Active Users Research Articles

Influencer Marketing, Personal Branding Image, And Affiliate Marketing Against Tiktok Shop Consumer Impulsive Buying

This research aims to identify the influence of Influencer Marketing, Personal Branding, and Affiliate Marketing on Consumer Impulsive Buying at the Tiktok Shop. The population in this research is consumers using Tiktok Shop (aged 11-39 years) in East Java with the sample size determined using the Slovin formula in this quantitative research being 110 respondents, who were selected using probability sampling techniques with random sampling type. In other words, there is an equal opportunity for people to be sampled with the characteristics of the sample used in the research, namely customers who have shopped on the TikTok Shop application and active TikTok Shop users. The data collection technique in this research uses questionnaire data with a linked measurement scale using data processing software tools, namely the Statistics Program for Social Science (SPSS). The results of the research obtained are that Influencer marketing, personal branding, Affiliate marketing influence impulsive buying at the Tiktok Shop. It can be concluded that there is a significant influence simultaneously or together between Influencer Marketing, Personal Branding, and Affiliate Marketing on the Impulsive Buying of Tiktok Shop Consumers.

TUMDOT–MUC: Data Collection and Processing of Multimodal Trajectories Collected by Aerial Drones

Currently available trajectory data sets undoubtedly provide valuable insights into traffic events, the behavior of road users and traffic flow theory, thus enabling a wide range of applications. However, there are still shortcomings that need to be addressed: (i) the continuous temporal recording (ii) of a coherent area covering several intersections (iii) with the detection of all road users, including pedestrians and cyclists. Therefore, this study focuses on the design of a large-scale aerial drone observation in the city of Munich, Germany, as well as the processing steps and the description of the resulting data set. Using twelve camera-equipped, unmanned aerial drones, the observation monitored an inner urban road section with a length of 700 m continuously for several hours during the afternoon peak hours on two working days. The trajectories of all road users were then extracted from the videos and post-processed in order to obtain a coherent and accurate data set. The resulting trajectories contain information on the category, dimensions, location, velocity, acceleration and orientation of each road user at each frame, merged continuously in time and space across several drone observation areas and subsequent time slots. The data, therefore, includes various interactions between different modes of motorized traffic and active mobility users like pedestrians and cyclists. The whole data set and the supporting data are available open source for research purposes to ensure global accessibility.

Aplikasi Sistem Laporan Kerja Harian Staf Pelayanan PT. Securindo Packatama Indonesia Berbasis Web dengan Metode Rapid Application Development

This study aims to develop a web-based daily work report information system that is more effective, efficient, and computerized. The application is designed to assist service staff in preparing daily reports and provides a data backup medium for work programs. The study employs the Rapid Application Development (RAD) method, which emphasizes fast development through iterative prototyping and active user involvement. RAD consists of several phases, including planning, design workshops, and implementation, allowing continuous user feedback to incrementally improve the system. Data was collected through field studies with direct observation, discussions with colleagues, and interviews with relevant parties. The application was built using PHP and MySQL and tested comprehensively to ensure performance and accuracy. The study concludes that the system effectively facilitates report recapitulation and performance data presentation, improving operational efficiency at PT. Securindo Packatama Indonesia.

Different Categories of Social Media Use and Their Association With Body Image Among Adolescents in 42 Countries.

Social media has become integrated into adolescents' lives and influences body image perceptions. Our study examined four patterns of social media use (SMU): non-active, active, intensive, and problematic. We hypothesised that intensive SMU and problematic SMU would be associated with negative body image (negative subjective body weight) and over/underestimated body weight congruence, compared to non-active and active SMU. In addition, we expect these associations to be stronger for girls. Data from 190,892 respondents aged 11, 13, and 15 from 42 countries involved in the Health Behaviour in School-aged Children study were analysed. Findings revealed higher rates of intensive or problematic SMU among adolescents who perceived themselves as too fat or too thin. Two-level regression analyses showed intensive and problematic SMU as more likely to perceive themselves as too fat or too thin than active users. The association was significant among intensive and problematic girl social media users, whereas, among boys, the relationship was only significant for problematic users. Our findings highlight the importance of assessing SMU patterns to evaluate associations with body image.

Improving the management of type 2 diabetes in China using a multifaceted digital health intervention in primary health care: the SMARTDiabetes cluster randomised controlled trial

There is limited evidence, mainly from high-income countries, that digital health interventions improve type 2 diabetes (T2DM) care. Large-scale implementation studies are lacking. A multifaceted digital health intervention comprising: (1) a self-management application ('app') for patients and lay 'family health promotors' (FHPs); and (2) clinical decision support for primary care doctors was evaluated in an open-label, parallel, cluster randomized controlled trial in 80 communities (serviced by a primary care facility for >1000 residents) in Hebei Province, China. People >40 years with T2DM and a glycated haemoglobin (HbA1c) ≥7% were recruited (∼25/community). After baseline assessment, community clusters were randomly assigned to intervention or control groups (1:1) via a web-based system, stratified by locality (rural/urban). Control arm clusters received usual care without access to the digital health application or family health promoters. The primary outcome was at the participant level defined as the proportion with ≥2 "ABC" risk factor targets achieved (HbA1c<7.0%, blood pressure < 140/80mmHg and LDL-cholesterol < 2.6mmol/L) at 24 months. A total of 2072 people were recruited from the 80 community clusters (40 urban and 40 rural), with 1872 (90.3%) assessed at 24 months. In the intervention arm, patients used FHPs for support more in rural than urban communities (252 (48.6%) rural vs 92 (21.5%) urban, p<0.0001). The mean monthly proportion of active app users was 46.4% (SD 7.8%) with no significant difference between urban and rural usage rates. The intervention was associated with improved ABC control rates (339 [35.9%] intervention vs 276 [29.9%] usual care; RR 1.20, 95% CI 1.02-1.40; p=0.025), with significant heterogeneity by geography (rural 220 [42.6%] vs 158 [31.0%]; urban 119 [27.9%] vs 118 [28.6%]; p=0.022 for interaction). Risk factor reductions were mainly driven by improved glycaemic control (mean HbA1C difference-0.33%, 95% CI-0.48 to-0.17; p=0.00025 and mean fasting plasma glucose difference-0.58mmol, 95% CI-0.89 to-0.27; p=0.00013). There were no changes in blood pressure and LDL-cholesterol levels. A multifaceted digital health intervention improved T2DM risk factor control rates, particularly in rural communities where there may be stronger relationships between patients and doctors and greater family member support. National Health and Medical Research CouncilGlobal Alliance for Chronic Diseases (ID 1094712).

Game psychotherapy intervention based on entertainment interactive robots for preventing depression in university students

This study designs an entertainment interactive robot system for gaming psychotherapy intervention and evaluates its effectiveness in preventing depression in students. In terms of the architecture and hardware of the entertainment interactive robot system, the robot adopts high-performance processors and sensors to achieve the ability to quickly respond and accurately perceive user actions. In terms of software, a mathematical model was established based on the structure and kinematic principles of the robot to describe its motion. Preprocess user gesture data by analyzing the features and patterns of user gestures, extracting key action information, and using machine learning algorithms or deep learning models to classify and recognize gestures to determine user intent and action types. Evaluate action recognition and effectiveness, and verify the accuracy and reliability of the gesture interaction module through comparison and testing with actual user gestures. The study designed an entertainment interactive robot assisted game that provides a fun and interactive experience to attract students’ attention and stimulate positive emotions. Through game design and behavioral pattern evaluation, the research team evaluated the preventive effect of entertainment interactive robot assisted games on depression and proposed optimization strategies.

Heterogeneity in activity and travel mode patterns of older Indonesians

Older adults aged 50 years and over may experience varied participation levels in out-of-home activities and travel modes, with their ability to walk being a significant factor. However, the heterogeneity of the activity–travel patterns of people at this age, especially in the Indonesian context, is not well understood. Using Yogyakarta as a case study and applying a latent class cluster analysis, this study is the first to categorize older adults’ participation in activities and use of travel modes and to understand the distinct characteristics of older adults. The model results revealed four discrete groups based on older adults’ activity participation and another four discrete groups based on their travel mode use. In the activity participation group, active older adults are the largest group, followed by inactive, working, and very active older adults. Meanwhile, those reliant on their motorized vehicles make up the largest travel mode group, followed by low-cost-vehicle users, captive riders of personal motorcycles, and car users. The model results also reveal that captive riders of personal motorcycles tend to be male and the youngest-old, while smartphone users are more likely to belong to the car users group. Walking ability constraints also significantly affect heterogeneity in older adults’ activity and travel mode, where those with no constraints on their ability to walk tend to be categorized as active older adult car users. The findings of this study may help policymakers identify older people with particular activity patterns and travel modes and develop policies to accommodate their mobility needs.

Interaksi Media Sosial Terhadap Niat Beli Merek Produk Fashion Lokal ‘Erigo’: Pengaruh Kredibilitas Sumber, Homofili Sumber, Dan Kualitas Konten Sebagai Variabel Mediasi

The purpose of this research is to analyze the factors influencing social media interaction regarding the purchase intention of the local fashion brand Erigo. The methodology employed in this study utilizes a quantitative approach with purposive sampling techniques. The criteria for respondents in this research are active Instagram users from the millennial and Generation Z cohorts, aged 19-36, residing in Indonesia. The valid number of respondents is 150. Data processing is carried out using the PLS-SEM method. The variables examined in this study include social media interaction, source credibility, source homophily, content quality, and purchase intention. The findings of this research indicate that social media interaction has a positive influence on source credibility, source homophily, and content quality. Source homophily and content quality positively affect purchase intention. However, source credibility does not have a significant impact on purchase intention. The results of this research are expected to assist companies and marketers in developing more intimate relationships with consumers and encouraging consumer intent to purchase through social media interaction.

An intelligent security mechanism in mobile Ad-Hoc networks using precision probability genetic algorithms (PPGA) and deep learning technique (Stacked LSTM)

The Mobile Ad-hoc Networks (MANETs) have gained a significant attention in the recent years due to their proliferation and huge application purposes. To defend from many types of modern cyber dangers like Distributed Denial of Service (DDoS) attacks, Advanced Persistent Threats (APTs), Insider Threats, Ransomware, Zero-Day Exploits, Social Engineering tactics, and etc is not easy when it comes to keeping MANETs security. These complex assaults focus on network infrastructure, take advantage of weaknesses in communication protocols and control user actions. Although there have been improvements in intrusion detection systems (IDS), it is still difficult to fully safeguard MANETs. The purpose of this research is to create advanced methods that can accurately find and decrease attacks inside MANETs. Applying Sensor-based Feature Extraction (SFE) to extract useful network features such as Received Signal Strength Indication (RSSI) and Time of Travel (TOT) from datasets NSL-KDD and CICIDS-2017. Utilizing the fresh method of Precise Probability Genetic Algorithm (PPGA) optimization for removing unrelated details, which enhances precision in detecting attacks. Predicting normal and attacking labels by applying Stacked Recurrent Long Short Term Memory (SRLSTM) method, fine-tuning classifier's parameters in every layer to improve outcomes. In order to authenticate and compare the suggested methods with current attack detection tactics, this study will make use of various evaluation measurements. The NSL-KDD, which is a benchmark dataset in network intrusion detection research, has a wide variety of network traffic data with instances that are labeled as normal and different attacks. CICIDS-2017 is similar because it contains an extensive dataset too - this includes real-world traces from network traffic where there's both regular activity and harmful actions. The purpose is to enhance the existing status of MANET security so as it can withstand more strongly against cyber dangers. According to the outcomes, it is analyzed that the attack detection accuracy has improved greatly 99 % when compared to other methods, as shown by the detailed assessment measurements. Better handling of big datasets with top detection accuracy reduces the time needed 8.9 s for training and testing models. Decrease in misclassification results and better ability to differentiate normal network actions from harmful intrusions. Improved resistance of MANETs to different cyber dangers, guaranteeing the safety and dependability of network communication in changing and non-centralized settings.

Sharenting and Digital Reputation: Pengaruh Sharenting Terhadap Presentasi Diri Orang Tua Milenial Di Media Sosial

Sharenting has now become a global trend on social media. The phenomenon that we can see in Indonesia is that many social media users who are millennial parents share content about their children's activities on social media. Sharenting is also considered a place for millennial parents to build their digital reputation. This research aims to prove the influence of sharenting on millennial parents' self-presentation on social media. The right approach to help researchers answer this research question is through a quantitative approach. Data collection techniques use online surveys distributed through advertising features on the Facebook platform. After filtering the online questionnaire data, the total number of respondents taken was 518 respondents. All respondents met the respondent criteria, namely millennial parents (age range 24-39 years), regularly sharing their children's activities on social media, and active users of popular social media. The research hypothesis will be tested using regression tests, correlation tests, partial tests (t), and simultaneous tests (f). The measurement method uses a Likert scale. Meanwhile, the data's validity was tested using validity and reliability tests. The research results prove a significant influence of sharenting activities on the self-presentation of millennial parents on social media. The relationship between the two variables is also stated to be positive. Then, the strength of the two variable relationships enters into a strong correlation. From the calculation results, the higher the sharenting, the higher the self-presentation. In the context of self-presentation, millennial parents use sharenting to display positive aspects of their lives, especially related to family life and their role as modern parents. For millennial parents, self-presentation on social media can also affect their reputation in the online environment. By building a positive image as caring and involved parents, they can strengthen their social connections and expand their support network online.

Hybrid Generalized Approximate Message Passing for Active User Detection and Channel Estimation With Correlated Group-Heterogeneous Activity

Hybrid Generalized Approximate Message Passing for Active User Detection and Channel Estimation With Correlated Group-Heterogeneous Activity

AI-Driven UX/UI Design: Empirical Research and Applications in FinTech

This study explores the transformative impact of AI-driven UX/UI design in the FinTech sector, examining current practices, user preferences, and emerging trends. Through a mixed-methods approach, including surveys, interviews, and case studies, the research reveals significant adoption of AI technologies in FinTech UX/UI design, with 78% of surveyed companies implementing such solutions. Personalization emerges as a dominant trend, with 76% of FinTech apps utilizing AI for tailored user interfaces. The study demonstrates a strong correlation between AI-enhanced features and improved user engagement, with apps incorporating advanced AI features showing a 41% increase in daily active users. Ethical considerations, including data privacy and algorithmic bias, are addressed as critical challenges in AI implementation. The research contributes a conceptual framework for AI-driven UX/UI design in FinTech, synthesizing findings from diverse data sources. Future trends, including emotional AI and augmented reality integration, are explored. The study concludes that while AI-driven UX/UI design offers significant potential for enhancing user experiences in FinTech, balancing innovation with ethical considerations is crucial for responsible implementation and user trust.

Digital Archives as Resisting Displacement

Abstract Amid wider concern about the emergence of vast data archives that document and instrumentalize everyday user activities for the purpose of marketing, research, and governance, this article turns to a series of creative and activist initiatives that preserve heterodox internet histories. Though a focus on three case studies—artistic engagements with GeoCities, traces left by Indymedia in contemporary activism, and emerging ethical frameworks for reusing social media data—the article examines the political and ethical significance of attempts to archive specific instances of participatory online cultures before these cultures disappear. Drawing from and advancing Jodi Dean's conception of displaced mediators, or entities that set in motion the forces that ultimately displace them, the article argues that the significance of these digital archives is in their capacity to resist linear, commercial logics of displacement that attempt to narrow user agency. Instead, the article argues, creative and activist engagements with digital archives generate questions about whether the infrastructures that govern everyday online interactions could be otherwise, through showing how they formerly have been otherwise.

Physical Side-Channel Attacks against Intermittent Devices

Intermittent (batteryless) devices operate solely using energy harvested from their environment. These devices turn on when they have energy and turn off during energy scarcity. Intermittent devices have recently become increasingly popular in smart buildings, manufacturing plants, and medical implantables as they eliminate the need for battery replacement and enable green computing. Despite their growing adoption in critical applications, the privacy implications of intermittent devices remain largely unexplored. In this paper, we introduce a novel remote side-channel attack. Our observation is that the network packet frequency of an intermittent device can be exploited to learn its turn-on/off patterns. From these patterns, we can infer the energy availability of a device, which reveals privacy-sensitive information about its operating environment, e.g., the presence or absence of individuals. To realize our attack, we develop a three-stage hierarchical inference framework that leverages the timestamped network packet sequence of intermittent devices. Our framework automatically extracts a set of temporal features from inter-packet-arrival timings. It then employs a series of models to uncover (1) whether a target intermittent device is present in the environment, (2) its energy harvester type (e.g., vibration or water flow), and (3) its energy availability conditions (e.g., high-vibration or no-vibration). To validate our attack effectiveness, we conduct experiments in two environments: a smart home and a miniature manufacturing plant equipped with three intermittent devices powered by solar energy, vibration, and temperature. By analyzing their energy availability patterns, we are able to infer user activities and presence in the smart home and the robot’s movement patterns in the manufacturing plant with an average accuracy of 85%. This sensitive information enables an adversary to launch domain-specific attacks, such as burglarizing a smart home when the user is asleep or timely tampering with plant sensors to cause maximum damage.

Enhancing Academic Integrity for Bangladesh's Educational Landscape

Integrating Artificial Intelligence (AI) tools in Bangladesh's academic landscape has ignited concerns about potentially eroding students' creative writing and critical thinking abilities. While AI offers efficient and personalized learning, there is a looming risk of students using it as a shortcut to success. Educators and policymakers must emphasize the cultivation of writing skills and critical thinking while guiding students to recognize the limitations of AI. While plagiarism checking is crucial for academic integrity, it often falls short in acknowledging students' originality. Natural Language Processing (NLP) emerges as a promising solution, augmenting plagiarism detection by comprehending context, nuances, and novel expressions. This paper proposed a model based on NLP that can incorporate a robust token identification mechanism into AI tools used by the students. A unique identification token is generated at the time of system login and stored in a publicly accessible resource, acting as a distinct marker for user activity. These identification tokens support a plagiarism detection system that continually reviews and compares activity records. It also enables a thorough examination of user involvement across various academic supplementary tools, such as academic writing, presentation creation, image generation, etc.

Increasing the precision of public transit user activity location detection from smart card data analysis via spatial–temporal DBSCAN

Smart Card (SC) systems have been increasingly adopted by public transit (PT) agencies all over the world, which facilitates not only fare collection but also PT service analyses and evaluations. Spatial clustering is one of the most important methods to investigate this big data in terms of activity locations, travel patterns, user behaviours, etc. Besides spatio-temporal analysis of the clusters provide further precision for detection of PT traveller activity locations and durations. This study focuses on investigation and comparison of the effectiveness of two density-based clustering algorithms, DBSCAN, and ST-DBSCAN. The numeric results are obtained using SC data (public bus system) from the metropolitan city of Konya, Turkey, and clustering algorithms are applied to a sample of this smart card data, and activity clusters are detected for the users. The results of the study suggested that ST-DBSCAN constitutes more compact clusters in both time and space for transportation researchers who want to accurately detect passengers’ individual activity regions using SC data.

생성형 AI가 가짜뉴스의 확산에 미치는 영향 연구

With the rapid development of generative artificial intelligence(AI) technology, concerns have been raised that it can be used to produce and disseminate fake news. Fake news is viewed as a digital organism that feeds on human interest and emotions, and how the Generative AI can increase the production and propagation power of fake news is considered. Through literature review and case analysis, the main characteristics of the Generative AI that can be abused in the production of fake news were derived. Deep learning, natural language processing, and multi-modal content synthesis are representative. Based on this, a conceptual model was proposed to explain the life cycle of AI-generated fake news and the factors of propagation within the digital platform. The research results suggest that the Generative AI can greatly amplify the scale and persuasiveness of fake news by automatically producing realistic and emotionally stimulating content. It also highlights the importance of digital platforms and user actions in the spread of AI-generated fake news. In order to cope with these threats, a multidimensional approach such as technical solutions, media literacy education, and regulation is required. An in-depth understanding of the co-evolution dynamics of AI technology and human society in the digital ecosystem is required. This study provides exploratory insights, but follow-up studies are needed for empirical verification and elaboration of the proposed model.

The effectiveness of active rehabilitation camp on wheelchair users’ self-esteem

Background: The Active Rehabilitation Camps (ARC) provide access to comprehensive rehabilitation. The main goal is to provide wheelchair users achieving the maximum possible independence in performing activities of daily living, to help them accept a new lifestyle, as well as comprehensive rehabilitation in the social and vocational fields. Higher self-esteem in wheelchair users help them overcome barriers in everyday living, increase initiative, improve their mental health and well-being.Aims of the study: The aim of the research was to evaluate the impact of Active Rehabilitation Camps on self-esteem of wheelchair users.Material and methods: 95 adult wheelchair users aged 18 to 75 took part in the study. 92 participants were included in the analysis (31 women and 61 men). The research tool was a questionnaire including first part with personal data and a second part - an international and validated questionnaire the Rosenberg Self-Esteem Scale (RSES). All participants filled the questionnaire at the beginning of the ARC and after two-week camp.Results: The results showed a significant improvement in self-esteem among participant after camps, total self-esteem according to RSES increased from 28.32 4.99 before a camp, to 30.8 4.85 after a camp (p&lt;0.0001). Our results showed that apart from general self-esteem, SE in such domains as “on the whole, I am satisfied with myself”, “I am able to do things as well as most other people” or “I take a positive attitude towards myself” increased significantly after the ARC (p&lt;0.001).Conclusions: The activities of the Active Rehabilitation Camp including access to physical activity, health-promoting education, experience exchange and integration of wheelchair users, increase the level of self-esteem of Active Rehabilitation Camp participants. Systematic physical activities, social and emotional support, as well as appropriate education should be provided in everyday rehabilitation among wheelchair users to improve their self-esteem and everyday life functioning.

A STUDY ON RELIABILITY TEST TO CONFIRM ANTECEDENT OF INFLUENCER MARKETING AND CONSUMER BEHAVIOR WITHIN PUNE CITY

This research study investigates the antecedents of social media influencer marketing. These antecedents were found and perceived to impact consumer behavior within Pune City. In particular, the research focuses on understanding the role of key factors such as trust &amp; credibility, consumer connect &amp; engagement, and social circle influence, the conceived belief system of the consumer antecedents that drive consumer decisions in influencer-led campaigns. By investigating these factors, the study aims to confirm their significance and reliability to firmly shape the construct of the impact of SMIs on consumer purchasing patterns, in a quickly developing urban market. The researchers employed a quantitative research approach, picking primary data from active social media users in Pune city. These users are followers of social media influencers using platforms like YouTube, Instagram, and Twitter. The Structured survey questionnaire was distributed to measure variables such as consumer trust &amp; credibility, influencer content richness &amp; authenticity, and purchase intent. Reliability testing was conducted using Cronbach’s alpha, to evaluate the consistency of the identified antecedents which influence consumer behavior. The analysis revealed that the alpha value of 0.754, fits contentedly within the prescribed range, indicating that the 42 items (defining antecedents) are well-designed. It also reveals that these items effectively acquire the envisioned concept. The inclusion of 42 items in the analysis advocates that a larger number of items normally increases the likelihood of capturing a comprehensive picture of the construct, thereby the reliability increases. This study confirms the importance of factors such as “Influencers are credible specialists, and following their advice is accepted socially” and "Consumers’ aristocracies support social media influencers’ recommendations". These factors when synthesized to “credibility” and “recommendation-centric”, bring into the limelight as crucial antecedents to be considered in the social media influencer marketing effectiveness within Pune City. The findings provide valuable perceptions for marketers looking to optimize their strategies. They can leverage social media influencer marketing to engage local viewers. This research study also contributes to the academic understanding : (a) how influencer marketing affects consumer behavior within Pune city (b) offers practical applications for businesses within the urban setting.

Motivating the Younger Generation in Reading: Integrating Technology in Understanding Digital Literacy

Reading is an important activity to develop insight, knowledge and critical thinking skills. Therefore, schools should organize literacy activities to increase interest in reading. Digital literacy is the ability to understand and use information from various digital sources and use technology and information from digital devices in various situations. The aim of this research is to develop an effective strategy for integrating technology in literacy promotion, to motivate the digital generation to read. The research technique is a qualitative method, with a case study approach, the research subjects are the digital generation, such as pupils, students and active social media users. The research object is the reading motivation of the digital generation and the integration of technology in the promotion of digital literacy. Assessment instruments (level of interest in reading, responsiveness, initiative to use technology in reading, and participation). The data collection techniques used are participant observation sheets to observe how the digital generation uses technology in reading, and document analysis to study existing digital literacy programs and how they integrate technology.