Sage Journals: Discover world-class research

Abstract

The relationship between academic stress and compulsive smartphone use among college students has been extensively studied, yet empirical findings remain inconsistent and inconclusive. Moreover, potential moderating factors affecting this relationship have received limited scholarly attention. This study addresses these research gaps by examining the relationship between academic stress and compulsive smartphone use and the moderating roles of academic buoyancy and future time perspective in this relationship. Using convenience sampling, data were collected from 439 English Language Learning Students (ELLS) at Ningxia Medical University. We employed covariance-based Structural Equation Modelling (CB-SEM) to test our hypotheses. Results revealed a significant positive relationship between academic stress and compulsive smartphone use. Furthermore, academic buoyancy emerged as a significant positive moderator of this relationship, whereas future time perspective showed no significant moderating effect. These findings offer practical implications for developing targeted interventions to mitigate compulsive smartphone use among college students.

Keywords

academic stress compulsive smartphone use academic buoyancy future time perspective English language learning students

Introduction

In modern society, the smartphone has permeated various aspects of everyday life. It serves as the primary tool for accessing information, entertainment, shopping, social interaction, and managing daily tasks like setting reminders and keeping track of time. While smartphones offer numerous benefits in daily life, they can also lead to excessive engagement in activities like social media and gaming. This overindulgence often interferes with work and social responsibilities, potentially causing negative outcomes such as deteriorating relationships or declining academic performance. Even though many users recognize these negative consequences, they still choose to invest significant amounts of time on their smartphones. Such use pattern is commonly known as compulsive smartphone use, which is defined as repetitive and irresistible smartphone use despite the recognition of its negative life consequences (Billieux et al., 2015).

College student has been identified as the social group that is particularly prone to compulsive smartphone use (Chen et al., 2017; Long et al., 2016; Yang et al., 2019). They, usually between the ages of 18 and 25, are navigating the early stages of adulthood and often experience a significant increase in independence as they enter college. This greater autonomy may pose challenges in self-regulation, where students might favour short-term pleasures without considering the adverse outcomes (Sussman & Arnett, 2014). In their pursuit of instant gratification, such tendency might lead them to excessively engage in specific smartphone activities (e.g. social media or watching video clips), despite knowing the potential negative consequences on their lives (i.e. compulsive use) (Aljomaa et al., 2016; Jiang et al., 2018; Long et al., 2016). Compulsive smartphone use was found to be associated with poor physical health (Kim et al., 2015), sleep impairment (Liu et al., 2017), academic failure (Samaha & Hawi, 2016), and suicidal ideation (Arrivillaga et al., 2020). It was also found to be highly prevalent across countries especially among college students (Olson et al., 2022). Given these impacts, scholars have stressed the need of identifying factors that either contribute to or prevent compulsive smartphone use among college students to enable more targeted interventions (Busch & McCarthy, 2021).

Academic stress, defined as “psychological distress arising from the pressure or strain of achieving academic goals” (Mun & Lee, 2024, p. 9079), is widely recognized as a significant factor contributing to compulsive smartphone use among college students (Chiu, 2014; Shen et al., 2021; Yaghoobi et al., 2024; Zhang et al., 2024). Stress-coping theory offers insights into why academic stress might lead to compulsive smartphone use. This theory suggests that when people encounter negative situations (e.g. heavy academic workload or difficult academic tasks) and experience stress, they may turn to behaviours that provide temporary relief. Although these coping mechanisms can temporarily make someone feel better by diverting attention away from stressful thoughts, they are essentially dysfunctional because they fail to tackle the underlying causes of stress. Consequently, relying on such coping strategies can result in adverse outcomes (e.g. poor performance and missed deadlines). This theory also emphasizes that individuals often persist with these behaviours in an attempt to maintain or regain a sense of calm, even when they are aware of the potential negative consequences and the need to regulate such behaviour (i.e. compulsive behaviour) (Kardefelt-Winther, 2014; Lazarus & Folkman, 1984).

Building on these understanding, researchers have suggested that students experiencing academic stress may be more likely to engage in short-sighted or addiction-like behaviours such as alcohol abuse (Metzger et al., 2017), illicit drug use (Ford & Schroeder, 2008), binge eating (Costarelli & Patsai, 2012), and heavy smoking (Jain & Verma, 2016). More relevant to this study, researchers have also found that students experiencing academic stress are more likely to engage in compulsive smartphone use (Yaghoobi et al., 2024; Zhang et al., 2024). However, it is important to note that the link between academic stress and compulsive smartphone use was found to be mixed or inconsistent. For instance, Yaghoobi et al. (2024) and Zhang et al. (2024) found their link to be positive, indicating that higher level of academic stress led to increased compulsive smartphone use. However, other studies found that academic stress had no effect on compulsive smartphone use (Chiu, 2014; Shen et al., 2021). Given the inconsistent findings, this study stresses the need to confirm the link between academic stress and compulsive smartphone use.

The aforementioned mixed results on the relationship between academic stress and compulsive smartphone use also indicate a need to explore potential contextual factors (e.g. environmental or individual differences) that could clarify under what circumstances academic stress might lead to compulsive smartphone use or have no effect at all (Yaghoobi et al., 2024; Zhang et al., 2024). Scholars argue that although students have the tendency to use smartphones to momentarily escape from stressful thoughts, not all of them necessarily engage in compulsive smartphone use. They emphasize that individual characteristics may play a significant role in determining susceptibility to such behaviour and thus it is crucial to identify which individual characteristics influence whether or the extent to which academic stress causes compulsive smartphone use (Wang et al., 2020; Xu et al., 2019; Zhang et al., 2024).

Since “a moderator specifies the conditions under which a given effect occurs, as well as the conditions under which the direction (nature) or strength of an effect vary” (Ramayah et al., 2018, p. 221), scholars have attempted to identify the individual characteristics that can moderate the link between academic stress and compulsive smartphone use such as problem-focused coping (Xu et al., 2019) and alienation/rumination (Zhang et al., 2024). Although some research has explored moderating variables in the link between academic stress and compulsive smartphone use, the specific individual characteristic that determines whether or the extent to which academic stress leads to compulsive smartphone use remain less investigated and warrant further investigations (Wang et al., 2020; Xu et al., 2019; Zhang et al., 2024).

Academic buoyancy, the perceived “ability to successfully deal with academic setbacks and challenges that are typical of the ordinary course of school life” (Martin & Marsh, 2008, p. 54), may be an individual characteristic that moderate the link between academic stress and compulsive smartphone use. Scholars have argued that students with high academic buoyancy typically exhibit confidence in their ability to overcome everyday learning setbacks and challenges, and they are often hopeful about reaching their educational goals. This mindset helps sustain their learning interest and effort in challenging situations, and encourages them to face the problems head-on and use adaptive learning strategies or proactive problem-solving coping approach. In contrast, students with low academic buoyancy may lack this confidence and hopefulness. In challenging learning contexts, they are more likely to feel hopeless about their chances to achieve what they would like to achieve. These can lead to diminished learning interest and a tendency to engage in maladaptive learning strategies and avoidance behaviours (Hirvonen et al., 2019; Lei et al., 2021; Ramasubramanian, 2017).

As discussed previously, stress-coping theory highlights that avoidant stress-coping is a foundational cause for the development of addiction-like behaviour (Kardefelt-Winther, 2014; Lazarus & Folkman, 1984; Maroney et al., 2019). As support for this notion, some scholars have found that students who employ problem-focused coping when they face academic stress are less prone to engage in compulsive smartphone use whereas those who employ avoidant-coping are more prone to engage in compulsive smartphone use. For example, Xu et al. (2019) have found that problem-focused coping negatively moderates the relationship between academic stress and compulsive smartphone use. Specifically, students with a high level of problem-focused coping are less prone to engage in compulsive smartphone use when experiencing academic stress compared to those with low level of this coping strategy. Given the notion that academic buoyancy can determine whether students use problem-solving coping or avoidant-coping approach (Hirvonen et al., 2019; Ramasubramanian, 2017), this study argues that academic buoyancy can potentially influence whether or the extent to which students who experience academic stress engage in compulsive smartphone use as a means of avoidant-coping. This area has not been empirically investigated, highlighting the need to investigate whether academic buoyancy moderates the link between academic stress and compulsive smartphone use.

Future time perspective, the tendency to prioritize long-term goals and consider the outcomes of future events in present decision-making and behaviour (Zimbardo & Boyd, 1999), may be another individual characteristic that can potentially moderate the link between academic stress and compulsive smartphone use. Social cognitive theory asserts that people are active agents in controlling their behaviours, not just passive entities driven by external forces. They exercise control over their behaviours through their personal goals, beliefs, and values, which steer their life choices and determine their judgements about appropriate actions. Consequently, people intentionally engage in behaviours that align with their life goals and avoid behaviours that conflict with or do not support these goals (Bandura, 1999). People with high future time perspective are inclined to prioritize achieving long-term goals, show little concern to immediate gratification, consider the outcomes of future events in current decision-making, and emphasize the mindset of “sacrifice today for greater rewards tomorrow” (Zimbardo & Boyd, 1999). Such forward-looking tendency typically steers people away from habits or behaviours that may generate immediate gratification but cause negative consequences in the long run and disrupt the achievement of their long-term objectives (Zimbardo & Boyd, 1999). Building on this understanding, future time perspective has been argued as a buffer against compulsive use of media/technology. People with low level of future time perspective may be more prone to compulsive use of media/technology than those with high level of future time perspective (Przepiorka & Blachnio, 2016; Przepiorka et al., 2019; Zhang et al., 2020). For example, Przepiorka and Blachnio (2016) have found that future time perspective is negatively related to the compulsive use of Internet and Facebook. Zhang et al. (2020) have found that future time perspective is negatively related to compulsive smartphone use. Inspired by these, this study argues that students’ future time perspective may influence how likely they are to cope with academic stress through compulsive smartphone use. Thus far, this is not empirically investigated, highlighting the need to investigate whether future time perspective moderates the link between academic stress and compulsive smartphone use.

Building on the discussions above, the objectives of this study are to investigate: (1) the relationship between academic stress and compulsive smartphone use, (2) the moderating effect of academic buoyancy on relationship between academic stress and compulsive smartphone use, and (3) the moderating effect of future time perspective on relationship between academic stress and compulsive smartphone use. Addressing these, the following hypotheses are tested: (H1) academic stress is positively related to compulsive smartphone use, (H2) academic buoyancy negatively moderates the relationship between academic stress and compulsive smartphone use, and (H3) future time perspective negatively moderates the relationship between academic stress and compulsive smartphone use. These were graphically depicted in Figure 1.

Figure 1.

Conceptual framework.

Methods

Participants

This study targeted college students at Ningxia Medical University in China. Although probability sampling is often considered ideal, convenience sampling was used due to the absence of a sampling frame. Saunders et al. (2020, p. 297) define a sampling frame as “a complete list of all the cases in the target population from which your sample will be drawn,” highlighting that “probability sampling techniques all necessitate some form of sampling frame.” They further state that when constructing a sampling frame is not possible, non-probability sampling techniques must be employed. Privacy concerns at Ningxia Medical University restricted access to a sampling frame, rendering probability sampling unfeasible. Hulland et al. (2018) support the use of non-probability sampling when the research aims to test theoretically derived hypotheses about relationships among variables and suggest that convenience sampling may be sufficient for such purpose. These collectively justify the use of convenience sampling in this study.

To facilitate data collection, lecturers who were accessible were approached. Five English lecturers from the School of Foreign Languages volunteered to distribute the link to the online questionnaire to their English Language Learning Students (ELLS) during class. While this sampling method may not produce a fully representative sample of all college students at Ningxia Medical University, it is appropriate for examining relationships among the targeted variables within a relevant and accessible population. Data were collected through a self-administered online questionnaire hosted on WenJuanXing. The data collection period spanned from 1 March 2024 to 1 April 2024. Initially, 463 responses were gathered; however, 24 responses were excluded due to suspicious patterns, such as straight-lining. Consequently, the final dataset consisted of 439 valid responses. The demographic profile of the respondents is provided in Table 1. Prior to data collection, a priori power analysis was conducted using G*Power software to determine the minimum sample size required for adequate statistical power. The analysis revealed that a minimum of 138 samples was needed to achieve a 95% power level, an effect size of 0.15, and a significance level of 5% (Faul et al., 2009). With 439 samples, the study was well-equipped to produce results with sufficient statistical power.

Table 1.

Demographic Profile of Respondents.

Variable	Classification	Frequency	Percentage (%)
Gender	Male	235	53.5
Gender	Female	204	46.5
Age	18–25	385	87.7
Age	26–30	54	12.3
Academic year	Year 1	68	15.5
	Year 2	37	8.4
	Year 3	216	49.2
	Year 4	118	26.9

Ethical Considerations

The questionnaires were accompanied by a cover letter that outlined the study’s objectives and assured respondents of the anonymity and confidentiality of their responses. The letter also emphasized that their participation was voluntary and they could withdraw at any time if they were not comfortable with the survey. Electronic informed consent was secured from all participants who agreed to take part in the survey. The data collection for this study took place in the absence of an ethical committee at the authors’ institution. Despite this, the research strictly followed the ethical principles of the 1964 Helsinki Declaration and its subsequent amendments to ensure that all procedures were conducted ethically.

Research Instruments

Academic Stress

A 10-item stress scale “Perceived Stress Scale (PSS-10)” was adapted to assess academic stress (Cohen & Williamson, 1988). The scale evaluates the frequency of stress-related feelings and thoughts experienced over the last month and uses a 5-point scale ranging from 1 (never) to 5 (very often) (Cohen & Williamson, 1988). While this scale is consistently recognized as a valid and reliable tool for measuring stress, experts pointed out that it only assesses a general perspective on stress and fails to capture the specific stressful emotions or thoughts associated with specific situations or domains (Cohen & Williamson, 1988; Lee, 2012). To tailor the scale to the academic context, the suggestions by Sheu et al. (2014) were followed. Terms specific to the academic context such as “schoolwork” or “in your academic life” were added and used to replace the general terminology. For examples, “how often have you felt nervous and stressed” was modified as “how often have you felt nervous or stressed because of your schoolwork” and “how often have you felt that you were unable to control the important things in your life” was modified as “how often have you felt that you were unable to control important things in your academic life” (refer to Appendix 1).

Compulsive Smartphone Use

Compulsive smartphone use was assessed through the 10 items adapted from Smartphone Addiction Inventory-Short Form (SPAI-SF) (Lin et al., 2017). These items were assessed based on a 4-point scale, ranging from 1 (strongly disagree) to 4 (strongly agree). This scale has been repeatedly found to be valid and reliable scale to assess compulsive smartphone use (i.e. the tendency to continue using smartphones even when aware of negative life consequences) (Andrade et al., 2023; Arpaci et al., 2022; Lin et al., 2017). Sample items include “I try to spend less time on smartphone, but the efforts were are in vain” and “To use smartphone has exercised certain negative effects on my schoolwork or job performance” (Lin et al., 2017, p. 3) (refer to Appendix 1).

Academic Buoyancy

Academic buoyancy was assessed through Academic Buoyancy Scale (ABS). The four items in this scale were assessed based on 7-point scale, ranging from 1 (strongly disagree) to 7 (strongly agree) (Martin & Marsh, 2008). This scale has been repeatedly found to be valid and reliable to assess academic buoyancy (i.e. the ability to successfully deal with academic setbacks and challenges that are typical of the ordinary course of school life (Martin, 2013; Martin & Marsh, 2008; Putwain et al., 2012). Sample items include “I think I am good at dealing with school work pressures” and “I do not let study stress get on top of me” (Martin & Marsh, 2008, p. 173) (refer to Appendix 1).

Future Time Perspective

Future time perspective was assessed through the five items adopted from the future subscale of Chinese Version of Zimbardo Time Perspective Inventory (ZTPI-C). It was assessed using a 5-point scale, ranging from 1 (extremely uncharacteristic of me) to 5 (extremely characteristic of me) (Li et al., 2022). This scale has been repeatedly found to be valid and reliable scale to assess future time perspective (i.e. the tendency to prioritize long-term goals and consider the outcomes of future events in present decision-making and behaviour) in the Chinese context (Lu et al., 2023; Mao et al., 2022). Sample items include “when I want to achieve something, I set goals and consider specific means for reaching those goals” and “I complete projects on time by making steady progress” (Li et al., 2022, p. 13550) (refer to Appendix 1).

Data Analysis

In this study, data analysis was performed using SPSS version 27.0 and AMOS version 24.0. Firstly, data cleaning procedures were performed to address issues of normality through skewness and kurtosis assessments. Secondly, the analysis examined multicollinearity by assessing variance inflation factor (VIF) and detection tolerance (TOL), as well as evaluating common method variance (CMV) using Harman’s single factor test. Thirdly, structural equation modelling (SEM) was performed using AMOS 24.0 to evaluate the measurement model (model fit, convergent validity, discriminant validity, composite reliability). Finally, in AMOS 24.0, data imputation was conducted to create latent variable scores and these latent scores were used for the analyses aimed to assess the influence of academic stress on compulsive smartphone use and explore whether academic buoyancy and future time perspective moderate the link between academic stress and compulsive smartphone use.

Result

Preliminary Analysis

Firstly, this study determined that the data were normally distributed, with skewness and kurtosis values for all constructs staying within the acceptable thresholds of −3 to 3 and −10 to 10 (Kline, 2016). Secondly, the multicollinearity diagnostic indicated that the detection tolerance (TOL) and variance inflation factor (VIF) values for all constructs in this study were within the acceptable thresholds, with TOL greater than 0.10 and VIF less than 5. These results indicate that there was no multicollinearity issue in the study. Thirdly, Harman’s Single Factor test indicated that common method variance (CMV) was not a concern in this study, as the first factor explained only 36.9% of the variance. This is below the threshold of 40% suggested by Hair et al. (2019) for indicating significant CMV.

Measurement Model

This study conducted confirmatory factor analysis (CFA) to examine the model fit, validity (convergent and discriminant validity) and reliability of the measurement scales. As proposed by Hair et al. (2019), a measurement model is considered to exhibit a good fit if it achieves the following criteria: (1) a parsimony normed fit index (PNFI) greater than 0.50, (2) a comparative fit index (CFI) above 0.90, (3) a Tucker-Lewis index (TLI) above 0.90, (4) a normed chi-square (χ²/df) below 3, and (5) a root mean square error of approximation (RMSEA) below 0.08. As indicated by a PNFI of 0.828, CFI of 0.939, TLI of 0.933, χ²/df of 2.722, and RMSEA of 0.063, the measurement model in this study demonstrates good model fit.

The evaluation of convergent validity for the constructs in this research adhered to the standards set by Hair et al. (2019). According to these criteria, convergent validity is confirmed when the factor loadings (FL) of all items reach or exceed 0.60, the average variance extracted (AVE) for each construct surpasses 0.50, and the composite reliability of each construct is greater than 0.70. Table 2 shows that, except for CSU3 (FL = 0.541), all measurement items have factor loadings above 0.60. Despite such slight discrepancy, it is still within acceptable range. As proposed by Hair et al. (2019), factor loadings between 0.4 and 0.7 are acceptable if the composite reliability (CR) and AVE meet their respective thresholds. In this regard, all variables have average variance extracted (AVE) above 0.50 and CR above 0.70. These findings demonstrate that the constructs have adequate convergent validity. The evaluation of discriminant validity for the construct utilized the Heterotrait-Monotrait (HTMT) ratio as its measure. Henseler et al. (2015) recommend that an HTMT value under 0.90 is indicative of discriminant validity. As indicated in Table 3, all HTMT values meet this criterion, suggesting that the discriminant validity is satisfactory. Taken together, these tests of validity affirm that the measurement instruments used are both reliable and valid.

Table 2.

Convergent Validity.

Constructs	Items	FL	AVE	CR
Academic Stress	AS1	0.747	0.512	0.913
	AS2	0.767
	AS3	0.626
	AS4	0.693
	AS5	0.703
	AS6	0.697
	AS7	0.700
	AS8	0.695
	AS9	0.763
	AS10	0.755
Compulsive Smartphone Use	CSU1	0.787	0.567	0.928
	CSU2	0.797
	CSU3	0.541
	CSU4	0.743
	CSU5	0.758
	CSU6	0.779
	CSU7	0.702
	CSU8	0.794
	CSU9	0.817
	CSU10	0.771
Academic Buoyancy	AB1	0.914	0.829	0.950
	AB2	0.929
	AB3	0.898
	AB4	0.896
Future Time Perspective	FTP1	0.918	0.827	0.960
	FTP2	0.918
	FTP3	0.893
	FTP4	0.903
	FTP5	0.921

Table 3.

Discriminant Validity (HTMT).

Note: The grey shading indicates symmetry in the HTMT matrix.

Hypotheses Testing

After confirming the quality of the measurement model, latent variable scores were generated by AMOS 24.0 and used to examine the effect of academic stress on compulsive smartphone use and the moderating effect of academic buoyancy and future time perspective on the relationship between academic stress and compulsive smartphone use. Firstly, as shown in Table 4, the effect of academic stress on compulsive smartphone use was found to be positive and significant (β = .41, t = 12.036, p > .001). Thus, H1 was supported. Additionally, following Cohen’s (1988) guidelines, the f² value was calculated to assess the magnitude of the direct effect. Cohen categorizes f² values of 0.02, 0.15, and 0.35 as small, medium, and large effects respectively. The f² value for the direct effect of academic stress on compulsive smartphone use was 0.331, indicating a medium effect size.

Table 4.

Hypothesis Testing.

Hypothesis	Relationship	Std beta	t-Values	Decision	Effect size (f²)
H1	AS→IA	.41	12.036	Supported	0.331 (Medium)
H2	AS × AB→IA	−.189	−2.046	Supported	0.01 (Medium)
H3	EM × HE→IA	.220	1.686	Not supported	N/A

Secondly, academic buoyancy (β = −.189, t = −2.046, p = .041) was found to have significant negative moderating effect on the relationship between academic stress and compulsive smartphone use. Thus, H2 was supported. Thirdly, the moderating effect of future time perspective on the relationship between academic stress and compulsive smartphone use was found to be insignificant (β = .220, t = 1.686, p = .092), indicating that future time perspective does not have moderating effect on the relationship between academic stress and compulsive smartphone use. Thus, H3 was not supported.

To better understand the nature of moderating effect of academic buoyancy, simple slope analysis was performed. As shown in Figure 2, the interpolation line is steeper for low academic buoyancy. It suggests that academic stress is more strongly related to compulsive smartphone use for college students with low academic buoyancy (one standard deviation below the mean) than those with high level (one standard deviation above the mean).

Figure 2.

Slope analysis for moderating effect of academic buoyancy.

Furthermore, to evaluate the magnitude of academic buoyancy’s moderating effect, this study adhered to the guidelines provided by Hair et al. (2022). Hair et al. categorize f² values of 0.005, 0.01, and 0.025 as representing small, medium, and large effect. The f² values for the moderating effects of academic buoyancy was 0.01, indicating medium effect.

Discussion

The mixed findings on the relationship between academic stress and compulsive smartphone use highlight the need for further empirical research, which this study aims to address by exploring the link between academic stress and compulsive smartphone use. Consistent with the findings of Yaghoobi et al. (2024) and Zhang et al. (2024), this study also identifies a significant positive effect of academic stress on compulsive smartphone use. This suggests that college students experiencing high levels of academic stress are more likely to engage in compulsive smartphone use compared to their peers experiencing lower levels of stress. This finding supports the stress-coping theory, which proposes that people facing negative situations (e.g. intense academic demands) may resort to behaviours that offer temporary relief. If these behaviours successfully distract them from stress and improve mood, people are likely to continue engaging in them to maintain or restore a sense of calm. This pattern may persist even when they are aware of the potential negative consequences and recognize the need to regulate such compulsive behaviours (Kardefelt-Winther, 2014; Lazarus & Folkman, 1984).

While research indicates a positive link between academic stress and compulsive smartphone use among college students, it is essential to recognize that not all students under academic stress will exhibit this behaviour. As previously mentioned, individual characteristics play a significant role in determining whether students are prone to engaging in compulsive smartphone use (Wang et al., 2020; Xu et al., 2019; Zhang et al., 2024). Supporting this, the study by Xu et al. (2019) illustrated that students who encounter academic stress but employ problem-focused coping strategies are less likely to engage in compulsive smartphone use compared to their peers who do not utilize such strategies. Zhang et al. (2024) reported that students who experience academic stress, coupled with high levels of rumination and feelings of alienation from their peers, are more likely to exhibit compulsive smartphone use than those who ruminate less and feel more connected to their peers.

Similarly, this study found that the link between academic stress and compulsive smartphone use was negatively moderated by academic buoyancy. Specifically, it reveals that college students who face academic stress are less likely to engage in compulsive smartphone use if they possess high levels of academic buoyancy compared to their counterparts with lower levels of this attribute. The relationship between academic buoyancy and coping styles provides insight into the negative moderating effect of academic buoyancy. Research has demonstrated that academic buoyancy is positively associated with problem-focused or adaptive coping styles and negatively associated with emotion-focused or maladaptive coping styles (Hirvonen et al., 2019; Lei et al., 2021). Students with high academic buoyancy tend to exhibit confidence in managing daily academic challenges and setbacks. Their optimistic perspective on achieving educational goals sustains their motivation and effort during difficult periods, encouraging them to tackle problems directly and adopt adaptive learning strategies or proactive problem-solving approaches. In contrast, students with low academic buoyancy often lack the confidence and optimism required to handle challenging academic situations. They may feel discouraged about their ability to achieve their goals, which can diminish their motivation and lead to reliance on maladaptive strategies or avoidance behaviours. Consequently, students with high academic buoyancy are more likely to adopt adaptive strategies, reducing their dependence on compulsive behaviours, such as excessive smartphone use, as a way to manage stress. Conversely, students with low academic buoyancy, lacking confidence in resolving problems, are more prone to maladaptive coping styles, increasing their likelihood of engaging in compulsive smartphone use as a means of escape or distraction from academic stress. Building on this, the study significantly contributes to the literature by pinpointing academic buoyancy as crucial factor that determines the nature (e.g. negative/positive/not significant) or intensity (stronger/weaker) of the link between academic stress and compulsive smartphone use. It elucidates “for whom” this link is more or less pronounced among college students.

On the other hand, this study hypothesized that future time perspective would negatively moderate the relationship between academic stress and compulsive smartphone use. However, the moderating effect of future time perspective was surprisingly found to be not significant. This indicates that future time perspective does not influence the extent to which academic stress is associated with compulsive smartphone use. The insignificant moderating effect of future time perspective on the relationship between academic stress and problematic smartphone use could be attributed to the limited variability in the dataset’s responses to the items measuring future time perspective. A substantial proportion of participants, approximately 80%, scored between 2, 3, and 4 on a five-point scale, with responses heavily concentrated around the values of 2 and 4. This concentration suggests a lack of representation at the scale’s extremes (1 and 5), which is critical for detecting significant interaction effects. Moderation analysis requires sufficient variability in the moderator to accurately assess its influence on the relationship between the independent and dependent variables. When such variability is restricted, as in this case, the moderating effect of future time perspective may not be detected, even if it is theoretically relevant (Aguinis et al., 2016). Despite this finding, this study does not suggest that future time perspective is not relevant to compulsive smartphone use. Consistent with previous research that has identified a negative relationship between future time perspective and compulsive technology use (Przepiorka & Blachnio, 2016; Przepiorka et al., 2019; Zhang et al., 2020), this study also found that future time perspective is negatively related to compulsive smartphone use (β = −.376, t = −4.701, p > 0.001). Therefore, even though the moderating effect was not significant, future time perspective may still play an important role in interventions aimed at reducing compulsive smartphone use among college students.

Conclusion and Practical Implications

This study reveals that academic stress can drive college students to engage in compulsive smartphone use. Moreover, it highlights the critical role of academic buoyancy in determining both whether and to what extent academic stress contributes to compulsive smartphone use. The contributions of this study to the literature are twofold. Firstly, it offers additional empirical evidence to validate the relationship between academic stress and compulsive smartphone use. Secondly, the study identifies academic buoyancy as a moderating factor, which sheds light on the question of “for whom” academic stress is more or less likely to lead to compulsive smartphone use.

These findings offer valuable insights into how to mitigate the risk of compulsive smartphone use among college students. Given that academic stress is positively associated with compulsive smartphone use, intervention programs can potentially focus on two primary strategies: (1) ensuring students have access to resources for stress reduction such as sports and entertainment amenities, and (2) guiding students to manage academic stress constructively by facing and resolving their academic challenges rather than avoiding them. These interventions can potentially reduce the tendency of students facing academic stress to engage in compulsive smartphone use as a form of avoidant coping. Additionally, this study found that college students who face academic stress are less likely to engage in compulsive smartphone use if they have high levels of academic buoyancy compared to those with lower levels of this attribute. Accordingly, enhancing academic buoyancy could be a key intervention for reducing compulsive smartphone use among college students. Providing academic support (e.g. additional tutoring, study groups, regular and constructive feedback) for struggling students can reinforce their belief in their ability to overcome academic challenges or setbacks, thereby reduce the likelihood that students resort to compulsive smartphone use as a form of avoidant coping.

Limitations and Future Research Directions

Despite its valuable contribution, this study is not without limitations. Firstly, this study is a cross-sectional study in which all data were collected simultaneously and that restricts the ability of this study to discern the causal relationships between the variables. Addressing this limitation, future research could employ a longitudinal design and that allows to generate a clearer understanding on the directions of causality. Secondly, the samples in this study were all collected using convenience sampling from Ningxia Medical University in China. Such sampling approach implies that the findings cannot be generalized to college students in other contexts such as those from different countries or even the broader population of college students within China. To enhance the generalizability of the findings, future studies could replicate the research with a more representative sample of college students. Thirdly, this study focused solely on the role of academic buoyancy and future time perspective in determining whether or the extent to which academic stress causes compulsive smartphone use. However, there are likely many other factors that could explain for whom academic stress is a stronger or weaker predictor of compulsive smartphone use. Identifying these factors could be a valuable direction for future research aimed at contributing the literature on compulsive smartphone use.

Footnotes

Appendix 1

Measurement Items.

Compulsive smartphone use
CSU1	Although using smartphone has brought negative effects on my interpersonal relationships, the amount of time spent on Internet remains unreduced.
CSU2	I use smartphone for a longer period of time and spend more money than I had intended.
CSU3	I try to spend less time on smartphone, but the efforts are in vain.
CSU4	I feel aches and soreness in the back or eye discomforts due to excessive smartphone use.
CSU5	I make it a habit to use smartphone and the sleep quality and total sleep time decreased.
CSU6	To use smartphone has exercised certain negative effects on my schoolwork or job performance.
CSU7	I feel restless and irritable when the smartphone is unavailable.
CSU8	I feel uneasy once I stop smartphone for a certain period of time.
CSU9	I find that I have been hooking on smartphone longer and longer.
CSU10	I have increased substantial amount of time using smartphone per week in recent three months.
Academic stress
AS1	How often have you been upset because of something that happened unexpectedly in your academic life?
AS2	How often have you felt that you were unable to control important things in your academic life?
AS3	How often have you felt nervous and stressed because of your schoolwork?
AS4	How often have you felt confident about your ability to handle your schoolwork?
AS5	How often have you felt that things in your academic life were going your way?
AS6	How often have you found that you could not cope with your academic life?
AS7	How often have you been able to control irritations in your academic life?
AS8	How often have you felt that you were able to manage your academic life?
AS9	How often have you been upset because your academic life was out of your control?
AS10	How often have you felt your schoolwork were piling up so high that you could not overcome them?
Academic buoyancy
AB1	I do not let study stress get on top of me.
AB2	I think I am good at dealing with school work pressures.
AB3	I do not let a bad mark affect my confidence.
AB4	I am good at dealing with setbacks at school (e.g. bad mark, negative feedback on my work).
Future time perspective
FTP1	When I want to achieve something, I set goals and consider specific means for reaching those goals.
FTP2	Meeting tomorrow’s deadlines and doing other necessary work comes before tonight’s play.
FTP3	I meet my obligations to friends and authorities on time.
FTP4	I complete projects on time by making steady progress.
FTP5	I keep working at difficult, uninteresting tasks if they will help me get ahead.

Acknowledgements

We are grateful to the anonymous reviewers for their insightful suggestions and constructive comments. We deeply thank the editors for their patient work on our manuscript.

ORCID iDs

Dongying Su

Boon Yew Wong

Ethical Considerations

All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Consent to Participate

Electronic informed consent was secured from all participants who agreed to take part in the survey.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

References

Aguinis

Edwards

J. R.

Bradley

K. J.

(2016). Improving our understanding of moderation and mediation in strategic management research. Organizational Research Methods, 20(4), 665–685. https://doi.org/10.1177/1094428115627498

Aljomaa

S. S.

Qudah

M. F. A.

Albursan

I. S.

Bakhiet

S. F.

Abduljabbar

A. S.

(2016). Smartphone addiction among university students in the light of some variables. Computers in Human Behavior, 61, 155–164. https://doi.org/10.1016/j.chb.2016.03.041

Andrade

A. L. M.

Spritzer

D. T.

Scatena

de Oliveira Pinheiro

da Silva

G. T.

Kim

H. S.

Breda

De Oliveira

W. A.

De Micheli

(2023). Psychometric properties of the Smartphone Addiction Inventory-Short Form (SPAI-SF) in Brazilian adolescents. Psychiatry Research, 319, 115001. https://doi.org/10.1016/j.psychres.2022.115001

Arpaci

Barzegari

Mahmoodi

Afrooz

Ranjbar

A. Z.

(2022). Psychometric characteristics of the Iranian Smartphone Addiction Inventory Short Form (SPAI-SF). In Al-Emran

Al-Sharafi

M. A.

Al-Kabi

M. N.

Shaalan

(Eds.), Proceedings of International Conference on Emerging Technologies and Intelligent Systems: ICETIS 2021 Volume 2 (pp. 633–641). Springer International Publishing. https://doi.org/10.1007/978-3-030-85990-9_51

Arrivillaga

Rey

Extremera

(2020). Adolescents’ problematic internet and smartphone use is related to suicide ideation: Does emotional intelligence make a difference?. Computers in Human Behavior, 110, 106375. https://doi.org/10.1016/j.chb.2020.106375

Bandura

(1999). Social cognitive theory of personality. In Pervin

L. A.

John

O. P.

(Eds.), Handbook of personality: Theory and research (2nd ed., pp. 154–196). Guilford Press.

Billieux

Maurage

Lopez-Fernandez

Kuss

D. J.

Griffiths

M. D.

(2015). Can disordered mobile phone use be considered a behavioral addiction? An update on current evidence and a comprehensive model for future research. Current Addiction Reports, 2(2), 156–162. https://doi.org/10.1007/s40429-015-0054-y

Busch

P. A.

McCarthy

(2021). Antecedents and consequences of problematic smartphone use: A systematic literature review of an emerging research area. Computers in Human Behavior, 114, 106414. https://doi.org/10.1016/j.chb.2020.106414

Chen

Zhang

K. Z.

Gong

Zhao

S. J.

Lee

M. K.

Liang

(2017). Examining the effects of motives and gender differences on smartphone addiction. Computers in Human Behavior, 75, 891–902. https://doi.org/10.1016/j.chb.2017.07.002

10.

Chiu

S. I.

(2014). The relationship between life stress and smartphone addiction on Taiwanese university student: A mediation model of learning self-efficacy and social self-efficacy. Computers in Human Behavior, 34, 49–57. https://doi.org/10.1016/j.chb.2014.01.024

11.

Cohen

(1988). Statistical power analysis for the social sciences. Hillsdale, NJ: Lawrence Erlbaum Associates.

12.

Cohen

Williamson

(1988). Perceived stress in a probability sample of the United States. In Spacapan

Oskamp

(Eds.), The social psychologyof health: Claremont symposium on applied social psychology. Sage.

13.

Costarelli

Patsai

(2012). Academic examination stress increases disordered eating symptomatology in female university students. Eating and Weight Disorders-Studies on Anorexia, Bulimia and Obesity, 17, e164–e169. https://doi.org/10.1007/bf03325343

14.

Faul

Erdfelder

Buchner

Lang

A. G.

(2009). Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/brm.41.4.1149

15.

Ford

J. A.

Schroeder

R. D.

(2008). Academic strain and non-medical use of prescription stimulants among college students. Deviant Behavior, 30(1), 26–53. https://doi.org/10.1080/01639620802049900

16.

Hair

J. F.

Black

W. C.

Babin

B. J.

Anderson

R. E.

(2019). Multivariate data analysis (8th ed.). Cengage Learning.

17.

Hair

J. F.

Hult

G. T.

Ringle

C. M.

Sarsted

M. A

. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.) Sage Publications.

18.

Henseler

Ringle

C. M.

Sarstedt

(2015). A new criterion for assessing discriminant validity in variance-based structural equation modelling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8

19.

Hirvonen

Yli-Kivistö

Putwain

D. W.

Ahonen

Kiuru

(2019). School-related stress among sixth-grade students–Associations with academic buoyancy and temperament. Learning and Individual Differences, 70, 100–108. https://doi.org/10.1016/j.lindif.2019.01.012

20.

Hulland

Baumgartner

Smith

K. M.

(2018). Marketing survey research best practices: evidence and recommendations from a review of JAMS articles. Journal of the Academy of Marketing Science, 46, 92–108. https://doi.org/10.1007/s11747-017-0532-y

21.

Jain

Verma

(2016). Prevalence of stress and coping strategies among college students. Journal of Advanced Medical and Dental Sciences Research, 4(6), 95. https://doi.org/10.21276/jamdsr.2016.4.6.21

22.

Jiang

Shypenka

(2018). Loneliness, individualism, and smartphone addiction among international students in China. Cyberpsychology, Behavior, and Social Networking, 21(11), 711–718. https://doi.org/10.1089/cyber.2018.0115

23.

Kardefelt-Winther

(2014). A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Computers in Human Behavior, 31, 351–354. https://doi.org/10.1016/j.chb.2013.10.059

24.

Kim

S. E.

Kim

J. W.

Jee

Y. S.

(2015). Relationship between smartphone addiction and physical activity in Chinese international students in Korea. Journal of Behavioral Addictions, 4(3), 200–205. https://doi.org/10.1556/2006.4.2015.028

25.

Kline

R. B.

(2016). Principles and practice of structural equation modeling. Guilford Publications.

26.

Lazarus

R. S.

Folkman

(1984). Stress, appraisal, and coping. Springer Publishing Company. https://doi.org/10.4135/9781412952576.n198

27.

Lee

E. H.

(2012). Review of the psychometric evidence of the perceived stress scale. Asian Nursing Research, 6(4), 121–127. https://doi.org/10.1016/j.anr.2012.08.004

28.

Lei

Zhang

Deng

Wang

Shao

(2021). Academic self-efficacy and test anxiety in high school students: A conditional process model of academic buoyancy and peer support. School Psychology International, 42(6), 616–637. https://doi.org/10.1177/01430343211039265

29.

Wang

Lyu

Worrell

F. C.

Mello

Z. R.

(2022). Psychometric properties of the Chinese version of the Zimbardo Time Perspective Inventory. Current Psychology, 42(16), 13547–13559. https://doi.org/10.1007/s12144-021-02622-6

30.

Lin

Y. H.

Pan

Y. C.

Lin

S. H.

Chen

S. H.

(2017). Development of short-form and screening cutoff point of the Smartphone Addiction Inventory (SPAI-SF). International Journal of Methods in Psychiatric Research, 26(2), e1525. https://doi.org/10.1002/mpr.1525

31.

Liu

Q. Q.

Zhou

Z. K.

Yang

X. J.

Kong

F. C.

Niu

G. F.

Fan

C. Y.

(2017). Mobile phone addiction and sleep quality among Chinese adolescents: A moderated mediation model. Computers in Human Behavior, 72, 108–114. https://doi.org/10.3389/fpsyg.2024.1379388

32.

Long

Liu

T. Q.

Liao

Y. H.

H. Y.

Chen

S. B.

Billieux

(2016). Prevalence and correlates of problematic smartphone use in a large random sample of Chinese undergraduates. BMC Psychiatry, 16, 1–12. https://doi.org/10.1186/s12888-016-1083-3

33.

Liang

Hong

(2023). Time matters: Time perspectives predict intertemporal prosocial preferences. Behavioral Sciences, 13(7), 590. https://doi.org/10.3390/bs13070590

34.

Mao

Chen

Wei

Luo

Liu

(2022). Future time perspective and bedtime procrastination: The mediating role of dual-mode self-control and problematic smartphone use. International Journal of Environmental Research and Public Health, 19(16), 10334. https://doi.org/10.3390/ijerph191610334

35.

Maroney

Williams

B. J.

Thomas

Skues

Moulding

(2019). A stress-coping model of problem online video game use. International Journal of Mental Health and Addiction, 17(4), 845–858. https://doi.org/10.1007/s11469-018-9887-7

36.

Martin

A. J.

(2013). Academic buoyancy and academic resilience: Exploring “everyday” and “classic” resilience in the face of academic adversity. School Psychology International, 34(5), 488–500. https://doi.org/10.1177/0143034312472759

37.

Martin

A. J.

Marsh

H. W.

(2008). Workplace and academic buoyancy: Psychometric assessment and construct validity amongst school personnel and students. Journal of Psychoeducational Assessment, 26(2), 168–184. https://doi.org/10.1177/0734282907313767

38.

Metzger

I. W.

Blevins

Calhoun

C. D.

Ritchwood

T. D.

Gilmore

A. K.

Stewart

Bountress

K. E.

(2017). An examination of the impact of maladaptive coping on the association between stressor type and alcohol use in college. Journal of American College Health, 65(8), 534–541. https://doi.org/10.1080/07448481.2017.1351445

39.

Mun

I. B.

Lee

(2024). A moderated mediation model of the relationship between academic stress and digital game addiction. Current Psychology, 43(10), 9078–9088. https://doi.org/10.1007/s12144-023-05002-4

40.

Olson

J. A.

Sandra

D. A.

Colucci

É. S.

Al Bikaii

Chmoulevitch

Nahas

Raz

Veissière

S. P.

(2022). Smartphone addiction is increasing across the world: A meta-analysis of 24 countries. Computers in Human Behavior, 129, 107138. https://doi.org/10.1016/j.chb.2021.107138

41.

Przepiorka

Blachnio

(2016). Time perspective in Internet and Facebook addiction. Computers in Human Behavior, 60, 13–18. https://doi.org/10.1016/j.chb.2016.02.045

42.

Przepiorka

Blachnio

Cudo

(2019). The role of depression, personality, and future time perspective in internet addiction in adolescents and emerging adults. Psychiatry Research, 272, 340–348. https://doi.org/10.1016/j.psychres.2018.12.086

43.

Putwain

D. W.

Connors

Symes

Douglas-Osborn

(2012). Is academic buoyancy anything more than adaptive coping?. Anxiety, Stress & Coping, 25(3), 349–358. https://doi.org/10.1080/10615806.2011.582459

44.

Ramasubramanian

(2017). Mindfulness, stress coping and everyday resilience among emerging youth in a university setting: A mixed methods approach. International Journal of Adolescence and Youth, 22(3), 308–321. https://doi.org/10.1080/02673843.2016.1175361

45.

Ramayah

Cheah

Chuah

Ting

Memon

M. A.

(2018). Partial least squares structural equation modeling (PLS-SEM) using smartPLS 3.0: An updated guide and practical guide to statistical analysis. Kuala Lumpur: Pearson.

46.

Samaha

Hawi

N. S.

(2016). Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Computers in Human Behavior, 57, 321–325. https://doi.org/10.1016/j.chb.2015.12.045

47.

Saunders

Lewis

Thornhill

(2020). Research methods for business students (8th ed.). Pearson.

48.

Shen

Wang

Sun

Liu

(2021). Chinese adolescents’ academic stress and smartphone addiction: A moderated-mediation model. Journal of Broadcasting & Electronic Media, 65(5), 724–740. https://doi.org/10.1080/08838151.2021.2014842

49.

Sheu

H. B.

Chong

S. S.

Chen

H. F.

Lin

W. C.

(2014). Well-being of Taiwanese and Singaporean college students: Cross-cultural validity of a modified social cognitive model. Journal of Counseling Psychology, 61(3), 447. https://doi.org/10.1037/cou0000018

50.

Sussman

Arnett

J. J.

(2014). Emerging adulthood: Developmental period facilitative of the addictions. Evaluation & the Health Professions, 37(2), 147–-155. https://doi.org/10.1177/0163278714521812

51.

Wang

J. L.

Rost

D. H.

Qiao

R. J.

Monk

(2020). Academic stress and smartphone dependence among Chinese adolescents: A moderated mediation model. Children and Youth Services Review, 118, 105029. https://doi.org/10.1016/j.childyouth.2020.105029

52.

T. T.

Wang

H. Z.

Fonseca

Zimmerman

M. A.

Rost

D. H.

Gaskin

Wang

J. L.

(2019). The relationship between academic stress and adolescents’ problematic smartphone usage. Addiction Research & Theory, 27(2), 162–169. https://doi.org/10.1080/16066359.2018.1488967

53.

Yaghoobi

Karimi

Asoudeh

Mohammadi

(2024). Associations between academic motivation, academic stress, and mobile phone addiction: Mediating roles of wisdom. International Journal of Mental Health and Addiction, 1–16. Advance online publication. https://doi.org/10.1007/s11469-024-01269-1

54.

Yang

Asbury

Griffiths

M. D.

(2019). An exploration of problematic smartphone use among Chinese university students: Associations with academic anxiety, academic procrastination, self-regulation and subjective wellbeing. International Journal of Mental Health and Addiction, 17, 596–614. https://doi.org/10.1007/s11469-018-9961-1

55.

Zhang

Han

Lian

Cao

Yan

(2024). How and when is academic stress associated with mobile phone addiction? The roles of psychological distress, peer alienation and rumination. PLoS One, 19(2), e0293094. https://doi.org/10.1371/journal.pone.0293094

56.

Zhang

Xiong

Zhou

(2020). Smartphone use disorder and future time perspective of college students: The mediating role of depression and moderating role of mindfulness. Child and Adolescent Psychiatry and Mental Health, 14, 1–11. https://doi.org/10.1186/s13034-020-0309-9

57.

Zimbardo

P. G.

Boyd

J. N.

(1999). Putting time in perspective: A valid, reliable individual-differences metric. Journal of Personality and Social Psychology, 77(6), 1271–1288. https://doi.org/10.1007/978-3-319-07368-2_2

The Moderating Role of Academic Buoyancy and Future Time Perspective on the Relationship Between Academic Stress and Compulsive Smartphone Use Among College Students: Evidence From English Language Learning Students at Ningxia Medical University in China

Abstract

Keywords

Introduction

Methods

Participants

Ethical Considerations

Research Instruments

Academic Stress

Compulsive Smartphone Use

Academic Buoyancy

Future Time Perspective

Data Analysis

Result

Preliminary Analysis

Measurement Model

Hypotheses Testing

Discussion

Conclusion and Practical Implications

Limitations and Future Research Directions

Footnotes

Appendix 1

Acknowledgements

ORCID iDs

Ethical Considerations

Consent to Participate

Funding

Declaration of Conflicting Interests

Data Availability Statement

References