Pa ge 1 Pa ge 12 1 American Journal of Human Psychology (AJHP) Boredom Proneness and Social Media Usage of College Students Rose Ann L. Bautista1*, Mark Luiz S. Demanawa1, Junna Liza D. Pedrigosa1, Claire Lynn B. Culajara1 Volume 2 Issue 1, Year 2024 ISSN: 2994-8878 (Online) DOI: https://doi.org/10.54536/ajhp.v2i1.2825 https://journals.e-palli.com/home/index.php/ajhp Article Information ABSTRACT Received: May 31, 2024 Accepted: June 29, 2024 Published: July 02, 2024 This descriptive-predictive study aimed to determine the correlation between the social media usage of college students and their boredom proneness in a University in Davao region. A total of 350 participants were chosen to participate in this study using simple random sampling techniques. A 22-item multidimensional state boredom questionnaire and 21-item social networking usage served as an instrument wherein the Jamovi was used for data analysis. The mean and standard deviation were utilized to test the level of both variables, and results showed that the level of social media usage and boredom proneness among students is high. The Shapiro-Wilk test was utilized to test the normality; it revealed that the data is not normally distributed. The Spearman rho correlation coefficient test revealed a significant relationship between social media usage and students’ boredom proneness, with a p-value of .001, indicating a strong positive correlation. Meanwhile, the result of Linear Regression showed 71% of the variance is explained by the three significant predictors contributing to the students’ social media usage which are Time Perception, Inattention and Disengagement. The study’s findings indicate that increased use of social media is associated with higher levels of boredom. In this case, school administration, local health authorities, and school counseling and guidance offices should enhance the implementation of policies regarding the responsible use of social media, integrate the findings into various programs, and increase public awareness of the effects of excessive social media use on people’s mental health and academic performance. Lastly, researchers should consider extending this topic with a quantitative approach and investigate the possible mediating factors between boredom and social media usage. Keywords Boredom, Boredom Proneness, Correlation, Social Media Usage, College Students 1 University of Mindanao Digos College, Digos City, Philippines * Corresponding author’s e-mail: r.bautista.60080.dc@umindanao.edu.ph INTRODUCTION Social media use has experienced rapid development and has become an important part of people’s day to day lives by connecting people, facilitating communication, sharing information, and even promoting businesses and causes. It has become a central platform for social interaction, networking, and staying informed about current events. Additionally, it provides opportunities for self-expression, creativity, and learning. It has become an essential tool for communication, collaboration, and community-building in today’s interconnected world (Ohara, 2023). The term “social media” describes websites and online platforms that enable users to produce, share, and exchange content with other social media users. According to Dixon (2023), in 2022, internet users around the world spent an average of 151 minutes of social media engagement per day, up slightly from 147 minutes the previous year. Currently, the Philippines is leading the way in daily social media usage, with people spending an average of 3 hours and 53 minutes online each day. On the other hand, Americans spend an average of 2 hours and 3 minutes a day on social media. In today’s era marked by technological progress, social media has emerged as an integral aspect of individuals’ everyday lives (Elrashidy, 2023). It has been easily accessible to almost everyone due to the availability of gadgets, specifically smartphones (Yang et al., 2021). In the modern era, characterized by the prevalence of gadgets and smartphones, communication has become simple. The emergence of social media has facilitated connectivity, resulting in people being connected to their friends and millions of other people (Radu, 2021). In the year 2022, 92.05 million individuals utilized social media in the country Philippines indicating 82.4% of the country’s population. But it is important to bear in mind that these statistics might not be accurate with the represented total number of social media users since some individuals possibly own several accounts on various platforms (Kemp, 2022). In 2014, social media usage was already widespread with Facebook becoming the most popular social media site. In addition, Instagram, Twitter, Pinterest, and LinkedIn saw a significant increase over the past year (Duggan, 2015). Subsequently, in 2024, social media will likely become even more pervasive and diversified. It was found that the total percentage of the world’s population who uses social media is 62.3%. With the rise of new platforms such as TikTok, Snapchat, and various messaging apps, people are spending more time than ever on social media Chaffey (2024). Furthermore, social media has negative consequences but it can be a powerful tool when used wisely; it can be a medium for staying connected with others, spreading information, encouraging important causes and beneficial goals (Lang, 2023). In short, social media isn’t all bad at all like most good things, it lies on moderate use. Pa ge 12 2 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 2(1) 121-131, 2024 To reiterate, social media use can be beneficial for users as a means for gathering information, communication, problem-solving, etc. (Nisar et al., 2019; Dimacangun & Guillena, 2023). However, problems have arisen in the numerous people with high levels of social media use. One of which has been found in existing local studies by (Gumasing et al., 2023; Conquilla et al., 2021) regarding student’s academic performance and their engagement to class (Gumasing et al, 2022). What’s more, Pawar & Shah’s study in 2019 showed results suggesting that aspects such as self-esteem is negatively influenced by social media use. A 2009 study by Mann and Robinson found that 30% of university students said they were bored in most of their lectures, while the majority of students (59%) said that almost half of their lectures were boring. Furthermore, according to Finkielstein (2020), between 40% and 59% of college students said they were bored. According to Sharp et al. (2019), most students experience high levels of boredom, which has been associated in recent studies to negatively impact undergraduate students’ performance, participation, and achievement (Sharp et al., 2020). Uncertainty has arisen regarding the potential effects of social media usage on people’s mental health and overall wellbeing (Yang et al. 2021). Feelings of isolation and loneliness are among the downsides of using social media (Khan, 2021). Consequently, smartphones have become the extensions of individual’s lives and it is not rare to see people, especially students engaged in a vast world of social media. However, behind the screens, a more profound foe is hidden - the prevailing feeling of boredom. This phenomenon has been the subject of numerous studies as one of the most important emotional problems. It has been associated with a negative psychological state that makes people seem drained and uninterested. A common experience that many people have at various points in their lives is boredom. According to Lv and Wang (2023), boredom is the incapacity of an individual to experience the proper levels of satisfaction in circumstances where there are minimal levels of both internal and external stimuli. This results in low arousal, persistent attention issues, and motivation depletion. Although it’s generally thought of as a negative emotion, boredom can actually have positive and negative effects on people. However, since boredom forces people to think outside the box and consider fresh concepts or viewpoints, it can also be a source of creativity and innovation. An individual can be inspired by boredom to try new things, find new hobbies, or push oneself to study and develop. For instance, the boredom brought on by isolation and lockdowns during the COVID-19 pandemic helped many in exploring new ideas and pursuing new interests. However, boredom can lead to harmful outcomes like productivity loss, poor mental health, and even physical health issues (Morse et al., 2021). Additionally, boredom seems to encourage individuals to make riskier behaviors or engage in riskier behaviors (Igou, 2020). For example, extremely bored people frequently take part in particular online activities, such as social media use, video gaming, and streaming movies and TV shows, as these activities may help them manage the stress brought on by these mood states (Blasi et al., 2019; Saiful Islam et al., 2020). The tendency to use these kinds of actions as coping mechanisms during crises, like the current pandemic, can lead to patterns that are hard to break (King et al., 2020). The COVID-19 pandemic has led to a rise in internet usage as a coping mechanism for psychological stressors and mood swings, such as boredom, which has been recognized as a significant negative psychological stressor (Brooks et al., 2020). Moreover, boredom, an aspect that the researchers seek to investigate regarding their correlation with social media use, is already in the body of knowledge thanks to the existing studies tackling it. Numerous studies have shown a strong connection between smartphone use and boredom among college students. In an instance where these individuals were asked why they use social media, they often identify that the primary motivator is to alleviate boredom (Barkley & Lepp, 2021d). Naturally then, the positive association between social media use and numerous self-report measures of boredom has been shown in quantitative research (Barkley & Lepp, 2021d; Aquino and Kimong, 2022; Rast et al., 2021; Pawar & Shah, 2019b; Orsolini et al., 2023; Wang et al., 2020; Whelan et al., 2020b; Cañete et al., 2022). However, there are few published studies that do not reveal a positive correlation between social media use and boredom proneness, namely the studies of Allahverdi in 2022 and by Manawatao & Sasan in 2015. Despite the numerous studies covering the two variables, social media usage and boredom proneness, there is an existing literature gap in finding which subscale of boredom proneness is the most significant predictor of social media use. After scouring the existing studies, only the research of Cañete et al., 2022 has presented which subscale of the boredom proneness scale, which was the Disengagement subscale. Hence, the researcher has deemed it necessary to investigate which scale of boredom proneness is the most significant predictor of social media use. Also, there are limited studies that investigated the correlation between social media use and boredom in the national level Philippines, thus, conducting this may help contribute to the body of information. The study’s findings on the correlation between social media usage and boredom proneness have an essential implication for society. In addition, the study emphasizes the potential impacts of the problematic use of social media in the boredom of the individuals, it emphasizes the need for individuals to use the social media logically and in a coordinated manner to avoid excessive boredom. The study’s findings can be applied to real-world situations by informing the strategies aimed at promoting healthier alternatives and limited social media use in contrast to the high levels of social media use that may cause problems. The beneficiaries of the study include people who use social media, as well as mental health professionals, and educators who can apply the results to develop guidelines and educational programs to promote awareness of moderated social media use. Pa ge 12 3 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 2(1) 121-131, 2024 Research Objectives This study generally aimed to determine the correlation between students’ social media usage and their boredom proneness. Specifically, it sought to find answers to the following objectives: 1. To assess the level of Social Media Usage in terms of: a. Academic b. Socialization c. Entertainment d. Informativeness e. Constraints 2. To assess the level of Boredom Proneness in terms of: a. Time perception b. Inattention c. High arousal d. Disengagement e. Internalizing aspects 3. To identify if there is a significant relationship between Social Media Usage and Boredom Proneness among college students. 4. To identify which specific domains under Social Media Usage that may influence Boredom Proneness among college students. Conceptual Framework Figure 1 shows the conceptual framework displaying the correlation between social media usage and boredom proneness. Social media usage is the dependent variable with five indicators, namely: academic, socialization, entertainment, informativeness, and constraints. Social media plays a crucial role in modern society as it offers a constant stream of content and interaction, which can potentially alleviate boredom by providing entertainment, information, and social engagement. Therefore, social media’s role in providing stimulation and opportunities for engagement helps combat boredom proneness in individuals’ daily lives. The dependent variable in this study is Boredom Proneness which has five indicators: time perception, inattention, high arousal, disengagement, and internalizing aspects. Mugon (2020), defined boredom proneness as the characterization of recurring extreme feelings of boredom. He also claims that being prone to boredom is a persistent individual distinct attribute linked to an array of adverse effects. In addition, related studies found that people who were likely to be bored used social media more frequently. The conceptual paradigm displayed study variables which are social media usage (DV) and boredom proneness (IV). Figure 1: Schematic Diagram Showing the Variables of the Study Source (author) MATERIALS AND METHODS Research Respondents The respondents of the study were college students from every department and academic level during the 2023- 2024 school year at a university in the Davao Region. The researchers utilized a simple random sampling method for their study. According to Thomas (2023), random sampling is the most suitable procedure to minimize the impact of possible confounding factors, which guarantees a high internal validity. Moreover, simple random sampling possesses a high level of external validity when the sample size is sufficiently large. The reason for this is because it indicates the characteristics of a large population. The respondents consist of 350 college students from a population of 3,868, calculated and validated using Slovin’s Formula. All the respondents’ information will be held or neglected based on their preferences. Table 1: Characteristics of Respondents (n=350) Profile f % Gender Female 195 56 Male 130 42 LGBTQ+ 5 3 Year Level First Year 134 34 Second Year 142 41 Pa ge 12 4 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 2(1) 121-131, 2024 Research Instrument The instruments that were utilized in this study to gather data are questionnaires through a survey adopted from the Social Networking Usage Scale and the Multidimensional State Boredom Scale (MSBS). The Social Networking Usage Scale by Gupta and Bashir (2018) is a 24-item survey with five indicators: Academic, Socialization, Entertainment, Informativeness, and Constraints. The questionnaire was developed as an attempt to clarify the construct of social networking usage for Indian students and has been shown to have acceptable validity in higher education. It was further validated for use in colleges and universities using random groups from 6 universities in India. The researchers wrote a permission letter to the researchers who formulated the questionnaires, Social Networking Usage and Multidimensional State Boredom Scale before conducting the survey. The questionnaire was tested to measure its reliability, resulting in Cronbach’s a (α=.830). Furthermore, the researchers conducted a pilot test to assess the reliability and validity of the questionnaire to test if the instrument was suitable to be used in the Philippine setting, particularly in the university in Davao Region. For Social Networking Usage Scale, all of the questions tested using Scale Reliability Analysis in JAMOVI are valid and indicated an almost perfect reliability with a total value of 0.905 using Cronbach’s Alpha. In addition, the Multidimensional State Boredom Scale from the study of Spoto et al. (2021) is a 23- item questionnaire with five factors: time perception, disengagement, inattention, high arousal, and low arousal. The Multidimensional State Boredom Scale, which has been demonstrated to have sufficient validity in adolescents, was to be validated in Italy using a cross- validation approach, which was also intended to be applied in other countries. The questionnaire was tested to measure its reliability which resulted in Cronbach’s a (α= .95) for the total score. Moreover, the researchers conducted a pilot test to evaluate the reliability and validity of the questionnaire to test if the instrument was suitable for use in the Philippine setting, particularly in the university in Davao Region. For Multidimensional State Boredom Scale, all of the questions tested using Scale Reliability Analysis in JAMOVI are valid and indicate an almost perfect reliability with an overall value of 0.939 using Cronbach’s Alpha. Furthermore, the Likert Scale is employed to assess the corresponding interpretations. By employing Likert scales, researchers can collect numerical evaluations of subjective attributes, resulting in numerical data that can be totaled and displayed in a manner similar to other quantitative data obtained during an assessment (South et al., 2022). The standard arrangement of a Likert scale item consists of a name, followed by a coding system comprising both descriptive terms and numerical values. These values typically range from 1 indicating “strongly disagree,” through 2 for “disagree,” 3 for “neither agree nor disagree,” 4 for “agree,” and 5 for “strongly agree” (Mcleud, 2023). Below are the interpretations used to evaluate the mean scores of Social Media Usage and Multidimensional State Boredom of college students at a university in Davao Region. Mean scores were evaluated using mean ranges from the Social Media Usage from the study of Bhuiyan & Islam in 2023 and Multidimensional State Boredom from (Nyutu et al., 2020). In the first column, the mean interval is presented, alongside its qualitative counterpart, which is detailed in both the second and last columns to illustrate variation in a descriptive equivalent. Third Year 72 21 Fourth Year 2 1 Department DAS 80 23 DCJE 84 24 DBA 52 15 DTE 76 22 DAE 21 6 DTP 38 11 Total 350 100.0 Table 2: Social Media Usage Mean Interpretation Mean Interval Interpretation Qualitative Description 4.21 - 5.00 Very High The level of social media usage among participants is very high. 3.41 - 4.20 High The level of social media usage among participants is high. 2.61 - 3.40 Moderate The level of social media usage among participants is moderate. 1.81 - 2.60 Low The level of social media usage among participants is low. 1.00 - 1.80 Very Low The level of social media usage among participants is very low. Pa ge 12 5 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 2(1) 121-131, 2024 Design and Procedure This study is quantitative and employs a descriptive- predictive research design. Initially, the researchers gathered related literature relevant to this study. The researchers validated questionnaires from (Gupta & Bashir, 2018) and (Spoto et al., 2021), tackling Social Media Usage and Multidimensional State Boredom, respectively with modifications and revisions. The researchers conducted a distribution of questionnaires through Google Forms and a traditional distribution of printed questionnaires inside the campus to the potential participants. Prior to distributing the questionnaires, the researcher obtained permission letters signed by the advisor and endorsed by the Dean of the Professional School. Additionally, letters were provided to participants to ensure the ethical considerations of the research process. Lastly, after gathering the data, the researchers tabulated and analyzed the data using the statistical treatment required with the aid of the tool, JAMOVI version 2.4, and interpreted it as the study’s main objective. Statistical Treatment The Shapiro Wilk Test was employed by the researchers to assess the normality of the data and find out whether the null hypothesis would be rejected. However, the test yielded no information regarding the presumed normal distribution of the continuous variables (W=.92, p-value = <0.001).According to Beers (2024), the null hypothesis is the initial claim about the population. The null hypothesis stated that the population that the sample data were drawn from had a normal distribution. Data are not normally distributed when p > 0.05 which indicates that the null hypothesis is accepted. The researchers used Mean, Standard Deviation, Spearman Correlation Coefficient, and Linear Regression Analysis. Andrade C. (2020), states that the average value is indicated by the mean and the average scatter of values around the mean is indicated by the standard deviation which also provides a spread indicator. Both are employed to measure the levels of variables. According to Sereno (2023), one of the most widely used statistical measures is determining the impact and trajectory of a monotonic relationship between two variables in which the variables typically move in the same or opposite direction, but not always in the same rate — contrast to a linear relationship, where the rate is constant is referred to as Spearman rho Correlation Coefficients (ρ). Positive and negative which indicate the direction of a relationship between variables are the two types of correlation coefficients. Correlation coefficients ranges from -1 to + 1; -1, when one variable change, the other variables change in the opposite way which indicates a perfect negative correlation, 0 indicates that there is no relationship between the variables, and +1, when one variable changes, the other variables change in the same way, which indicates a perfect positive correlation (Bhanduri, 2023). P-value is always used in statistical analysis. Statistical tests unique to the kind of correlation analysis are used to determine the p- value. The p-value shows the likelihood of finding a correlation coefficient as extreme as the one seen in your data. The most widely used range is p < 0.05. The null hypothesis is rejected if the p-value is small—that is, less than the desired significance level, such as 0.05—it is statistically significant, and the null hypothesis is rejected (Kaabar, 2023). In addition, the relationship between the mean value of one variable (dependent) and the corresponding values of one or more other variables (independent) is determined by linear regression (Lawton, 2023). Whether or not these relationships are statistically significant is shown by the p values for the coefficients. p-values in regression helps to ascertain whether the relationships that are observed in the sample data are also present in the broader population. The null hypothesis, which posits no correlation between a variable and the dependent variable, is evaluated for each independent variable through the linear regression’s p-value. There cannot be any relationship between changes in the dependent variable and changes in the independent variable if there is no correlation (Frost, 2023). Ethical Consideration The researchers of this study adhered to the University College’s Research ethics respondents. According to Scribbr (2012), ethical considerations in research are guidelines that influence how you plan and carry out your research. These also ensure that participants receive equitable treatment, improve the dependability of your study, and maintain integrity. Voluntary Participation The researchers asked the respondents whether they were willing to participate in the study before proceeding. In addition, all respondents have a freedom to withdraw or discontinue their involvement in the study at any time without feeling obligated to continue. The decisions made by respondents should be accepted and respected by researchers without question. Table 3: Multidimensional State Boredom Mean Interpretation Mean Interval Interpretation Qualitative Description 4.21 - 5.00 Very High The level of boredom among participants is very high. 3.41 - 4.20 High The level of boredom among participants is high. 2.61 - 3.40 Moderate The level of boredom among participants is moderate. 1.81 - 2.60 Low The level of boredom among participants is low. 1.00 - 1.80 Very Low The level of boredom among participants is very low. Pa ge 12 6 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 2(1) 121-131, 2024 Right to Revoke The researcher of this study guaranteed respondents the right to revoke their involvement at any moment without facing any consequences. Privacy and Confidentiality All the personal information provided by the respondents is not disclosed to unauthorized individuals and kept secure in a plastic envelope and put in a secured cabinet. Additionally, it will not be used outside the study. The personal information of the respondents of the study is ensured that is protected and they have control how their data is collected, used and shared during the study. Informed Consent Process The respondents of this study were informed by the researchers before signing the informed consent about the purpose, risk, benefits, and procedure of the study before they decided to join or decline. Benefits The researchers ensured that the study is beneficial and helpful to others. In addition, the respondents are informed about the possible benefits of the study which lead to personal growth and increased awareness. Risks The respondents of this study are informed about the possible risks or negative consequences involved which a reasonable individual would consider when deciding whether to take part in the research study. Additionally, the researchers of this study will minimize the potential risks to the extent possible. Plagiarism The study was conducted with careful attention to avoid plagiarism. In addition, the researchers provided appropriate credits and proper acknowledgements for all sources that contributed significantly to this study. Fabrication The researchers made sure not to create or fabricate data, results, or outcomes that do not actually exist. All the information provided is genuine and it has not been invented or manipulated. Falsification The study was based on reliable and accurate studies. In addition, the researchers ensured that there’s no act of manipulating, altering, and distorting data in order to misrepresent the truth or support a particular hypothesis or conclusion. Conflict of Interest The researchers of this study avoided potential bias and upheld transparency and honesty throughout the research process, ensuring any bias and external factors did not influence their work. Deceit The researchers prioritized and ensured that the respondents provided truthful responses that wouldn’t cause harm to anyone. In addition, the researchers avoid misleading respondents and providing false data that would compromise the trustworthiness of the findings. Permission from Organization/Location The researchers of this study asked permission from the school dean as well as the class adviser to conduct the survey properly. Upon approval, the survey questionnaires were distributed to the chosen students of a university in Davao Region. RESULTS AND DISCUSSION The Level of Social Media Usage of College Students Table 4 shows the level of social media usage among students across various subscales: Academic, Socialization, Entertainment, Informativeness, and Constraints. The mean values indicate the average level of social media usage in each subscale, while the standard deviations reflect the variability of the data. Based on the presented results, there is an overall total of (x̄ =3.73, SD=.72) for the level of social media usage among respondents, interpreted as “high”. This indicates that the level of social media usage among college students at the university in Davao Region is high. In addition, all of the subscales are interpreted as ‘high’. It means students’ use social media for various purposes, whether it is for academic, entertainment, socialization, informativeness and constraints. Similarly, in the study by Tus (2021; Kutu et al., 2022; Israel, 2024), it was found that the social media usage is high primarily in terms of academics and entertainment. However, in the study by (Erdogan, A., & Icen, R. 2017), the analysis revealed that the level of social media usage of students in Balikesir University is low. Additionally, users have reported that they use social media for socialization purposes in a study conducted by (Kolhar et al., 2021; Stockdale & Coyne, 2020). To reiterate, entertainment has been one of the reasons why individuals spend time on social media platforms, specifically the platform, YouTube (Israel, 2024). Also, individuals have been shown to use social media for the purpose of informativeness (Si et al., 2023b). Lastly, social media users in higher education have experienced Table 4: Level of Social Media Usage Among Respondents Indicator x̄ SD Interpretation Academic 3.82 0.617 High social media use Socialization 3.62 0.662 High social media use Pa ge 12 7 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 2(1) 121-131, 2024 constraints during their utilization of different social media platforms which was reported in the study of Kutu et al., 2022. The Level of Boredom Proneness of College Students Table 5 exhibits the results that measures the level of boredom proneness of college students at the university in Davao Region in 5 subscales: Disengagement, Inattention, Internalizing Aspects, High Arousal and Time Perception. The level of boredom proneness among respondents has an overall mean score and standard deviation of (x ̄ =3.42 SD=.79) with a quantitative description of ‘high’. The table 5 indicates the level of boredom among college students at the university in Davao Region is high. The study’s findings are parallel with the findings of the study of Cañete et al. (2022), which indicated that the respondents’ level of boredom was at a high level. Similarly, in the study of Lv and Wang (2022), which indicated that the respondents’ level of boredom was at a high level. However, the study of Orsolini et al. (2023), indicates a variety of results with high and low levels of boredom. Additionally, Disengagement is a significant predictor of boredom proneness among individuals. The “Disengagement” indicator shows how willing respondents are to disconnect or disengage when they are bored. The respondents’ ability to disengage from activities or distractions when bored is significant, as evidenced by their mean score of 3.61, which indicates a high level of boredom proneness. According to the study of Bergdahl (2020), disengagement was interpreted as “less engaged”, which suggests that when people find their surroundings boring or uninteresting, they may withdraw from them physically or emotionally. High arousal has also been a significant predictor of boredom proneness among individuals. The” High Arousal’ indicator shows how people who have high levels of emotional intensity to their surroundings get bored easily. This happens when their arousal level is not satisfied, resulting in individuals feeling frustrated. (Wolff et al., 2022) claimed that this subscale of boredom correlated moderately with frustration. This implies that people who are bored could experience frustration or disappointment when their present circumstances don’t meet their demands, which serves as a signal for them to make a change. Lastly, Inattention is a significant predictor of boredom proneness among individuals. The “Inattention” indicator shows how easily respondents can get distracted or lose focus when they are bored. A mean score of 3.53 indicates a high level of boredom proneness and indicates a significant inattentional capacity among respondents. According to Furlong et al. (2021), boredom experiences are associated with low interest in class-specific learning activities, which demonstrates how difficult it is for people to concentrate or pay attention to tasks or senses when they are bored. Both indicators show that respondents’ boredom proneness correlates with high levels of inattention and disengagement. While individuals typically exhibit significant tendencies toward inattention and disengagement when experiencing boredom, it is essential to implement interventions aimed at enhancing intrinsic motivation and fostering a stimulating learning environment. Entertainment 4.05 0.704 High social media use Informativeness 3.73 0.689 High social media use Constraints 3.44 0.935 High social media use Total 3.73 0.72 High social media usage Table 5: Level of Boredom Proneness among Respondents Indicator x̄ SD Interpretation Time Perception 3.21 0.770 Moderate levels of boredom Inattention 3.53 0.840 High levels of boredom High Arousal 3.36 0.861 High levels of boredom Disengagement 3.61 0.647 High levels of boredom Internalizing Aspect 3.39 0.820 Moderate levels of boredom Total 3.42 0.79 High Levels of Boredom Relationship between Social Media Usage and Boredom Proneness of College Students The result in Table 6 shows the Spearman rho correlation coefficient test, which is used to determine the association. The findings revealed a strong positive correlation between the two variables with a p-value .001 indicating a strong positive correlation. To put in another way, it demonstrates that there is a statistically significant association between students’ social media usage and boredom proneness. Similar findings were found by Pawar and Shah (2019), showing that there is a considerable association between social media addiction and boredom. Additionally, the study of Menders (2023), found a statistically significant relationship between social media usage and leisure boredom. However, this contradicts the findings of several studies. The study of (Catedrilla Pa ge 12 8 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 2(1) 121-131, 2024 et al., 2020) revealed that loneliness and boredom does not have an influence on problematic social media usage. in the result of the study of (Siebers et al., 2021) which attested that participants who spend more time on social media are experiencing more distraction. Additionally, the association between excessive social media use and distraction has been exhibited by (Xie et al., 2021), emphasizing that excessive social media usage can result in users to experience distraction in attention-related tasks. In addition, results also emphasized that Time Perception (p=.032) is a significant predictor of social media usage in individuals. This indicated that social media usage can cause a person to have problems with time perception. High levels of social media usage have been linked to symptoms of time perception distortion. This is stressed by (Rast, 2021) with results stated by participants that they are unaware of the time spent on social media. Participants initially intended to use social media quickly, but turned out to be longer than initially intended. Lastly, results also emphasized that Disengagement (p=.002) is a significant predictor of social media usage in individuals. This indicated that social media usage can result in an individual to be disengaged towards their environment. Boredom is defined as an unpleasant feeling resulting from not being engaged in satisfying activities (Donati et al., 2022). Although there is a lack of recent studies tackling the disengagement part of boredom, it can still be linked to social media usage since disengagement is technically a result of an individual’s boredom. This is affirmed by the study of Barkley and Lepp (2021), which stated that smartphone facilitated social media use caused an individual to have an increase in boredom. Table 6: Spearman rho Correlation Coefficient analysis among variable Independent variable Overall Social Media Usage Boredom Proneness r p-value remarks .719 <.001 Significant Regression Analysis for Variables Predicting Boredom Proneness Table 7 shows the Regression Analysis for Variables Predicting Students Boredom Proneness. The table shows the three significant predictors that contributed to the students’ boredom proneness. Linear regression with coefficients of correlation applied with the significance was tested at the level α=0.05. . Results showed that 23% of the variance is explained by the five predictors, F(5, 344)=12.9, p=0.001. Specifically, time perception, (β=0.010, t=2.15, p<0.005), inattention, (β=0.21, t=3.78, p<0.005), and disengagement, (β=0.23, t=3.17, p<0.005) are positively associated with the social media usage. On the other hand, high arousal (β=0.10, t=- 1.69, p=n.s.) and internalizing aspects (β=0.010, t=-1.44, p=n.s.) are negatively associated with social media usage. These subscales are Time Perception (p=.032), Inattention (p=< .001), and Disengagement (p=< .002). On per item evaluation, results emphasized that Inattention (p=< .001) is a significant predictor of boredom proneness. This indicates that inattention has the most impact on an individual’s social media usage. This is also shown Table 7: Linear Regression of the Indicators Predicting Boredom Proneness Predictor β SE t p Intercept 2.5390 0.1664 15.26 < .001 Time Perception 0.0958 0.0445 2.15 0.032 Inattention 0.2172 0.0575 3.78 < .001 High Arousal -0.1005 0.0593 -1.69 0.091 Disengagement 0.2259 0.0714 3.17 0.002 Internalizing Aspects -0.0972 0.0677 -1.44 0.152 CONCLUSION This study determines the correlation between Social Media Usage (SMU) and Multidimensional State boredom (MSB). The data was gathered from the College Students at a University in Davao Region. The results revealed that the level of social network usage among respondents was ‘high’. On per-item evaluation, academics, entertainment, socialization, informativeness, and constraints are ‘high.’ It indicates that students use social media for various purposes, whether it is for academics, entertainment, socialization, informativeness, or constraints. Meanwhile, the level of multidimensional state boredom among respondents was revealed to be ‘high’. Upon item evaluation, disengagement yielded the highest mean score, followed by inattention, both interpreted as ‘high.’ This suggests that college students experience a high level of boredom, possibly attributable to the repetitive and monotonous nature of school activities. Additionally, the normality of the gathered data was assessed using the Shapiro-Wilk analysis, revealing non-normal distribution. Spearman’s Rho Correlation Coefficient was then employed to ascertain the relationship between social media usage and multidimensional state boredom among students, unveiling a significant and strong correlation. Further analysis through Linear Regression revealed three significant predictors influencing social media usage among college students: time perception, inattention, and disengagement. This implies that distortion in Pa ge 12 9 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 2(1) 121-131, 2024 time perception, distraction, and disengagement from one’s surroundings notably increase social media usage. Moreover, acknowledging the significant relationship between social media usage and boredom proneness suggests that excessive social media use may elevate levels of boredom proneness, potentially impacting satisfaction with offline activities, attention abilities, and overall well-being. To address these concerns, raising awareness and implementing programs and policies to promote moderated social media use among students are recommended. Lastly, future research is advised to replicate the study in different settings to assess potential variations in results. Additionally, exploring potential mediators between the variables overlooked in this study could provide valuable insights for future researchers. RECOMMENDATION Considering the findings and conclusions conducted, these are the following suggestions that were made: School Administrators Utilize the findings to implement policies aimed at promoting healthy social media usage among students, tailored to their levels of boredom. This proactive approach can contribute to fostering a more balanced and mindful digital presence among students. Additionally, insights from the study can inform the development of support programs and services by the School Counseling and Guidance Office, specifically targeting students at risk of excessive social media use and related boredom issues. Educators Incorporate hands-on activities and experiential learning opportunities into their teaching methods. Activities such as experiments, field trips, simulation games, artistic expression, and outdoor education can foster active engagement and direct interaction with materials and experiences, thereby creating a dynamic and stimulating learning environment. This approach can help mitigate boredom among students and enhance their overall learning experience. Students Educate their peers about the potential consequences of excessive social media use and collaborate on campaigns to formulate strategies for alleviating boredom. Encouraging peers to explore alternative activities that are meaningful and fulfilling, beyond social media, can help combat boredom effectively. Additionally, raising awareness about the negative effects of unregulated social media use can empower students to develop coping strategies for managing boredom. Mental Health Practitioners and Community-Based Organizations Leverage the research findings to develop awareness programs and interventions aimed at addressing the influence of social media on students’ susceptibility to boredom. By integrating these insights into their initiatives, practitioners can offer valuable support and resources to individuals struggling with boredom and excessive social media use, ultimately promoting mental well-being among students. Future Researchers Use the study as a reference or guide when conducting quantitative research in related fields. The findings can serve as a foundation for further exploration of the relationship between social media usage and boredom proneness, as well as the potential mediating factors that may have been overlooked in this study. This iterative approach to research can contribute to a deeper understanding of the complex dynamics between social media, boredom, and psychological well-being. Acknowledgements We, the researchers, would like to thank the individuals who contributed to completing this research. First, we would like to thank God for his continued guidance, and for giving us the wisdom we need to pursue this academic endeavor, and for giving us the strength to continue. Without His guidance and blessings, we would not be able to pursue our research. Second, we would like to extend our utmost appreciation and sincere gratitude to our instructor, Ms. Claire Lynn Culajara, for providing continued guidance, support, advice, insightful comments, suggestions, and provision that contributed greatly in completion of this study. Working with our paper under her guidance was a privilege and an honorable experience. We would also like to express our utmost gratitude to our parents for their unwavering support and love. Your sacrifices, prayers, and guidance has made it possible to continue with our daily endeavors. We would not have come this far without all the things you do for us. Our relatives, friends, and subject teachers who helped us find participants and made our quantitative research possible within a limited time frame. Lastly, we would like to express our sincerest gratitude and appreciation to everyone who helped us, may it be directly or indirectly. Thank you for all the hands you lended to those who needed it. Above all, to God be the glory. REFERENCES Allahverdi, F. Z. (2022). Trait boredom and perceived social media addiction. Journal of Positive School Psychology, 6(2), 5024 - 5036. https://journalppw.com/ index.php/jpsp/article/view/3014/1973 Andrade, C. (2020). Understanding the Difference Between Standard Deviation and Standard Error of the Mean, and Knowing When to Use Which. Indian Journal of Psychological Medicine, 42(4), 409–410. https://doi.org/10.1177/0253717620933419 Aquino, A. P. S., & Kimong, P. J. (2022). Boredom and distress tolerance on problematic internet use among public university students. Malaysian Journal of Social Pa ge 13 0 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 2(1) 121-131, 2024 Sciences and Humanities, 7(5), e001477. https://doi. org/10.47405/mjssh.v7i5.1477 Barkley, J. E., & Lepp, A. (2021). The effects of smartphones facilitated social media use, treadmill walking, and schoolwork on boredom in college students: Results of a within subjects, controlled experiment. Computers in Human Behavior, 114, 106555. https://doi.org/10.1016/j.chb.2020.106555 Beers, B. (2024, March 5). P-Value: What It Is, How to Calculate It, and Why It Matters. Investopedia. Retrieved March 10, 2024, from ttps://www. investopedia.com/terms/p/p-value.asp Bhuiyan, M. A., & Islam, A. B. (2023). Assessing citizen satisfaction of urban local government service and infrastructure in Bangladesh: A case study of Pabna municipality. Environment & Social Psychology, 8(3). https://doi.org/10.54517/esp.v8i3.1671 Bai, J., Mo, K., Peng, Y., Hao, W., Qu, Y., Lei, X., & Yang, Y. (2021). The Relationship Between the Use of Mobile Social Media and Subjective Well-Being: The Mediating Effect of Boredom Proneness. Frontiers in Psychology, 11(1664–1078), 568492. https://doi. org/10.3389/fpsyg.2020.568492 Cañete, M. J. A., Cuartero, A. N. R., Montenegro, N. P. D., Pasilan, A. J. M., Pitogo, C. L. G., Pueblas, S. M. A., Rocabo, I. N., & Yrog-Irog, R. C. (2022). The relationship between loneliness and boredom in the purchasing behavior among college students during COVID-19 pandemic. Zenodo CERN European Organization for Nuclear Research. https://doi. org/10.5281/zenodo.7206700 Catedrilla, J., Limpin, L., Ebardo, R., de la Cuesta, J., Ching, M., Leano, C., & Trapero, H. (2020). Loneliness, Boredom and Information Anxiety on Problematic Use of Social Media during the COVID-19 Pandemic. researchgate. https://www.researchgate. net/publication/365761238_Health_Anxiety_ Information_Anxiety_and_Internet_Self-Efficacy_ on_Cyberchondria_among_Fil ipino_Young_ Professionals_during_the_COVID-19_Pandemic/ citation/download Conquilla, J., Espiritu, N. A., Paras, N. E., Garcia, S. R., Garcia, Rodriguez, K. E., Gado, S., Escoto, Ma. R., De Jesus, S. B., Cruz, R., & Perante, L. (2021). The Social Media Usage and Its Impact on the Filipino Learners’ Academic Performance Amidst the Online Education. International Journal of Psychology and Counseling, 11(3), 2278–5833. https://doi. org/10.6084/m9.figshare.16997119.v Dimacangun, F. E., & Guillena, J. B. (2023). Social media usage and the academic performance of Filipino Junior high school students. International Journal of Educational Management and Development Studies, 4(2), 187–206. https://doi.org/10.53378/3529 Frost, J. (2023, March 21). How to interpret P-values and coefficients in regression Analysis. Statistics by Jim. https://statisticsbyjim.com/regression/interpret- coefficients-p-values-regression/ Gumasing, Ma. J., Dahilig, J. A., Taw, C. A., & Valeriano, C. M. (2023). Effects of Boredom on the Academic Engagement of Students during Online Class. IEOM Society International. https://doi.org/10.46254/ na07.20220521 Gupta, S., & Bashir, L. (2018). Social Networking Usage Questionnaire: Development and validation in an Indian Higher Education context. The Turkish Online Journal of Distance Education, 214–227. https://doi. org/10.17718/tojde.471918 He, X. (2024). An Investigation into the Effect of Foreign Language Learning Boredom on the English Proficiency of Chinese EFL Learners. Frontiers in Educational Research, 7(2). https://doi.org/10.25236/ fer.2024.070203\ Kılıç, A., Van Tilburg, W. A., & Igou, E. R. (2019). Risk‐taking increases under boredom. Journal of Behavioral Decision Making, 33(3), 257–269. https:// doi.org/10.1002/bdm.2160 Israel, D. J. (2024). A Study On Millennial Consumption of Entertainment on Social Media Platforms. ResearchGate, 12(Special Issue 8), 3247–3248. https://doi. org/10.48047/ecb/2023.12.si8.2362023.03/07/2023 Joseph, R. (2019, December 10). What is the difference between Correlation and P value. The Data School. https://dataschool.com/fundamentals-of-analysis/ correlation-and-p-value/ Khan, Z. (2021, June 9). Social Media and its impact on our mental health. Linkedin. Retrieved March 3, 2024, from https://www.linkedin.com/pulse/social-media- its- impact-our-mental-health-zara- Kolhar, M., Kazi, R. N. A., & Alameen, A. (2021). Effect of social media use on learning, social interactions, and sleep duration among university students. Saudi Journal of Biological Sciences, 28(4), 2216–2222. https:// doi.org/10.1016/j.sjbs.2021.01.010 Kutu, I. F., Olajide, O., & Kutu, J. O. (2022). Awareness, Accessibility and Challenges of Social Media as experienced by Postgraduate Information Studies students, University of KwaZulu-Natal during the COVID-19 Pandemics lockdown. African Journal of Library, Archives and Information Science, 32(1). https:// www.ajol.info/index.php/ajlais/article/view/226804 Lawton, G. (2023, August 15). linear regression. Enterprise AI. https://www.techtarget.com/searchenterpriseai/ definition/linear-regression Lv, S., & Wang, H. (2023). The Effect of College Students’ Boredom Proneness on Phubbing: The Chain-Mediating Effects of Fear of Missing Out and Online Vigilance. Perspectives in Psychiatric Care, 2023, 1–6. https://doi.org/10.1155/2023/9713789 Manawatao, I. V., & Sasan, P. R. (2015). The influence of the level of leisure boredom to the extent of internet use and its implication to occupational therapy practice among first year students of Cebu Doctors’ University. HERDIN. Retrieved March 3, 2024, from https://www.herdin.ph/index. php?view=research&cid=70671 Pa ge 13 1 https://journals.e-palli.com/home/index.php/ajhp Am. J. Hum. Psychol. 2(1) 121-131, 2024 Mann S., Robinson A. (2009). Boredom in the lecture theatre: An investigation into the contributors, moderators, and outcomes of boredom amongst university students. British Educational Research Journal, 35(2), 243–258. https://doi. org/10.1080/01411920802042911 Mcleud, S., (2023, July 31). Likert Scale Questionnaire: Meaning, Examples & Analysis. https://www. simplypsychology.org/likert-scale.html Murphy, S., Hill, T., McDonagh, P., & Flaherty, A. (2022). Mundane emotions: Losing yourself in boredom, time and technology. Sage Journals, 23(2), 275–293. https://doi.org/10.1177/14705931221138617 Ndetei, D. M., Nyamai, P., & Mutiso, V. (2023). Boredom– understanding the emotion and its impact on our lives: an African perspective. Frontiers in Sociology, 8. https://doi.org/10.3389/fsoc.2023.1213190 Nisar, T. M., Prabhakar, G. P., & Strakova, L. (2019). Social media information benefits, knowledge management and smart organizations. Journal of Business Research, 94, 264–272. https://doi.org/10.1016/j. jbusres.2018.05.005 Nyutu, E., Cobern, W. W., & Pleasants, B. A.-S. (2020). Correlational study of student perceptions of their undergraduate laboratory environment with respect to gender and major. International Journal of Education in Mathematics, Science and Technology, 9(1), 83–102. https://doi.org/10.46328/ijemst.1182 Orsolini, L., Longo, G., & Volpe, U. (2023). The mediatory role of the boredom and loneliness dimensions in the development of problematic internet use. International Journal of Environmental Research and Public Health, 20(5), 4446. https://doi.org/10.3390/ijerph20054446 Pawar, T., & Shah, J. (2019). The relationship between social media addiction, SelfEsteem, sensation seeking and boredom among college students. Indian Journal of Mental Health, 6(4), 333. https://doi.org/10.30877/ ijmh.6.4.2019.333-339 Pimentel, J. (2019). Some Biases in Likert Scaling Usage and its Correction. International Journal of Sciences: Basic and Applied Research, 45(1), 183–191. https://gssrr. org/index.php/JournalOfBasicAndApplied/article/ download/9874/4329 Pitogo, C. L. G., Pueblas, S. M. A., Rocabo, I. N., & Yrog-Irog, R. C. (2022). The relationship between loneliness and boredom in the purchasing behavior among college students during COVID-19 pandemic. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.7206700 Radu, C. (2021, June 15). The impact of mobile technology in our lives. Mobiversal. Retrieved March 3, 2024, from https://blog.mobiversal.com/the- impact-of-mobile- technology-in-our-daily-life.html Rast, R., Coleman, J. T., & Simmers, C. S. (2021, December 30). The darkside of the like: The effects of social media addiction on digital and in-person communication. https://www.thejsms.org/index. php/JSMS/article/view/839 Sereno. (2023, September 14). Comparison of Pearson vs Spearman Correlation Coefficients. Analytics Vidhya. Retrieved March 10, 2024, from https://www. analyticsvidhya.com/blog/2021/03/comparison-of- pearson-and- spearman-correlation-coefficients/ Sharp J. G., Hemmings B., Kay R., Sharp J. C. (2019). Academic boredom and the perceived course experiences of final year education studies students at university. Journal of Further and Higher Education, 43(5), 601–627. https://doi.org/10.1080/030987 7x.2017.1386287 Sharp J. G., Sharp J. C., Young E. (2020). Academic boredom, engagement and the achievement of undergraduate students at university: A review and synthesis of relevant literature. Research Papers in Education, 35(2), 144–184. https://doi.org/10.1080/ 02671522.2018.1536891 Si, K., Jalees, T., Zaman, S. I., Alam, S. H., & Khan, S. (2023). The role communication, informativeness, and social presence play in the social media recruitment context of an emerging economy. Cogent Business & Management, 10(3). https://doi.org/10.1080/2 3311975.2023.2251204 South, L., Saffo, D., Vitek, O., Dunne, C., & Borkin, M. A. (2022). Effective use of Likert scales in visualization evaluations: A systematic review. Computer Graphics Forum, 41(3), 43–55. https://doi.org/10.1111/ cgf.14521 Spoto, A., Iannattone, S., Valentini, P et al.,(2021). Boredom in adolescence: Validation of the Italian version of the Multidimensional State Boredom Scale (MSBS) in adolescents. Children, 8(4), 314. https:// doi.org/10.3390/children8040314 Stockdale, L. A., & Coyne, S. M. (2020). Bored and online: Reasons for using social media, problematic social networking site use, and behavioral outcomes across the transition from adolescence to emerging adulthood. Journal of Adolescence, 79(1), 173–183. https://doi.org/10.1016/j.adolescence.2020.01.010 The Times of India (2023, February 17). The Power of Social Media: Connecting and engaging in the digital age. Reader’s Blog. Retrieved March 3, 2024, from https://timesofindia.indiatimes.com/readersblog/ elrashidy-media-group/the- Wang, Y., Yang, H., Montag, C., & Elhai, J. D. (2020). Boredom proneness and rumination mediate relationships between depression and anxiety with problematic smartphone use severity. Current Psychology, 41(8), 5287–5297. https://doi.org/10.1007/s12144- 020-01052-0 Whelan, E., Najmul Islam, A. K. M., & Brooks, S. (2020). Is boredom proneness related to social media overload and fatigue? A stress–strain–outcome approach. Internet Research, 30(3), 869–887. https:// doi.org/10.1108/intr-03-2019-0112 Wolff, W., Radtke, V. C., & Martarelli, C. S. (2022). Same same but different: What is boredom actually?. In The Routledge International Handbook of Boredom (pp. 5-29). Routledge.https://doi.org/10.31234/osf.io/jze5k