violations (β = 1.50, SE = 0.19, p < .001, F = 61.89, R^2 = 0.45). 

According to Cohen’s d, the between group effect size was large (a mean difference of 

more than 1 standard deviation) (Cohen’s d = 1.79).  I anticipated that the between 

condition difference would be associated with a large portion of variance in perceived 

phubbing expectancy violations because of the experimental manipulation (i.e., 

confederate mobile phone checking). None of the control variables were related to 

 

61 

perceived phubbing expectancy violations; hence, the Adjusted R Square was smaller 

in the full model (Adj. R^2 = 0.40) compared to the simple bivariate model (R^2 = 0.45).  

 

 

      Table 2 Regression Results for Perceived Phubbing 

EV as Dependent Variable  

 
 
 SE 
0.21 

    
     
     t  
 7.50 

 

 
 
p-value 

  < .001 

   
        

 
 
   b 
 1.59 

 

   

Condition 

(1 = PC) 

Phone Mimicry 

Age 

Female 

Asian 

 

 

 

 

 

 

  

 

 

.16 

.78 

-0.76 

0.53 

-1.44 

 0.03 

0.08 

 0.32 

-0.05 

0.22 

-0.22 

      .83 

-0.16 

0.31 

-0.50 

     .62 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

African 

  0.03 

0.37 

 0.07 

 

.95 

American 

Other 

Extraversion 

Positive Affect 

Mobile Phone 

 

 

 

 

 0.05 

0.39 

 0.13 

-0.00 

0.11 

-0.04 

-0.08 

0.15 

-0.55 

-0.10 

0.15 

-0.70 

Habit 

 

 

 

 

 

Constant 

  1.34 

1.92 

  0.70 

 

 

 

 

 

 

.90 

.97 

.59 

.48 

 

.49 

F 

  6.14 

Adjusted R^2 

 

  .40 

 

 

 

 

  < .001 

 

 

  Notes: N= 77, PC = phubbing condition  

 
Regression Results for Perceived Liking (H1b partially supported) 
 
 

H1b was partially supported. Statistically significant between condition 

differences were only observed for liking and not for empathy or trust. Specifically, 

 

62 

participants in the phubbing condition were more likely to report lower levels of liking 

than participants in the control condition (β = -0.20, SE = 0.09, p < .05, R^2 = 0.06). The 

Cohen’s d was = 0.5, which indicates a medium effect size. The bivariate regression 

between study condition and liking indicated that the between condition difference 

explained about 6% of the variance in liking. In addition, the association between study 

condition and liking was potentially mediated by perceived phubbing expectancy 

violation, which will be discussed below in the section on H2. In the full model (Step 3), 

perceived phubbing expectancy violations had a statistically significant and inverse 

association with perceived liking (β = -0.16, SE = 0.05, p < .01). Table 3 below shows 

the independent variables entered in steps.     

 

 

 

 

 

 

 

 

 

 

 

63 

 

 

   

 

 

Table 3 Regression Results for Perceived Liking as 
Dependent Variable     

   

Condition 
(1 = PC) 

Phubbing EV 

Phone Mimicry 

Age 

Female 

Asian 

African 
American 

Other 

Extraversion 

Positive Affect 

Mobile Phone 
Habit 

 

Constant 

F 

R^2/Adjusted 
R^2 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Step 1 
  b 
(se) 

-0.20* 
(0.53) 

 
 
  t 

0.09 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 Step 2 
    b  
  (se) 

  0.08 
 (0.12) 

 -0.17** 
 (0.05) 

 -0.36 
 (0.23) 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
 
   t 

 0.63 
  

-3.12 

-1.53 

 

 

 

 

 

 

 

 

 

 4.90*** 
(0.06) 

 4.87* 

   .06 

6.91 

 

 

 5.08*** 
(0.08) 

 5.36** 

   .15 

  60.57 

 

 

 

 

 
  

Step 3 
  b 
(se) 

   
        

 0.11 
(0.13) 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-0.16** 
(0.05) 

-0.40 
(0.23) 

-0.01 
(0.04) 

 0.08 
(0.10) 

 0.06 
(0.14) 

 0.21 
(0.16) 

 0.13 
(0.17) 

 0.04 
(0.05) 

 0.12 
(0.07) 

 0.01 
(0.07) 

 

 4.50*** 
(0.84) 

 2.17* 

   .15 

 

 

 
 
t 

 0.87 

-2.95 

-1.72 

-0.19 

 0.79 

 0.46 

 1.31 

 0.80 

 0.92 

 1.77 

 0.21 

 

 5.39 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  Notes: standard errors for each test statistic are shown in parentheses. ***p < .001, **p < .01, *p < .05. N 

= 77 

 
Regression Results for Perceived Trust and Empathy 

 

On average, participants in both conditions reported high levels of perceived trust 

(M = 4.2, SD = 0.61, range = 1 to 5). The between condition mean difference was in the 

 

64 

predicted direction (e.g., control group scored higher than phubbing group) but the 

effect size was small according to Cohen’s d and not statistically significant. In addition, 

the between condition difference only explained about 1% of the variance in perceived 

trust (R Square = 0.01) and the regression results were not statistically significant (p = 

0.30) (Table 4). As such, the findings do not suggest that mobile phone checking 

influenced perceptions of perceived trust (i.e., on average both conditions reported the 

same response option, M = 4.1 and M = 4.3, both round to the nearest integer response 

option “4”).      

 

Similarly, the mean perceived empathy scores for participants in both conditions 

were high (M = 4.4, SD = 0.49, range = 1 to 5). Much like perceived trust, the between 

condition mean difference was in the predicted direction (e.g., control group scored 

higher than phubbing group) but the effect size was small according to Cohen’s d. In 

addition, the between condition difference only explained less than 1% of the variance 

in perceived empathy (R Square = -0.01) and was not statistically significant (p = 0.52). 

The findings do not suggest that mobile phone checking influenced perceptions of 

perceived trust (i.e., on average both conditions reported the same response option - M 

= 4.3 and M = 4.4, both also round to the nearest integer response option “4.”     

 

 

 

 

65 

 

 

    

 

Table 4 Regression Results for Perceived Trust and 
Empathy as Dependent Variables     

   

 

Condition 
(1 = PC) 

Phubbing EV 

Phone Mimicry 

Age 

Female 

Asian 

African 
American 

Other 

Extraversion 

Positive Affect 

Mobile Phone 
Habit 

Constant 

F 

Adjusted R^2 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Trust 
  b 
(se) 

 

 0.05 
(0.19) 

-0.10 
(0.08) 

 0.24 
(0.36) 

-0.04 
(0.06) 

 0.02 
(0.15) 

-0.38 
(0.21) 

-0.01 
(0.25) 

 0.23 
(0.26) 

 0.14 
(0.07) 

 0.05 
(0.10) 

 0.10 
(0.10) 

 4.06** 
(1.28) 

 1.87 

  .11 

 
 
   t 

 

 0.28 

-1.24 

 0.67 

-0.67 

 0.10 

-1.81 

-0.06 

 0.88 

 1.87 

 0.50 

1.01 

3.17 

 

 

 Empathy 
    b  
  (se) 

 

   0.08 
  (0.16) 

  -0.07 
   0.07 
  

   0.18 
  (0.29) 
  

   0.04 
  (0.05) 

   0.18 
  (0.12) 

  -0.23 
  (0.17) 

   0.32 
  (0.21) 

   0.17 
  (0.21) 

   0.07 
  (0.06) 

   0.04 
  (0.08) 

  0.03 
 (0.08) 

 2.91** 
(1.05) 

 1.67 

   .09 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
 
   t 

 

 0.49 

-1.00 

  0.60 

  0.95 

  1.45 

 -1.35 

  1.58 

  0.83 

  1.25 

  0.47 

  0.43 

  2.78 

 

 

 
  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
  
 

 

 

 
 

 
 

 
 

 
 

  

  
 

  
 

  
 

  
 

  
 

  
 

 

  

 

 
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

  

  

  

 

 

 

 

 

 

  Notes: standard errors for each test statistics are shown in parentheses. ***p < .001, **p < .01, *p < 

.05. N = 77 

 

 

66 

Regression Results for Self Disclosure (H1c partially supported)  

 

H1c was partially supported (Table 5). On average, participants in the treatment 

condition scored lower on all the self-disclosure measures than participants in the 

control condition (self-report and behavioral measure). However, statistically significant 

between group differences were only observed for the behavioral measure of self-

disclosure (β = -80.47, SE = 21.06, p < .001) – net of all controls. In the full model, 

mobile phone mimicry was also positively associated with longer lengths of initial self-

disclosure compared to participants that did not mimic their partner’s mobile phone use 

(β = 136.20, SE = 38.50, p < .001). In addition, participants that had a self-reported race 

of “other” engaged in longer self-disclosure than white participants (β = 58.36, SE = 

27.53, p < .05).    

 

Without accounting for control variables, the between condition mean difference 

was about 59 seconds (β = -58.97, SE = 15.52, p < .001, R^2 = 0.16) and there was an 

80 second difference when accounting for all control variables (control group engaged 

in more than a full minute of initial self-disclosure compared to the phubbing group) (Adj. 

R^2 = 0.30). The predictor variables in the full model explained about 30% of the total 

variance in the initial amount of time the participant engaged in self-disclosure. The 

Cohen’s d test statistic also indicated a large effect size between the two study 

conditions (0.87). The boxplot below (Figure 8) highlights that a large portion of the 

participants in the treatment condition stopped engaging in self-disclosure within several 

seconds of their confederate-partner’s first mobile phone checking behavior 1-minute 

into the interaction (in the phubbing condition).   

 

67 

 

 

      Table 5 Regression Results for Self Disclosure  

Behavioral Measure as Dependent Variable 

   

Step 1 

Condition  

(1 = PC) 

Constant 

F 

R^2 

Step 2 

Condition 

(1 = PC) 

Phubbing EV 

Phone Mimicry 

Age 

Female 

Asian 

African 

American 

Other 

Extraversion 

Positive Affect 

Mobile Phone 

Habit 

Constant 

F 

Adjusted R^2 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
 
   b 
 

 
 
 SE 
 

    
     
     t  
 

 

 

 
 
p-value 
 

 -58.97 

15.52 

 -3.80 

  < .001 

134.21 

10.90 

12.31 

  < .001 

  14.44*** 

  00.16 

 

 

 

 

 

 

 

 

 

 

 

 

 

 -80.47 

20.59 

-3.91 

  < .001 

 

  10.54 

  8.79 

 1.20 

135.20 

38.50 

 3.54 

   -1.67 

  6.00 

-0.28 

  

 

 

 

   .24 

   .001 

   .78 

   -2.76 

16.00 

-0.17 

         .87 

  34.90 

22.28 

 1.57 

        .12 

    4.36 

26.37 

 0.17 

 

   .87 

  58.36 

27.53 

 2.12 

  11.59 

  7.82 

 1.48 

   -7.81 

11.04 

-0.71 

 -15.18 

10.61 

-1.43 

 

 

 

 

  .04 

  .14 

  .48 

  .16 

199.20 

137.33 

 1.45 

  < .001 

    3.89*** 

     .30 

 

 

 

 

 

 

 

 

 
  

 

 

 

 

 

 

 
 

 

 

 

 

 

 

 

   
        

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  Notes: ***p < .001, **p < .01, *p < .05. N = 77 

 

68 

Figure 8 Between Condition Difference in Self-Disclosure 

 

NOTE: The self-disclosure measure had 3 key outliers, however removal of those cases did not lead to 
meaningful changes in the results. Interestingly, within the phubbing condition, 2 of the 3 outliers for the 
behavioral measure of self-disclosure also happened to be in the minority of participants that engaged in 
mobile phone mimicry, which supports the prediction that phone mimicry may lead to prosocial outcomes.  
                      
 

On average, participants in both conditions reported moderate to high levels of 

self-disclosure (M = 3.7, SD = 0.90). The between condition mean difference was in the 

predicted direction (control group scored higher than phubbing group) but the effect size 

was small according to Cohen’s d (d = 0.30). In addition, the between condition 

difference only explained about 1% of the variance in self-disclosure (R^2 = 0.01) and 

was not statistically significant (p = 0.20) (See Table 6). The group means also both 

round to response option 4, which suggests that there was not a substantive between 

group difference with this measure. The mobile phone mimicry results will be discussed 

in the mobile phone checking mimicry subsection below and interpreted through the use 

of boxplots. 

 

69 

 

 

      Table 6 Regression Results for the Self-Reported Self 

Disclosure Measure as Dependent Variable 

 
 
p-value 
  .14 

 
  

 
 

 

   
        

 
 
 SE 
0.30 

    
     
     t  
-1.49 

 
 
   b 
-0.44 

 

 0.06 

0.13 

 0.45 

 1.13 

0.56 

 2.04 

 

 

 

 

  .66 

  .05 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   

Condition 

(1 = PC) 

Phubbing EV 

Phone 

Mimicry 

Age 

Female 

Asian 

African 

American 

-0.02 

0.09 

-0.25 

 

  .81 

-0.07 

0.23 

-0.32 

        .75 

-0.52 

0.32 

-1.62 

       .11 

-0.15 

0.38 

-0.40 

 

  .69 

Other 

 

-0.75 

0.40 

-1.88 

Extraversion 

  0.002 

0.11 

-0.01 

Positive Affect 

Mobile Phone 

Habit 

 

Constant 

F 

Adjusted R^2 

 

 

 

 

 

 

 0.03 

0.16 

 0.21 

 0.02 

0.15 

 0.11 

 

 

 

 4.22 

1.98 

 2.13 

 1.14 

   .02 

 

 

 

 

 

 

 

 

 

 

 

 

  .07 

  .99 

  .83 

  .91 

 

  .04 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  Notes: N = 77 

 

Indirect Effect Between Mobile Checking and Liking (H2 partially supported)  

 

Only one interpersonal evaluation outcome (liking) met the necessary 

preconditions to test for mediation analysis (H2a). Sobel and Process mediation 

analyses were used to assess if perceived phubbing expectancy violations mediated the 

 

70 

association between study conditions and liking. Both the Hayes PROCESS mediation 

analysis (indirect effect: -0.25, SE = 0.13, CI: -0.51, -0.03) and Sobel analysis (z = -

2.95, SE = 0.09, p = 0.003) suggested that there was an indirect association between 

mobile phone checking and perceived liking that was mediated by perceived phubbing 

expectancy violation.  

 

The size of the association between the study conditions and liking decreased by 

85% and was not statistically significant when perceived phubbing expectancy violation 

was entered into the regression model (Figure 9). The associations between study 

condition and perceived phubbing expectancy violations explained around 13% of the 

variance in perceived liking. Specifically, the results suggest that participants in the 

treatment condition were more likely to perceive phubbing expectancy violations and 

subsequently to report lower levels of partner liking – which supported H2a.  

Figure 9 Mediation Analysis 

Note: The coefficients in parentheses indicate the association between mobile phone checking and liking 
before perceived phubbing expectancy violation was entered in the regression model 

 

Mobile Phone Checking Mimicry Results 

 

Only 8% of the participants in the treatment condition engaged in mobile phone 

mimicry and none of the participants in the control condition engaged in mobile phone 

checking mimicry. Specifically, 2 of the 3 participants in the treatment condition that 

 

71 

engaged in mobile phone checking mimicry also had the outlier scores on the 

behavioral measure of self-disclosure. Within the treatment condition, the 3 participants 

that engaged in mobile phone mimicry reported higher levels of trust (H6), empathy 

(H6), and self-disclosure (H8) and lower levels of perceived phubbing expectancy 

violations (H4) compared to the rest of the participants in the treatment condition. 

However, statistically significant differences with large effect sizes were observed 

between mobile phone checking mimicry and self-disclosure – both with the behavioral 

measure (β = 136.20, SE = 38.50, p < .01) and the short 3-item self-disclosure measure 

(β = 1.13, SE = 0.56, p < .05).  

 

The between condition moderation hypotheses, H3, H5, and H7 were not tested 

because phone mimicry (nor phone checking) occurred in the control condition. Though 

mobile phone mimicry was inversely associated with perceived phubbing expectancy 

violations (β =-0.76, SE = 0.53, p = .16) and positively associated with interpersonal 

evaluations (e.g., trust (β = 0.24,  SE = 0.36, p > .05) and empathy (β = 0.18, SE = 0.29, 

p > .05), the mean differences were not statistically significant. The differences shown in 

Figures 10 and 11 were statistically significant for both indicators of self-disclosure (p < 

.05). 

 

 

 

 

 

 

72 

Figure 10 Phone Mimicry and Behavioral Measure of Self-Disclosure 

 
Figure 11 Phone Mimicry and Self-Reported Measure of Self-Disclosure 

 

 

 

73 

 

CHAPTER 5: DISCUSSION  

Chapter 5 Overview 

 

In Chapter 5, I recap the results, discuss the findings in the context of EVT theory 

and previous literature, consider practical implications, and scrutinize the study 

limitations. The remaining chapters/sections highlight the potential prosocial and 

antisocial effects of mobile phone checking, during FtF first encounters among young 

adults. The findings point to the dynamic aspect of FtF interpersonal communication 

and extend phubbing research by identifying possible novel mechanisms of action (e.g., 

mobile phone checking mimicry).  

Summary of Results 

 

This study investigated the potential effects of mobile phone checking during FtF 

first encounters, among a sample of young adult college students (aged 18-25). 

Specifically, I assessed whether mobile phone checking may influence a specific 

communication outcome, self-disclosure. In addition, I examined whether mobile phone 

checking mimicry potentially mitigate adverse mobile checking effects. Participants in 

the treatment condition reported higher levels of perceived phubbing expectancy 

violations than participants in the control condition (H1a supported). Participants in the 

control condition reported higher levels of perceived liking, trust, and empathy than 

participants in the treatment condition; however, only the mean difference for perceived 

liking was statistically significant (H1b partially supported). Participants in the treatment 

condition self-reported lower levels of self-disclosure and scored lower on the 

behavioral measure of self-disclosure compared to participants in the control condition; 

however, only the between condition mean differences were statistically significant for 

 

74 

the behavioral measure (H1c partially supported).  

 

I also observed evidence that perceived phubbing expectancy violations 

mediated the association between study conditions and perceived liking (H2a partially 

supported) but not self-disclosure (H2b not supported). Perceived phubbing expectancy 

violations had an inverse bivariate association with the behavioral measure of self-

disclosure, which suggested each unit increase in perceived phubbing expectancy 

violations was associated with about 13 seconds less on the behavioral measure of 

initial self-disclosure. Moreover, only 8% of the participants in the treatment condition 

and none of the participants in the control condition engaged in mobile phone checking 

mimicry. Mobile phone checking mimicry was inversely associated with perceived 

phubbing expectancy violations (H4) and positively associated with indicators of 

interpersonal evaluations (e.g., trust and empathy) (H6) and self-disclosure (H8). 

However, within the phubbing condition, only the association between mobile phone 

checking mimicry and self-disclosure was statistically significant (with both the 

behavioral measure and self-reported measure) (H8 supported). Because none of the 

participants in the control condition engaged in mobile phone checking mimicry, I was 

not able to test the between condition mimicry moderation hypotheses (H3, H5, & H7). 

Eight of the 11 predictions were tested, with 5 of the 8 receiving at least partial support.  

Discussion of Results 

 

Inappropriate mobile phone use can cause conflict within relationships and 

potentially be detrimental for well-being (e.g., Halpern & Katz, 2017; Roberts & David, 

2016). Phubbing can have effects on interpersonal interactions, such as hinder 

interpersonal evaluations and influence perceptions of communication quality 

 

75 

(Chotpitayasunondh & Douglas, 2016, 2018; Misra et al., 2016). Prior to this study, the 

existing research did not yet indicate whether phubbing can influence actual 

communication outcomes, such as self-disclosure, or the sharing of information about 

one’s self to another person (thoughts, feelings, emotions) (Tardy & Smithson, 2018; 

Utz, 2015). When people meet for the first time, self-disclosure tends to be one of the 

most common forms of interpersonal communication and it can help build affiliation and 

intimacy within relationship (Wenzel, Sprecher & Harvey, 2018). Other phubbing 

researchers have also considered the notion that phubbing may influence aspects of 

interpersonal communication, such as self-disclosure, but had yet to investigate it (e.g., 

Vanden Abeele et al., 2016, Vanden Abeele, forthcoming).  

 

The findings from the present study suggest that mobile phone checking 

behaviors can disrupt self-disclosure processes. Within the phubbing condition, about 

70% of participants responded to the confederates first mobile checking behavior by 

immediately ending their turn to discuss their meaningful event. However, this 

association was not mediated by perceived phubbing expectancy violations, which 

could suggest that mobile checking, whether deemed as inappropriate (i.e., phubbing) 

or not, may influence self-disclosure. Alternatively, other mechanisms of action, such as 

specific attentional cues (e.g., gaze diversion), could facilitate the association between 

mobile checking and adverse interpersonal outcomes (Vanden Abeele, forthcoming). 

One may perceive that their interaction partner is communicating nonverbal cues that 

signal inattention and disinterest, which may hinder self-disclosure, without necessarily 

perceiving a negative violation of mobile etiquette. Nevertheless, the result highlights 

the salience of considering how mobile checking habits may influence other 

 

76 

components of everyday interpersonal conversations, which could be altered in subtle 

but potentially meaningful ways.  

 

Counter to previous research (e.g., Karadag et al., 2016), participants in this 

study showed nearly flawless behavioral control over their checking behaviors. No 

mobile checking or mimicry occurred in the control condition and non-confederates only 

checked their mobile phone while mimicking the confederate. But, the mobile phone 

checking mimicry findings in the phubbing condition support the notion that 

synchronized mobile checking could have prosocial outcomes (Humphreys & 

Hardeman, 2019), which contradicts the assumption that all mobile phone checking 

during FtF interactions has either negative or benign effects. For example, mobile 

phone mimicry was positively associated with self-disclosure.  

 

Though the mobile phone mimicry findings were limited by the sparse occurrence 

of mobile phone mimicry behaviors (potentially due to the length of the interaction, 

experimental setting, and unacquainted compared to established relationship between 

interaction partners), the findings suggest that when mobile phone checking occurs, it 

can disrupt self-disclosure during first encounters, but this effect might be lessened by 

mobile phone checking mimicry. Specifically, mobile phone mimicry was positively 

associated with self-disclosure. This study may be the first to observe evidence that 

suggests that mobile phone checking behaviors may influence a specific type of 

interpersonal communication (self-disclosure) required for affiliation formation and 

relationship development during FtF first encounters. However, in line with the 

expectancy violation theory framework applied to the phubbing context (Vanden Abeele 

et al., in press; Vanden Abeele, forthcoming), mobile phone checking mimicry may 

 

77 

promote prosocial outcomes as well (e.g., higher levels of self-disclosure). 

Contributions to EVT Theory  

 

This study was informed by several different theories and bodies of literature, but 

the theoretical underpinning for this study came primarily from expectancy violation 

theory (EVT). EVT proposes that expectancy violations, or deviations from expected 

etiquette or interpersonal behavior can disrupt interpersonal evaluations and 

communication processes (Burgoon, 2015). The theory assumes that when people 

encounter a departure from expected interpersonal behavior, one is likely to experience 

an emotional arousal that can be positively or negatively valanced (Burgoon, 1993). 

Phubbing constitutes a negatively valanced expectancy violation because being 

snubbed or ignored can be perceived as a threat to one’s need for affiliation (Roberts & 

David, 2016; Vanden Abeele et al., 2016; Vanden Abeele, forthcoming). In addition, 

negatively valanced expectancy violations can hinder interpersonal evaluations and 

communication processes, during FtF first encounters among young adults – even 

though young adults tend to have permissive views towards mobile phone use during 

FtF interactions (Rainie & Zickuhr, 2015; Richter, 2018).  

 

Premised on EVT, a key mechanism of action for why phubbing is a negative 

expectancy violation is gaze aversion or shifting one’s gaze away from their interaction 

partner and towards another object of focus (Miller-Ott & Kelly, 2015, 2017; Vanden 

Abeele & Postma-Nilsenova, 2018). During FtF interpersonal interactions, gaze 

aversion can diminish affiliation formation and negatively influence interpersonal 

evaluations of one’s interaction partner. Gazing at one’s phone is more likely to be 

perceived as ostracizing, by threating one’s need for affiliation, compared to non-

 

78 

interactive media such as averting one’s gaze towards a newspaper (Vanden Abeele & 

Postma-Nilsenova, 2018). Because negative expectancy violations that involve gaze 

aversion can be disruptive to interpersonal communication in general (Burgoon, 2015) 

and specifically to self-disclosure (e.g., Omarzu, 2000), this study purported to extend 

the EVT by investigating a relevant communication outcome that is also inextricably 

linked to affiliation formation processes (e.g., the exchange of self-disclosure). The 

findings provide evidence that mobile phone checking may influence self-disclosure 

processes (i.e., lead to less self-disclosure), which could have implications for 

relationship formation.  

 

However, perceived phubbing expectancy violations only mediated the 

association between mobile checking and interpersonal judgements (specifically, 

perceived liking), but not with the behavioral indicator of self-disclosure. Perhaps, 

perceptions of phubbing expectancy violations could play more of a role in shaping 

interpersonal evaluations, compared to self-disclosure. Nonverbal communication cues 

can trigger automatic affiliation formation processes (Burgoon, 2015). Mobile checking 

behaviors that lead one to avert their gaze from their interaction partner may inhibit self-

disclosure because people view phubbing and gaze aversion as a sign of disinterest 

and lack of desire to affiliate (Hales, Dvir, Wesselmann, Kruger & Finkenauer, 2018). It 

is possible that even if participants were not consciously aware that they stopped 

engaging in self-disclosure, immediately following their partner’s responsive mobile 

phone check, they could have automatically processed the nonverbal cues of disinterest 

and lack of immediacy, which hinders interpersonal evaluations and perhaps 

communication processes without one even realizing it.  

 

79 

 

EVT also maintains that perceived similarity and imitation (or mimicry) may make 

one less likely to view a specific behavior as a negative expectancy violation (Burgoon, 

2015). Following this premise, mobile phone checking mimicry may also lessen 

phubbing effects – though mimicry might occur more often in social settings, compared 

to first encounters in an experimental environment such as in this study. In addition, 

other phubbing researchers have recently proposed that prosocial phone use can 

mitigate negative phubbing effects (e.g., Vanden Abeele, forthcoming). This notion is 

supported by recent findings that suggest that young people commonly share their 

screen with their interaction partners (e.g., “look at this picture”) during conversations 

that also include phubbing (i.e., perceived phone snubbing). As such, the dual potential 

for prosocial and antisocial consequences of phubbing and mobile phone checking may 

have a myriad of practical implications.   

Practical Implications  

 

Evidence suggests that young adults’ mobile communication habits are activated 

ubiquitously throughout daily life (Bayer, Dal Cin, Campbell & Panek, 2016; LaRose, 

2010, 2015). As such, it was reasonable to assume that in both conditions participants 

would engage with their mobile phones more than was observed in the study. Although 

the mimicry results were limited, I recommend that phubbing researchers should not 

throw out the notion that mobile phone checking mimicry may also mitigate perceptions 

of interpersonal judgements in situations that invoke more natural checking habits, 

relative to a laboratory setting.  

 

Conditions in which mobile checking occurs normatively are less likely to 

engender negative phubbing expectancy violations and perhaps more likely to promote 

 

80 

the potential prosocial benefits of phone mimicry, perhaps through the mechanism of 

perceived similarity. It was anticipated, for example, that mobile phone checking 

mimicry would occur in both conditions – which did not happen because of the lack of 

checking in the control condition. This “non-finding” could also have practical 

implications that encourage a focus on mobile communication habits and dissuade 

researchers from foregoing the conclusion that most young adults are addicted to their 

mobile phones, which inherently implies a lack of behavioral control that differs from 

cognitive automaticity/habit (LaRose, 2015; Verplanken & Orbell, 2003).  

 

This study provides support for the notion that young adults have strong mobile 

phone checking habits (evidenced by their responses to the mobile phone checking 

habit scale), but that they exerted behavioral control by not checking their mobile phone 

in the control condition (even though they were NOT instructed that they had to abstain 

from use). To the best of my knowledge, this study was the first phubbing experiment to 

allow participants in the control condition to have their phones during the interaction.  

 

Consistent with the notion of mobile checking habit, some participants in the 

treatment condition (all of whom encountered mobile phone checking) also engaged in 

mobile phone checking mimicry – which could indicate the activation of the participant’s 

own mobile checking habits. This research is aimed at provoking a broader discussion 

of the potential interpersonal communication processes couched in widely pervasive 

mobile checking habits, which are relevant for all types of interpersonal relationships. 

The future of mobile technologies, though widely unknown, will likely involve even more 

embeddedness of mobile communication habits in everyday life.  Emerging mobile 

technologies such as augmented and mixed reality (e.g., Magic Leap headset) may 

 

81 

likely involve novel sets of evolving accepted norms and behaviors (e.g., mixed-reality 

snubbing), which could also have implications for interpersonal communication and 

relationships. Because interpersonal communication is a core component of everyday 

life, it is paramount to consider the prosocial and antisocial effects of mobile 

communication habits in daily life.   

Study Limitations 

 

Like all studies, this investigation is not without limitations. One limitation is that 

the sample design only included student participants from one university. Though the 

study achieved statistical power, a larger and more diverse sample of young adults 

could benefit future research to improve external validity and generalizability of the 

findings. Given that this study was the first to examine self-disclosure in the phubbing 

context, I used a self-reported and behavioral measure of self-disclosure. However, 

future research could benefit from the use of other self-disclosure measures as well, 

including assessments of one’s interaction partners self-disclosure, text mining/total 

word count measures, and/or qualitative approaches. The experimental study design 

allowed me to rule out confounding factors that are potentially associated with the study 

hypotheses, such as relationship history, conversational topic, status, age, and sex; yet, 

the laboratory setting could have limited the activation of norms and expectations 

associated with social settings. For example, previous field experiments have reported 

much higher rates of mobile phone checking than was observed in this study, which 

could reflect the activation of normative expectations to be responses via one’s mobile 

phone that might increase the possibility of mobile phone checking mimicry. In addition, 

future work could specifically match participants by their language abilities as well, 

 

82 

particularly if the sample will include both domestic and international students/young 

adults.      

 

Moreover, this study suggests that phubbing may lead to mild negative effects on 

interpersonal evaluations and self-disclosure processes. But, the incorporation of 

longitudinal data would allow for the assessment of these processes over time, which 

could shed light on potential effects that disruption of such processes may have on 

affiliation formation and relationship development. For example, on average, 

participants reported high levels of perceived trust. However, phubbing within romantic 

relationships has been shown to decrease trust over time (Halpern & Katz, 2017). As 

such, the relationship between mobile phone checking/mimicry, perceived phubbing 

expectancy violations, and perceived trust may yield divergent patterns (more distinct 

differences) if assessed outside of the context of unacquainted strangers as well as by 

types of phubbing (e.g., responsive phone checking, discussion length, etc.) (Miller-Ott 

& Kelly, 2015) and phone mimicry behaviors.  

 

It is plausible that the short length of the interaction (10 minutes), use of 

responsive mobile checking (which is less severe than proactive checking violations), 

and the context of a get-to-know-you activity that required the discussion of a 

meaningful event were limiting factors for phone mimicry. In addition, further intercoder 

reliability test statistics could be used to ensure accurate interpretation of the 

video/behavioral data for mimicry and self-disclosure. The mimicry results are also 

limited because they were only assessed within the phubbing condition and they did not 

occur in the control condition. Therefore, I cannot rule out the possibility that the mimicry 

findings were indicative of individual differences. It is also possible that how participants 

 

83 

placed their phone on the table (facing up vs facing down) could have confounded the 

mimicry findings because confederates always kept their phone facing upwards, while 

non-confederate participants placed their devices either way. The following section 

elaborates further on study conclusions and directions for future research.   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

84 

CHAPTER 6: CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS 

The affordances of mobile communication allow for people to build affiliation and 

connect with others more than ever before in recorded history (Chayko, 2017). An 

underlying assumption of this study rests on decades of research that suggests that 

humans have a need for affiliation and are motivated to avoid affiliative threats, such as 

experiencing social rejection (Nezlek, Wesselmann, Wheeler & Williams, 2015; 

Wesselmann & Williams, 2017). When mobile communication habits occur during FtF 

interactions, it is possible for phubbing, a negative expectancy violation, to occur. Given 

that mobile phone checking habits, for example, occur commonly throughout the day – 

especially among young adults that tend to check their mobile phones more than other 

age cohorts (e.g., older adults) (Rainie and Zickuhr, 2015). In addition, most young 

adults encounter phubbing daily (Chotpitayasunondh & Douglas, 2018). Consistent with 

previous phubbing and EVT research, I suspected that mobile checking would hinder 

interpersonal judgements and self-disclosure during first encounters among young adult 

college students, which was anticipated to be mediated by perceptions of phubbing 

expectancy violations. Partial support was found for the predictions.  

 

Despite the limitations, the study contributes to EVT theory by extending the 

theory to the investigation of phubbing and communication outcomes (self-disclosure), 

which is both consistent with the theory and novel in the phubbing context. In addition, 

EVT proposes that nonverbal behavioral mimicry naturally promotes affiliation, in part by 

mitigating perceptions of negative expectancy violations. To this end, the present study 

also makes a modest contribution to theory by reporting on the potential role of mobile 

phone mimicry during first encounters among young adults. In lieu of the findings and 

 

85 

given the prevalence of mobile communication habits and phubbing among young 

adults, it is important that future research continues to examine how such mobile 

communication behaviors potentially influence key aspects of interpersonal 

communication and affiliation formation. The following subsection further elaborates on 

potential future research directions, open questions, and conclusions.  

 

Although this study sheds light on novel research questions, it also sets the stage 

for future research. In this conclusion subsection, I lay out a framework for a future 

research agenda and propose several open research questions. Much is yet to be 

learned about both the causes and effects of phubbing behaviors. In addition, the 

application of EVT based approaches to this domain has much to offer. Moreover, it is 

plausible that phubbing processes may become reincarnated in the context of emerging 

mobile media and communication technologies (Vanden Abeele, forthcoming); for 

instance, other types of technology-use-snubbing behaviors may develop during FtF 

interactions. In short, interactive mobile media habits, which are thought to be a driving 

force behind phubbing behaviors, surface in the context of a myriad of interactive media 

technologies (e.g., mixed-reality technology such as Magic Leap) and will likely continue 

to do so in the future (LaRose, 2015). As such, considering how widely pervasive 

interactive media habit behaviors may influence interpersonal evaluations, 

communication processes, and relationships is paramount to understanding the 

potential impacts of use.  

 

At the present time, however, there is still much to be discovered about how 

mobile checking habits, phubbing, expectancy violations, and interpersonal 

communication processes work together. One area that is especially in need of 

 

86 

continued research attention is on the potential prosocial effects of mobile phone use 

during FtF interactions, which may mitigate the adverse effects of negative phubbing 

expectancy violations and disruptive mobile phone checking. The present study was the 

first to attempt to assess whether mobile phone checking mimicry can work to engender 

prosocial outcomes (e.g., more favorable interpersonal evaluations, less likely to 

perceive negative expectancy violations and/or promote self-disclosure). In addition, 

recent formulations of EVT in the phubbing context have also attempted to explicate 

additional mechanisms that could help explain potential prosocial effects of mobile 

phone use during FtF interactions (Vanden Abeele, forthcoming).  

 

A new study by Vanden Abeele et al. (in press) suggests that phubbing occurs in 

most FtF interactions; however, a sizable portion of such interactions involve sharing 

one’s screen with their interaction partner, thus highlighting another possibly related 

avenue to pursue prosocial phone use during FtF interactions. Another recent field 

study also indicates that parallel mobile phone use, such as two partners holding hands 

while they are both interacting with their own mobile phones, may lead to prosocial 

outcomes (Humphreys & Hardeman, 2019). As such, future research could continue to 

investigate mobile phone mimicry, parallel use, and forms of device sharing in relation 

to negative expectancy violations and interpersonal outcomes.     

 

Moreover, there is still much unknown about the potential adverse effects of 

phubbing – especially in regard to the exchange of verbal messages. This study 

focused on self-disclosure given its relevance for affiliation formation and relationship 

development. However, self-disclosure and other important forms of interpersonal 

communication, such as the exchange of social support, can develop over time 

 

87 

throughout a given relationship. As such, future research could continue to examine 

phubbing, expectancy violations, and self-disclosure in a myriad of relationship 

domains. 

 

 

Much of the phubbing research, though diverse in some respects (e.g., 

culturally), tends to focus on young adults and romantic relationship domains. Previous 

research, however, has examined phubbing within intergenerational family relationships 

(e.g., Kadylak et al., 2018; SawChuk & Crow, 2012). If one does take on the pursuit of 

investigating phubbing and interpersonal communication within other relationship 

domains, it may also be important to consider different types of phubbing (e.g., 

proactive vs responsive phubbing) (Vanden Abeele et al., 2016). Given previous 

findings, one may expect to potentially observe more severe phubbing effects on self-

disclosure than in the present study if proactive, or unsolicited phubbing, is assessed in 

conjunction with responsive phubbing (which was the only type of phubbing tested in 

this study).  

 

 

Future mobile communication research could also consider gaze length 

variations in the context of phubbing. Such an approach might help parse out the 

mechanisms of action between mobile phone checking and prosocial/antisocial 

outcomes during FtF interactions. By investigating gaze length, mimicry, perceptions of 

phubbing expectancy violations, and varying types of phubbing behaviors one might 

procure a deeper understanding of how mobile communication habits develop and 

influence interpersonal communication and affiliation formation processes.   

 

In summary, this study suggests that mobile phone checking and phubbing 

among young adults may influence key aspects of interpersonal communication and 

 

88 

affiliation formation. Though mobile checking habits and phubbing occur frequently 

throughout the daily lives of young adults, potential phubbing effects (i.e., less favorable 

interpersonal evaluations and less self-disclosure) should generally be thought of as 

unintended consequences of mobile checking habits that tend to occur with limited to no 

deliberation (Bayer et al., 2016). In other words, phone-snubbing (phubbing) occurs as 

a result of mobile phone checking habits – instead of deliberately attempting to make 

one’s interaction partner feel ignored. Similarly, automatic behavioral mimicry may also 

play a key role in shaping potential phubbing effects. By advancing this research 

agenda, we will be able to better grasp how widely pervasive mobile communication 

habits influence daily life.   

 

 

 

 

 

 

 

 

 

 

 

89 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

APPENDICES 

90 

Appendix A: Confederate Instructions 
 
Purpose/Overview:  
The purpose of the study is to assess emerging adults during a face-to-face get-to-
know-you activity. Specifically, you will interact with your study partner for 10-minutes. 
During the interaction, you and your study partner will take turns discussing a 
meaningful event that has occurred in both your lives during the past year. Based 
on SONA IDs, study personnel have already randomized which participant will start the 
discussion. When you are in the speaker role, you will discuss a meaningful event that 
has occurred in your life. While you are in the listener role you will discuss the 
meaningful event that occurred in your study partner’s life.  

You have been randomly selected to start in the listener role. In addition, you 
have also been selected to be the confederate in this study. This means we need 
you to perform one extra task during the interaction. Specifically, you will receive 
3 text messages from study personnel during the interaction. We need you to 
visually confirm that each message is delivered. And, we need you to clear the 
message as naturally as possible, while lifting your phone from the table. Aside 
from naturally checking your phone for messages, we would like you to engage in 
the discussion as you otherwise would.      

Checklist/Procedures:  

•  Please confirm that you reviewed the consent form and completed the pre-

interaction survey 
 

•  Please sit in the seat that the researcher has asked you to sit in and take a few 
moments to consider a meaningful event that you might wish to discuss during 
the get-to-know-you-activity  
 

•  Do not worry about the time during the interaction 

• 

 
If you have a backpack, purse, coat, or any other small personal item make sure 
they are in the specified location 
  

•  During the interaction, please put your mobile phone on the table in front of you 

and please make sure your phone is in silent mode and facing upwards 
 

•  As instructed above, please check your phone for 3 “test messages” throughout 
the interaction. When the message alert appears on your mobile phone, please 
read the message while lifting your phone from the table.  
 

 

91 

•  Please do not rearrange or move the chairs or other furniture in the interaction 

room 
 

•  Once the Interaction has ended, please complete the brief post-interaction 

survey  
 

THANK YOU FOR YOUR PARTICIPATION!  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

92 

Appendix B: Measures  

Self-Disclosure (Laurenceau, Barrett & Pietromonaco,1998) 
1. During the interaction I disclosed my thoughts  
2. During the interaction I disclosed my emotions 
3. During the interaction I disclosed intimate information about myself  
Response options: 1 (very little) to 5 (a great deal) 

Interpersonal Evaluations 
Liking/Social Attraction (Weisband & Atwater, 1999, Vanden Abeele et al., 2016) 
1. I like my interaction partner  
2. I dislike my interaction partner (R) 
3. I would like to see my interaction partner again (dropped due to factor analysis) 
Response options:1 (strongly disagree) to 5 (strongly agree) 

Perceived Trust (Larzelere & Huston, 1980)  
1. My interaction partner was perfectly honest and trustful 
2. My interaction partner was truly sincere 
3. I feel that I can trust my interaction partner  
4. I feel that my interaction partner can be counted on 
Response Options: 1 (strongly disagree) to 5 (strongly agree) 

 Perceived Emotional Empathy (Reis and Carmichael, 2006) 
1. My Interaction partner: expressed liking and encouragement for me. 
2. Seemed interested in what I was thinking and feeling.  
3. Seemed interested in discussing things with me.  
4. Valued my opinions.  
5. Respected me.  
Response Options: 1 (Not true at all) to 5 (Completely true) 

Perceived Phubbing Expectancy Violation 
Expectancy Violation of Phubbing Norm (Chotpitayasunondh and Douglas, 2016) 
(Manipulation Check questions) 
1. My interaction partner’s mobile phone use was appropriate during our interaction (R) 
2. My interaction partner’s mobile phone use made me feel ignored  
Response Options: 1 (strongly disagree) to 5 (strongly agree) 

Control Variables  
Mood/Positive Affect (Przybylski & Weinstein, 2013 observed an α = 0.95) 
How much do you feel?  
1. Happy  
2. Joyful 
3. Pleased  
4. Enjoyment/Fun       
5. Depressed 
6. Unhappy 
7. Frustrated   
8. Angry 

 

93 

9. Worried/Anxious 
Response options: 1 (not at all) to 5 (extremely) 

Extraversion (α = 0.63) 
1. I see myself as someone who is reserved (this item was reverse coded)  
2. I see myself as someone who is outgoing and sociable 
Response options: 1 (strongly disagree) to 5 (strongly agree) 

Mobile Phone-checking Habit (Bayer, Dal Cin, Campbell & Panek (2016) observed an α = 0.95) 

Please rate your level of agreement or disagreement with the following 
statements: 

Checking my mobile phone is something… 
1. I do automatically 
2. I do without thinking 
3. I do without having to consciously remember 
4. I start doing before I realize I am doing it. 
Response options: 1 (Strongly disagree) to 5 (Strongly agree) 

How old are you?  

What is your Sex? 

1. Female 
2. Male 
 
What is your race? Please select all that apply. 

a. Asian / Pacific Islander 
b. Black or African American 
c. White or Caucasian 
e. Other 

Education Level  
What is your current education level?  
(please circle only one option) 
1. Freshman 
2. Sophomore 
3. Junior 
4. Senior 
5. Graduate Student 
6. Other 

 

 

 

94 

 

 

 

 

 

 

 

 

 

 

 

 

REFERENCES   

 

95 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

REFERENCES 

consequences for attractiveness. Human Communication Research, 26(2), 203-233. 

perception. Emotion Review, 8(2), 115-128. 

control in social cognition. Handbook of social cognition, 1, 1-40. 

mental processes. Psychological bulletin, 126(6), 925. 

generation of interpersonal closeness: A procedure and some preliminary  
findings. Personality and Social Psychology Bulletin, 23(4), 363-377. 

of interpersonal closeness. Journal of Personality and Social Psychology, 63(4), 596– 
612. 

Afifi, W. A., & Burgoon, J. K. (2000). The impact of violations on uncertainty and the  
  
 
Altman, I., & Taylor, D. (1973). Social penetration theory. New York: Holt, Rinehart & Winston. 
 
Argyle, M., & Dean, J. (1965). Eye-contact, distance and affiliation. Sociometry, 28(3), 289-304. 
 
Aron, A., Aron, E. N., & Smollan, D. (1992). Inclusion of other in the Self Scale and the structure  
  
  
 
Aron, A., Melinat, E., Aron, E. N., Vallone, R. D., & Bator, R. J. (1997). The experimental  
  
  
 
Balcetis, E. (2016). Approach and avoidance as organizing structures for motivated distance  
  
 
Bargh, J. A. (1994). The four horsemen of automaticity: Awareness, intention, efficiency, and  
  
 
Bargh, J. A., & Ferguson, M. J. (2000). Beyond behaviorism: on the automaticity of higher  
  
 
Bayer, J. B., Dal Cin, S., Campbell, S. W., & Panek, E. (2016). Consciousness and self- 
  
 
Bayer, J. B., Campbell, S. W., & Ling, R. (2016). Connection cues: Activating the norms and   
  
 
Berg, J. H. (1987). Responsiveness and self-disclosure. In Self-disclosure (pp. 101-130).  
  
 
Berger, C. R. (1993). Uncertainty and social interaction. Annals of the International  
  
 
Berger, C. R., & Calabrese, R. J. (1974). Some explorations in initial interaction and beyond:  
  
  
 
Bernieri, F. J. (1988). Coordinated movement and rapport in teacher-student  
  
 
Bianchi, A., & Phillips, J. G. (2005). Psychological predictors of problem mobile phone  
  
 

Toward a developmental theory of interpersonal communication. Human communication  
research, 1(2), 99-112. 

Boston, MA: Springer. 

Communication Association, 16(1), 491-502. 

interactions. Journal of Nonverbal behavior, 12(2), 120-138. 

use. CyberPsychology & Behavior, 8(1), 39-51. 

regulation in mobile communication. Human Communication Research, 42(1), 71-97. 

habits of social connectedness. Communication Theory, 26(2), 128-149. 

 

96 

comparison. International Journal of Communication, 1(1), 738-757. 

collectivism, and focus of the setting. Communication Reports, 21(2), 70-81. 

hierarchy of habits. Psychological review, 6(4), 345-375. 

time. Mobile Media & Communication, 3(1), 36-52. 

initial test. Human Communication Research, 4(2), 129-142. 

communication. Journal of Language and Social Psychology, 12(1-2), 30-48. 

interpersonal communication, 1-9. 

Routledge. 

application to immediacy behaviors. Communications Monographs, 55(1), 58-79. 

Bryan, W.L., & Harter, N. (1899). Studies on the telegraphic language: The acquisition of a  
  
 
Burchell, K. (2015). Tasking the everyday: Where mobile and online communication take  
  
 
Burgoon, J. K. (1978). A communication model of personal space violations: Explication and an  
  
 
Burgoon, J. K. (1993). Interpersonal expectations, expectancy violations, and emotional  
  
 
Burgoon, J. K. (2015). Expectancy violations theory. The international encyclopedia of  
  
 
Burgoon, J. K., Guerrero, L. K., & Floyd, K. (2016). Nonverbal communication. New York, NY:  
  
 
Burgoon, J. K., & Hale, J. L. (1988). Nonverbal expectancy violations: Model elaboration and  
  
 
Campbell, S. W. (2007). Perceptions of mobile phone use in public settings: A cross-cultural  
  
 
Campbell, S. (2008). Perceptions of mobile phone use in public: The roles of individualism,  
  
 
Campbell, S. W. (2013). Mobile media and communication: A new field, or just a new  
  
 
Campbell, S. W., & Ling, R. (2017). Mobile communication: Bringing us together and tearing us  
  
 
Carpenter, A., & Greene, K. (2016). Social Penetration Theory. The International Encyclopedia  
  
  
 
Castells, M., Fernandez-Ardevol, M., Qiu, J. L., & Sey, A. (2009). Mobile communication and  
  
 
Chartrand, T. L., & Bargh, J. A. (1999). The chameleon effect: the perception–behavior link and  
  
 
Chartrand, T. L., & Lakin, J. L. (2013). The antecedents and consequences of human behavioral  
 
 
Chayko, M. (2007). The portable community: envisioning and examining mobile social  
  
 
Chayko, M. (2017). Superconnected: The internet, digital media, and techno-social life.  
  

of Interpersonal Communication. https://wp.comminfo.rutgers.edu/kgreene/wp- 
content/uploads/sites/51/2018/02/ACGreene-SPT.pdf  

connectedness. International Journal of Web Based Communities, 3(4), 373-385. 

journal?. Mobile Media & Communication, 1(1), 8-13. 

apart. In Mobile Communication (pp. 11-26). New York, NY: Routledge. 

Thousand Oaks, CA: Sage Publications. 

 

97 

society: A global perspective. MIT Press. 

social interaction. Journal of personality and social psychology, 76(6), 893. 

mimicry. Annual review of psychology, 64, 285-308. 

210. 

relationship management. Mobile Media & Communication, 4(1), 19-36. 

interaction. Journal of Applied Social Psychology, 48(6), 304-316. 

review. Psychological bulletin, 116(3), 457-475. 

The impact of restricting wireless mobile device use on anxiety levels among low,  
moderate and high users. Computers in Human Behavior, 37, 290-297. 

Cheever, N. A., Rosen, L. D., Carrier, L. M., & Chavez, A. (2014). Out of sight is not out of mind:  
  
  
 
Chotpitayasunondh, V., & Douglas, K. M. (2016). How “phubbing” becomes the norm: The  
antecedents and consequences of snubbing via smartphone. Computers in Human  
  
  
Behavior, 63, 9-18. 
 
Chotpitayasunondh, V., & Douglas, K. M. (2018). The effects of “phubbing” on social  
  
 
Collins, N. L., & Miller, L. C. (1994). Self-disclosure and liking: a meta-analytic  
  
 
Cozby, P. C. (1973). Self-disclosure: a literature review. Psychological bulletin, 79(2), 73-91. 
 
Cui, D. (2016). Beyond “connected presence”: Multimedia mobile instant messaging in close  
  
 
Dance, F. E. (1970). The “concept” of communication. Journal of communication, 20(2), 201- 
  
 
David, M. E., & Roberts, J. A. (2017). Phubbed and alone: Phone snubbing, social exclusion,  
  
  
 
Deloitte, U. S. (2016). Global mobile consumer survey: US edition,”. Deloitte US.  
 
  
 
Desjarlais, M., & Joseph, J. J. (2017). Socially interactive and passive technologies enhance  
  
 
 
Dindia, K., & Allen, M. (1992). Sex differences in self-disclosure: a meta- 
  
 
Duffy, K. A., & Chartrand, T. L. (2015). Mimicry: causes and consequences. Current Opinion in   
 
 
Eagleman, D. (2015). The brain: The story of you. New York, NY: Penguin Books.   
 
Eisenberger, N. I., & Lieberman, M. D. (2004). Why rejection hurts: a common neural alarm  
  
 
Ellis, D. A., Dr, Davidson, B. I., Shaw, H., & Geyer, K. (2018, November 21). Do smartphone  
  
 
Feldman, R., Weller, A., Zagoory-Sharon, O., & Levine, A. (2007). Evidence for a  
  

friendship quality: An investigation of the mediating roles of online and offline self- 
disclosure. Cyberpsychology, Behavior, and Social Networking, 20(5), 286-291. 

analysis. Psychological bulletin, 112(1), 106-124. 

Behavioral Sciences, 3, 112-116. 

and attachment to social media. Journal of the Association for Consumer  
Research, 2(2), 155-163. 

https://www2.deloitte.com/us/en/pages/technology-media-and- 
telecommunications/articles/global-mobile-consumer-survey-us-edition.html 

system for physical and social pain. Trends in cognitive sciences, 8(7), 294-300. 

usage scales predict behaviour?. https://doi.org/10.31234/osf.io/6fjr7  

neuroendocrinological foundation of human affiliation: plasma oxytocin levels across  

 

98 

induced ostracism threatens fundamental needs. The Journal of social  
psychology, 158(4), 460-473. 

65(5), 1003-1026. 

communication, private talk, public performance, 227-242. 

in Mobile Communication (Dissertation).  

and age differences in cell phone etiquette and use. Computers in Human Behavior, 31,  
314-321. 

Cambridge handbook of personal relationships, 409-427.  
http://himaforstaunair.url.ph/soal/Self%20Disclsoure%20in%20Personal%20Relationship 
s%20copy.pdf  

  
  
 
Finkel, J. A., & Kruger, D. J. (2012). Is cell phone use socially contagious? Human Ethology  
  
 
Forgays, D. K., Hyman, I., & Schreiber, J. (2014). Texting everywhere for everything: Gender  
  
  
 
Gergen, K. J. (2002). The challenge of absent presence. Perpetual contact: Mobile  
  
 
Gray, R. (2018). Breaks in Connectedness? The Meaning and Experience of Response Delays  
  
 
Greene, K., Derlega, V. J., & Mathews, A. (2006). Self-disclosure in personal relationships. The  
  
  
  
 
Hales, A. H., Dvir, M., Wesselmann, E. D., Kruger, D. J., & Finkenauer, C. (2018). Cell phone- 
  
  
 
Hall, E. T. (1963). A system for the notation of proxemic behavior. American anthropologist,  
  
 
Hall, E. T. (1966). The hidden dimension (Vol. 609). Garden City, NY: Doubleday. 
 
Hall, J. A. (2006). Women's and Men's Nonverbal Communication: Similarities, Differences,  
  
  
  
 
Hall, J. A., Baym, N. K., & Miltner, K. M. (2014). Put down that phone and talk to me:  
  
  
 
Halpern, D., & Katz, J. E. (2017). Texting's consequences for romantic relationships: A cross- 
  
 
Hampton, K. N. (2016). Persistent and pervasive community: New communication technologies  
 
 
Hjorth, L., & Richardson, I. (2017). Pokémon GO: Mobile media play, place-making, and the  
  
 
Hove, M. J., & Risen, J. L. (2009). It's all in the timing: Interpersonal synchrony increases  
  

Stereotypes, and Origins. In V. Manusov & M. L. Patterson (Eds.), The Sage handbook  
of nonverbal communication (pp. 201-218).  
http://dx.doi.org/10.4135/9781412976152.n11  

and the future of community. American Behavioral Scientist, 60(1), 101-124. 

digital wayfarer. Mobile Media & Communication, 5(1), 3–14. 

Understanding the roles of mobile phone norm adherence and similarity in  
relationships. Mobile Media & Communication, 2(2), 134-153. 

lagged analysis highlights its risks. Computers in Human Behavior, 71, 386-394. 

pregnancy and the postpartum period predict mother-infant bonding. Psychological  
Science, 18(11), 965-970. 

Bulletin, 27(1-2), 15–17.  

affiliation. Social Cognition, 27(6), 949-960. 

 

99 

69th International Communication Association Annual Meeting.   

phones in Japanese life. MIT press. 

among U.S. Older Adults.  

(2015). Determinants of phubbing, which is the sum of many virtual addictions: A  
structural equation model. Journal of behavioral addictions, 4(2), 60-74. 

(2016). The virtual world’s current addiction: phubbing. Addicta: Turkish journal on  
addictions, 3(2), 252-269. 

Disrupted copresence: Older adults’ views on mobile phone use during face-to-face  
interactions. Mobile Media & Communication, 6(3), 331-349.  

of romantic relationship status. Personality and Social Psychology Bulletin, 34(7), 939- 
950. 

Humphreys; H. Hardeman (2019). Mobiles in Public: Social Interaction in a Smartphone Era L. 
 
 
Ito, M. E., Okabe, D. E., & Matsuda, M. E. (2005). Personal, portable, pedestrian: Mobile  
  
 
Kadylak, T. (under review). An Investigation of Perceived Phubbing Effects and Well-Being,  
  
 
Kadylak, T., Makki, T. W., Francis, J., Cotten, S. R., Rikard, R. V., & Sah, Y. J. (2018).  
  
 
 
Karadağ, E., Tosuntaş, Ş. B., Erzen, E., Duru, P., Bostan, N., Şahin, B. M., ... & Babadağ, B.  
  
  
 
Karadağ, E., Tosuntaş, Ş. B., Erzen, E., Duru, P., Bostan, N., Şahin, B. M., ... & Babadağ, B.  
  
  
 
Karremans, J. C., & Verwijmeren, T. (2008). Mimicking attractive opposite-sex others: The role  
  
 
 
Katz, J. E. (2006). Mobile communication and the transformation of daily life: The next phase of  
  
 
Katz, J. E., Katz, J. E., & Aakhus, M. (Eds.). (2002). Perpetual contact: Mobile communication,  
  
 
Katz, J. E. (2011). Magic in the air: Mobile communication and the transformation of social  
  
 
Kelly, L., Miller-Ott, A. E., & Duran, R. L. (2017). Sports scores and intimate moments: An  
  
  
 
Korb, S., Malsert, J., Rochas, V., Rihs, T. A., Rieger, S. W., Schwab, S., ... & Grandjean, 
 
 
 
Krasnova, H., Abramova, O., Notter, I., & Baumann, A. (2016, June). Why Phubbing is Toxic for  
your Relationship: Understanding the Role of Smartphone Jealousy among" Generation  
  
  
y" Users. In ECIS (p. ResearchPaper109). 
 
Lacohée, H., Wakeford, N., & Pearson, I. (2003). A social history of the mobile telephone with a  
  
 
LaFrance, M., & Hecht, M. A. (1995). Why smiles generate leniency. Personality and Social  
  

expectancy violations theory approach to partner cell phone behaviors in adult romantic  
relationships. Western Journal of Communication, 81(5), 619-640. 

D. (2015). Gender differences in the neural network of facial mimicry of smiles–An rTMS 
study. Cortex, 70, 101-114. 

research on mobiles. Knowledge, Technology & Policy, 19(1), 62-71. 

private talk, public performance. Cambridge University Press. 

life (Vol. 1). New Brunswick, NY: Transaction Publishers. 

view of its future. BT Technology Journal, 21(3), 203-211. 

Psychology Bulletin, 21(3), 207-214.  

 

100 

psychology of communication technology, 32, 365-380. 

practical primer for t-tests and ANOVAs. Frontiers in psychology, 4.  
https://www.frontiersin.org/articles/10.3389/fpsyg.2013.00863/full  

affiliation and rapport. Psychological science, 14(4), 334-339. 

cultural study of the consequences of social media connection. Information Systems  
Management, 31(1), 59-73. 

attachments as a fundamental human motivation. In Interpersonal Development (pp. 57- 
89). Routledge. 

Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a  
  
  
 
Lakin, J. L., & Chartrand, T. L. (2003). Using nonconscious behavioral mimicry to create  
  
 
LaRose, R. (2010). The problem of media habits. Communication Theory, 20(2), 194-222. 
 
LaRose, R. (2015). The psychology of interactive media habits. The handbook of the  
  
 
LaRose, R., Connolly, R., Lee, H., Li, K., & Hales, K. D. (2014). Connection overload? A cross  
  
  
 
Laurenceau, J. P., Barrett, L. F., & Pietromonaco, P. R. (1998). Intimacy as an interpersonal  
process: the importance of self-disclosure, partner disclosure, and perceived partner  
  
responsiveness in interpersonal exchanges. Journal of personality and social  
  
  
psychology, 74(5), 1238-1251. 
 
Leary, M. R., & Baumeister, R. F. (2017). The need to belong: Desire for interpersonal  
  
  
 
Leary, M. R., & Cox, C. B. (2008). Belongingness motivation: A mainspring of social action. In J.  
Y. Shah & W. L. Gardner (Eds.), Handbook of motivation science (pp. 27-40). New York,  
  
  
NY, US: Guilford Press. 
 
Licoppe, C. (2004). ‘Connected’presence: The emergence of a new repertoire for managing  
  
  
 
Licoppe, C., & Smoreda, Z. (2005). Are social networks technologically embedded?: How  
  
  
 
Ling, R. (1998). 'One can talk about common manners!': The use of mobile telephones in  
  
 
Ling, R. (2002). The social juxtaposition of mobile telephone conversations and public  
  
 
Ling, R. (2004). The mobile connection: The cell phone's impact on society. San Francisco, CA:  
  
 
Ling, R. (2005). The mobile connection. International Journal of Technology and Human  
  
 
Ling, R. S. (2008). New tech, new ties. Cambridge, MA: MIT press. 

social relationships in a changing communication technoscape. Environment and  
planning D: Society and space, 22(1), 135-156. 

Elsevier. 

Interaction, 1(4), 101-104. 

networks are changing today with changes in communication technology. Social  
networks, 27(4), 317-335. 

inappropriate situations. Telektronikk, 94, 65-78. 

spaces. In Conference on the Social Consequence of Mobile Telephones. 

 

101 

MIT Press. 

actions on social order. Etikk i praksis-Nordic Journal of Applied Ethics, (2), 11-26. 

contact: Mobile communication, private talk, public performance pp. 139-169. 

college students. International Journal of Consumer Studies, 31(1), 46-56. 

(MFQ-FF). Measurement instrument database for the social science. 

disclosure. Journal of personality and social psychology, 44(6), 1234. 

couple relationships and implications for women’s personal and relational well- 
being. Psychology of Popular Media Culture, 5(1), 85. 

Ling, R. (2012). Taken for grantedness: The embedding of mobile communication into society.  
  
 
Ling, R., & McEwen, R. (2010). Mobile communication and ethics: implications of everyday  
  
 
Ling, R., & Yttri, B. (2002). Hyper-‐coordination via mobile phones in Norway. In Perpetual  
  
 
Lipscomb, T. J., Totten, J. W., Cook, R. A., & Lesch, W. (2007). Cellular phone etiquette among  
  
 
McDaniel, B. T., & Coyne, S. M. (2016). “Technoference”: The interference of technology in  
  
  
 
Mehrabian, A. (2017). Nonverbal communication. New York, NY: Routledge.  
 
Mendelson, M. J., & Aboud, F. (2012). McGill Friendship Questionnaire & Friendship Functions  
  
 
Miller, L. C., Berg, J. H., & Archer, R. L. (1983). Openers: Individuals who elicit intimate self-
 
 
Miller-Ott, A. E., Kelly, L., & Duran, R. L. (2012). The effects of cell phone usage rules on  
  
 
Miller-Ott, A., & Kelly, L. (2015). The presence of cell phones in romantic partner face-to-face  
  
  
 
Miller-Ott, A. E., & Kelly, L. (2017). A Politeness Theory Analysis of Cell-Phone Usage in the  
  
 
Misra, S., Cheng, L., Genevie, J., & Yuan, M. (2016). The iPhone effect: the quality of in-person  
social interactions in the presence of mobile devices. Environment and Behavior, 48(2),  
  
  
275-298. 
 
Nezlek, J. B., Wesselmann, E. D., Wheeler, L., & Williams, K. D. (2015). Ostracism in everyday  
  
  
 
Nowak, K. (2001, May). Defining and differentiating copresence, social presence and presence  
  
 
Omarzu, J. (2000). A disclosure decision model: Determining how and when individuals will  
  
 
Pettigrew, J. (2009). Text messaging and connectedness within close interpersonal  
  

interactions: An expectancy violation theory approach. Southern Communication  
Journal, 80(4), 253-270. 

self-disclose. Personality and Social Psychology Review, 4(2), 174-185. 

relationships. Marriage & Family Review, 45(6-8), 697-716. 

life: The effects of ostracism on those who ostracize. The Journal of social  
psychology, 155(5), 432-451. 

as transportation. In Presence 2001 Conference, Philadelphia, PA (pp. 1-23). 

satisfaction in romantic relationships. Communication Quarterly, 60(1), 17-34. 

Presence of Friends. Communication Studies, 68(2), 1-18. 

 

102 

does indeed promote attraction in live interaction. Journal of personality and social  
psychology, 101(3), 557. 

https://www.statista.com/chart/12403/smartphone-addiction/  

phone: Partner phubbing and relationship satisfaction among romantic partners.  
Computers in Human Behavior, 54, 134-141. 

Countries but Plateaus Across Developed Ones. Pew Research Center, 22.  
http://assets.pewresearch.org/wp-content/uploads/sites/2/2018/06/15135408/Pew- 
Research-Center_Global-Tech-Social-Media-Use_2018.06.19.pdf  

connectivity poses new challenges for users about when to be present with those nearby  
or engaged with others on their screens. Pew Research Center. Available at:  
http://www.pewinternet.org/files/2015/08/2015-08-26_mobile-etiquette_FINAL.pdf. 

Poushter, J., Bishop, C., & Chwe, H. (2018). Social Media Use Continues to Rise in Developing  
  
  
  
 
Przybylski, A. K., & Weinstein, N. (2013). Can you connect with me now? How the presence of  
mobile communication technology influences face-to-face conversation quality. Journal  
  
  
of Social and Personal Relationships, 30(3), 237-246. 
 
Rainie, L., & Zickuhr, K. (2015). American’s views on mobile etiquette: Always on mobile  
  
  
  
 
Rammstedt, B., & John, O. P. (2007). Measuring personality in one minute or less: A 10-item 
short version of the Big Five Inventory in English and German. Journal of research in 
 
 
Personality, 41(1), 203-212. 
 
Reis, H. T., Maniaci, M. R., Caprariello, P. A., Eastwick, P. W., & Finkel, E. J. (2011). Familiarity  
 
 
 
Richter, Felix. (June 20, 2018). Digital Detox, America’s Smartphone Addiction.   
 
Roberts, J. A., & David, M. E. (2016). My life has become a major distraction from my cell  
  
  
 
Roberts, J. A., & David, M. E. (2017). Put down your phone and listen to me: How boss  
  
  
 
Sawchuk, K., & Crow, B. (2012). “I'm G-Mom on the Phone” Remote grandmothering, cell  
  
 
Sbarra, D., Briskin, J., & Slatcher, R. B. (in press). Smartphones and Close Relationships: The  
  
 
Scissors, L. E., Gill, A. J., & Gergle, D. (2008, November). Linguistic mimicry and trust in text- 
  
  
 
Schrock, A. R. (2015). Communicative affordances of mobile media: Portability, availability,  
 
 
Spengler, F. B., Scheele, D., Marsh, N., Kofferath, C., Flach, A., Schwarz, S., ... & Hurlemann,  
R. (2017). Oxytocin facilitates reciprocity in social communication. Social cognitive and  
  
  
affective neuroscience, 12(8), 1325-1333. 
 
Sprecher, S., & Treger, S. (2015). The benefits of turn‐taking reciprocal self‐disclosure in get‐ 
  

phubbing undermines the psychological conditions necessary for employee  
engagement. Computers in Human Behavior, 75, 206-217. 

acquainted interactions. Personal Relationships, 22(3), 460-475. 

 

103 

phones and inter-generational dis/connections. Feminist Media Studies, 12(4), 496-505. 

Case for an Evolutionary Mismatch. Perspectives on Psychological Science.  

based CMC. In Proceedings of the 2008 ACM conference on Computer supported  
cooperative work (pp. 277-280). ACM. 

locatability, and multimediality. International Journal of Communication, 9, 1229-1246. 

Reciprocal self-disclosure promotes liking in initial interactions. Journal of Experimental  
Social Psychology, 49(5), 860-866. 

information technology, 24(2), 111-129. 

personal and professional relationships 1. In The handbook of communication skills (pp. 
217-258). Routledge. 

other. Hachette UK. 

positive and entertaining self-disclosures increase the feeling of connection. Computers  
in Human Behavior, 45, 1-10. 

Sprecher, S., Treger, S., Wondra, J. D., Hilaire, N., & Wallpe, K. (2013). Taking turns:  
  
  
 
Srivastava, L. (2005). Mobile phones and the evolution of social behaviour. Behaviour &  
  
 
Tardy, C. H., & Smithson, J. (2018). Self-disclosure: Strategic revelation of information in 
 
 
 
Turkle, S. (2017). Alone together: Why we expect more from technology and less from each  
  
 
Utz, S. (2015). The function of self-disclosure on social network sites: Not only intimate, but also  
  
  
 
Vanden Abeele, M. M. P. (forthcoming). The social consequences of phubbing: A framework  
and a research agenda. In R. Ling, G. Goggin, L. Fortunati, S. S. Lim, & Y. Li (Eds.),  
  
  
Handbook of Mobile Communication, Culture, and Information (pp. xx-xx). Oxford  
  
University Press. 
 
Vanden Abeele, M. M., Antheunis, M. L., & Schouten, A. P. (2016). The effect of mobile 
 
 
 
Vanden Abeele, M. V., De Wolf, R., & Ling, R. (2018). Mobile media and social space: How  
  
  
 
Vanden Abeele, M. M. P., Hendrickson, A., Pollmann, M. M. H., & Ling, R. (in press). 
 
 
 
Vanden Abeele, M. M., & Postma-Nilsenova, M. (2018). More Than Just Gaze: An Experimental  
  
  
  
 
Vanden Abeele, M. V., Schouten, A. P., & Antheunis, M. L. (2017). Personal, editable, and  
  
  
  
 
van't Veer, A. E., & Giner-Sorolla, R. (2016). Pre-registration in social psychology—A discussion  
  
 
Verplanken, B., & Orbell, S. (2003). Reflections on Past Behavior: A Self‐Report Index of Habit  
  

always accessible: An affordance approach to the relationship between adolescents’  
mobile messaging behavior and their friendship quality. Journal of Social and Personal  
Relationships, 34(6), 875-893. 

Vignette Study Examining How Phone-Gazing and Newspaper-Gazing and Phubbing- 
While-Speaking and Phubbing-While-Listening Compare in Their Effect on  
Affiliation. Communication Research Reports, 1-11. 

and suggested template. Journal of Experimental Social Psychology, 67, 2-12. 

Strength 1. Journal of applied social psychology, 33(6), 1313-1330. 

messaging during a conversation on impression formation and interaction quality. 
Computers in Human Behavior, 62, 562-569. 

anytime, anyplace connectivity structures everyday life. Media and Communication, 6(2),  
5-14. 

Phubbing behavior in conversations and its relation to perceived conversation intimacy 
and distraction: An exploratory observation study. Computers in Human Behavior. 

 

104 

 

https://journals-sagepub-com.proxy1.cl.msu.edu/doi/pdf/10.2466/pr0.1969.25.3.914  

Relationship. In Handbook of Relationship Initiation (pp. 164-185). Psychology Press. 

and rejection in groups. Group Processes & Intergroup Relations, 20(5), 693-706. 

among married Chinese adults: The roles of relationship satisfaction and relationship  
length. Personality and Individual Differences, 110, 12-17. 

Werry, I. (2005, August). The influence of subjects' personality traits on personal spatial 
zones in a human-robot interaction experiment. In Robot and Human Interactive 
Communication, 2005. ROMAN 2005. IEEE International Workshop on (pp. 347-352).  

Vondracek, F. W. (1969). Behavioral measurement of self-disclosure. Psychological Reports. 
 
 
Walters, M. L., Dautenhahn, K., Te Boekhorst, R., Koay, K. L., Kaouri, C., Woods, S., ... &  
 
 
 
 
Wang, X., Xie, X., Wang, Y., Wang, P., & Lei, L. (2017). Partner phubbing and depression  
  
  
 
Weisband, S., & Atwater, L. (1999). Evaluating self and others in electronic and face-to-face  
  
 
Wenzel, A., Sprecher, S., & Harvey, J. (2018). Self-Disclosure and Starting a Close 
 
 
Wesselmann, E. D., & Williams, K. D. (2017). Social life and social death: Inclusion, ostracism,  
  
 
White, C. H. (2008). Expectancy violations theory and interaction adaptation theory. Engaging  
  
 
Williams, K. D. (2007). Ostracism. Annual review of psychology, 58. 425-452.  
 
Williams, K. D. (2009). Ostracism: A temporal need‐threat model. Advances in experimental  
  
 
Williams, K. D., & Jarvis, B. (2006). Cyberball: A program for use in research on interpersonal  
  
 
 
 
 
 
 
 
 
 
 

groups. Journal of Applied Psychology, 84(4), 632-639. 

theories in interpersonal communication, 189-202. 

social psychology, 41, 275-314. 

ostracism and acceptance. Behavior research methods, 38(1), 174-180. 

 

105