The Effect of Sports Science Students’ Social Media Addictions on Redundant Purchasing Behavior

Doğaner, Seçkin; Akoğlu, Halil Erdem

doi:10.20448/journal.522.2020.64.616.626

Cited by 6 publications

(4 citation statements)

References 61 publications

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“…Social media networking sites usage (SMNSU) has substantially increased since the beginning of the COVID-19 pandemic (Ostic et al, 2021). SMNSU facilitates interaction between users through its various platforms by allowing individuals to share opinions, images, information, and content (Doganer & Akoglu, 2020). Similarly, SMNSU creates other opportunities such as communication, searching for information, and entertainment.…”

Section: Introductionmentioning

confidence: 99%

Social Media Networking Sites Usage and Depression Among University Students During the COVID-19 Pandemic: The Mediating Roles of Social Anxiety and Loneliness

Iqbal

Asghar

Ashraf

et al. 2022

Social Media + Society

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The current COVID-19 pandemic has resulted in increased psychological issues such as excessive social media networking sites usage (SMNSU), loneliness, social anxiety, and depression. In this quantitative study, we examined how SMNSU can directly and indirectly influence depression, with loneliness and social anxiety examined as mediator variables. A 39-item questionnaire was used to collect survey data on SMNSU, loneliness, social anxiety, and depression from 244 blended learning undergraduate students from universities in the Hunan province in China. Partial least squares structural equation modeling was conducted using SmartPLS 3.3.3 to measure the relationships between the stated variables of interest. Results indicated that SMNSU has a direct, significant, and positive relationship with depression. In terms of mediating effects, both loneliness and social anxiety have an intervening role in the association between SMNSU and depression. This study focused on the higher education sector of China by recruiting students who were enrolled in blended learning courses during the COVID-19 pandemic and experiencing psychological problems. We found that excessive SMNSU is associated with depression. Loneliness and social anxiety also increase depression along with excessive SMNSU among blended learning students during unprecedented situations, in this case, the COVID-19 pandemic. The valuable implications of these findings for teachers, counselors, and university managers are discussed, along with a consideration of future research directions.

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Section: Introductionmentioning

confidence: 99%

Social Media Networking Sites Usage and Depression Among University Students During the COVID-19 Pandemic: The Mediating Roles of Social Anxiety and Loneliness

Iqbal

Asghar

Ashraf

et al. 2022

Social Media + Society

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“…Since the 21st century, Internet technology and computer hardware technology have developed rapidly and become popular [1]. e data stock of the Internet has increased exponentially, but there are only a handful of data containing valuable information [2].…”

Section: Introductionmentioning

confidence: 99%

Discussion on Redundant Processing Algorithm of Association Rules Based on Hypergraph in Data Mining

Zhu

2022

Journal of Robotics

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With the rapid advancement of big data, it is becoming a great problem for people to nd objective information in the database.e relevance data processing rule for digging the information can be the way. Relevance data processing rule for digging the information is mainly studied in three aspects: data dimension, data abstraction level, and processing variable type. In the aspect of rules, the research mainly focuses on three aspects: active relationships, passive rules, and uncommon relationship rules. Association rules of digging the data can be the most well-employed investigation goal and aim for the data digging. Along with the advancement of the information scale, the time rate of traditional relationship rules exploration of counting ways is too low. How to increase the time rate for the way of counting is the main research content of relevance data processing rules for digging the information. Current relevance data processing rules for digging the information have two limitations: (1) metrics such as support, con dence, and lift rely too much on expert knowledge or complex adjustment processes in value selection; (2) it is often di cult to explain rare association rules. Based on the existing research, this paper proposes a Markov logic network framework model of association rules to address the above shortcomings. e theory of a hypergraph and system is proposed, and the method of a hypergraph in 3D matrix modeling is studied. Aiming at the new characteristics of big data analysis, a new super edge de nition method is introduced according to the de nition of the system, which greatly enhances the ability to solve problems. In the cluster analysis and calculation of hypergraph, this paper will use the hypergraph segmentation operator hMETIS to carry out the cluster analysis method in order to achieve higher accuracy in cluster analysis and calculation. As for the test of cycle ones, which is in line with the relevance of the hypergraph with clear directions, the thesis will o er a brand-new way to make an analysis and turn the rule of relevance into the hypergraph with clear directions with a new de nition of the near linking matrix, and it will change the dealing way from the test of the cycle and more ones into the linking f bricks and circles, which is a new way to explore. is paper uses two datasets of di erent sizes to conduct rule prediction accuracy experiments on the Markov logic network framework model algorithm of association rules and the traditional association rule algorithm. e results show that compared with the traditional association rule algorithm, the rules obtained by the Markov logic network framework model of association rules have a higher prediction accuracy.

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“…This increase has been further amplified by the preventive measures taken by most countries to limit the spread of the virus [3]. These sites have facilitated user interaction by allowing them to share opinions, pictures, information, and content [4]. According to statistics individuals spend 2.25 hours daily on SNSu [5].…”

Section: Introductionmentioning

confidence: 99%

Analytical study of social networking sites usage and social anxiety among physical education students

A’mir,

Alzughailat,

Abdel Fattah

et al. 2024

IJPHS

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<div class="translate-tooltip-mtz green sm-root translate hidden_translate"><div class="header-wrapper">Social networking sites usages (SNSu) are online platforms used for communication, sharing, and connecting with others. This paper aims to investigate the current status of SNSu among students in physical education college, as well as the differences in SNSu based on gender. Additionally, we aimed to assess the level of social anxiety among the study participants and examine whether it varied by gender and SNSu. We conducted a descriptive survey among 1,109 physical education students (688 females and 421 males) enrolled in public universities. We administered the social anxiety disorder-adult and SNSu questionnaire using Google Forms. We analyzed the data using means, standard deviations, frequencies, percentages, One-way ANOVA, and chi-square tests with a confidence level of 95% (p<0.05). The results of the study showed that both male and female students used social networking sites (SNS) more than 10 times a day, with percentages of 46.3% and 46.1%, respectively. Additionally, 43.7% of male and 49.1% of female students reported using SNS for more than three hours per day. The mean score for social anxiety was 20.20, indicating a severe level of social anxiety among the study participants. Based on the responses of the sample study, the results showed an increase in negative indicators associated with SNSu and these unfavorable indicators were accompanied by an increase in the level of social anxiety.</div></div>

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The Effect of Sports Science Students’ Social Media Addictions on Redundant Purchasing Behavior

Cited by 6 publications

References 61 publications

Social Media Networking Sites Usage and Depression Among University Students During the COVID-19 Pandemic: The Mediating Roles of Social Anxiety and Loneliness

Social Media Networking Sites Usage and Depression Among University Students During the COVID-19 Pandemic: The Mediating Roles of Social Anxiety and Loneliness

Discussion on Redundant Processing Algorithm of Association Rules Based on Hypergraph in Data Mining

Analytical study of social networking sites usage and social anxiety among physical education students

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