Abstract
Background and aims
Smartphones have been so widely adopted that many consider them essential for modern life. However, some people use their phone excessively, which can cause functional impairment or harm, termed problematic smartphone use (PSU). Smartphone use motives may help explain why users engage in general smartphone use and PSU, but existing measures may not capture certain motives which research suggests are important to smartphone use. To address this, across two studies, we constructed and validated a Motives for Smartphone Use Questionnaire (MSUQ) among young adults.
Methods and results
In Study 1, the Delphi method was used, whereby engagement with a panel of 23 international academic experts resulted in a pool of 62 smartphone use motives items that measure 11 proposed motives. In Study 2, the 62 items were administered to 680 young adults aged 18–25 years (Mage = 22.50, SD = 2.16). Results from exploratory and confirmatory factor analyses found that the MSUQ has a seven-factor structure, assessing smartphone use to cope, pass time, socialize, obtain social comfort, feel safe, fulfil social obligations, and seek information. These motives differentially influenced PSU and smartphone usage.
Conclusions
The MSUQ is a valid measure of motives for smartphone use. It was developed specifically for smartphone use and it includes motives not captured in prior measures.
Introduction
Smartphone ownership has become the norm in many countries globally, with young adults adopting the technology more rapidly than other age groups (Pew Research Centre [PRC], 2022). Unsurprisingly, research has examined what motivates people to use smartphones (e.g., Lepp, Barkley, & Li, 2017; Moon & An, 2022; Vanden Abeele, 2016). Motives for smartphone use are proposed to be key factors in the etiology of problematic smartphone use (PSU; Chen et al., 2017; Li, Zhan, Zhou, & Gao, 2021; Shen, Wang, Rost, Gaskin, & Wang, 2021) which, like general smartphone use, is higher among young adults (Busch & McCarthy, 2021). Despite promising findings linking motives to PSU, there are issues related to the measurement of motives for smartphone use that limit understanding of what motivates smartphone use and differentiates use from PSU. To address this, two studies were conducted which constructed and validated a Motives for Smartphone Use Questionnaire (MSUQ) among young adults.
Conceptualization and consequences of problematic smartphone use
PSU refers to uncontrolled smartphone use that leads to harm or functional impairment (James, Dixon, Dragomir, Thirlwell, & Hitcham, 2023). While there has been some debate in the past, it is generally accepted that PSU reflects problematic engagement in smartphone content (e.g., social networks, internet messaging, games, and pornography), rather than the device itself (Elhai, Yang, & Montag, 2019; Montag, Wegmann, Sariyska, Demetrovics, & Brand, 2021; Yu & Sussman, 2020). Given this, some authors have asserted that PSU is not a distinct construct but rather an umbrella construct that masks a range of specific behaviors that can be engaged in on a smartphone (Panova & Carbonell, 2018; Starcevic et al., 2021). However, others have suggested that generalized PSU, which reflects the problematic engagement in a range of content on a smartphone, is a distinct construct (Griffiths, 2021; Montag et al., 2021). Notably, research directly investigating this question with network analysis has found that PSU operated as a distinct construct, contrary to what was hypothesized (Baggio et al., 2018). Rozgonjuk, Sindermann, Elhai, and Montag (2021) also found that PSU was associated with higher levels of distress and impairment compared with the problematic use of specific social networking and online communication applications. Perhaps this is due to the smartphone providing simultaneous access to multiple different types of content. Therefore, PSU warrants ongoing investigation.
While PSU is often likened to a behavioral addiction, including symptoms such as salience, withdrawal, and loss of control (Paterna et al., 2024), it is not recognized as an addictive disorder in diagnostic manuals (American Psychiatric Association, 2022; World Health Organization, 2018). Multiple reasons for this have been cited. First, it has been argued that the evidence for and conceptualization of certain symptoms that are core to substance use disorders, particularly tolerance and withdrawal, remain limited in the context of PSU. Second, as noted above, people are not addicted to their smartphone devices; rather, they problematically engage in one or more highly reinforcing activities accessible via a smartphone (Domoff et al., 2025; Gjoneska, Bőthe, Potenza, Szabo, & Demetrovics, 2025). Third, some authors have argued that treating PSU like an addictive disorder is limiting, as it includes unique characteristics such as smartphone use while driving and impaired productivity (Billieux, Maurage, Lopez-Fernandez, Kuss, & Griffiths, 2015; Su et al., 2024). Regardless of whether PSU should (or should not) be considered an addictive disorder, it has been linked with a range of negative consequences. For example, in their systematic review, Busch and McCarthy (2021) found that possible consequences of PSU include depression/anxiety, musculoskeletal injury, sleep problems, motor vehicle collision, difficulties with concentration, impaired work/academic performance, and impaired social/romantic relationships. Given these potential consequences, it is imperative that research works to better understand the etiology of PSU, so that effective interventions can be developed.
Research has found that there are several etiological pathways to PSU (e.g., excessive reassurance, impulsive, and extraversion pathways), each characterized by different psychological factors (Billieux et al., 2015; Canale et al., 2021; Pivetta, Harkin, Billieux, Kanjo, & Kuss, 2019). A range of psychopathological (e.g., anxiety), neurodevelopmental (e.g., attention-deficit/hyperactivity disorder), and personality (e.g., neuroticism) factors have been found to influence PSU (Busch & McCarthy, 2021). In a seminal article, Kardefelt-Winther (2014) asserted that motives may link the aforementioned psychological factors to PSU and provide a better understanding of why people engage problematically with their smartphones (or other technologies). Thus, motives may represent key components in the etiology of PSU and represent targets of intervention. However, which motives are relevant to PSU is unclear, in large part due to how smartphone use motives are measured (Mostyn Sullivan & George, 2023).
What are motives for smartphone use and how do they relate to problematic smartphone use?
Motives have been identified as important factors in the etiology of a range of problematic/addictive behaviors. For example, research has found that motives for gaming (Bäcklund, Elbe, Gavelin, Sörman, & Ljungberg, 2022), gambling (Stewart & Zack, 2008), pornography use (Bőthe et al., 2021), and social media use (Kircaburun, Alhabash, Tosuntaş, & Griffiths, 2020) are associated with problematic engagement in each respective behaviour. It is therefore unsurprising that motives have been examined in relation to PSU. Research investigating motives for smartphone use, and their relationship to PSU, has primarily drawn from two theories—the uses and gratifications theory (Katz, 1974) and compensatory internet use theory (Kardefelt-Winther, 2014). The uses and gratifications theory asserts that people choose to engage in different types of media to gratify their needs and desires, with motives reflecting the specific gratifications sought (Rubin, 2002). Traditionally, the uses and gratifications literature has considered motives to reflect innate personal needs, but more recent theoretical development considers motives to also derive from the environment (e.g., the possible functions of a specific type of media; Rathnayake & Winter, 2018; Sundar & Limperos, 2013). Kardefelt-Winther's (2014) compensatory internet use theory builds on the uses and gratifications theory by proposing that research which has examined motives for media use should be integrated with research that has investigated the association of psychosocial wellbeing factors with problematic internet use. Specifically, it is suggested that people use the internet excessively to compensate for negative emotions and life circumstances. Thus, motives reflecting efforts to avoid or reduce distress should influence the association of psychosocial wellbeing factors with problematic internet use.
The alcohol use motivational model (Cox & Klinger, 1988) has also been applied in some PSU research (e.g., Chen et al., 2017; Zhang, Chen, & Lee, 2014). This model is relevant for conceptualizing what motives are and how they influence behavior, including PSU. The theory conceptualizes motives similarly to the uses and gratification theory, with Cox and Klinger (2004, p. 124) describing them as “the value placed on the particular effects they want to achieve, which motivate them to drink”. Moreover, they emphasized that motives are: (a) reinforcing; (b) reflective of personal and environmental influences; and (c) proximal to behavior, mediating the effects of other psychosocial factors (Cox & Klinger, 1988, 2004). Applying these theoretical models to smartphone use and PSU, motives can be considered proximal drivers of smartphone use to achieve desired effects, which are influenced by a range of psychosocial factors. When the desired effect of smartphone use is achieved, use is reinforced and perpetuated.
While smartphone use motives are theoretically relevant for understanding smartphone use and PSU, there remains some conceptual ambiguity about what constitutes motives for smartphone use. Cheng and Meng (2021) recently argued that motives for smartphone use have been confused with ways of using a smartphone. Use refers to frequency/intensity of engagement in certain actions and/or types of content on a smartphone (Panova, Carbonell, Chamarro, & Puerta-Cortés, 2020) rather than internal motivations for engaging in that content. In the context of smartphones, use is generally operationalized broadly to include frequency of engagement in types of content (e.g., sending/receiving messages, social networking sites, internet/websites; Elhai et al., 2018) or makes small distinctions between actions (e.g., reading social media content and posting social media content; Panova et al., 2020). Noting the multitude of applications accessible on a smartphone, there are a range of more specific actions that can be performed on a smartphone not captured by measures of smartphone use (see Romero et al., 2023 for an expanded discussion of Instagram use). The application of the alcohol use motivational model (Cox & Klinger, 2004) is one way of conceptualizing smartphone use motives to ensure they remain distinct from different functions of the smartphone; that is, categorizing motives by the valence (reward/avoidance) and source (internal/external) of the desired effect (Kuntsche, Wiers, Janssen, & Gmel, 2010). If a proposed smartphone use motive does not have an identifiable valence and source, it may be better conceptualized as a type of use. For example, information seeking or instrumental motives, which reflect smartphone use to obtain information or for their many other functions, are relatively common in the smartphone use literature (Hwang & Park, 2015; Lin, Fang, & Hsu, 2014; Meng et al., 2020), but do not have a clear valence. Therefore, it remains open as to whether they should be considered a motive for smartphone use or a type of use.
There also appears to have been some conflation of motives and the related construct of expectancies in this literature. Drawing a distinction between motives and expectancies in the context of alcohol use, Kuntsche et al. (2010) noted that expectancies are beliefs about the effects of drinking (i.e., after drinking I expect X), while motives are the value placed on achieving those effects (i.e., I drink to achieve X). Some smartphone use motives measures include items that better reflect expectancies (e.g., “using my smartphone is fun”; Zhang et al., 2014). Another measure intended to assess smartphone use expectancies included items that are more consistent with motives (e.g., “I use my smartphone to have fun”; Elhai, Yang, Dempsey, & Montag, 2020). Given the potential importance of motives to general smartphone use and PSU, a clear understanding of what constitutes motives for smartphone use is essential so they can be accurately measured and investigated. This may enhance understanding of the etiology of PSU and inform interventions.
Measures of smartphone use motives
Several measures of smartphone use motives have been developed. Some authors have categorized smartphone use motives according to the theoretically derived motives categories from the alcohol use motivational model (i.e., coping, enhancement, conformity, and social motives; Chen et al., 2017; Zhang et al., 2014). Others have drawn on the uses and gratifications theory literature, adapting various motives domains and items developed for other media (e.g., pagers, telephones, the internet) to smartphone use (Moon & An, 2022; Park & Lee, 2014; Tirado-Morueta, García-Umaña, & Mengual-Andrés, 2021; Vanden Abeele, 2016). In their systematic review, Mostyn Sullivan and George (2023, p. 22) noted that this scale development approach had resulted in “a significant diversity in smartphone use motives measures, with 19 different measures and 55 different labels applied to individual motives dimensions”. Some motives domains with similar labels appeared to measure different constructs and others with different labels appeared to measure similar constructs, termed the “jingle” and “jangle” fallacies, respectively (Whiteside & Lynam, 2001). For example, escape (Kim, 2017), mood regulation (Zhang et al., 2014), and negative smartphone use expectancies (Elhai, Yang, et al., 2020) all appear to measure similar constructs. Conversely, escape (Kim, 2017) and escapism (Wang, Wang, Gaskin, & Wang, 2015) motives appear to measure somewhat different constructs. It has been noted that such fallacies may inhibit the accumulation of scientific knowledge (Whiteside & Lynam, 2001). Mostyn Sullivan and George (2023) qualitatively synthesized existing smartphone use motives domains into seven categories (i.e., mood regulation, pass time, enhancement, self-identity/conformity, social, safety, and information seeking), most of which were generally positively associated with PSU. However, quantitative factor analytic research is required to integrate existing smartphone use motives measures and identify the core motives for smartphone use.
Additionally, existing smartphone use motives measures may not include motives unique to smartphone use that could only be identified through bottom-up qualitative research (Mostyn Sullivan & George, 2023). This issue has previously been discussed by Sundar and Limperos (2013) in the broader context of the uses and gratification theory literature. The authors noted that “nuanced (and perhaps ‘new’) gratifications obtained from using the Internet and other new communication technologies have not been fully specified” (p. 509). Thus, developing new motives measures by adapting items developed for older media/behaviors—as is typically done with smartphone use motives measures—may result in motives unique to the context of newer media being missed. This is consistent with the theoretical conceptualization of motives as both personally and environmentally derived (Cox & Klinger, 1988) and contingent on the possible functions of a given behavior (Sundar & Limperos, 2013). Supporting this, research from the alcohol use literature suggests there are motives which are unique to different patterns of the same behavior (George, Zamboanga, Martin, & Olthuis, 2018). For example, Cooper (1994) originally identified that the core motives for drinking were coping, enhancement, conformity, and social. However, subsequent research identified a range of additional motives for different patterns of drinking, such as playing drinking games (Zamboanga et al., 2019) and pre-drinking (Bachrach, Merrill, Bytschkow, & Read, 2012). Moreover, recent qualitative research suggests that there may be motives which uniquely influence different patterns of smartphone use that were not included in existing smartphone use motives measures, such as smartphone use to avoid feeling awkward in social situations and to conform to perceived social obligations (Lepp et al., 2017; Mostyn Sullivan, George, & Rickwood, 2024; Ochs & Sauer, 2022). A more comprehensive and current smartphone use motives measure is clearly warranted.
The utility of the Delphi method as a step in psychometric scale development
The Delphi method (Dalkey, Brown, & Cochran, 1969) is a useful tool that to date has not yet been utilized to support the development of a smartphone use motives questionnaire. The Delphi method is a group consensus research technique that broadly involves the: (a) administration of a set of potential questionnaire items to a group of experts for agreement on applicability; (b) revision of the original items based on the group's responses; (c) provision of anonymous feedback to all individuals within the group, allowing them to compare their responses to those provided by the group as a whole; and (d) re-administration of a revised version of the questionnaire items across multiple rounds, giving group members an opportunity to modify their responses based on feedback after each round. The Delphi method has a range of applications, including scale development (Jorm, 2015), although it is rarely used to develop scales in psychological research (Dragostinov et al., 2022).
Despite its infrequent use for scale development in psychological research, the Delphi method has clear benefits for such an application. It is useful for establishing content validity, mapping items to the concepts they are intended to measure (Colton & Hatcher, 2004) and can overcome the lack of transparency in scale development reporting by outlining the process through which items were generated, modified, and retained (Dragostinov et al., 2022). A key strength of the Delphi method is also the ability to help identify the fundamental underpinnings of a field (Shang, 2023). Therefore, it may be particularly useful for parsing the current heterogeneous smartphone use motives measures and distilling their core features into a single comprehensive measure. Finally, because the Delphi method excels at defining foundational concepts (Jorm, 2015), it may have particular utility for developing and refining motives that were not well captured by prior smartphone use motives measures.
The present studies
We conducted two studies to address key gaps in the measurement of smartphone use motives. Specifically, prior smartphone use motives measures are highly heterogenous, suffering from the “jingle” and “jangle” fallacies (Whiteside & Lynam, 2001). That is, there are a range of motives domains with different labels which appear to measure similar constructs and some motives domains with similar labels which appear to measure different constructs. Moreover, prior smartphone use motives measures were generally developed with a confirmatory approach, adapting items and domains developed for other behaviors to smartphone use. As Sundar and Limperos (2013) have previously argued, because motives reflect both personal needs and the specific affordances provided by a given behavior, a confirmatory approach to scale development means motives unique to a given behavior that could only be identified through qualitative research may be missed. Finally, it has been argued that there has been some conflation of smartphone use motives with the related, yet distinct, constructs of smartphone use expectances and usage types (Cheng & Meng, 2021; Mostyn Sullivan & George, 2023).
To address these gaps, Study 1 aimed to develop a comprehensive pool of smartphone use motives items and motives domains which were distinct from expectancies and smartphone usage types. To this end, the Delphi method was used. Study 2 then used the pool of items developed in the first study to construct and validate the MSUQ among young adults. Young adults were the focus, given smartphone use and PSU are generally higher among this age group (Busch & McCarthy, 2021; PRC, 2022). To achieve the aim of Study 2, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and multiple linear regression were used to establish the MSUQ's construct and concurrent validity.
STUDY 1
Method
Participants and procedure
We used the Delphi method to develop possible smartphone use motives domains and associated items intended to measure each domain. The process was conducted online via Qualtrics over two rounds. Questionnaire items were administered in Round 1, modified based on the participants' responses, and then readministered in Round 2. Feedback was provided after each round with participants given a tabular summary of their own responses, the overall mean, median, minimum, and maximum, as well as the percentage of respondents that rated each item ≥ 4 (i.e., very important/representative or essential/extremely representative) and ≤ 2 (i.e., not very important/representative or not important/representative) on a 5-point scale.
Eligibility criteria for participation were holding an academic position (including PhD candidates) with at least one published article in a relevant domain (either motivation, PSU, other problematic behaviors, and/or addictions). Potential participants were identified through a review of the literature and sent individual email invitations. Participants had the option to be included in the acknowledgement section of any published article with the study's findings. A total of 23 experts (Mage = 40.35, SD = 7.16) participated in the first round of our Delphi. The attrition rate was 13%, with only three experts not returning for the second round (n = 20, Mage = 39.5, SD = 6.29). The most common academic appointments were Assistant Professor and Associate Professor (Round 1: 22%), followed by PhD student/candidate (Round 1: 17%). Our panel of experts was based across multiple geographic regions globally, including North America, South America, Europe, the Middle East, and East Asia. A complete summary of participants' gender, academic roles, areas of expertise, and geographic locations are presented in Table 1.
Participant characteristics
| Characteristic | Round 1 | Round 2 |
| n (%) | n (%) | |
| Gender | ||
| Male | 10 (44%) | 9 (45%) |
| Female | 13 (57%) | 11 (55%) |
| Field of expertisea | ||
| Motivation | 10 (44%) | 10 (50%) |
| PSU | 16 (70%) | 13 (65%) |
| Problematic behaviors | 13 (57%) | 11 (55%) |
| Addictions | 9 (39%) | 8 (40%) |
| Academic role | ||
| Assistant professor | 5 (22%) | 5 (25%) |
| Associate professor | 5 (22%) | 4 (20%) |
| PhD student/candidate | 4 (17%) | 4 (20%) |
| Professor | 3 (13%) | 2 (10%) |
| Senior research fellow | 2 (9%) | 2 (10%) |
| Lecturer | 2 (9%) | 1 (5%) |
| Postdoctorate | 1 (4%) | 1 (5%) |
| Research fellow | 1 (4%) | 1 (5%) |
| Country | ||
| Italy | 6 (26%) | 5 (25%) |
| United States | 3 (13%) | 3 (15%) |
| Argentina | 2 (9%) | 2 (10%) |
| Australia | 2 (9%) | 2 (10%) |
| Germany | 2 (9%) | 2 (10%) |
| China | 2 (9%) | 2 (10%) |
| Hungary | 1 (4%) | 1 (5%) |
| Japan | 1 (4%) | 1 (5%) |
| Switzerland | 1 (4%) | 1 (5%) |
| Turkey | 1 (4%) | 1 (5%) |
| United Arab Emirates | 1 (4%) | |
| Canada | 1 (4%) | |
Note. a = participants could report multiple fields of expertise.
Materials
Most items for Round 1 of the Delphi were drawn from 17 existing smartphone use motives measures (AlBarashdi & Bouazza, 2019; Elhai, Yang, et al., 2020; Hwang & Park, 2015; Khang, Kim, & Kim, 2013; Kim, 2017; Kim, Seo, & David, 2015; Park, Kim, Shon, & Shim, 2013; Lee & Lee, 2017; Lin et al., 2014; Meng et al., 2020; ; Park & Lee, 2014; Shen et al., 2021; Van Deursen, Bolle, Hegner, & Kommers, 2015; Vanden Abeele, 2016; Wang et al., 2015; Zhang et al., 2014; Zhang, Chen, Zhao, et al., 2014). These measures were identified through a systematic review of the literature (Mostyn Sullivan & George, 2023). Prior to the commencement of this study, additional non-systematic searches were also conducted to identify new smartphone use motives measures not captured by Mostyn Sullivan and George (2023); no relevant measures were found. All 17 measures are summarized in Appendix A. Of these, we had access to all the items for 12 measures, but only sample items for the remaining five measures (AlBarashdi & Bouazza, 2019; Park et al., 2013; Meng et al., 2020; Park et al., 2013; Shen et al., 2021; Wang et al., 2015). As such, we had a pool of 206 items. To reduce fatigue, 120 duplicate or very similarly worded items, and items considered to be very low-quality by the research team, were excluded. Small amendments were made to the wording of several items to ensure consistency, including changing items originally constructed as expectancies (e.g., “using smartphone is interesting”; Zhang et al., 2014) to be consistent with motives (e.g., “I use my smartphone because it is interesting”). An additional 24 items were written to assess motives identified in qualitative research (Lepp et al., 2017; Mostyn Sullivan et al., 2024, under review; Ochs & Sauer, 2022) that were not represented by items in the prior smartphone use motives measures. This resulted in 110 items included in Round 1.
Items were a priori categorized into nine proposed motives domains (mood regulation, boredom reduction, social, avoid social awkwardness, safety, conformity, validation, pleasure, and instrumental), developed based on a recent systematic review (Mostyn Sullivan & George, 2023), qualitative research (Lepp et al., 2017; Mostyn Sullivan et al., 2024; Ochs & Sauer, 2022), and discussion amongst the research team. In Round 1, experts were asked to rate the importance of each of the nine motives domains in relation to PSU on a 5-point scale (1 = not important, 2 = not very important, 3 = important, 4 = very important, 5 = essential). They were then asked to rate how representative each item was of the domain it was proposed to measure on a 5-point scale (1 = not representative, 2 = not very representative, 3 = representative, 4 = very representative, 5 = extremely representative). Based on feedback, experts were asked in Round 2 to rate the importance of including each motives domain in a measure of smartphone use motives, instead of their importance in relation to PSU.
In Round 1, participants could provide qualitative feedback regarding each domain and item. They were also provided with several questions to guide feedback (e.g., “Is the specified motive better conceptualized as a type of smartphone use?”, “Do you think certain items need rewording?”, “Do you think items intended to measure a certain motive actually better measure a different motive?”). Participants could provide qualitative feedback after each block of items categorized as corresponding to a particular domain, including the following question to help guide responses: “What other items would help accurately measure this motives domain?”. To help reduce fatigue, participants in Round 2 were only given the option to provide qualitative feedback after each block (each block included one proposed domain and the corresponding items for it). Qualitative feedback was used to refine domains and items between rounds.
Data analysis
Quantitative data was analyzed to determine whether items reached consensus. There is no one definition of consensus, with studies using cutoffs for percentage agreement, measures of central tendency, or a combination of both (Nasa, Jain, & Juneja, 2021). We employed Dragostinov et al.’s (2022) definition of consensus which avoids excessive refinement of items. This was important, given this study intended to develop a preliminary pool of smartphone use motives items which could subsequently be subjected to further psychometric validation, rather than construct a final smartphone use motives questionnaire. Specifically, consensus on inclusion of a motives item was reached if ≥ 70% of participants responded 4 (very important/very representative) or 5 (essential/extremely representative) and ≤ 15% responded 1 (not important/not representative) or 2 (not very important/not very representative). Conversely, items were considered to have reached consensus for exclusion if ≥ 70% of participants responded 1 (not important/not representative) or 2 (not very important/not very representative) and ≤ 15% responded 4 (very important/very representative) or 5 (essential/extremely representative). Qualitative data was used to refine motives domains and items. Items that reached consensus in Round 1 were not included again in Round 2. Due to the degree of consensus, the study concluded after Round 2. Items that did not reach consensus were moderated by the research team for a final decision regarding inclusion or exclusion from the final pool of smartphone use motives items.
Ethics
The study procedures were carried out in accordance with the Declaration of Helsinki. The Human Research Ethics Committee of the University of Canberra approved the study (HREC 12015). All subjects were informed about the study, and all provided informed consent.
Results
The Delphi process for smartphone use motives items is presented in Fig. 1. All items, including indicators for those that reached consensus, are presented in Supplementary material 1. A total of 110 items proposed to measure smartphone use motives were presented in Round 1, 24 of which experts thought were very representative or extremely representative of the motives domains they were proposed to measure, as per the inclusion criteria. A total of 74 motives items were presented in round 2, 25 of which reached consensus for inclusion and none for exclusion.
Delphi process for smartphone use motives items
Note. All items presented in Round 1 and Round 2 of the Delphi are listed in Supplementary material 1 and all items in the final item pool are listed in Supplementary material 2.
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2025.00006
Motives domains
Round 1. The quantitative results for all motives domains included in Rounds 1 and 2 of the Delphi are presented in Table 2. Only three of nine proposed smartphone use motives domains reached consensus (i.e., participants thought they were very important or essential in relation to PSU) for inclusion and none met consensus for exclusion. Several domains were modified based on feedback. Firstly, it was suggested that the mood regulation motives domain was too broad. As such, we separated this into three proposed domains (reduce loneliness, coping, escapism) and reorganized relevant items under each category for Round 2. Secondly, items which reflected use of a smartphone due to a perception that others expect you to always be contactable and responsive were recategorized from the conformity motives domain to a new proposed motives domain labelled social obligation motives. The remaining items that were in the conformity and validation motives categories were combined under a single new motives domain labelled positive impression. The avoid social awkwardness motives domain was relabeled social comfort. Finally, the instrumental motives category was relabeled information seeking, based on the content of items which reached consensus. The remaining items, which were originally proposed to measure instrumental motives, were recategorized under a new proposed domain labelled convenience motives.
Round 1 and 2 importance ratings of smartphone use motives domains
| Motives domain | Mean | Median | Min | Max | Percentage that rated ≥4 | Percentage that rated ≤2 |
| Round 1 | ||||||
| 1) Mood regulation* | 4.30 | 4 | 2 | 5 | 91.3% | 4.3% |
| 2) Boredom reduction* | 4.13 | 4 | 3 | 5 | 82.6% | 0% |
| 3) Avoid social awkwardness | 3.57 | 4 | 2 | 5 | 52.2% | 8.7% |
| 4) Safety | 2.52 | 2 | 1 | 5 | 21.7% | 52.2% |
| 5) Conformity | 2.65 | 3 | 1 | 5 | 13.0% | 39.1% |
| 6) Validation | 2.91 | 3 | 1 | 5 | 26.1% | 30.4% |
| 7) Pleasure | 3.78 | 4 | 1 | 5 | 65.2% | 13.0% |
| 8) Instrumental | 3.70 | 4 | 1 | 5 | 65.2% | 17.4% |
| 9) Social* | 4.00 | 4 | 1 | 5 | 78.3% | 8.7% |
| Round 2 | ||||||
| 1) Reduce loneliness* | 3.95 | 4 | 2 | 5 | 70% | 5% |
| 2) Coping+ | 3.95 | 4 | 2 | 5 | 60% | 5% |
| 3) Escapism* | 4.25 | 4 | 3 | 5 | 80% | 0% |
| 4) Social comfort* | 3.90 | 4 | 2 | 5 | 75% | 5% |
| 5) Safety | 3.25 | 3 | 1 | 5 | 40% | 20% |
| 6) Social obligation+ | 3.45 | 3 | 2 | 5 | 45% | 10% |
| 7) Positive impression | 2.80 | 3 | 2 | 4 | 20% | 40% |
| 8) Enjoyment* | 4.25 | 4 | 3 | 5 | 90% | 0% |
| 9) Information seeking* | 4.40 | 4 | 3 | 5 | 90% | 0% |
| 10) Convenience+ | 3.90 | 4 | 1 | 5 | 65% | 10% |
Note. There were 23 participants in round 1 and 20 in round 2. * = domain reached consensus for inclusion (≥70% of participants responded 4 (very important) or 5 (essential) and ≤ 15% responded 1 (not important) or 2 (not very important). + = domain was close to consensus after round 2 (≤15% responded 1 (not important) or 2 (not very important).
Round 2. Of the 10 motives domains presented, five reached consensus for inclusion and none reached consensus for exclusion. Small changes were made to domains based on qualitative feedback; i.e., the reduce boredom domain was relabeled pass time. There was some qualitative feedback that the convenience motives domain was not a relevant construct; it was suggested that smartphones are a convenient way to satisfy various motives, rather than convenience itself being a motive for smartphone use.
Moderation of remaining items
The authors decided whether to include or exclude the remaining five motives domains and 49 motives items that did not reach consensus. We examined domains and items which ≤ 15% of experts responded 1 (not important/not representative) or 2 (not very important/not very representative) on the 5-point scale. That is, ≥ 85% of experts responded 3 (important/representative), 4 (very important/very representative), or 5 (essential/extremely representative). Three of the five proposed domains and 13 of the 49 proposed motives items reached this threshold (domains and items are presented in Table 2 and Supplementary material 1, respectively). All 13 items were retained as they were close to consensus and a conservative approach to exclusion was warranted, given we intended to conduct psychometric validation.
The complete pool of items and domains are presented in Supplementary material 2. There were 62 items that were proposed to measure 11 motives domains reflecting smartphone use: (a) to reduce emotional distress caused by a lack of social connection (reduce loneliness; 3 items); (b) to reduce or regulate general emotional distress (coping; 5 items); (c) to escape negative circumstances (escapism; 6 items); (d) to reduce boredom (pass time; 6 items); (e) to cope with feelings of discomfort elicited by in-person social interactions (social comfort; 5 items); (f) to feel safe and reduce fears related to physical safety (safety; 9 items); (g) because of the perceived need to respond to others quickly (social obligation; 3 items); (h) for fun, enjoyment, and entertainment (enjoyment; 6 items); (i) to socialize, including initiating new relationships and maintaining ongoing relationships (social; 11 items); (j) because it makes my way of life easier (convenience; 4 items); (k) to learn and obtain information you want to know (information seeking; 4 items).
STUDY 2
Method
Participants and procedure
To construct and validate the MSUQ, the pool of items developed from the Delphi were administered to a sample of 680 young adults aged 18–25 years (Mage = 22.50, SD = 2.16). Only those aged 18–25 years who used a smartphone and resided in Australia were eligible to participate. Participants aged 22–25 years accounted for substantially more than half (67.6%) of the total sample. There were more female than male participants (62% female, 37% male, 1% non-binary/third gender). Most participants were employed (81%) and not currently studying at university (64%). Of those that were employed, most worked full-time (50%), with the remainder working part-time (30%) and casually (21%). Current students were mostly full-time (63%) as opposed to part-time.
Data were collected cross-sectionally with an online survey on Qualtrics during December 2023. The Online Research Unit (ORU; an Australia-based data collection agency) was used to recruit participants. The ORU uses both online and offline (e.g., telephone, print, and postal) recruitment methods to ensure their panels are as representative of the Australian population as possible, including strong regional representation. They use a mixed incentives scheme to keep participants interested and engaged. Participants provided informed consent and demographics information, then responded to motives items. The presentation order of motives items was randomized to control for order effects. Following the motives items, participants responded to measures of PSU, smartphone usage, social anxiety, negative urgency, lack of premeditation, and sensation seeking. The presentation order of PSU, smartphone usage, social anxiety, negative urgency, lack of premeditation, and sensation seeking measures were randomized, as were the presentation order of each item within all measures.
Measures
Motives for Smartphone Use Questionnaire items. The 62 motives for smartphone use items measured 11 proposed motives domains: reduce loneliness, coping, escapism, pass time, social comfort, safety, social obligation, enjoyment, social, convenience, and information seeking. Participants indicated on a 5-point scale (1 = almost never/never, 2 = some of the time, 3 = half of the time, 4 = most of the time, 5 = almost always/always) how often they used their smartphone for each of the 62 reasons.
Smartphone Addiction Scale—Short Version. The short version of the Smartphone Addiction Scale (SAS-SV; Kwon, Kim, Cho, & Yang, 2013) was included to measure PSU. We chose this short measure (10 items) to reduce respondent burden despite it being unidimensional and thus not capturing the three theoretically distinct facets of PSU (i.e., addictive, antisocial, dangerous; Billieux et al., 2015). Additionally, it is one of the more commonly used PSU measures (Busch & McCarthy, 2021; Harris, Regan, Schueler, & Fields, 2020; Mostyn Sullivan & George, 2023) so suitable for psychometric validation. Participants indicated their level of agreement with each statement on a 6-point scale (1 = strongly disagree, 2 = disagree, 3 = somewhat disagree, 4 = somewhat agree, 5 = agree, 6 = strongly agree). Consistent with Duke and Montag (2017) and Elhai, Gallinari, et al. (2020), we slightly modified items to ensure consistent use of first-person perspective. For example, we changed “Won't be able to stand not having a smartphone” to “I won't be able to stand not having a smartphone”. The SAS-SV had good internal reliability (α = 0.89).
Smartphone usage. The frequency that different types of smartphone usage were engaged in was assessed with 11-items developed by Elhai et al. (2018). Participants indicated on a 6-point scale (1 = never, 2 = rarely, 3 = sometimes, 4 = somewhat often, 5 = often, 6 = very often) how often they engaged in the following features of a smartphone: video and voice calls (making and receiving), text/instant messaging (sending and receiving), email (sending and receiving), social networking sites, internet/websites, games, music/podcasts/radio, taking pictures or videos, watching videos/TV/movies, reading books/magazines, and maps/navigation. The internal reliability for smartphone usage was acceptable (α = 0.78).
Statistical analyses
Like Demetrovics et al. (2011) and Romero Saletti et al. (2023) in their development of the Motives for Online Gaming Questionnaire and the Instagram Motives Questionnaire, respectively, we performed EFA and CFA. It is best practice to run EFA and CFA on independent samples (Worthington & Whittaker, 2006). Therefore, consistent with the approach taken in prior scale development studies (e.g., Demetrovics et al., 2011; Hogg, Stanley, O'Brien, Wilson, & Watsford, 2021), two subsamples of participants were created (sample one: N = 365; sample two: N = 315). The Random Sample of Cases function in SPSS 29.0 was used to create the subsamples, which has been used in prior factor analysis research (e.g., Aaby, Lykkegaard Ravn, Kasch, & Andersen, 2021). This function split the sample by approximately 50%. There were no significant differences between the demographic profiles (i.e., age, gender, employment and student status) of the subsamples.
First, we conducted an EFA to explore the dimensionality of the original 62 MSUQ items among sample one (n = 359 due to listwise deletion). The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy (Kaiser, 1974) and Bartlett's test of sphericity (Bartlett, 1954) were used to check assumptions. KMO and Bartlett's test were considered acceptable if > 0.70 and significant at p ≤ 0.05, respectively. We used principal axis factoring to extract the factor model and the direct quartimin rotation. Kaiser's eigenvalue criterion (Kaiser, 1960), visual scree plot analysis (Cattell, 1966), parallel analysis (Horn, 1965), and use of guiding theory and interpretability of different solutions (Belhekar, 2016) were examined to determine the number of factors to retain.
To confirm the factor structure identified in sample one, we performed CFA with Lavaan (Rosseel, 2012) in RStudio among sample two (n = 313 due to listwise deletion; the code used for the analysis is in Supplementary material 3). The factor structure identified through EFA in sample one was compared against a one factor structure. Maximum Likelihood Robust (MLR) analysis was the estimation method used. We used MLR due to it being robust to non-normality (Lei & Shiverdecker, 2020) and it is commonly used in psychological research which employs confirmatory factor analysis (e.g., Baggio et al., 2018; Li, Zhang, Jia, & Kong, 2021), including in the development of the Motives for Online Gaming Questionnaire (Demetrovics et al., 2011). Several indices were used to assess goodness of fit: the Root Mean Square Error of Approximation (RMSEA), the Standardized Root Mean Square Residual (SRMSR), the Comparative Fit Index (CFI), and the Tucker-Lewis index (TLI). We considered values of RMSEA ≤ 0.05, SRMR ≤ 0.08, and CFI and TLI ≥ 0.95 to indicate good fit (Hu & Bentler, 1999; Lai & Green, 2016). While we report the χ2 goodness of fit index, it was not used to assess model fit because research has established that it is overly sensitive in larger samples (Meade, Johnson, & Braddy, 2008).
The reliability of the MSUQ was assessed with the Omega coefficient. The Omega coefficient and 95% confidence intervals for each of the MSUQ subscales were calculated with the MBESS package (Kelley & Lai, 2012) in RStudio, as per the procedure outlined in Dunn, Baguley, and Brunsden (2014). Omega coefficient values ≥0.70, ≥0.80, and ≥0.90 were considered acceptable, good, and excellent, respectively (Hunsley & Mash, 2008). Finally, consistent with the broader scale development literature (e.g., Brytek-Matera, Plasonja, & Décamps, 2020; Kittel et al., 2023; Sarling, Sundin, & Jansson, 2024; Walton, Nazari, Bobos, & MacDermid, 2023), to test the MSUQ's concurrent validity, we used Pearson correlations and hierarchical linear regression to examine its association with PSU and smartphone usage among the entire sample. Age and gender were included as covariates at step 1, followed by the MSUQ at step 2. Only eight cases reported identifying as non-binary/third gender, so were excluded from the regression analyses.
Ethics
The study procedures were carried out in accordance with the Declaration of Helsinki. The Human Research Ethics Committee of the University of Canberra approved this study (HREC 13451). All subjects were informed about the study, and all provided informed consent.
Results
Exploratory factor analysis
We conducted an EFA with principal axis factoring and direct quartimin rotation on the 62 smartphone use motives items developed in Study 1. Results for the KMO test was 0.96. Bartlett's test of sphericity was significant (χ2 = 14,324.83, df = 1,830, p < 0.001), indicating assumptions of factor analysis had been met. Parallel analysis with R (Patil, Surendra, Sanjay, & Donavan, 2017) and examination of the scree plot supported a four-factor solution, whereas Kaiser's eigenvalue criterion indicated an eight-factor solution. Therefore, both four and eight factor solutions were computed. The eight factor solution was more interpretable and consistent with theoretical motives constructs identified in the literature (Mostyn Sullivan & George, 2023) and Study 1.
We followed an item deletion process outlined in Acar Güvendir and Özer Özkan (2022). Items with high cross loadings (factor loading ≥0.30 and the difference between factors loadings <0.10) on more than two factors were deleted one at a time, starting with the item with the lowest difference between factor loadings. The same process was then carried out on items with high cross loadings on two factors. Initially, a total of 11 cross loading items were deleted. After these items were removed, factor eight included only two items, so was removed from further analyses. Two additional items were removed because they had weak factor loadings (<0.32; Tabachnick & Fidell, 2019). When the analyses were rerun, one additional cross loading item and one item with a weak factor loading were removed. Finally, two items that loaded on factor two, but which did not appear to clearly measure the same construct as other items were removed. A final EFA was conducted on the remaining 43 items, resulting in a highly interpretable and theoretically relevant seven-factor solution (items and factor loadings are presented in Table 3; inter-factor correlations are in Supplementary material 4).
Final factor loadings from exploratory factor analysis
| Items | Coping | Safety | Pass time | Social | Comfort | Info | Obligation |
| I use my smartphone… | |||||||
| 1) because it helps me to forget about my problems. | 0.782 | ||||||
| 2) because it helps me forget about worries and concerns. | 0.771 | ||||||
| 3) because it helps to distract me from my problems. | 0.675 | ||||||
| 4) because it helps me feel better when I am upset. | 0.665 | ||||||
| 5) because it helps me feel better when I am down. | 0.653 | ||||||
| 6) to escape from the stress of everyday life. | 0.614 | ||||||
| 7) because it helps me forget about study or work. | 0.597 | ||||||
| 8) because it helps me escape from reality. | 0.596 | ||||||
| 9) because it helps me to reduce stress. | 0.547 | ||||||
| 10) because it helps me feel less anxious. | 0.449 | ||||||
| 11) to be able to contact the emergency services if necessary. | 0.749 | ||||||
| 12) to help me in case of emergency situations. | 0.734 | ||||||
| 13) to be able to contact someone in case I am in danger. | 0.717 | ||||||
| 14) to talk to others when I feel like I am in danger. | 0.649 | ||||||
| 15) so that family can let me know if they need help. | 0.483 | ||||||
| 16) to feel safer when I go out. | 0.336 | ||||||
| 17) when I'm feeling bored. | −0.723 | ||||||
| 18) when I have nothing better to do. | −0.653 | ||||||
| 19) when there is nothing to do. | −0.586 | ||||||
| 20) because it helps me pass the time. | −0.585 | ||||||
| 21) to avoid boredom. | −0.581 | ||||||
| 22) to kill time. | −0.538 | ||||||
| 23) I use my smartphone for entertainment. | −0.530 | −0.311 | |||||
| 24) because it is enjoyable. | −0.334 | ||||||
| 25) because it helps me maintain good relationships with others. | 0.644 | ||||||
| 26) to interact with people. | 0.574 | ||||||
| 27) to socialize with others. | 0.554 | ||||||
| 28) because it helps me develop relationships with others. | 0.511 | ||||||
| 29) to let others know I care about them. | 0.468 | ||||||
| 30) to maintain relationships with others. | 0.447 | ||||||
| 31) to keep in touch with others. | 0.431 | ||||||
| 32) because it increases my feelings of connection with others. | 0.402 | ||||||
| 33) because it helps me avoid in person conversations with people. | 0.742 | ||||||
| 34) because it helps me avoid interacting with other people in person. | 0.685 | ||||||
| 35) because it helps me avoid talking to people in social situations I do not know very well. | 0.670 | ||||||
| 36) because it helps me manage discomfort I experience when I am around other people. | 0.501 | ||||||
| 37) because I find it easier to communicate by smartphone than in real life. | 0.491 | ||||||
| 38) in order to stay up to date on the latest news. | −0.660 | ||||||
| 39) to be informed about what is going on in the world. | −0.523 | −0.336 | |||||
| 40) to obtain the information I need. | −0.320 | ||||||
| 41) because others expect me to always be contactable. | −0.770 | ||||||
| 42) because others expect me to be easily contactable. | −0.612 | ||||||
| 43) because I feel like I should respond quickly to others. | −0.386 |
Note. Factor loadings <0.30 are not reported. Comfort = social comfort. Information = information seeking. Obligation = social obligation.
Confirmatory factor analysis
We next performed a CFA on sample two to test a seven-factor solution and compare it with a one-factor solution. Consistent with the approach taken by Demetrovics et al. (2011) for their development of the Motives for Online Gaming Questionnaire, prior to conducting the CFA, we performed an item selection to limit the number of items per factor. This ensured that the MSUQ would be concise and useful for application in research. A maximum of four items per factor were selected, as three items is generally considered the minimum number required to define a latent construct (El-Den, Schneider, Mirzaei, & Carter, 2020). Items with high factor loadings and which variably measured the content of the latent construct were retained (items which were removed and reasons for their removal are summarized in Supplementary material 5). This would allow a content valid operationalization of motives for smartphone use. The seven-factor model provided a good fit to the data, χ2 (278) = 404.91, p < 0.001, CFI = 0.96, TLI = 0.96, RMSEA = 0.04 (90% CI [0.03–0.05], SRMR = 0.05). This contrasted with the one-factor solution, which had poor fit, χ2 (299) = 1356.83, p < 0.001, CFI = 0.68, TLI = 0.66, RMSEA = 0.12 (90% CI [0.11–0.13], SRMR = 0.10.
Characteristics of the seven-factor model
The seven-factor model with factor loadings is presented in Supplementary material 6 (inter-factor correlations are presented in Supplementary material 4). All factor loadings were >0.60, which is considered acceptable (e.g., Baharum et al., 2023; Zainudin Awang, Asyraf Afthanorhan, Mahadzirah Mohamad, & Asri, 2015). Factor one reflects items that assess smartphone use to cope with emotional distress and negative life circumstances (coping motives). This factor contains four items with factor loadings from 0.74 to 0.80. Factor two represents smartphone use to pass time and avoid boredom (pass time motives). This factor comprises four items with factor loadings from 0.72 to 0.76. Factor three is smartphone use to initiate and maintain social connections (social motives). It includes four items with factor loadings from 0.72 to 0.74. Factor four measures smartphone use to cope with discomfort experienced during in-person social interactions (social comfort motives). It contains four items with factor loadings from 0.75 to 0.80. Factor five assesses smartphone use to feel safe (safety motives). It includes four items with factor loadings from 0.72 to 0.81. Factor six is smartphone use because of the perceived need to respond to others quickly (social obligation). It comprises three items with factor loadings from 0.63 to 0.77. Factor seven reflects smartphone use to obtain information and be up to date on current events (information seeking). It contains three items with factor loadings from 0.63 to 0.80. Omega coefficients with 95% bootstrap confidence intervals indicated the internal reliabilities for all subscales were acceptable to good (coefficients and confidence intervals are in Supplementary material 6). The complete MSUQ is in Appendix B.
Concurrent validity
Descriptive statistics and correlations between smartphone use motives subscales and relevant outcome variables are presented in Table 4. The mean PSU score was around the mid-point of the scale, while the mean smartphone usage score was slightly higher. Information seeking motives was the most reported and social comfort was the least. All motives were positively associated with PSU and smartphone usage, with small to medium effect sizes.
Correlations for all study variables in entire sample (N = 648)
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
| 1) Coping | – | |||||||||
| 2) Pass time | 0.60** | – | ||||||||
| 3) Comfort | 0.72** | 0.49** | – | |||||||
| 4) Safety | 0.35** | 0.37** | 0.37** | – | ||||||
| 5) Obligation | 0.43** | 0.46** | 0.42** | 0.55** | – | |||||
| 6) Social | 0.50** | 0.52** | 0.46** | 0.52** | 0.61** | – | ||||
| 7) Information | 0.38** | 0.53** | 0.34** | 0.52** | 0.54** | 0.58** | – | |||
| 8) Age | −0.16** | −0.14** | −0.09* | −0.01 | 0.00 | −0.07 | 0.00 | – | ||
| 9) PSU | 0.44** | 0.23** | 0.46** | 0.21** | 0.26** | 0.29** | 0.18** | −0.04 | – | |
| 10) Usage | 0.21** | 0.31** | 0.16** | 0.35** | 0.33** | 0.37** | 0.40** | 0.03 | 0.22** | – |
| Mean | 3.26 | 3.71 | 3.03 | 3.61 | 3.48 | 3.51 | 3.74 | 22.55 | 3.25 | 4.15 |
| SD | 1.04 | 0.90 | 1.06 | 1.02 | 0.91 | 0.85 | 0.87 | 2.13 | 1.08 | 0.73 |
Note. Comfort = social comfort. Information = information seeking. Obligation = social obligation. PSU = problematic smartphone use (Kwon et al., 2013). Usage = smartphone usage (Elhai et al., 2018). MSUQ = total motives for smartphone use questionnaire.
*p < 0.05. **p < 0.001. (2-tailed).
Two hierarchical linear regression analyses tested whether motives for smartphone use explained PSU and smartphone usage (see Table 5). Regarding PSU, at step 1, age and gender did not explain a significant amount of variance, F(2,640) = 0.57, p < 0.57, f2 = 0.00. When motives were entered at step 2, they collectively explained 25% of variance in PSU, ΔF(7,633) = 30.02, p < 0.001, f2 = 0.25. Only coping and social comfort motives predicted higher levels of PSU, both with small effects. Pass time motives predicted lower levels of PSU, but the effect was very small. As for smartphone usage, at step 1, age and gender explained 4% of variance, F(2,645) = 12.22, p < 0.0001, f2 = 0.04. When motives were entered at step 2 they explained an additional 19% variance in smartphone usage, ΔF(7,638) = 22.40, p < 0.001, f2 = 0.19. Only gender (being female), pass time, safety, social, and information seeking motives predicted higher levels of smartphone usage, all with small effects.
Smartphone use motives as predictors of smartphone usage and problematic smartphone use
| Variable | PSU (N = 641) | Usage (N = 648) | ||
| β | sr2 | β | sr2 | |
| Step 1 | ||||
| Age | −0.04 | 0.00 | 0.02 | 0.00 |
| Gender (female) | −0.02 | 0.00 | 0.19** | 0.04 |
| Step 2 | ||||
| Age | 0.02 | 0.00 | 0.04 | 0.00 |
| Gender (female) | −0.02 | 0.00 | 0.14** | 0.02 |
| Coping | 0.26** | 0.03 | 0.02 | 0.00 |
| Pass time | −0.11* | 0.01 | 0.10* | 0.00 |
| Comfort | 0.28** | 0.04 | −0.09 | 0.00 |
| Safety | 0.02 | 0.00 | 0.11* | 0.01 |
| Obligation | 0.03 | 0.00 | 0.06 | 0.00 |
| Social | 0.08 | 0.00 | 0.12* | 0.01 |
| Information | −0.02 | 0.00 | 0.21** | 0.02 |
Note. PSU = problematic smartphone use measured with the Smartphone Addiction Scale (Kwon et al., 2013). Usage = smartphone usage (Elhai et al., 2018). Comfort = social comfort. Information = information seeking. Obligation = social obligation. sr2 = squared semi-partial correlation.
*p < 0.05. **p < 0.001.
Discussion
The aim of these two studies was to develop and validate a current measure of smartphone use motives. In Study 1, using the Delphi method, we developed 62 items that measure 11 proposed domains of smartphone use motives. In Study 2, the 62 items were administered to a sample of young adults that was then randomly split with separate EFA and CFA conducted. This resulted in the construction of the 26-item MSUQ. The MSUQ comprehensively assesses seven motives domains (coping, safety, pass time, social, social comfort, information seeking, and social obligation) and demonstrated good concurrent validity. Social comfort and social obligation motives identified in the current measure are not assessed in prior measures of motives for smartphone use.
Improvements over prior smartphone use motives measures
The MSUQ has several benefits over prior smartphone use motives measures. Mostyn Sullivan and George (2023) noted considerable heterogeneity in prior measures of smartphone use motives; the measures suffered from the jingle and jangle fallacies and no one single measure included all potentially relevant motive domains. Additionally, smartphone use motives measures were typically developed with a confirmatory approach, meaning that items were adapted from measures designed to assess different behaviors (not smartphone use). This confirmatory approach certainly led to the identification of valid motives for smartphone use. However, given that smartphones provide unique functions not afforded by other technologies/media, it may have resulted in motives unique to smartphone use being missed. The MSUQ overcomes these limitations through its development processes, including a comprehensive review of prior measures (Mostyn Sullivan & George, 2023), qualitative research (Lepp et al., 2017; Mostyn Sullivan et al., 2024; Ochs & Sauer, 2022), and expert consensus (i.e., the Delphi method). Moreover, the administration of items to two subsamples of young adults and the EFA and CFA findings allow us to empirically confirm a smartphone use motives typology that was constructed on a sound theoretical and methodologically rigorous foundation.
Importantly, some of the motives that constitute the MSUQ are, to our knowledge, not included in any prior smartphone use motives measure. The MSUQ includes social comfort motives, which reflect smartphone use to cope with discomfort experienced during in-person social interactions. This motive is a nuanced form of coping motives previously identified in the alcohol use literature (Cooper, Kuntsche, Levitt, Barber, & Wolf, 2016) relevant specifically to smartphone use. Despite it being related to general coping motives (which is also present in the MSUQ), our EFA and CFA findings suggested that it was an independent construct. Notably, while social comfort motives have not been included in a prior smartphone use motives measure, it was identified in prior qualitative research (Mostyn Sullivan et al., 2024). Moreover, it was considered important by academic experts that participated in our Delphi prior to being administered to participants and identified in our factors analyses. This supports the assertion that prior smartphone motives typologies/measures missed motives unique to smartphone use due to not employing bottom-up qualitative research methods.
An additional motive included in the MSUQ that is not present in prior smartphone use motives measures was social obligation. Social obligation motives reflect smartphone use because of the perceived need to respond to others quickly. This motivation appears to be a nuanced form of conformity motives specific to smartphone use. Conformity motives were originally identified within the alcohol use motivational model, reflecting drinking to fit in and avoid social disapproval (Cooper et al., 2016). Conformity motives in the context of smartphone use are generally operationalized with items reflecting smartphone use to: appear cool (e.g., “I use my phone to be in fashion”; Vanden Abeele, 2016); be liked by others (e.g., “I use my smartphone to be liked by my friends”; Zhang et al., 2014); or fit in with one's peer group (e.g., “I use my smartphone because my friends use it”; Lee & Lee, 2017). Findings from our Delphi suggested that such items were outdated, with smartphones now so ubiquitous and normalized that people do not tend to use them to look cool or fit in. However, consensus from the panel of experts was that smartphone use to conform to a perceived social obligation to quickly respond to communications was important for smartphone use. This corroborated findings from prior qualitative research (Lepp et al., 2017; Mostyn Sullivan et al., 2024), and was further confirmed to be a distinct motive for smartphone use in our factor analyses.
The MSUQ is conceptually distinct from expectancies and smartphone functions/uses. Throughout the item pool generation process in Study 1, we ensured that all items were written to reflect motives for smartphone use rather than expectancies. This improves prior smartphone use motives measures which included items that more closely resembled expectancies (e.g., Chen, Li, & Liu, 2021; Zhang et al., 2014). On theoretical grounds, we questioned whether information seeking motives may be better conceptualized as a type of smartphone use, due to the lack of a clearly identifiable valence, as per the alcohol use motivational model's valence/source categorization framework (Cooper et al., 2016). However, experts did not provide qualitative feedback that information seeking motives (or any other proposed motives domain) were conflated with different uses of a smartphone. Instead, the information seeking motives domain was considered by our panel of experts to be important for inclusion on a smartphone use motives questionnaire.
Association of motives with problematic smartphone use and smartphone usage
All MSUQ motives were associated with higher levels of PSU and smartphone usage. We note that only the correlations of coping and social comfort motives with PSU had medium effect sizes, the latter approaching large. The remainder had small effect sizes. Conversely, pass time, safety, obligation, social, and information seeking motives were associated with higher smartphone usage, all with medium effect sizes; the associations of coping and social comfort motives with smartphone usage were small. Consistent with this, when age, gender, and motives were controlled for, only coping and social comfort motives predicted higher levels of PSU. Pass time motives predicted lower PSU, but the effect was very small and incongruent with the bivariate correlation, suggesting it may be a statistical artifact. Pass time, safety, social, and information seeking motives predicted higher levels of smartphone usage. However, all except information seeking motives explained a particularly small amount of unique variance so those effects should be interpreted with caution.
The differential association of motives with PSU and smartphone usage is consistent with findings from research examining the association of motives with other potentially problematic behaviors (Bőthe et al., 2021) and substance use (Cooper et al., 2016). Specifically, these findings suggest that while a range of motives (particularly information seeking) influence general smartphone usage, PSU may in large part be motivated by efforts to compensate for emotional discomfort (i.e., coping and social comfort motives). Moreover, the small associations of coping and social comfort motives with smartphone usage highlights that more frequent use of a smartphone does not equate to problematic use. This aligns with the compensatory internet use theory (Kardefelt-Winther, 2014) and is consistent with the robust association of coping motives with PSU identified in prior research (reviewed in Mostyn Sullivan & George, 2023). Crucially and further highlighting the benefits of the MSUQ, social comfort (which does not appear in any prior smartphone use motives measure) explained slightly more unique variance in PSU compared with coping motives, corroborating findings from recent qualitative research (Mostyn Sullivan et al., 2024).
Limitations and directions for future research
Despite the present study taking a novel and methodically rigorous approach to developing the MSUQ, limitations must be noted. First, the Delphi method allowed us to develop a pool of smartphone use items with good content validity, and our subsequent psychometric analyses allowed us to construct a measure with good construct and concurrent validity among young adults in Australia. However, further research is required to determine if the validity of the MSUQ generalizes to other age groups and populations globally. This is important, as the experience of young adults in Australia may differ substantially from the experience of those in other countries or even older and younger people within Australia. Thus, there may be locale/population specific motives not currently included in the MSUQ. Second, the cross-sectional design employed in the study means the longitudinal reliability and validity of the MSUQ cannot be assessed. Third, our findings may have been influenced by the partially confirmatory approach used in Study 1. That is, we a priori categorized motives items before administering them in our Delphi, which may have informed the subsequent assessment of each motivation. Fourth, while we employed Kaiser's eigenvalue criterion (Kaiser, 1960), visual scree plot analysis (Cattell, 1966), and parallel analysis (Horn, 1965), we extracted factors based on Kaiser's eigenvalue criterion. Kaiser's eigenvalue criterion is considered less optimal than visual scree plot and parallel analysis for factor extraction (Howard, 2016), but was adopted because it resulted in the most interpretable factors that were consistent with relevant theory and research (Mostyn Sullivan & George, 2023). Finally, we note that there were moderate cross loadings on items 23 and 39 in our EFA. We retained both items since they did not meet our a priori defined criteria for high cross loadings (Acar Güvendir & Özer Özkan, 2022; i.e., while factor loadings were >0.30 on two factors, the differences between factor loadings were >0.10). Future research may seek to investigate whether these items accurately measure their respective factors in different samples.
We propose several key avenues for further validation of the MSUQ and how it may be used to better understand the etiology of PSU. Future research should examine the association of the MSUQ with objective measures of smartphone use (both general smartphone use and the use of specific applications). This would be useful to further substantiate the association of motives with smartphone use and whether they differentially relate to different patterns of smartphone use. Future research also needs to use the MSUQ to explore which motives are associated with distinct dimensions of PSU, such as the addictive, antisocial, and risky patterns of smartphone use outlined in Billieux et al.'s (2015) pathway model. Such research would help explain why people develop certain problematic patterns of PSU and help direct/develop interventions. Moreover, given motives are considered to be the final common pathway to behavior (Cooper et al., 2016), research should use the MSUQ to examine whether and which motives mediate the association of various personality and psychopathological factors with different patterns of PSU. Finally, research influenced by the compensatory internet use theory has found that certain motives are associated with PSU, but only among those with already high levels of PSU (Shen et al., 2021; Wang et al., 2015). Therefore, future research should use the MSUQ to examine the association of motives with PSU among samples of people with high levels of PSU. Such research may provide insight into the role of motives in maintaining high levels of PSU.
Conclusions
The present studies used a rigorous two-stage approach (i.e., the Delphi method and subsequent psychometric validation) to construct and validate the MSUQ. The MSUQ improves on prior smartphone use motives measures in the following ways: (a) it integrates the core relevant smartphone use motives identified in prior research; (b) it assesses relevant smartphone use motives identified in qualitative research that were not included in prior smartphone use motives measures; and (c) it is conceptually distinct from related yet distinct constructs (i.e., expectancies, types of smartphone uses). Additionally, the MSUQ explains a considerable amount of variance in both PSU and self-reported smartphone usage. This suggests that the MSUQ has robust content, concurrent, and construct validity. Use of the MSUQ will support future research to enhance knowledge of the motivations that influence different patterns of smartphone use and PSU, to inform the development of intervention strategies to be applied in clinical practice.
Funding sources
No financial support was received for this study.
Authors' contribution
All authors had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. The specific contribution by each author follows. BMS: conceptualization, methodology, formal analysis, investigation, resources, data curation, writing—original draft, writing—review & editing, visualization, project administration. AG: conceptualization, methodology, validation, writing—review & editing, supervision. DR: conceptualization, methodology, resources, writing—review & editing, supervision.
Conflict of interest
The authors declare no conflict of interest.
Acknowledgements
We sincerely thank all the experts who participated in the Delphi for Study 1. Experts who participated were offered the opportunity to be acknowledged in this manuscript, with those that agreed listed in alphabetical order as follows: Alessandro Musetti, Barbara Mervo, Cristina Zogmaister, David Caelum Arness, Emily Rooney, Giulia Fioravanti, James T. Neill, Jin Tao Zhang, Joël Billieux, Juliana Beatriz Stover, Lea-Christin Wickord, María Laura Lupano Perugini, Melih Sever, Michela Vezzoli, Sabrina Hegner, Seyhan Özdemir, Silvia Casale, Tommaso Manari, Wajeeha Aslam, Xi Shen.
Supplementary materials
Supplementary data to this article can be found online at https://doi.org/10.1556/2006.2025.00006.
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Appendix A
Questionnaires and their factors assessing smarthone use motives
| Title/Description | Authora | Motive Dimension Labelb |
| Smartphone Usage Behaviour Questionnaire | AlBarashdi and Bouazza (2019) |
|
| Smartphone Use Expectancies Scale | Elhai, Yang, et al. (2020) |
|
| Motives for Mobile Phone Use | Hwang and Park (2015) |
|
| Dispositional Media Use Motives | Khang et al. (2013) |
|
| Motivations for Smartphone Use | Kim (2017) |
|
| Motivations for Mobile Phone Use | Kim et al. (2015) |
|
| Motives for Smartphone Use | Lee and Lee (2017) |
|
| Motives for Mobile Phone Application Use | Lin et al. (2014) |
|
| Smartphone Use Motivations | Meng et al. (2020) |
|
| Smartphone Use Motivations | Park and Lee (2014) |
|
| Motivation for Social Inclusion and Instrumental Use | Park et al. (2013) |
|
| Smartphone Usage Motivation Scale | Shen et al. (2021) |
|
| Mobile Phone Uses and Gratifications Scale | Vanden Abeele (2016) |
|
| Process and Social Smartphone Usage | Van Deursen et al. (2015) |
|
| Smartphone Usage Motivation Scale | Wang et al. (2015) |
|
| Smartphone Use Motives | Zhang et al. (2014) |
|
| Reinforcement Motives for Smartphone Use | Zhang, Chen, Zhao, et al. (2014) |
|
Note. The measures in this table were identified in a systematic review conducted by Mostyn Sullivan and George (2023) of the literature investigating the association of motives with PSU. This table is a modified version of a table found in that study. Additional non-systematic searches for additional motives measures were conducted, but none were found.
Appendix B
Motives for Smartphone Use Questionnaire (MSUQ)
| Thinking of all the times you use your smartphone, please indicate on a 5-point scale (1 = almost never/never, 2 = some of the time, 3 = half of the time, 4 = most of the time, 5 = almost always/always) how often you use your smartphone for each of the following reasons? |
| I use my smartphone… |
| 1) because it helps me to forget about my problems. |
| 2) to be able to contact the emergency services if necessary. |
| 3) when I'm feeling bored. |
| 4) because it helps me maintain good relationships with others. |
| 5) because it helps me avoid in person conversations with people. |
| 6) in order to stay up to date on the latest news. |
| 7) because others expect me to always be contactable. |
| 8) because it helps me forget about worries and concerns. |
| 9) to help me in case of emergency situations. |
| 10) when I have nothing better to do. |
| 11) to interact with people. |
| 12) because it helps me avoid interacting with other people in person. |
| 13) to be informed about what is going on in the world. |
| 14) because others expect me to be easily contactable. |
| 15) because it helps me feel better when I am upset. |
| 16) to be able to contact someone in case I am in danger. |
| 17) because it helps me pass the time. |
| 18) to socialize with others. |
| 19) because it helps me avoid talking to people in social situations I do not know very well. |
| 20) to obtain the information I need. |
| 21) because I feel like I should respond quickly to others. |
| 22) to escape from the stress of everyday life. |
| 23) to talk to others when I feel like I am in danger. |
| 24) to avoid boredom. |
| 25) because it helps me develop relationships with others. |
| 26) because it helps me manage discomfort I experience when I am around other people. |
Note. Motives scores are generated by calculating the mean of relevant item scores. Coping = items 1, 8, 15, 22. Safety = items 2, 9, 16, 23. Pass time = item 3, 10, 17, 24. Social = items 4, 11, 18, 25. Social comfort = items 5, 12, 19, 26. Information seeking = items 6, 13, 20. Social obligation = items 7, 14, 21.
