Behavior Drug Use Research Articles

Interactive Interface Design for Adaptive and User Behavioural Habits

This study aims to explore adaptive interaction design in relation to user behavior habits, optimizing the user interface (UI) of mobile applications through statistical analysis of user habits. Specifically, the research analyzes user behavior regarding landscape and portrait browsing modes when using mobile applications. The results indicate that adjusting the UI according to different browsing habits can significantly enhance user satisfaction. Therefore, adaptive interaction design strategies play a crucial role in accommodating user behavior habits, contributing to improved user experience and satisfaction.

Validity of a wrist-worn consumer-grade wearable for estimating energy expenditure, sedentary behaviour, and physical activity in manual wheelchair users with spinal cord injury

Purpose To evaluate the validity of a consumer-grade wearable for estimating energy expenditure, sedentary behaviour, and physical activity in manual wheelchair users with spinal cord injury (SCI). Materials and methods Fifteen manual wheelchair users with SCI (C5-L1, four female) completed activities of daily living and wheelchair propulsion (2–8 km·h−1). Wrist-worn accelerometry data were collected using consumer-grade (z-Track) and research-grade (ActiGraph GT9X) devices. Energy expenditure was measured via indirect calorimetry. Linear regression was used to evaluate the prediction of criterion metabolic equivalent of task (MET) by each accelerometer’s vector magnitude (VM). Area under the receiver operating characteristic curve (ROC-AUC) evaluated the accuracy of VM for discriminating between physical activity intensities and for identifying accelerometer cut-points. Results Standardised β-coefficients for the association between z-Track and ActiGraph VM for criterion MET were 0.791 (p < 0.001) and 0.774 (p < 0.001), respectively. The z-Track had excellent accuracy for classifying time in sedentary behaviour (ROC-AUC = 0.95) and moderate-to-vigorous physical activity (ROC-AUC = 0.93); similar values to the ActiGraph (ROC-AUC = 0.96 and 0.88, respectively). Cut-points for the z-Track were ≤37 g·min−1 for sedentary behaviour and ≥222 g·min−1 for moderate-to-vigorous physical activity. Conclusions This study supports the validity of a consumer-grade wearable to measure sedentary time and physical activity in manual wheelchair users with SCI.

Cascading graph contrastive learning for multi-behavior recommendation

Traditional recommendation techniques often prioritize target behavior in practical recommendation scenarios(e.g., follow, play and buy). However, these approaches suffer from data sparsity issues and may not fully capture user’s personal preferences. To address this deficiency, multi-behavior recommendation technology has emerged, leveraging users’ multi-behavioral interactions for recommendation. Nevertheless, certain multi-behavior recommendation methods learning behavioral information from each behavior separately and then aggregate them before making recommendation, which inadvertently neglects the intrinsic connections between different behaviors. In some scenarios, user behavior often occurs in a fixed order, such as view -> cart -> buy in e-commerce platforms. In this work, we propose a novel Cascading Graph Constrastive Learning (CGCL) framework for Multi-Behavior recommendation. Specifically, we devise a graph contrastive learning block to learn distinctive user behavioral representations for each type of interaction. Leveraging the recommendation task, we aim to capture user preferences, while the contrastive learning provides supplementary supervisory signals to refine the user and item representation. By acknowledging the sequential order of behaviors, we utilize the cascading structure within our model to iteratively propagate and purify the personalized preferences of users. Extensive experimental results and ablation studies on three real-world datasets have shown that our CGCL framework outperforms various state-of-the-art recommendation methods and validated the effectiveness of our approach.

Drug Use Behaviors and the Risk of HIV Infection Among Drug Users in China Between 2014 and 2021: Cross-Sectional Study.

Drug users are a high-risk group for HIV infection and are prominent HIV carriers. Given the emergence of new drugs, we explored current drug-using behaviors, HIV infections, and the correlation between drug-using behaviors and HIV infection risk among drug users from 2014 to 2021. We aimed to identify the prevalence of HIV infection risk among drug users and explore drug use behaviors based on the updated data, which could provide evidence for the precision of HIV prevention strategies among drug users. Data were collected from sentinel surveillance of drug users in rehabilitation centers and communities in Hangzhou (2014-2021), including sociodemographic characteristics, HIV awareness, drug use, risky sexual behaviors, and HIV infection status. Multivariate logistic regression was used to identify the factors influencing HIV infection and risky sexual behaviors among drug users. In total, 5623 drug users (male: n=4734, 84.19%; age: mean 38.38, SD 9.94 years) were included. New drugs dominated among the participants (n=3674, 65.34%). The main mode of drug use was noninjection (n=4756, 84.58%). Overall, for 27.45% (n=1544) of injected drugs in the last month before the investigation, the average daily injection frequency was 3.10 (SD 8.24). Meanwhile, 3.43% of participants shared needles. The incidence of sexual behaviors after drug use was 33.13% (n=1863), with 35.75% (n=666) of them using a condom in the last time. Overall, 116 participants tested positive for HIV antibodies (infection rate=2.06%). New drug users exhibited more postuse sexual behaviors than traditional drug users (odds ratio [OR] 7.771, 95% CI 6.126-9.856; P<.001). HIV-aware drug users were more likely to engage in risky sexual behaviors (OR 1.624, 95% CI 1.152-2.291; P=.006). New-type drug users were more likely to engage in unprotected sexual behavior (OR 1.457, 95% CI 1.055-2.011; P=.02). Paradoxically, drug users with greater HIV awareness were more prone to engaging in unprotected sexual behavior (OR 5.820, 95% CI 4.650-7.284; P<.001). Women engaged less in unprotected sex than men (OR 0.356, 95% CI 0.190-0.665; P=.001). HIV rates were higher among injecting drug users (OR 2.692, 95% CI 0.995-7.287; P=.04) and lower among drug users who used condoms during recent sex than those who did not (OR 0.202, 95% CI 0.076-0.537; P=.001). Higher education levels were associated with higher HIV infection rates. However, there was no significant correlation between HIV cognition level and HIV infection. New drug types and noninjection were the main patterns in last 7 years. Using new types of drugs, rather than traditional drugs, was associated with an increased risk of HIV infection. Injection drug use was a risk factor for HIV infection. HIV awareness among drug users was high, but the incidence of risky sexual behaviors remained high. Therefore, it is important to promote the behavioral transformation of high-risk populations from cognition to attitude, and then to taking protective measures.

Multi-dimensional feature fusion-based expert recommendation in community question answering

PurposeCommunity question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.Design/methodology/approachTo address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.FindingsThe proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.Practical implicationsThis work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.Originality/valueThis work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

Anomalous behavior detection based on optimized graph embedding representation in social networks

Anomalous behaviors in social networks can lead to privacy leaks and the spread of false information. In this paper, we propose an anomalous behavior detection method based on optimized graph embedding representation. Specifically, the user behavior logs are first extracted into a social network user behavior temporal knowledge graph, based on which the graph embedding representation method is used to transform the network topology and temporal information in the user behavior knowledge graph into structural embedding vectors and temporal information embedding vectors, and the hybrid attention mechanism is used to merge the two types of vectors to obtain the final entity embedding to complete the prediction and complementation of the temporal knowledge graph of user behavior. We use graph neural networks, which use the temporal information of user behaviors as a time constraint and capture both user behavioral and semantic information. It converts the two parts of information into vectors for concatenation and linear transformation to obtain a comprehensive representation vector of the whole subgraph, as well as joint deep learning models to evaluate abnormal behavior. Finally, we perform experiments on the Yelp dataset to validate that our method achieves a 9.56% improvement in the F1-score.

Game-theoretic implications for uncovering the effects of virtual tipping in complex user networks

Consumer-generated media (CGM), including social live streaming service (SLSS), often incorporate gamification elements to engage users. Online virtual tips and gifts, such as “bits” in Twitch, exemplify this approach by fostering interactive dynamics between live streamers and their audience. However, issues remain in understanding the impact of (virtual) tips on user behavior and how platform commissions, which are partly collected from the tips provided by other users, influence the platform’s overall revenue and user behavioral strategies. This study considers a CGM/SLSS platform, such as Twitch, as a variation of the public goods game and proposes a game-theoretical model integrated with a platform-mediated tipping system with a certain commission rate. In experiments, the proposed model was applied to artificial and actual SLSS networks, demonstrating that the influence of a tipping system on user behavior is significant, impacting the motivation for live streaming and the effort to improve its quality. Depending on individual preferences for psychological or monetary reward, the commission rate is determined by the platform depending on the users’ network positions, such as the degree of connectivity, which corresponds to the number of followers/followees in SLSS networks. Our findings offer valuable insights into the design and management of SLSS platforms and further suggest that there may be an appropriate commission rate to maximize platform revenue.

Aggressive Behavior in the Perspective of Extroverted and Introverted Personality Types of Online Game Users

This study aimed to investigate the relationship between extroverted and introverted personality types and aggressive behaviour in online game users. The research focuses on teenagers who are avid players of the popular online game Player Unknown's Battlegrounds (PUBG). This research method uses a quantitative-descriptive approach with 127 adolescents aged 12-16 years who have played or are playing online games. We took samples based on incidental sampling techniques found in game centers. The results showed that extroverted and introverted personality types contributed to aggressive behaviour in adolescent online game users both partially and simultaneously.

Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation

Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user preferences across different behaviors and fail to account for diverse item preferences within behaviors. Various user preference factors (such as price or quality) entangled in the behavior may lead to sub-optimization problems. Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items. To tackle these challenges, we propose the Disentangled Cascaded Graph Convolutional Network (Disen-CGCN), a novel multi-behavior recommendation model. Disen-CGCN employs disentangled representation techniques to effectively separate factors within user and item representations, ensuring their independence. In addition, it incorporates a multi-behavioral meta-network, enabling personalized feature transformation across user and item behaviors. Furthermore, an attention mechanism captures user preferences for different item factors within each behavior. By leveraging attention weights, we aggregate user and item embeddings separately for each behavior, computing preference scores that predict overall user preferences for items. Our evaluation of benchmark datasets demonstrates the superiority of Disen-CGCN over state-of-the-art models, showcasing an average performance improvement of 7.07% and 9.00% on respective datasets. These results highlight Disen-CGCN’s ability to effectively leverage multi-behavioral data, leading to more accurate recommendations.

Objective Neighborhood-Level Disorder Versus Subjective Safety as Predictors of HIV Transmission Risk and Momentary Well-Being

Mental health and HIV risk behavior have been studied with ecological momentary assessment (EMA), but this approach has not been combined with tracking of activity space (where people go and what they encounter there) in people with HIV and their social relations, who may be HIV+ or HIV−. Activity space represents a modifiable risk or protective factor for behavior related to health status and quality of life, in both clinical and nonclinical populations. We conducted an observational study with 286 participants (243 HIV+ and 43 HIV−), roughly matched for socioeconomic status and neighborhood of residence via three waves of snowball sampling. Each participant carried a smartphone for up to 4 weeks, making 5 randomly prompted entries and 1 end-of-day entry each day, plus self-initiated event-contingent entries for sexual activity and drug use. Responses to randomly prompted items provided subjective evaluations of the safety of the participant’s current social and physical environment (the place they were and the people they were with). GPS-based location tracking—coupled with publicly available statistic indicating neighborhood-level physical disorder and socioeconomic disadvantage—provided an indicator of each participant’s exposure to objective psychosocial hazard. We examined possible relationships of these objective and subjective environmental exposures with risky sexual and intravenous drug-use behavior, knowledge and utilization of antiretroviral treatment and prophylaxis, and momentary mental health (mood and stress, which relate to risky behavior and overall well-being). We found that both risky behavior and mental health were more related to participants’ subjective evaluations of their activity space than to objective measures of neighborhood-level disorder, suggesting that, even within an objectively hazardous neighborhood, people who find a niche they perceive as socially and physically safe may engage in less risky behavior and have better well-being.Trial registration Clinicaltrials.gov Identifier NCT01571752.

Impact of Alcohol and Opium Use on Aggressive Behavior in Substance Users

The aim of the study is to compare the aggression levels in male drug patients who were taking alcohol and opium. A sample of 54 male drug addicts used in this study to compare verbal and physical aggression, hostility and anger. The Buss Perry Aggression Questionnaire was used in this study. The results of the study show the significant difference between alcohol and opium when it comes to physical aggression, verbal aggression, anger, and hostility. The result shows that physical aggression was higher in alcohol addicts (M= 29.00) as compared to opium addicts (M= 22.70). Similarly, verbal aggression was significantly higher (M= 12.14) in alcohol addicts as compared to opium addicts (M= 11.57). Likewise, anger was also higher in alcohol addicts (M= 16.86) than opium (M= 14.96). Furthermore, hostility was also higher in alcohol addicts (M= 21.28) as compared with opium addicts (M= 20.04). The results are significant in spreading awareness and developing optimal treatment plans for substance users.

Associations among drug acquisition and use behaviors, psychosocial attributes, and opioid-involved overdoses

AimsThis study sought to develop and assess an exploratory model of how demographic and psychosocial attributes, and drug use or acquisition behaviors interact to affect opioid-involved overdoses.DesignWe conducted exploratory and confirmatory factor analysis (EFA/CFA) to identify a factor structure for ten drug acquisition and use behaviors. We then evaluated alternative structural equation models incorporating the identified factors, adding demographic and psychosocial attributes as predictors of past-year opioid overdose.Setting and participantsWe used interview data collected for two studies recruiting opioid-misusing participants receiving services from a community-based syringe services program. The first investigated current attitudes toward drug-checking (N = 150). The second was an RCT assessing a telehealth versus in-person medical appointment for opioid use disorder treatment referral (N = 270).MeasurementsDemographics included gender, age, race/ethnicity, education, and socioeconomic status. Psychosocial measures were homelessness, psychological distress, and trauma. Self-reported drug-related risk behaviors included using alone, having a new supplier, using opioids with benzodiazepines/alcohol, and preferring fentanyl. Past-year opioid-involved overdoses were dichotomized into experiencing none or any.FindingsThe EFA/CFA revealed a two-factor structure with one factor reflecting drug acquisition and the second drug use behaviors. The selected model (CFI = .984, TLI = .981, RMSEA = .024) accounted for 13.1% of overdose probability variance. A latent variable representing psychosocial attributes was indirectly associated with an increase in past-year overdose probability (β = .234, p = .001), as mediated by the EFA/CFA identified latent variables: drug acquisition (β = .683, p < .001) and drug use (β = .567, p = .001). Drug use behaviors (β = .287, p = .04) but not drug acquisition (β = .105, p = .461) also had a significant, positive direct effect on past-year overdose. No demographic attributes were significant direct or indirect overdose predictors.ConclusionsPsychosocial attributes, particularly homelessness, increase the probability of an overdose through associations with risky drug acquisition and drug-using behaviors. Further research is needed to replicate these findings with populations at high-risk of an opioid-related overdose to assess generalizability and refine the metrics used to assess psychosocial characteristics.

Strategies, processes, outcomes, and costs of implementing experience sampling-based monitoring in routine mental health care in four European countries: study protocol for the IMMERSE effectiveness-implementation study

BackgroundRecent years have seen a growing interest in the use of digital tools for delivering person-centred mental health care. Experience Sampling Methodology (ESM), a structured diary technique for capturing moment-to-moment variation in experience and behaviour in service users’ daily life, reflects a particularly promising avenue for implementing a person-centred approach. While there is evidence on the effectiveness of ESM-based monitoring, uptake in routine mental health care remains limited. The overarching aim of this hybrid effectiveness-implementation study is to investigate, in detail, reach, effectiveness, adoption, implementation, and maintenance as well as contextual factors, processes, and costs of implementing ESM-based monitoring, reporting, and feedback into routine mental health care in four European countries (i.e., Belgium, Germany, Scotland, Slovakia).MethodsIn this hybrid effectiveness-implementation study, a parallel-group, assessor-blind, multi-centre cluster randomized controlled trial (cRCT) will be conducted, combined with a process and economic evaluation. In the cRCT, 24 clinical units (as the cluster and unit of randomization) at eight sites in four European countries will be randomly allocated using an unbalanced 2:1 ratio to one of two conditions: (a) the experimental condition, in which participants receive a Digital Mobile Mental Health intervention (DMMH) and other implementation strategies in addition to treatment as usual (TAU) or (b) the control condition, in which service users are provided with TAU. Outcome data in service users and clinicians will be collected at four time points: at baseline (t0), 2-month post-baseline (t1), 6-month post-baseline (t2), and 12-month post-baseline (t3). The primary outcome will be patient-reported service engagement assessed with the service attachment questionnaire at 2-month post-baseline. The process and economic evaluation will provide in-depth insights into in-vivo context-mechanism-outcome configurations and economic costs of the DMMH and other implementation strategies in routine care, respectively.DiscussionIf this trial provides evidence on reach, effectiveness, adoption, implementation and maintenance of implementing ESM-based monitoring, reporting, and feedback, it will form the basis for establishing its public health impact and has significant potential to bridge the research-to-practice gap and contribute to swifter ecological translation of digital innovations to real-world delivery in routine mental health care.Trial registrationISRCTN15109760 (ISRCTN registry, date: 03/08/2022).

Inferiority Complex in Adolescent Users of The Tiktok Application

In today's digital era, the role of social media such as TikTok can influence the formation of adolescent self concepts. If it is not accompanied by a positive attitude, the various content seen can create a stimulus that forms a negative self-concept for adolescents. Inferiority complex is a condition in which individuals cannot compensate for feelings of inferiority so they tend to view their self-concepts negatively and feel powerless to face the environment. This study aims to determine the description of inferiority complex behavior in adolescent users of the TikTok application. This research is a mix method with purposive sampling technique. Quantitative data collection using an inferiority feeling scale. Qualitative data collection using interviews to 6 respondents with low, medium, and high inferiority complex categories. The results of the study describe that the inferiority complex behavior of adolescent users of the TikTok application is related to aspects of social confidence, school abilities, self-regard, physical appearance, and physical abilities. The survey results also found that 8.82% of teenagers using the TikTok application had a low level of inferiority complex; 85.30% at moderate level; and 5.88% at high levels.

Examining the social and behavioral dynamics of substance use in a longitudinal network study in rural Appalachia

BackgroundPrior studies have shown that individuals and their peers often have similar substance use behaviors, but the mechanisms driving these similarities – particularly in rural settings, are not well understood. The primary objectives of this analysis are to (1) identify factors that contribute to relationship turnover and maintenance within a rural network of persons who use drugs (PWUD), (2) determine whether assimilation and/or homophily shape participants use of injection drugs, heroin, and stimulants (methamphetamine and cocaine), and (3) assess the extent that these mechanisms influence networks ties and/or behaviors and whether these effects vary across time. MethodsSociometric network data were collected from a cohort of PWUD in rural Eastern Kentucky at baseline (2008–2010) and at four follow-up visits conducted approximately semiannually. Stochastic actor-oriented models (SAOMS) were used to model network structure and participant behaviors as jointly dependent variables and to identify characteristics associated with the maintenance, dissolution, and formation of network ties and changes in drug use behaviors. ResultsFindings suggest (1) greater network stability over time for reciprocal and transitive relationships, (2) both homophily and assimilation played a greater role in shaping injection drug use (IDU) initiation and cessation than they did in shaping heroin and stimulant use, and (3) the importance of these mechanisms appeared consistent over time. ConclusionGiven the stability of particular network structures and evidence of both homophily and assimilation with respect to drug-use behaviors, interventions that leverage social networks could be used to motivate health-promoting behaviors.

THE INFLUENCE OF VALUE PERCEPTION AND RISK PERCEPTION ON GREEN BUYING BEHAVIOR

Indonesia is the second largest contributor of plastic waste in the sea in the world, causing the government to issue policies to tackle plastic waste. The retail entrepreneurs association as the largest contributor of plastic waste follows government policy by providing environmentally friendly shopping bags/tote bags. Therefore, Niceso Ciruas, Serang Regency has adopted the policy. The policy affects consumer behavior. Green buying behavior is influenced by many factors, one of which is the perception of value and perception of risk. This study aims to determine the effect of value perception and risk perception on green purchasing behavior in shopping bag / tote bag users in Niceso Ciruas, Serang Regency. This research uses quantitative methods with a descriptive approach. The population criteria were consumers of Niceso Ciruas, Serang Regency and a sample of 100 respondents using the formula proposed by Slovin and Mustafa with a confidence level of 90% with a value of e = 10%. Data collection was carried out through the distribution of questionnaires to consumers of Niceso Ciruas, Serang Regency. The analysis method used is descriptive analysis using SmartPLS 4.0. This study found that value perception has a positive and significant effect on green purchasing behavior and risk perception has a positive and significant effect on green purchasing behavior in tote bag users in Niceso, Ciruas, Serang Regency.

PENGARUH FEAR OF MISSING OUT TERHADAP PERILAKU PHUBBING PADA REMAJA PENGGUNA MEDIA SOSIAL

The development of increasingly advanced technological times and the presence of the internet in Indonesia has reached hundreds of millions of users who use it for various purposes, one of which is for accessing social media, which reaches more than ninety percent, of which Indonesian teenagers in the 13-18 year age group use it the most. The aim of this research is to determine the influence of fear of missing out on phubbing behavior among adolescent social media users. The sampling techinique uses purposive sampling, the sample is 158 teenage respondents who use social media. The phubbing behavior scale compiled is based on aspects: communication disturbance and phone obsession, the fear of missing out scale compiled in this research is based on aspects: self and relatedness. This research uses a simple regression testing technique using SPSS version 25 for MacOS. The results of this research are that the hypothesis is accepted with a significance value of 0.000 (p&lt;0.05), namely that fear of missing out has a very significant effect on phubbing behavior in adolescent social media users.

Consumer Behaviour in Online Travel: How Does Continuous Purchase Behaviour Formed in Digital Age?

Research aims: This study aims to understand the role of perceived ease of use, information service function, consumer expectation, and user satisfaction in determining continuous purchase behavior in online travel application users, while considers consumer experience as a moderating variable.Design/Methodology/Approach: This study is carried out using quantitative approach. The data is collected by distributing online questionnaire to users of e-travel application in West Java, Indonesia chosen using purposive sampling. The data is processed using Structural Equation Modeling with Partial Least Square (SEM PLS).Research findings: The results indicate that perceived ease of use, information service function, consumer expectation, and user satisfaction can explain customers’ continuous purchase behavior. In addition, consumer experience can moderate the relationship between user satisfaction and continuous purchase behavior, by which higher experience strengthens the relationship. Theoretical Contribution/Originality: This study contributes to enrich the understanding of how user satisfaction affects continuous purchase behavior by looking at the variables of perceived ease of use, information service function, and user expectation.Practitioners/Policy Implications: This study can be of interest to service providers, especially e-travel application, to provide a good, easy to understand services, as well as information and comfort for its users to remain loyal in using the application.Research Limitations/Implications: This study is only limited to one object, namely users of e-travel application. Future research can use different objects and consider additional variables such as review credibility and perceived enjoyment in determining continuous purchase behavior.

The Relationship Between Self-Control and Self-Esteem with Narcissistic Behavior in Teenage Tiktok Users

This research aims to determine the relationship between self-control and self-esteem and narcissistic behavior in adolescent Tiktok users at SMA Negeri 2 Gorontalo City. This research uses a correlational quantitative design with a population of 840 students, a sample size of 90 students using purposive sampling techniques. The data collection method uses a self-control scale, self-esteem scale and narcissistic behavior scale using multiple correlation analysis techniques (Multivariate Correlation) with the SPSS 23.0 for Windows program. Data analysis on the correlation coefficient between self-control variables and narcissistic behavior resulted in a value of R = -0.514 p = 0.000 and the relationship between self-esteem and narcissistic behavior was R = -0.668 p = 0.000. This means, it can be said, there is a significant negative relationship between self-control and narcissistic behavior and there is also a significant negative relationship between self-esteem and narcissistic behavior in teenage TikTok users. Furthermore, the results of multiple correlations to measure the relationship between self-control and self-esteem with narcissistic behavior obtained a value of R = 0.670, R Square 0.449 and p = 0.000 (p &lt; 0.05). This is shows, there is a relationship between self-control and self-esteem and narcissistic behavior in teenage TikTok users at SMA Negeri 2 Gorontalo City.

User Behavior on Actual Use of Academic Information System in Makassar State University: Is Information Technology Facilities Have a Moderating Role to Strengthen the Behavior?

This study aims to determine factors that influence the use of information technology in higher education, especially at Makassar State University. The type of information technology studied is academic information systems. This study combines two theories, namely the Technology Acceptance Model (TAM) and The Theory of Planned Behavior (TPB) to explain the user behavior for the use of academic information systems at Makassar State University. The results of this study indicate that perceived usefulness and ease of use significantly affect user behavioral intention to use academic information systems. Behavioral intention significantly affected actual use of the academic information system. An interesting finding is that the facilities available for the use of the system in the campus environment have no significant effect on the use of the academic information system at Makassar State University.