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Abstract

Co-present smartphone use—the use of smartphones in the presence of others—is a prevalent behavior in social contexts. However, the situational mechanisms underlying this phenomenon remain poorly understood. While previous research has primarily conceptualized co-present smartphone use as a stable individual disposition and focused on person-level determinants, the role of specific situational characteristics has remained largely unexplored. Addressing this gap, the present study investigates co-present smartphone use from a situational perspective, aiming to gain insights into the situational predictors of co-present smartphone use. We conducted an experience sampling study with 87 participants, reporting on 829 social interactions to examine how co-present use is linked to different types of connection cues (technical, spatial, and mental). The findings show that a person's smartphone use during an interaction is related to the smartphone use of others present and the number of notifications received.

Keywords

Co-present smartphone use phubbing connection cues situational predictors experience sampling

Introduction

Smartphone use in the presence of others is a ubiquitous phenomenon and has become common among many people (Chotpitayasunondh & Douglas, 2016; Reiter et al., 2024). This behavior is often referred to—along with other terms—as phubbing or co-present smartphone use (Aagaard, 2020; Courtright & Caplan, 2020). Among studies in the field of communication, the term phubbing (a portmanteau of the words phone and snubbing) prevails. However, the term phubbing has been subject to ongoing debate regarding its conceptual clarity and validity (e.g., Frackowiak, Ochs, et al., 2024) as it conflates the observable behavior (i.e., smartphone use) with its potential effects (i.e., at least one person feels snubbed). To enhance conceptual precision, we adopt the term co-present smartphone use, as it allows us to disentangle the actual behavior from its outcomes.

A big strand of research on co-present use focuses on temporally stable personal characteristics (e.g., age, personality traits) that lead to a general predisposition to use the smartphone in social situations (for a review see Arenz & Schnauber-Stockmann, 2024). While most studies examine co-present use at the person level, research on situational factors linked to smartphone use in specific social situations remains scarce (see e.g., Al-Saggaf, 2021; Büttner et al., 2022). A recent meta-analysis comparing person-level and situation-level variation in media use shows that the situational level is crucial for understanding and measuring media use (Schnauber-Stockmann, Scharkow, et al., 2025). Thus, by focusing mostly on person-specific factors, existing research lacks an important facet to understand co-present use fully. Looking at the situational level can help broaden our understanding of co-present use and offer new insights (Schnauber-Stockmann, Bayer, et al., 2025).

To address this research gap, we adopted a situational perspective and conducted an in-situ study with event-contingent experience sampling (ESM) to investigate the situational conditions of co-present use in everyday life. Building on Bayer et al. (2016), we distinguish between different types of connection cues and examine how these relate to individuals’ smartphone use during social interactions. Thereby, the present paper complements existing trait-level research with a state-level perspective on co-present use.

Co-Present Smartphone Use and Connection Cues

Research on the situational factors that lead people to reach for their smartphones in certain social settings is scarce. However, the literature on mobile media habits offers insights into situational factors that prompt smartphone use in general, which can be applied to social interactions. It is argued that mobile behavior is often automatically activated by specific contextual cues that are present in a given situation (e.g., Bayer et al., 2016; Oulasvirta et al., 2012; Wood & Neal, 2007). In a broader sense, cues, as perceived by individuals, constitute the fundamental building blocks of a situation (Rauthmann et al., 2015). In this understanding, they can either automatically trigger subsequent behavior or serve as information chunks in decision processes (Sundar et al., 2007). Bayer et al. (2016) suggest three different types of cues that lead users to check their mobile phone, referred to as connection cues: technical cues; spatial cues; and mental cues. The latter are internal cues, representing psychological states of a person such as emotions or motivations, while the former two represent context cues related to the media and the environment (Schnauber-Stockmann, Bayer, et al., 2025). We argue that this theoretical framework is suitable for studying co-present smartphone use and that connection cues may help explain why users reach for their smartphones in some social situations but not in others.

Technical Cues: The Influence of Notifications

Technical cues are signals originating from a mobile device itself, such as ringing or vibrations. According to Bayer et al. (2016), technical cues are the most important form of connection cues. “The technology actively reaches out to its owner” (Bayer et al., 2016, p. 134) and informs them about something new happening online that requires their attention (Sun & Yoon, 2023). The most obvious form of technical cues is notifications, which are a central design feature of mobile media (Meier, 2022). A “notification is any kind of alert generated by applications. These alerts can be visual, audio, haptic or somatic” (Devrim, 2023, p. 64).

Studies using data donations—such as logging data—indicate that most people receive numerous notifications per day and that notifications are an integral and influential part of everyday life (Lee et al., 2014; Liao & Sundar, 2022; Pielot et al., 2014). The average number of daily notifications reported in these studies varies between 50 (Liao & Sundar, 2022) and 400 (Lee et al., 2014). Furthermore, the empirical evidence suggests that notifications (whether acoustic, haptic, or visual) lead to more frequent smartphone checking. Lee et al. (2014) showed that around 79% of logged smartphone usage sessions were triggered by notifications. Liao and Sundar (2022) found that the number of notifications per day was related to how often users picked up their smartphones and their overall screen time. While Liao and Sundar (2022) also controlled for notification mode and found that turning off sounds and vibrations led to increased checking and screen time, their results suggest that the number of notifications itself is already a meaningful driver of smartphone use.

After receiving a notification, most people check their smartphones on average within ten minutes (Halfmann et al., 2024; Pielot et al., 2014). In contrast, without receiving a notification, people check their smartphones on average after more than 30 minutes (Halfmann et al., 2024). The literature explains these short reaction times by the fact that notification signals stimulate the user's brain and attract their attention, leading users to reach for their smartphone when they receive a notification (Sun & Yoon, 2023). Other explanations are that people often feel social pressure to respond promptly to incoming messages (Park et al., 2017) or feel an internal urge to maintain a clean mobile interface and remove all notification indicators (Burchell, 2015).

As notifications usually appear unannounced and unexpectedly, they can potentially interrupt the user's primary task (Meier, 2022; Park et al., 2017). If a user receives such a notification in the presence of other people, it can draw their attention to their smartphone and thus cause co-present smartphone use. Klein (2014) found that notifications are one reason why people use their smartphones during social interactions, with around 36% of participants reporting incoming messages and push notifications as one of the main reasons they reached for their smartphone in the presence of others. In line with this, 70% of participants in a study by Park et al. (2017) stated that they reach for their smartphones during social interactions due to notifications. Therefore, we hypothesize:

H1: An increase in the number of notifications received during a face-to-face interaction is associated with an increased probability of co-present smartphone use during that interaction.

Spatial Cues: The Influence of Others' Smartphone Use

Spatial cues occur in the person's environment and include objects, places, or other people and their behavior (Bayer et al., 2016). A spatial cue especially relevant for co-present use is the smartphone use of others present, as it can prompt others to also engage with their devices, creating a social cascade effect (Bayer et al., 2016). For instance, if just one person uses their smartphone at a social gathering, this can remind everyone else of their smartphone, initiating a chain reaction of smartphone use.

The literature provides strong cross-sectional evidence indicating that one's smartphone use during social interactions is moderately correlated with the smartphone use of others (e.g., Chotpitayasunondh & Douglas, 2016; Li, 2023). These findings point to between-person differences, indicating that the general experience of others using their smartphones during social interactions leads to a general tendency to use one's smartphone as well. Initial observational evidence supports these findings and indicates that this correlation can also be found within specific situations (i.e., at the state level). Finkel and Kruger (2012, p. 16) found “feedback effect[s] of mutual influence” in smartphone use in public dyads: participants observed used their smartphones significantly more often when the other person also used their smartphone. This leads to the following hypothesis:

H2: Other people using their smartphones during a face-to-face interaction (vs. others not using their smartphones) is associated with an increased probability of co-present smartphone use during that interaction.

Mental Cues: The Influence of Relatedness Need Experiences and Fear of Missing Out

Mental cues arise from the person's internal cognition and include psychological states, such as thoughts, emotions, motivations, intrinsic goals, and needs (Bayer et al., 2016). In the context of co-present use, where people navigate between in-person and digital social connection, the need for relatedness might be a particularly important mental cue. According to self-determination theory (Ryan & Deci, 2000), the need for relatedness refers to the desire to belong and to feel a meaningful bond with others (Xie & Xie, 2020) and can either be satisfied or frustrated: need satisfaction fosters a feeling of connectedness, whereas need frustration leads to feelings of loneliness and exclusion (Schneider et al., 2022).

Smartphones offer various opportunities to interact with others and can satisfy this need by enhancing “feelings of connectedness” (Schneider et al., 2022, p. 252). Indeed, the social use of smartphones (i.e., to stay in touch with others) is the most common form of use (We Are Social, 2025). Research on smartphone activities during face-to-face interactions indicates that people most often use social media, messenger services, chat programs, and email (Klein, 2014; Yang & Christofferson, 2020). Other smartphone functions (such as music or gaming) are used less frequently. Thus, people often use their smartphones to engage with their online contacts, which addresses their need for relatedness. Especially for a person whose need for relatedness is frustrated in a certain situation, smartphone use may serve as a coping mechanism to satisfy the previously frustrated need for relatedness (Butt & Arshad, 2021). Conversely, when people's need for relatedness is already satisfied by their interaction partner(s), they have little reason to use their smartphone to bond with others. Consequently, a satisfied need for relatedness is expected to be negatively and a frustrated need positively associated with smartphone use in a social situation. These assumptions are supported by Butt and Arshad (2021), who find a negative association of need satisfaction and a positive association of need frustration with co-present smartphone use at the trait level. This leads to the following two hypotheses:

H3: Higher frustration of the need for relatedness during a face-to-face interaction is associated with an increased probability of co-present smartphone use during that interaction.

H4: Higher satisfaction of the need for relatedness during a face-to-face interaction is associated with a decreased probability of co-present smartphone use during that interaction.

Another construct frequently studied in the literature on co-present smartphone use that can be seen as a relevant mental cue is fear of missing out (FoMO). FoMO describes the anxiety of missing out on rewarding experiences and the urge to stay constantly connected with others (Przybylski et al., 2013). Just like the need for relatedness, FoMO is linked to smartphone use, which can counteract the fear of missing out on something (Buff & Burr, 2018; Butt & Arshad, 2021). Those who experience FoMO are prone to check their smartphones to see what others are doing and to avoid missing any worthwhile experiences (Akat et al., 2022). Studies show that FoMO is not only associated with smartphone checking behavior (Przybylski et al., 2013; Tanhan et al., 2022) but also with smartphone use during social interactions. Arenz and Schnauber-Stockmann (2024) demonstrate in a recent meta-analytical review that FoMO is one of the most frequently studied predictors of co-present use, with a medium overall effect (see also Ansari et al., 2024). Although mostly studied at the trait level (e.g., Akat et al., 2022; Permata et al., 2023), FoMO likely also acts as a momentary cue at the state level. Therefore, we hypothesize:

H5: A higher level of FoMO during a face-to-face interaction is associated with an increased probability of co-present smartphone use during that interaction.

Figure 1 summarizes the proposed hypotheses.

Figure 1.

Conceptual Model of the Proposed Hypotheses H1–H5.

Method

The hypotheses, procedure, measures, and data analyses were preregistered (see https://osf.io/mcd4v). The preregistration, materials (questionnaires, protocol, and study briefing), datasets, code, an amendment documenting all deviations from the preregistration as well as preregistered analyses not reported in the manuscript are available online at https://osf.io/jcvf7/.

Procedure and Participants

To test the hypotheses, an event-contingent, self-initiated experience sampling study (ESM)¹ was conducted (field period: February/March 2024). The study consisted of three parts: (1) an intake survey including person-level questions and recruitment for the ESM phase; (2) the ESM study; and (3) an exit survey on the study experience and registration for the incentive (individuals who participated in all three phases of the study received €10). SoSci survey was used for all study parts (Leiner, 2019). Participants received an email with a stable link to access the ESM protocols and detailed instructions on when and how to complete a protocol one day before the start of the field period (see https://osf.io/ja8fd). During the field period, participants received email reminders every evening at 7 pm. ESM protocols could be completed on any device with Internet access and a browser, no specific app installation was needed.

For seven consecutive days, participants completed an ESM protocol following a private social interaction (e.g., not work-related) with an individual aged 18 or older (to exclude, e.g., parental technoference, see e.g., Frackowiak, Ochs, et al., 2024) that lasted at least 15 minutes. In a pretest, participants reported difficulties answering questions about situations involving more than ten people, as such larger group settings deviate from typical everyday face-to-face encounters. Therefore, we excluded these situations and focused only on interactions with up to ten people. Participant instructions are available at https://osf.io/ja8fd, and the inclusion criteria for relevant social interactions are available at https://osf.io/gqu5b. To minimize participant burden, participants reported a maximum of five interactions per day, which is a common threshold for the number of daily prompts in ESM research (Schnauber-Stockmann & Karnowski, 2020).

The study was conducted in Germany and included smartphone users aged 18 and above. To obtain a heterogenous sample, participants were recruited in two ways. First, they were recruited through mailing lists of courses at the University of Mainz. To complement this strategy focused on students, we also recruited through personal networks (e.g., via messengers or social media) and used snowball sampling by asking participants to share the link to the intake questionnaire with others.

A minimum sample size of 50 participants was targeted because simulation studies in the field of multilevel analyses show that this is the minimum sample size required to estimate stable fixed effects and variance components (Maas & Hox, 2005). Since common guidelines for ESM studies recommend a sample size of 100 participants (Silvia et al., 2014), and comparable studies in the field have also targeted a sample of 100 participants (e.g., Dwyer et al., 2018; Kushlev & Heintzelman, 2018), we aimed for a sample of 100 participants. In total, 117 participants consented to take part in the ESM study. Of those, 30 did not complete at least one ESM protocol (attrition: 26%). Thus, the final sample on the person level consisted of 87 participants who completed the intake questionnaire and at least one ESM protocol. The 87 participants completed a total of 829 ESM protocols, with an average of 9.53 protocols per participant (SD = 5.86, Minimum = 1, Maximum = 32). Table 1 summarizes the sample characteristics.

Table 1.

Sample Characteristics.

	n	%	M	SD	Range
Gender
Female	62	71.3
Male	25	28.7
Age (years)			37.30	16.50	19−67
Education^a
High	62	71.3
Low	25	29.7
Occupation
Employed, civil servant	40	45.9
Self-employed	5	5.8
Student	34	39.1
Other	8	9.3
Marital status
Single	26	29.9
In a relationship	32	36.8
Married	25	28.7
Other	4	4.6
Living situation
Alone	20	22.9
With at least one other person	67	77.1

Note. N = 87; ^ahigh = high school diploma with or without university degree.

Measures

Only those measures relevant to the following analyses are reported. The complete intake, ESM, and exit questionnaires can be found at https://osf.io/ubptc.

Intake and Exit Questionnaires

The intake questionnaire served to recruit and screen participants (criteria: 18 years and older, smartphone users), measured socio-demographics as well as control variables, and obtained participants’ informed consent for the ESM study. Next to participants’ gender, age, education, occupation, relationship status, and living situation (alone or with others), we measured five control variables at the person level which are connected to our state predictors: Trait co-present use (“How frequently do you use your smartphone when interacting with someone (e.g., having a conversation with someone)?”; scale from 1 = Never to 7 = Very regularly; M= 2.53, SD = 1.00) and general smartphone use frequency (“How frequently do you use your smartphone on an average day?”, scale from 1 = Less than once per day to 7 = Every five minutes or more; M = 4.11, SD = 1.43) were measured with one item. The relatedness need frustration and satisfaction subscales of the Basic Psychological Need Satisfaction and Frustration Scale (Chen et al., 2015; Heissel et al., 2018) were used to measure trait need satisfaction (e.g., “I feel that the people I care about also care about me”) and frustration (e.g., “I feel that people who are important to me are cold and distant towards me”). Both subscales consisted of four items and demonstrated good internal consistency (need satisfaction: Cronbach's α = 0.85, M = 6.03, SD = 0.93; need frustration: Cronbach's α = 0.74, M = 1.99, SD = 0.96; scale from 1 = Strongly disagree to 7 = Strongly agree). Finally, fear of missing out was measured with ten items using the Fear of Missing Out Scale by Przybylski et al. (2013). Example items are “I fear others have more rewarding experiences than me” or “I get worried when I find out my friends are having fun without me”. The scale showed acceptable internal consistency (Cronbach's α = 0.73, M = 3.63, SD = 0.95; scale from 1 = Strongly disagree to 7 = Strongly agree).

The exit questionnaire mainly collected contact information to administer incentives.

ESM Protocol

The ESM protocol served to describe the social interaction. For one, it contained descriptions of the situation that: (a) were used to check the inclusion criteria (see above; i.e., duration of the social interaction, number of individuals present); and (b) served to describe the context (e.g., relationship with the individuals present) as well as control variables for the following analyses (i.e., latency between the interaction and the time of completing the protocol). For the other, the central variables were assessed: Participants indicated whether they had used their smartphones during the social interaction (“Which of the following statements apply to the social interaction?” answering option “I used my smartphone” selected; co-present use), how many notifications they had received during the social interaction (technical cue; “Please estimate how many notifications (e.g., beeps or signals that indicate incoming messages or similar) you received on your smartphone during the social interaction”), and whether others had used their smartphones (spatial cue; “Which of the following statements apply to the social interaction?” answering option “At least one of the people I spent time with used their smartphone” selected). The mental cues satisfaction and frustration of the need for relatedness were assessed with a single item each derived from the Balanced Measure of Psychological Needs Scale (“During the social interaction, I felt a strong sense of intimacy with the people I spent time with” and “During the social interaction, I have felt unappreciated by one or more people around me”, scale from 1 = Not at all to 7 = Very much; Neubauer & Voss, 2016; Sheldon & Hilpert, 2012). FoMO as a mental cue was assessed using the following question: “To what extent did you feel that you were missing out on alternative activities and experiences in your environment during the social interaction?” (adapted from Hayran et al., 2020; Milyavskaya et al., 2018; scale from 1 = Not at all to 7 = Very much).

Data Analysis

The data have a two-level structure, as ESM protocols (level 1) are nested within participants (level 2). Therefore, we ran a logistic multilevel model (MLA) with co-present use by the participant (0 = No, 1 = Yes) as the dependent variable and the number of notifications (H1), smartphone use by others (H2), relatedness needs frustration (H3), relatedness needs satisfaction (H4), and FoMO (H5) as the independent variables. We included trait co-present use to control for the general tendency to use one's smartphone in social situations. Additionally, the following person-level variables were included as control variables, as they are commonly correlated with co-present use (Arenz & Schnauber-Stockmann, 2024): general smartphone use frequency; trait need satisfaction and frustration; trait FoMO; age; and gender. At the situational level, we used the duration of the social interaction, latency, and study day as control variables. The former was included to control for the fact that longer interactions make smartphone use more likely, while the latter two served as methodological controls.

All metric level-1 predictors were person-mean-centered, and all metric level-2 predictors were grand-mean-centered. The analyses were run in R (R Core Team, 2021) using rmcorr (Bakdash & Marusich, 2024), lme4 (Bates et al., 2015), and lmerTest (Kuznetsova et al., 2017).

Results

Descriptive Analyses

In most social situations, participants were together with 1.63 other persons (SD = 1.00)—that is, one or two. Mostly, they were with their partner (40% of all situations) and/or (close) friends (31%). Overall, the need for relatedness was satisfied in many situations (M = 5.45; SD = 1.57) and seldom frustrated (M = 1.74, SD = 1.39). The level of state FoMO was rather low (M = 1.56, SD = 1.06). The participants received an average of 2.76 notifications (SD = 3.80) during the social interaction. In 475 (57%) of the reported social interactions, a smartphone was used by either the participants themselves (N = 253, 30%) and/or others present (N = 321, 39%) alone, or together (N = 93, 11%). Table 2 contains the within-person and between-person correlations of the central variables.

Table 2.
Zero-Order Within-Person and Between-Person Correlations.

Co-present use Smartphone use by others Notifications Fear of missing out (FoMO) Need satisfaction Need frustration

Co-present use (1 = yes) 0.29* 0.15* 0.07* −0.02 0.03

Smartphone use by others (1 = yes) 0.59* 0.14* 0.10 0.01 0.12*

Notifications −0.09 −0.06 0.11 0.12* 0.00

FoMO 0.05 0.00 0.16 −0.17*** −0.05

Need satisfaction −0.08 −0.04 −0.17 −0.25* −.04

Need frustration 0.03 0.00 0.04 0.31** −0.20

Note: Coefficients above the diagonal line: within-person correlations; and coefficients below the diagonal line: between-person correlations.

* p < 0.001; p <0 .01, * p <0 .05.

Confirmatory Analyses

Table 3 reports the results of the logistic MLA. The intraclass correlation coefficient (ICC) indicates that co-present smartphone use is largely determined by variation on the situational level (ICC = 0.27). None of the trait-level control variables in the model were significantly associated with co-present use on the situational level.

Table 3.
Logistic Multilevel Model With Co-Present Use as the Dependent Variable, Including Control Variables.

Odds Ratios Confidence Intervals p

Fixed effects

(Intercept) 0.26 0.11–0.59 0.001

State-level predictors

Notifications 1.12 1.05–1.19 <0.001

Others’ smartphone use 3.48 2.27–5.34 <0.001

Need frustration 0.98 0.81–1.18 0.819

Need satisfaction 0.87 0.73–1.04 0.119

Fear of missing out (FoMO) 1.11 0.88–1.40 0.381

Control variables (state)

Duration of interaction 1.00 1.00–1.00 0.171

Latency 1.00 1.00–1.00 0.194

Study day 1.02 0.92–1.13 0.668

Control variables (trait)

Trait co-present use 1.10 0.73–1.66 0.659

General smartphone use 1.41 0.84–2.37 0.199

Trait need satisfaction 1.15 0.67–1.97 0.622

Trait need frustration 1.24 0.75–2.05 0.405

Trait FoMO 0.93 0.60–1.45 0.754

Age 0.99 0.96–1.03 0.754

Gender (1 = female) 0.60 0.24–1.50 0.274

Random effects

σ² 3.29

τ₀₀ _id 1.21

Notes: Intraclass Correlation Coefficient = 0.27, N_Person = 87, N_Situation = 829, Marginal R² = 0.17; σ²: residual variance; τ₀₀ _id: between-person variance; metric L1 variables are person-mean-centered, and metric L2 variables are grand-mean-centered; significant associations are shown in bold.

Supporting H1 and H2, the probability of co-present use was positively associated with the number of notifications received (OR = 1.11, p < 0.001) and smartphone use by others (OR = 3.46, p <0 .001). However, need frustration, need satisfaction, and FoMO showed no significant association with the probability of co-present use (OR_frustration ₌ 1.00, OR_satisfaction ₌ 0.91, OR_FoMO ₌ 1.10, all p > 0.05). Thus, H3, H4, and H5 were rejected.

Exploratory Analyses

In many social situations, participants spend time alone with their partner and studies show that co-present use is especially common in such contexts (see e.g., Frackowiak, Hilpert, et al., 2024). To examine this relationship more closely, we conducted a non-pre-registered exploratory analysis where we included the presence of the partner (0 = Others present; 1 = Only partner present) into the model (see Online Table A1 in the additional analyses: https://osf.io/u4pv2). Our results support this assumption. In addition to the significant associations of co-present use with the number of notifications received and others’ smartphone use, we found that co-present use was more likely when only one's partner was present than when other people were around (OR = 1.87, p =0 .018). Additionally, need satisfaction showed the expected association (see H4): Higher satisfaction of the need for relatedness was associated with a lower probability of smartphone use (OR = 0.82, p =0 .037).

Discussion

While research on media use and effects has long been dominated by a person-level-centered perspective, current research focuses more on the situational level. A growing number of studies highlight the role of situation-specific factors for media use and call for their integration into theory and empirical research (for an overview, see Schnauber-Stockmann, Bayer, et al., 2025). Research on co-present smartphone use, however, largely focuses on the person level, examining, for example, how the general tendency to use a smartphone in a social setting is related to stable personal characteristics such as personality (for an overview, see Arenz & Schnauber-Stockmann, 2024). The situational level, in contrast, has received little attention in research on co-present use to date. However, we argue that co-present use per se is rather an inherently situational concept—individuals may use their smartphone in one situation but not in another—and that situational factors are key to better understanding the actual processes involved in co-present use (see e.g., Al-Saggaf, 2021; Büttner et al., 2022). Our results show that 73% of the variation of co-present use occurs at the situational level. Therefore, focusing on person-level predictors ignores central explanations for co-present use. The present paper thus complements research on co-present smartphone use by examining situational conditions of co-present use.

Following Bayer et al. (2016), we distinguished three types of connection cues (technical, spatial, and mental) and tested their association with situational co-present use in an event-contingent ESM study. The results suggest that mainly technical and spatial cues relate to co-present use: notifications and others’ smartphone use were linked to a higher probability of co-present smartphone use. These results support current research showing that notifications are among the most important drivers of smartphone use in general (e.g., Lee et al., 2014; Pielot et al., 2014) and in social situations specifically (e.g., Klein, 2014; Park et al., 2017). Likewise, mainly cross-sectional results indicate that others’ smartphone use in social situations correlates with one's smartphone use (e.g., Chotpitayasunondh & Douglas, 2016; Li, 2023). Our results support these findings on the situational level.

Both technical and spatial cues are context cues—either stemming from the media or the environmental context (Schnauber-Stockmann, Bayer, et al., 2025). Our findings suggest that context cues are more influential than internal cues: using one's smartphone in the presence of others is primarily associated with the users’ environmental and media context, not their cognitions and feelings.

The exploratory analysis additionally shows that the relationship with the person(s) present is associated with the probability of co-present use: being alone with one's partner—which is also a spatial cue—is associated with smartphone use. This finding is consistent with studies showing that people use their smartphones more when interacting with close others (e.g., partners) than with more distant contacts (e.g., colleagues; Al-Saggaf & MacCulloch, 2019) and that co-present smartphone use has become normalized in couples’ everyday lives (e.g., McDaniel et al., 2021). This may be because partners, especially when living together, spend time together daily, and therefore are more likely to be exposed to each other's smartphone use.

Although theoretically plausible, there is little research on the relationship between co-present smartphone use and relatedness need frustration and satisfaction (for an exception, see Butt & Arshad, 2021). In our confirmatory analysis, neither relatedness need frustration nor satisfaction was associated with co-present use. However, when controlling for the presence of one's partner, higher levels of relatedness need satisfaction were significantly associated with a decreased probability of co-present use. This exploratory result suggests that, when not only the partner is present, need satisfaction may act as an “inverse” cue that prevents co-present use. This finding underscores the importance of emphasizing relationships between interaction partners when researching co-present use. This emphasis is potentially attributable to the existence of divergent norms regarding the appropriateness of co-present use, which are contingent on the specific social setting (e.g., Büttner et al., 2022). Future research should build on these exploratory findings and engage with the interrelations between norms and social context on the situational level.

Contrary to the well-established link between co-present use and FoMO on the trait level (for an overview, see Ansari et al., 2024; Arenz & Schnauber-Stockmann, 2024), we did not find a significant association between state FoMO and smartphone use in social situations. This result can be seen as a caution against inferring within-person processes from between-person data, which only reveal co-occurrences. Thus, a general tendency to use smartphones in social situations may co-occur with higher FoMO, without indicating a causal link.

There are several possible explanations for the lack of significant association between mental cues and co-present smartphone use. First, limited variability in state-level measures may have constrained the detection of associations. Participants generally reported high relatedness need satisfaction and low levels of frustration and FoMO. This lack of variation could have reduced the likelihood of observing associations in the data. Future studies could benefit from more sensitive or context-aware measures. Second, associations with internal states may depend on situational moderators and co-occurring factors that were not accounted for. FoMO, for instance, may only relate to co-present use when self-regulatory capacity is low (cf. Meier et al., 2023), or need satisfaction may be relevant only in specific social settings (see exploratory analysis). Moreover, mental cues likely do not operate in isolation. FoMO, for example, may coincide with other internal states such as boredom, while smartphone use by others may also signal descriptive norms or social pressure. Some associations—or lack thereof—may reflect interacting cues. Future research should therefore explore these complex interrelations between cues.

While our study is among the first to demonstrate the general importance of different connection cues for co-present smartphone use, we see several directions for future research that were beyond the scope of this study but appear relevant to understanding co-present smartphone use. Future research could benefit from examining the processes linking cues to smartphone behavior in more detail and evaluating the extent to which these associations are activated automatically or deliberately. Moreover, beyond the cues studied, many other potential cues, including mood and the mere presence of smartphones, remain unexplored, offering the opportunity for a more comprehensive understanding of co-present smartphone use. In addition, more granular forms of smartphone use deserve closer attention. Whereas we focused on more sustained use episodes in which individuals devoted their full attention to the device, brief glances at the smartphone may also play a meaningful role in social interactions, and exploring these subtler forms of use could further illuminate the nuances of co-present smartphone use.

Limitations

The study comes with some limitations in the research design that need to be considered when interpreting the results. Compared to, for example, signal-contingent sampling, the chosen event-contingent, self-initiated ESM design has the disadvantage that the data quality is completely dependent on the participants remembering to complete the protocols on their own. This poses three potential risks that can affect data quality: participants may forget to complete the protocols; have different thresholds for what they consider to be a relevant social interaction; or complete the protocols with a considerable time delay.

The first problem arises when participants forget to complete a protocol after an interaction, resulting in relevant situations not being recorded. Compared to other studies with event-based, self-initiated designs that examined social interactions (e.g., Côté & Moskowitz, 1998; Elmer et al., 2023), slightly fewer protocols were completed in the present study. Therefore, it seems likely that the participants in our study may have somewhat underreported their daily interactions. However, whether this is mainly an issue of reduced power due to a smaller sample size on the situational level or leads to biased results depends on whether the interactions not reported differ from those reported, foremost in whether co-present use occurred or not. Whether there are systematic reasons for non-reported interactions, unfortunately, remains unclear.

The second problem concerns possible variation in how participants defined social interaction. Despite detailed instructions, individual differences may have influenced which interactions were reported, contributing to variability in the number of reported interactions. While subjective thresholds cannot be ruled out, we assume that the variation likely reflects actual differences in how many social interactions participants experienced.

The third problem arises when participants do not complete the protocols in situ, but at a later point, since long time lags between the situation and the completion of the protocols can lead to recall bias (Schnauber-Stockmann & Karnowski, 2020). Overall, the range of latency times was wide, and some protocols were completed with a significant delay (range: 0–36 h), which may have affected the quality of the study. However, the data analyses showed that latency had no significant influence on the results (see Table 3). We therefore assume that the partly long time lags are not problematic for data quality. Nevertheless, it would be advisable for future studies to find ways to reduce long response latencies.

One way to address these three issues might be to choose an alternative research design. A promising approach for future studies would be to use a signal-based event-contingent ESM design, meaning that a signal to fill out a protocol would be automatically triggered on the participant's smartphone after each social interaction. This could be technically implemented by combining an ESM design with smartphone-based mobile sensing to identify social interactions through mobile sensors embedded in smartphones (e.g., microphone sensors or Bluetooth data; see e.g., Harari et al., 2017; Reiter et al., 2024).

A further limitation related to the research design is that we could not empirically test the causality of the assumed relationships. The present paper argues that connection cues influence co-present use. While the direction of the relationship between co-present use and notifications is very plausible, the nature of the other relationships is less clear. Further research is needed to test the causality of relationships by either analyzing the temporal pattern of the occurrence of co-present use and connection cues or by experimentally manipulating their occurrence.

Additionally, whereas our results indicate that most of the variation in co-present smartphone use resides on the situational level, approximately one-fourth (ICC =0 .27) can be ascribed to the person level. Although trait variables were incorporated as controls, the intricate interplay between trait-level and state-level variables was not the focal point of this study. This is a promising and essential avenue for future research.

Finally, our sample size and composition were satisfactory given the highly demanding study design. Still, they leave room for improvement. Replicating these findings in larger and more varied samples would help establish their robustness.

Conclusion

The present study contributes to a better understanding of the situation-specific factors associated with co-present use. We identified two relevant cues by demonstrating that notifications and others’ smartphone use are linked to co-present use. This study is among the first in the research field on predictors of co-present smartphone use to employ an in-situ approach (see also, e.g., Dwyer et al., 2018; Kushlev & Heintzelman, 2018; Reiter et al., 2024). This approach allows for the investigation of situation-specific variation in co-present smartphone use and provides a crucial complement to prior correlational cross-sectional research, which has mainly focused on person-specific influences on co-present use. While cues are frequently discussed from a theoretical perspective, empirical investigations of their role in everyday life remain limited. Our study contributes to theory development by providing situational-level evidence that supports and extends connection cue theory. This represents an important advance toward a more nuanced understanding of the complex dynamics associated with co-present smartphone use.

	Co-present use	Smartphone use by others	Notifications	Fear of missing out (FoMO)	Need satisfaction	Need frustration
Co-present use (1 = yes)		0.29***	0.15***	0.07*	−0.02	0.03
Smartphone use by others (1 = yes)	0.59***		0.14***	0.10**	0.01	0.12***
Notifications	−0.09	−0.06		0.11**	0.12***	0.00
FoMO	0.05	0.00	0.16		−0.17***	−0.05
Need satisfaction	−0.08	−0.04	−0.17	−0.25*		−.04
Need frustration	0.03	0.00	0.04	0.31**	−0.20

	Odds Ratios	Confidence Intervals	p
Fixed effects
(Intercept)	0.26	0.11–0.59	0.001
State-level predictors
Notifications	1.12	1.05–1.19	<0.001
Others’ smartphone use	3.48	2.27–5.34	<0.001
Need frustration	0.98	0.81–1.18	0.819
Need satisfaction	0.87	0.73–1.04	0.119
Fear of missing out (FoMO)	1.11	0.88–1.40	0.381
Control variables (state)
Duration of interaction	1.00	1.00–1.00	0.171
Latency	1.00	1.00–1.00	0.194
Study day	1.02	0.92–1.13	0.668
Control variables (trait)
Trait co-present use	1.10	0.73–1.66	0.659
General smartphone use	1.41	0.84–2.37	0.199
Trait need satisfaction	1.15	0.67–1.97	0.622
Trait need frustration	1.24	0.75–2.05	0.405
Trait FoMO	0.93	0.60–1.45	0.754
Age	0.99	0.96–1.03	0.754
Gender (1 = female)	0.60	0.24–1.50	0.274
Random effects
σ²	3.29
τ₀₀ _id	1.21

Footnotes

Acknowledgments

We thank the Alumni Foundation of the Department of Communication (University of Mainz) for providing incentives for participation and Leonard Reinecke for his helpful comments on earlier versions of the manuscript.

ORCID iDs

Anisha Arenz

Anna Schnauber-Stockmann

Informed Consent

All participants provided written informed consent to participate and written informed consent for publication.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability

The preregistration, materials (questionnaires, protocol, study briefing), dataset, code, an amendment documenting all deviations from the preregistration as well as preregistered analyses not reported in the manuscript are available at .

Notes

Author biographies

Anisha Arenz is a PhD student at the Amsterdam School of Communication Research at the University of Amsterdam, the Netherlands. Her research is in the field of media psychology and focuses on the uses and effects of digital media, including mobile communication, social influence processes, and mental health/well-being.

Anna Schnauber-Stockmann is a postdoctoral researcher at the Department of Communication at the University of Mainz, Germany. Her research focuses on media habits, mobile media, self and feedback effects, situational influences on media use and effects, and in situ methods.

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The Role of Connection Cues for Co-Present Smartphone Use in Everyday Life: Insights from an Experience Sampling Study

Abstract

Keywords

Introduction

Co-Present Smartphone Use and Connection Cues

Technical Cues: The Influence of Notifications

Spatial Cues: The Influence of Others' Smartphone Use

Mental Cues: The Influence of Relatedness Need Experiences and Fear of Missing Out

Method

Procedure and Participants

Measures

Intake and Exit Questionnaires

ESM Protocol

Data Analysis

Results

Descriptive Analyses

Confirmatory Analyses

Exploratory Analyses

Discussion

Limitations

Conclusion

Footnotes

Acknowledgments

ORCID iDs

Informed Consent

Funding

Declaration of Conflicting Interests

Data Availability

Notes

Author biographies

References