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Abstract

Objective

This study investigates the influence of psychological factors—specifically affective and cognitive risk perceptions, social distancing attitudes, subjective norms, and cabin fever syndrome—on smartphone usage intensity during the COVID-19 pandemic, with a particular focus on university students.

Methods

Utilizing a cross-sectional survey design, data were collected from 314 university students from South Korea and Vietnam. Structural equation modeling was employed to analyze the relationships between the psychological constructs and their impact on smartphone usage.

Results

The analysis confirms that both affective and cognitive risk perceptions significantly influence attitudes towards social distancing. Furthermore, these social distancing attitudes are found to significantly affect cabin fever syndrome, suggesting that positive attitudes towards social distancing are closely associated with higher reports of cabin fever. Notably, cabin fever syndrome emerges as a significant predictor of increased smartphone usage, underscoring its role as a mediator between prolonged isolation and digital engagement. Additionally, subjective norms are also shown to significantly influence smartphone usage intensity, highlighting the impact of social expectations on digital behaviors during the pandemic.

Conclusion

The study highlights the complex interplay between psychological distress induced by social restrictions and increased reliance on digital technology for social connectivity. These insights suggest that mental health interventions and digital literacy programs tailored to university students’ needs can be effective in managing the negative impacts of prolonged social isolation.

Keywords

Behavioral responses cabin fever syndrome cognitive risk perception pandemic smartphone usage social distancing

Introduction

The global outbreak of COVID-19 has brought unprecedented changes to daily life, impacting individuals’ social interactions, work, and education systems worldwide.¹ The pandemic has necessitated the adoption of various health guidelines, including social distancing, quarantine measures, and the widespread use of masks, fundamentally altering how communities function and individuals interact.^2,3 As people have adapted to these changes, there has been a significant shift in the use of digital technologies, particularly smartphones, which have become central to maintaining personal connections, accessing information, and managing daily tasks during periods of isolation.^4,5 This shift is particularly pronounced among university students, who have faced unique challenges due to the sudden transition to online learning and restrictions on campus activities. The impact of these changes on students’ social behaviors, psychological well-being, and technology usage provides a critical area of study. The extended periods of isolation and reduced physical social interactions have led to significant psychological stress and changes in behavioral patterns, including increased reliance on digital communication tools. This situation presents a unique opportunity to explore the interplay between perceived risks, behavioral attitudes, and technology usage during a health crisis.

Theoretical frameworks such as the health belief model (HBM) and the theory of planned behavior (TPB) offer valuable insights into understanding these dynamics. The HBM suggests that personal beliefs about the severity of a health threat and the benefits of taking preventive action influence individuals’ readiness to act in health-promoting ways.⁶ In the context of a pandemic, this model helps to explain how perceptions of the risks associated with COVID-19 can motivate students to adhere to recommended behaviors such as social distancing.^7,8 Similarly, the TPB emphasizes the role of subjective norms and perceived behavioral control in shaping behavioral intentions.⁹ According to this theory, individuals’ behaviors are influenced not only by their beliefs about the consequences of their actions but also by social pressures and their confidence in their ability to perform the behaviors. This framework is particularly pertinent for understanding how social influences and individual assessments of control over preventive measures impact individuals’ adherence to health guidelines during the pandemic.^10,11

Previous research has extensively explored the role of risk perception in influencing health behaviors in various contexts, including vaccine uptake and responses to public health emergencies.^12–16 However, less is known about how these perceptions specifically affect young adults in educational settings, who may experience the pandemic differently due to their social environments and lifestyle. Moreover, studies have indicated that prolonged social restrictions can lead to “cabin fever”—a state characterized by feelings of restlessness and irritability due to prolonged confinement.^17,18 This study aims to bridge these gaps by examining how cognitive and affective risk perceptions relate to social distancing attitudes and how these attitudes, in turn, influence smartphone usage among university students.

Furthermore, the study considers the influence of subjective norms—social pressures from peers and significant others—that could affect students’ behavioral intentions regarding health measures. The current global health crisis offers a unique context to examine these theoretical constructs and their interactions, providing new insights into behavior changes among young adults. By focusing on university students, this research seeks to understand a demographic that is not only crucial for spreading or containing the virus but also represents a technologically savvy segment that frequently adopts digital solutions.

This article aims to address the following research questions:

How do affective and cognitive risk perceptions influence university students’ attitudes towards social distancing?

To what extent do social distancing attitude impact cabin fever syndrome during the pandemic?

How does experiencing cabin fever and subjective norms affect the intensity of smartphone usage among students?

By addressing these questions, this study not only contributes to the theoretical literature on health behavior and technology use but also offers practical insights for policymakers and educational institutions seeking to support students during and beyond the pandemic. The findings are expected to inform strategies that enhance compliance with health guidelines and address the psychological and social needs of students during prolonged periods of virtual learning and social isolation.

Theoretical background

Theory of planned behavior

The TPB posits that individual behavioral intentions and subsequent behaviors are influenced by attitudes, subjective norms, and perceived behavioral control.⁹ In the context of COVID-19, TPB has been applied extensively to understand and predict protective behaviors such as social distancing,^19–21 mask-wearing,^22,23 and vaccination uptake.^24,25 Studies leveraging TPB demonstrate that positive attitudes towards the effectiveness of preventive measures, perceived social pressure from significant others to engage in these behaviors, and confidence in one's ability to perform them predict higher compliance rates.^26–28 For instance, research indicates that individuals who believed in the benefits of masks and perceived a high level of social endorsement were more likely to consistently wear masks.²⁹ Additionally, perceived behavioral control has been shown to significantly affect both the intention and the actual practice of social distancing, especially when individuals feel capable of implementing these behaviors amidst various constraints.³⁰ These applications of TPB in the COVID-19 era highlight its utility in crafting targeted public health interventions and policies.

Smartphone

With the spread and settlement of smartphones, a number of studies have explored the behavioral intentions of smartphone users.^31,32 At the time of the advent of smartphones, research mainly focused on the acceptance and use of smartphones.^33–35 Recently, attempts have been made to explain the behavior of users by targeting specific applications such as m-learning, m-banking, and m-commerce.^36–38 Some scholars investigated the roles of subjective norm and perceived behavioral control on intention to use m-learning among smartphone users.^36,39 Several studies figured out that the subjective norm of smartphone users plays a crucial role in generating the intention to use m-banking.^37,38 Also, they have proven to influence smartphone application purchase intention.⁴⁰

After the COVID-19 outbreak, a great deal of study began to identify the changed behaviors of smartphone users. One study examined the use patterns of smartphones during the pandemic, observing how much people used their smartphones to watch the news or talk to others.⁴¹ It was noted that smartphones are essential tools that support people in staying informed and connected during the pandemic. Another study investigated the impact of smartphone usage on high school students’ higher-order thinking skills in physics learning, finding that these skills were determined by smartphone usage intensity.⁴² Further research uncovered that COVID-19 decreases users’ smartphone engagement but increases WiFi usage, revealing significant correlations between WiFi access frequency, network switches, and memory usage.⁴³ Moreover, user behavior toward smartphones was strongly correlated with the pandemic. Another study explored the association between COVID-19 anxiety and problematic smartphone use severity, discovering that anxiety correlates with increased problematic smartphone use, anxiety, and depression.⁴⁴ Additionally, research demonstrated the effects of a smartphone application and social media on physical activity in psychiatric outpatients during COVID-19, highlighting significant correlations between general smartphone use, digital social engagement, and physical activity.⁴⁵ These findings suggest that individuals use smartphones to gain social support to cope with mental stress and social deprivation. In summary, while numerous studies have examined smartphone use during the pandemic, little research has addressed the role of risk perception, attention to social measures, and psychological pressure regarding smartphone usage intensity.

Risk perception

Risk perception represents value judgments or subjective beliefs about unspecific situations brought from a certain risk.⁴⁶ Some researchers have divided it into affective risk perception and cognitive risk perception.^47,48 On the other hand, other scholars have classified risk perception into “risk as analysis” and “risk as feelings.”⁴⁹ Comparing the operational definitions of scales and indicators, affective risk perception resembles “risk as feelings,” and cognitive risk perception is similar to “risk as analysis.” It was identified that the effects of risk perception on heath-protection actions under COVID-19.¹³ The “feelings of risk” positively influence protective behaviors. Additionally, “risk analysis” was found to significantly impact social distancing. Both affective risk perception and cognitive risk perception are significantly associated with behavioral intention toward contactless tourism.⁵⁰ Global research found that risk perception leads to the adoption of preventive health behaviors.⁵¹ Existing studies have investigated the effects of risk perception on preventive activities, but studies verifying the effects of risk perception on smartphone use have not yet been actively conducted.

Cabin fever syndrome

Cabin fever syndrome is characterized by a cluster of distress symptoms experienced by individuals confined to their homes for extended periods, particularly relevant during the COVID-19 pandemic. This syndrome encompasses a range of psychological responses including loneliness, restlessness, reduced motivation, difficulty concentrating, and irritability, as identified in several studies.^52–54 It has been noted that a lack of sufficient social interaction can exacerbate feelings of inertia and loneliness, especially during periods of enforced isolation like lockdowns.⁵⁵

Further research explores cabin fever syndrome in young individuals, highlighting symptoms such as vicarious trauma, nihilism, anxiety, compulsion, and severe procrastination, which contribute to a complex emotional and psychological landscape during prolonged isolation.⁵⁶ Additionally, cabin fever syndrome has been shown to significantly influence social network site usage among young adults, suggesting that digital engagement may serve as a coping mechanism for isolation-related stress and anxiety.¹⁷ Lastly, gender differences in cabin fever symptoms were explored, with female college students particularly reporting difficulties with food cravings and concentration during lockdowns.⁵⁷

These studies collectively underscore the multifaceted impact of cabin fever syndrome on mental health and behavior. However, despite these insights, there remains a gap in understanding how specifically cabin fever influences smartphone usage intensity beyond general online engagement. Addressing this gap could offer deeper insights into behavioral adaptations to prolonged indoor confinement and inform strategies to mitigate negative psychological impacts.

Conceptual model and hypotheses

The overarching theoretical framework for this research is grounded in the HBM and the TPB, which are widely recognized in the literature for explaining health-related behavior changes in response to perceived threats.^6,9 These models collectively inform the hypothesized relationships and the selection of factors in our study, providing a robust theoretical basis for examining how perceptions and social norms influence individual behaviors during the COVID-19 pandemic.

The HBM posits that personal beliefs about a disease and its perceived threats influence health-related behaviors. This model supports the inclusion of affective and cognitive risk perceptions, positing that individuals who perceive a higher threat are more likely to engage in protective behaviors.⁵⁸ This is crucial for understanding the motivations behind social distancing attitudes and subsequent behaviors during a pandemic. The TPB extends this framework by incorporating the role of subjective norms, suggesting that social pressures significantly predict behavior. This theory explains the inclusion of subjective norms, arguing that individuals’ actions during a pandemic are influenced not just by personal threat assessments but also by the expectations and behaviors of others around them. The integration of these theories helps to explain why individuals may respond differently to similar health crises based on their perceptions and social influences. Figure 1 illustrates the research model.

Figure 1.

Conceptual framework.

Affective risk perception

Affective risk perception is characterized by individuals’ emotional responses toward the threat posed by the virus, including feelings of concern and anxiety about being infected or having loved ones infected.⁵⁹ Research indicates that when individuals perceive a higher emotional risk regarding health crises, they are more likely to support and adhere to preventive measures.^14,60 This is grounded in the notion that emotional responses can significantly influence decision-making processes and behavioral intentions.⁶¹ Moreover, studies have shown that heightened risk perceptions can motivate adherence to recommended protective behaviors during pandemics, aligning with protective health behavioral theories.^62–64 Thus, this study suggests the following hypotheses.

H1. Affective risk perception is positively associated with social distancing attitude.

Cognitive risk perception

Cognitive risk perception involves the subjective assessment of the likelihood of experiencing a negative event, such as contracting COVID-19, and its potential severity.⁶⁵ This perception plays a crucial role in shaping individuals’ attitudes towards health behaviors.^15,66 The TPB suggests that beliefs about the likelihood and consequences of risks are central to the formation of attitudes toward behaviors, such as social distancing.⁹ Further, individuals who perceive a higher cognitive risk are more likely to engage in behaviors they believe will mitigate those risks, thus fostering a positive attitude towards measures like social distancing.^67–69 Therefore, this study suggests the following hypotheses.

H2. Cognitive risk perception is positively associated with social distancing attitude.

Social distancing attitude

Social distancing attitude refers to the favorable or unfavorable evaluations that individuals hold about maintaining physical distance to mitigate the spread of COVID-19.^7,70 This positive attitude can have unintended psychological and behavioral consequences. As individuals seek alternative means to connect and entertain themselves while isolating, their usage of smartphones intensifies.⁷¹ This increase in smartphone usage emerges as a coping mechanism to combat the isolation imposed by social distancing. Thus, the study presents these hypotheses, anticipating that a positive attitude towards social distancing will be directly linked to both increased symptoms of cabin fever and greater smartphone usage.

H3. Social distancing attitude is positively associated with cabin fever syndrome.

Cabin fever syndrome

Cabin fever syndrome, characterized by feelings of irritability and restlessness due to prolonged confinement, may lead individuals to seek digital forms of engagement.^17,72 As people experience limited physical social interactions and increased isolation, smartphones become a critical tool for connection and entertainment. The increase in smartphone usage under these conditions serves as a coping mechanism to alleviate feelings of confinement and maintain social connections, thus enhancing their usage intensity.^73–75 Consequently, the hypothesis proposes that the psychological strain induced by cabin fever syndrome is likely to escalate individuals’ reliance on their smartphones, reflecting an increased intensity in their usage.

H4. Cabin fever syndrome is positively associated with smartphone usage intensity.

Subjective norms

Subjective norms involve the perceived social expectations regarding behavior and are shaped by the influence of significant others.⁹ In the context of COVID-19, these norms particularly relate to the adherence to protective measures like social distancing and mask-wearing, which are promoted by key social contacts. Studies have indicated that when individuals perceive strong social endorsement for specific behaviors, they are more likely to engage in those behaviors themselves.⁷⁶ During the pandemic, this can extend to smartphone usage as smartphones become crucial for communicating with others, obtaining health updates, and maintaining social connections while physical interactions are restricted. Thus, robust subjective norms supporting COVID-19 protective measures are expected to enhance smartphone usage intensity. Consequently, this study suggests the following hypotheses.

H5. Subjective norms are positively associated with smartphone usage intensity.

Methods

This study employs a quantitative, cross-sectional research design, which is well-suited for examining the associations between psychological variables and smartphone usage among university students during a specific period. Cross-sectional studies are ideal for capturing a snapshot of data at one point in time, which allows for the analysis of relationships and correlations between variables without the extended duration required for longitudinal studies.⁷⁷ This method is particularly effective for exploring the immediate impacts of the COVID-19 pandemic on behavioral responses across different cultural contexts, providing valuable insights into how students in varied geographical locations adapt to similar global challenges.

Measurement

The questionnaire for this study was meticulously designed to capture nuanced information about the constructs under investigation, namely, affective risk perception, cognitive risk perception, cabin fever syndrome, subjective norms, and smartphone usage intensity. It was comprised of questions inspired by previous studies, adapted for the specific context of this research, and carefully selected to provide comprehensive data.

All measurement indicators used in this study were carefully selected from established studies to ensure relevance and accuracy. Affective and cognitive risk perceptions, essential for understanding individual responses to COVID-19, were adapted from previous research,⁷⁸ with items concerning personal and familial risk concerns further contextualized for this study. Social distancing attitude indicators were derived from established behavioral models,⁹ focusing on the influence of personal beliefs on pandemic control practices. Cabin fever syndrome, captured by relevant psychological scales,¹⁷ measured the psychological impact of prolonged home confinement, including feelings of isolation and increased food cravings. Additionally, subjective norms, also adopted from behavioral models,⁹ included items reflecting societal expectations and individual self-efficacy regarding participation in preventative health behaviors. Smartphone usage intensity, a critical variable, was measured through indicators assessing changes in smartphone interaction during lockdown.¹⁷ Table 1 provides a detailed list of these measurement items along with their respective sources.

Table 1.

Measurement items.

Construct	Item	Description	Reference
Affective risk perception	ARP1	I'm concerned about catching COVID-19	Brug et al.⁷⁸
Affective risk perception	ARP2	I'm anxious about my family members getting COVID-19	Brug et al.⁷⁸
Cognitive risk perception	CRP1	It's very likely that one could get COVID-19 compared to other diseases	Brug et al.⁷⁸
Cognitive risk perception	CRP2	It's very likely to die from COVID-19	Brug et al.⁷⁸
Social distancing attitude	SDA1	I believe that practicing social distancing will positively influence COVID-19 control	Ajzen⁹
	SDA2	Social distancing is helpful in managing patient care
	SDA3	I find the practice of social distancing interesting for controlling COVID-19
Cabin fever syndrome	CFS1	I experience food cravings while at home during social distancing/lockdown	Chakraborty et al.¹⁷
Cabin fever syndrome	CFS2	I feel socially isolated while staying at home during social distancing/lockdown	Chakraborty et al.¹⁷
Subjective norms	SNO1	The significant people in my life support my participation in social actions to prevent COVID-19	Ajzen⁹
	SNO2	The significant people in my life understand my involvement in social actions to curb COVID-19
	SNO3	The significant people in my life are in agreement with my involvement in social actions to mitigate COVID-19
Smartphone usage intensity	SPI1	I use my smartphone more than usual during social distancing/lockdown	Chakraborty et al.¹⁷
	SPI2	I log into my smartphone more often during social distancing/lockdown
	SPI3	I am utilizing various functions on my smartphone more than usual during social distancing/lockdown

In this study, we carefully adapted measurement items from established scales, adjusting only the context to align with our specific research objectives while preserving the original scale structures. During the validity analysis phase, items with factor loadings below the acceptable threshold were excluded to ensure robust psychometric properties. Specifically, two items from both the affective and cognitive risk perception scales, and two from the cabin fever syndrome scale, were removed. Additionally, from the smartphone usage intensity scale,¹⁷ we excluded the item related to using social networking sites for reading news and attending social gatherings due to its limited scope, which did not align with the broader aims of our study.

The questions were formulated in a way that ensures readability and understandability, and used a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree) to measure the respondents’ attitudes. The questionnaire was initially developed in Korean, considering the local nuances and colloquialisms, and then translated into Vietnamese by a professional translator to maintain the essence of the questions. To ensure the accuracy of the translation, it underwent a back-translation process, where another translator translated the Vietnamese version back into Korean. Any discrepancies identified were rectified.

Subject and data collection

The focus of this study was on current smartphone users, specifically, university students residing in two Asian countries, South Korea and Vietnam. South Korea is regarded as a developed country⁷⁹ while Vietnam is experiencing rapid economic growth.⁸⁰ Smartphone usage can potentially alleviate cabin fever by providing numerous activities such as connecting with friends, streaming videos, and engaging in social networking across expansive areas. The inclusion criteria required participants to be university students who own smartphones, while the exclusion criteria ruled out non-students and those without access to smartphones.

The questionnaire was disseminated through a Google survey, which was active from 11 June 2021 to 27 September 2021. The data collection was facilitated through Google Surveys and was conducted with the help of university professors in both countries, ensuring a broad and diverse sample. The analysis of the survey data was carried out in South Korea by the author. This study adhered to the Declaration of Helsinki guidelines. The nature of the research and the type of data collected did not involve sensitive information, as defined under Article 23 of the Personal Information Protection Act of Korea; Consequently, based on the guidelines of the author's affiliated institution (HJ Institute of Technology and Management), this study was exempt from requiring ethical approval. All participants were informed about the purpose of the study, and their rights as participants, including the right to withdraw at any time without consequences, and they were assured that their responses would be anonymized and used only for the purposes of this research. Informed consent was obtained in written form from all individual participants included in the study. Consent to publish the findings was also obtained from the participants. Demographic information, such as nationality, gender, and age, was collected as part of the questionnaire. In the demographic section, participants were asked to provide these details.

After removing any incomplete responses, a total of 314 responses were analyzed. Table 2 outlines the demographic characteristics of the 314 subjects in the sample. It details nationality, with 22.6% from South Korea and 77.4% from Vietnam. Gender distribution shows 39.2% male and 60.8% female participants. Age-wise, 14.6% are 19 or younger, 81.5% fall within the 20–23 range, and 3.8% are 24 or older. We acknowledge the uneven distribution between South Korean and Vietnamese participants, and between the age groups, which may have influenced the results of the study. Future analyses could consider the implications of these disparities in demographic representation.

Table 2.

Sample characteristics.

Demographics	Item	Subjects (N = 314)
Demographics	Item	Frequency	Percentage
Nationality	South Korea	71	22.6%
Nationality	Vietnam	243	77.4%
Gender	Male	123	39.2%
Gender	Female	191	60.8%
Age (minimum: 18 maximum: 47 mean: 20.720 SD: 1.983)	19 or younger	46	14.6%
	20–23	256	81.5%
	24 or older	12	3.8%

Results

This study employs structural equation modeling (SEM) for data analysis. The evaluation of the models was done using partial least squares (PLS).⁸¹ The reason for choosing PLS in this article was due to the untested nature of the suggested framework in previous studies. For the analysis of the structural model, the Smart PLS software (4.1.0.2) was used.

Measurement instrument

The current research estimated the composite reliability (CR) and Cronbach's alpha value to evaluate reliability. As shown in Table 3, the CR value and Cronbach's alpha exceed 0.7, presenting that the model is satisfied. This work calculated the average variance extracted (AVE) and factor loading to assess convergent validity. In order for the latent variables to describe more than half of the variability of their items, AVE must be larger than 0.50.^82,83 Also, factor loadings should exceed 0.70.⁸² AVE and loadings are over the acceptable limit, indicating an adequate level of convergence.

Table 3.

Descriptive statistics, and test results of reliability and convergent validity.

Construct	Item	Mean	St Dev.	Factor loading	Cronbach's alpha	CR (rho_a)	CR (rho_c)	average variance extracted (AVE)
Affective risk perception	ARP1	5.226	1.742	0.951	0.876	0.889	0.941	0.889
Affective risk perception	ARP2	5.541	1.584	0.935	0.876	0.889	0.941	0.889
Cognitive risk perception	CRP3	5.561	1.722	0.930	0.793	0.821	0.905	0.827
Cognitive risk perception	CRP4	5.169	1.856	0.888	0.793	0.821	0.905	0.827
Social distancing attitude	SDA1	5.611	1.513	0.874	0.819	0.853	0.891	0.731
	SDA2	5.637	1.424	0.888
	SDA3	5.347	1.565	0.801
Subjective norms	SNO1	5.844	1.323	0.843	0.902	0.924	0.939	0.838
	SNO2	6.115	1.148	0.953
	SNO3	6.131	1.134	0.946
Cabin fever syndrome	CFS3	5.172	1.767	0.907	0.783	0.783	0.902	0.822
Cabin fever syndrome	CFS4	4.908	1.889	0.906	0.783	0.783	0.902	0.822
Smartphone usage intensity	SPI1	5.627	1.480	0.914	0.883	0.888	0.927	0.810
	SPI2	5.561	1.561	0.900
	SPI3	5.535	1.533	0.886

The discriminant validity of the constructs in this study was assessed using both the Fornell–Larcker criterion and the Heterotrait–Monotrait ratio (HTMT) as indicated in Tables 4 and 5, respectively. Discriminant validity is confirmed when the square root of the AVE for each construct (shown on the diagonal in Table 4) is greater than the correlations between that construct and all other constructs in the model.⁸⁴ The data illustrate that all constructs meet this criterion, as the diagonal values are consistently higher than the corresponding off-diagonal values in their respective rows and columns.

Table 4.

Fornell-Larcker criterion (test results of discriminant validity).

Construct	1	2	3	4	5	6
1. Affective Risk Perception	0.943
2. Cognitive Risk Perception	0.349***	0.909
3. Social Distancing Attitude	0.297***	0.382***	0.855
4. Cabin Fever Syndrome	0.062	0.354***	0.360***	0.907
5. Subjective Norms	0.271***	0.326***	0.533***	0.154**	0.915
6. Smartphone Usage Intensity	0.253***	0.280***	0.415***	0.490***	0.348***	0.900

Note: Diagonal entries are the square root of the average variance extracted (AVE).

Table 5.

Heterotrait–Monotrait ratio (HTMT) matrix (test results of discriminant validity).

Construct	1	2	3	4	5
1. Affective risk perception
2. Cognitive risk perception	0.412
3. Social distancing attitude	0.341	0.459
4. Cabin fever syndrome	0.075	0.456	0.435
5. Subjective norms	0.300	0.366	0.618	0.179
6. Smartphone usage intensity	0.292	0.329	0.488	0.587	0.386

Furthermore, the HTMT values, as shown in Table 5, offer additional support for discriminant validity. HTMT ratio is a criterion for assessing discriminant validity in structural equation models. It measures the ratio of the averages of the heterotrait-heteromethod correlations to the geometric mean of the monotrait-heteromethod correlations for any two constructs. A value of HTMT < 0.90 generally suggests adequate discriminant validity between the constructs, indicating that they are sufficiently distinct from each other.⁸⁵ The HTMT ratios in this study are well below these thresholds, underscoring strong discriminant validity across all constructs.

Structural model

The current study performed a bootstrap method with 5000 resamples to confirm the significance of the relationships among the constructs.⁸² The structural model accounted for 31.6% of the variance in smartphone usage intensity. The outcomes are detailed in Table 6.

Table 6.

Structural equation modeling (SEM) results.

H	Predictor	Outcome	β	t	p	Result
H1	Affective risk perception	Social distancing attitude	0.187	3.125	0.001	Supported
H2	Cognitive risk perception	Social distancing attitude	0.317	5.485	0.000	Supported
H3	Social distancing attitude	Cabin fever syndrome	0.360	6.440	0.000	Supported
H4	Cabin fever syndrome	Smartphone usage intensity	0.447	9.642	0.000	Supported
H5	Subjective norms	Smartphone usage intensity	0.279	5.428	0.000	Supported

Multi-group analysis (MGA)

In our study, we performed an MGA to compare the effects of social and psychological factors on behaviors between South Korean and Vietnamese respondents (Table 7). Key findings from this analysis revealed significant differences in several pathways. Firstly, the association between affective risk perception and social distancing attitude was significantly stronger in the Korean sample (β = 0.423, p < 0.001) compared to the Vietnamese sample (β = 0.134, p < 0.05), with a difference of β = 0.289 (p < 0.05). Secondly, there was a notable contrast, with a negative association between social distancing attitude to cabin fever syndrome in Korea (β = −0.100) and a strong positive association in Vietnam (β = 0.497, p < 0.001). Lastly, the relationship between cabin fever syndrome and smartphone usage intensity was significantly stronger in Korea (β = 0.551, p < 0.001) than in Vietnam (β = 0.3, p < 0.001).

Table 7.

Multi-group analysis (MGA) results.

H	Predictor	Outcome	β_Korea	β_Vietnam	β_Korea–β_Vietnam
H1	Affective risk perception	Social distancing attitude	0.423***	0.134*	0.289*
H2	Cognitive risk perception	Social distancing attitude	0.228*	0.244***	−0.015
H3	Social distancing attitude	Cabin fever syndrome	−0.100	0.497***	−0.597***
H4	Cabin fever syndrome	Smartphone usage intensity	0.551***	0.300***	0.251**
H5	Subjective norms	Smartphone usage intensity	0.197	0.372***	−0.175

Note: *: p < 0.05; **: p < 0.01; ***: p < 0.001.

These significant differences underscore the cultural variations in how psychological responses and social attitudes towards the pandemic influence behaviors, suggesting the need for region-specific strategies in public health messaging and technology use interventions.

Discussion

The results from the SEM provided several insights into the factors influencing smartphone usage intensity during the COVID-19 pandemic.

For H1, the positive relationship between affective risk perception and social distancing attitude was significant, with a coefficient (β = 0.187) and a p-value (p = 0.001) that suggests a modest yet statistically significant influence. This finding is in line with previous research that suggests affective components of risk perception, such as fear and anxiety about health, can motivate people to engage in preventive behaviors.⁵⁹ These results support the notion that emotional responses to the pandemic can play a crucial role in shaping attitudes toward social distancing, highlighting the impact of affective risk on behavior regulation during public health crises.⁶¹ From a theoretical perspective, this reinforces the role of affect in the health belief model as a critical driver of behavior adaptation in crisis conditions. Practically, it underscores the importance of addressing emotional components in communication strategies to enhance public adherence to health guidelines.

H2 examined the impact of cognitive risk perception on social distancing attitude. The results indicated a relatively strong positive correlation (β = 0.317) that was highly significant (p < 0.001). This finding suggests that cognitive perceptions of the risk associated with COVID-19 significantly enhance the propensity to adhere to social distancing guidelines, likely due to an increased awareness of the dangers of contagion in various settings. This supports previous research that demonstrated that cognitive assessments of environmental risk factors critically influence health-related behaviors.⁶² This confirms the theory that rational appraisals of risk are pivotal in shaping health behaviors, particularly in pandemic contexts. On a practical level, this suggests that public health communications should focus on clear, factual information about the risks of COVID-19 to foster protective behaviors among the population.

The influence of social distancing attitude on cabin fever syndrome (H3) was notably strong and significant (β = 0.360, p < 0.001), indicating that more favorable attitudes towards social distancing are strongly linked with higher reports of cabin fever. This finding suggests that while social distancing is adopted as a protective measure, it can also lead to significant psychological stress and discomfort. This result complements findings that prolonged isolation can exacerbate feelings of restlessness and distress.⁸⁶ Theoretically, this introduces an important consideration for behavioral health models incorporating the cost of compliance in long-term health behavior strategies. In practice, it suggests a need for mental health support mechanisms for individuals strictly practicing social distancing, aiming to mitigate the adverse effects of isolation.

H4 demonstrated a robust positive relationship between cabin fever syndrome and smartphone usage intensity (β = 0.447, p < 0.000). This significant result suggests that feelings of irritability and restlessness associated with prolonged confinement can lead to a substantial increase in smartphone use, possibly as a coping mechanism to alleviate psychological discomfort or to seek social connection remotely. This aligns with findings that isolation can drive individuals toward increased use of digital communication to mitigate feelings of loneliness.⁷² The strong correlation here underscores the impact of cabin fever on behavior, suggesting that as discomfort from confinement increases, so does reliance on smartphones for mental escape and social interaction.⁷³ It theoretically contributes to discussions on coping mechanisms during isolation and their reliance on technology, offering practical insights into the need for designing user-centric digital tools that can address the specific needs of isolated individuals during extensive lockdowns.

Lastly, H5 highlighted a significant relationship between subjective norms and smartphone usage intensity (β = 0.279, p < 0.000), supporting the hypothesis that social influences strongly affect digital communication behaviors during the pandemic. This supports the TPB, which posits that social pressures can significantly dictate individual actions.⁹ This result is consistent with findings that demonstrated the influence of peer norms on technology adoption and usage.⁸⁷ This result enriches the understanding of how social norms influence technology use, suggesting that normative pressures during pandemics can increase reliance on digital tools for maintaining social connections. Practically, this implies that technology interventions during health crises should consider the social drivers of technology adoption and usage.

The MGA results offer intriguing insights into how social and psychological factors influence behaviors differently across cultural contexts, specifically comparing South Korean and Vietnamese responses.

In South Korea, affective risk perception has a significantly stronger impact on social distancing attitude (β = 0.423) compared to Vietnam (β = 0.134), indicating a greater sensitivity to emotional responses towards COVID-19 in Korean participants. This variance suggests that Koreans may be more influenced by their emotional evaluations of the pandemic risk than the Vietnamese. The finding aligns with studies suggesting cultural differences in emotional responses to health crises.⁵⁹ Theoretically, this supports the notion that emotional engagement in health behaviors can vary significantly by culture, emphasizing the need for culturally adapted health communication strategies. Practically, it suggests that interventions in Korea might benefit from targeting emotional appeals to enhance public health compliance.

The impact of cognitive risk perception on social distancing attitude is similar in both countries (β = 0.228 in Korea and β = 0.244 in Vietnam), suggesting a universally strong influence of rational risk assessments on behavior across different cultural backgrounds. This supports previous findings that cognitive evaluations of health risks consistently predict protective behaviors regardless of cultural context.⁶⁵ Theoretically, this highlights the universal applicability of cognitive models in public health strategies. Practically, it underscores the importance of providing clear, accurate information about COVID-19 risks to the public in both countries.

Interestingly, the relationship between social distancing attitude and cabin fever syndrome diverges significantly between the countries, with a negative association in Korea (β = −0.100) and a strong positive one in Vietnam (β = 0.497). This suggests that while Vietnamese participants may experience increased psychological distress with more stringent adherence to social distancing, Korean participants do not show such a pattern. This divergence could reflect different societal norms or levels of support systems available in coping with isolation.⁸⁶ Theoretically, this underscores the complexity of psychological outcomes resulting from health behaviors. Practically, it indicates a need for targeted mental health interventions in Vietnam to address the psychological impact of prolonged social distancing.

Cabin fever syndrome shows a more substantial effect on smartphone usage intensity in Korea (β = 0.551) than in Vietnam (β = 0.300), suggesting that Koreans may turn to their smartphones more frequently as a coping mechanism for isolation-related distress. This aligns with research that links increased technology use with attempts to mitigate feelings of loneliness and isolation.⁷² Theoretically, this supports the notion of technology as a significant coping tool in mental health management. Practically, it points to the potential benefits of enhancing digital resources to support individuals experiencing isolation during pandemics in Korea.

Finally, the influence of subjective norms on smartphone usage intensity is more pronounced in Vietnam (β = 0.372) than in Korea (β = 0.197), highlighting the stronger role of social influences in Vietnamese smartphone behavior. This is consistent with the TPB, which posits that social norms significantly shape individual actions.⁹ Theoretically, this finding enriches our understanding of the social underpinnings of technology use. Practically, this suggests that Vietnamese smartphone usage interventions might focus more on leveraging social influence strategies.

This study, while insightful, has limitations that could be addressed in future research. It primarily focuses on university students, potentially limiting the generalizability of the findings to other demographics. Future studies could explore similar dynamics in different age groups or in varied professional settings to broaden understanding. Additionally, the cross-sectional nature of this research restricts its ability to capture changes over time. Longitudinal studies would provide a deeper understanding of how attitudes and behaviors evolve throughout the course of a pandemic. Further, incorporating qualitative methods could enrich the quantitative findings, offering more nuanced insights into individual experiences and motivations behind smartphone usage and social distancing practices during health crises. Finally, the significant discrepancies in the number of South Korean versus Vietnamese participants and the varied age groups present a limitation in this study. Future research should aim to achieve a more balanced sampling to ensure that findings are generalizable across different demographic groups.

Conclusion

In conclusion, this study provides valuable insights into the factors influencing smartphone usage intensity during the COVID-19 pandemic, particularly among university students in South Korea and Vietnam. The results reveal that both affective and cognitive risk perceptions significantly shape social distancing attitudes, which in turn affect behaviors like smartphone usage. Additionally, cabin fever syndrome emerged as a strong predictor of increased smartphone usage, highlighting the psychological toll of prolonged isolation. The differences found between South Korean and Vietnamese participants further emphasize the importance of cultural context in shaping responses to public health crises. While the findings contribute to understanding the psychological and behavioral impacts of the pandemic, they also underscore the need for targeted mental health interventions and culturally sensitive communication strategies. However, this study is not without limitations. The sample consisted primarily of university students, which may limit the generalizability of the results to other demographic groups. Additionally, the cross-sectional design prevents capturing changes in attitudes and behaviors over time. Future research should consider longitudinal studies and include a broader demographic to expand the applicability of the findings. Despite these limitations, the study offers important theoretical and practical implications for managing digital engagement and mental well-being during prolonged health crises.

Footnotes

Contributorship

HJ was responsible for the original article of this study. DS was responsible for the revision.

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.

Ethical approval

This study adhered to the Declaration of Helsinki guidelines. The nature of the research and the type of data collected did not involve sensitive information, as defined under Article 23 of the Personal Information Protection Act of Korea; Consequently, based on the guidelines of the author's affiliated institution (HJ Institute of Technology and Management), this study was exempt from requiring ethical approval.

Funding

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

ORCID iD

Hyeon Jo

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Influence of social and psychological factors on smartphone usage during the COVID-19 pandemic

Abstract

Objective

Methods

Results

Conclusion

Keywords

Introduction

Theoretical background

Theory of planned behavior

Smartphone

Risk perception

Cabin fever syndrome

Conceptual model and hypotheses

Affective risk perception

Cognitive risk perception

Social distancing attitude

Cabin fever syndrome

Subjective norms

Methods

Measurement

Subject and data collection

Results

Measurement instrument

Structural model

Multi-group analysis (MGA)

Discussion

Conclusion

Footnotes

Contributorship

Declaration of conflicting interests

Ethical approval

Funding

ORCID iD

References