Usage Behavior Research Articles

Knowledge and Attitude of self-medication with leftover antibiotics in Saudi Arabia: A cross-sectional study

Background Antimicrobial resistance is increasing at an alarming rate. The use of antibiotics without a prescription by a patient or other family members and their inappropriate storage have caused serious health issues as it would lead to antibiotic resistance and exposure to the risk of harmful adverse effects unnecessarily. Therefore, the study aimed to evaluate the current behaviour of antibiotic usage, storage, re-usage and misuse among the residents of Saudi Arabia. Methods This is a cross-sectional study. Our target study population was the residents of Saudi Arabia. Data were collected by an online questionnaire and analysed by SPSS. Results A total of 738 participants answered the online questionnaire from all ages, genders, nationalities, and socioeconomic backgrounds residing in different regions across the Kingdom of Saudi Arabia. 76.42% knew that an antibiotic is a chemical substance used to treat infections. The participants were questioned about when they started using antibiotics, to which 95.66% (n=706) responded after consulting a physician, 3.25% (n=24) said when they felt ill for any reason, and 1.08% (n=8) replied after first attempting herbal medicine. A total of 147 participants admitted that they store excess pills of antibiotics after being prescribed for an infection and reuse them later on for symptoms like sore throat and fever. Conclusions The results indicated that nearly half of the participants used leftover antibiotics. Participants having children in their homes significantly reuse antibiotics. However, one-third of the participants didn’t complete the antibiotics course. A large portion of the population, regardless of age, level of education, or professional background, have continued to store leftover antibiotics after an infection treatment and reuse them once they think they need them for new symptoms. This advice further revises the current measures to fill those gaps and reduce this habit.

Optimal Dispatching Strategy for Textile-Based Virtual Power Plants Participating in GridLoad Interactions Driven by Energy Price

The electricity consumption of the textile industry accounts for 2.12% of the total electricity consumption in society, making it one of the high-energy-consuming industries in China. The textile industry requires the use of a large amount of industrial steam at various temperatures during production processes, making its dispatch and operation more complex compared to conventional electricity–heat integrated energy systems. As an important demand-side management platform connecting the grid with distributed resources, a virtual power plant can aggregate textile industry users through an operator, regulating their energy consumption behavior and enhancing demand-side management efficiency. To effectively address the challenges in load regulation for textile industry users, this paper proposes a coordinated optimization dispatching method for electricity–steam virtual-based power plants focused on textile industrial parks. On one hand, targeting the impact of different energy prices on the energy usage behavior of textile industry users, an optimization dispatching model is established where the upper level consists of virtual power plant operators setting energy prices, and the lower level involves multiple textile industry users adjusting their purchase and sale strategies and changing their own energy usage behaviors accordingly. On the other hand, taking into account the energy consumption characteristics of steam, it is possible to optimize the production and storage behaviors of textile industry users during off-peak electricity periods in the power market. Through this electricity–steam optimization dispatching model, the virtual power plant operator’s revenue is maximized while the operating costs for textile industry users are minimized. Case study analyses demonstrate that this strategy can effectively enhance the overall economic benefits of the virtual power plant.

Regional variations in digital transformation: the impact of industry 5.0 on business quality performance

After criticism of the technological orientation of Industry 4.0, many authors began to highlight the importance of Industry 5.0, which considers human-oriented values. This opened space for a lot of research on how people perceive new technologies in this environment and their relationship with the new paradigm – Industry 5.0. The paper aims to examine the relationship between familiarity with Industry 5.0 and business quality performance in SMEs through the workers’ perception of digitalisation (attitudes toward digitalisation, digitalisation usage behaviour, and digitalisation behavioural intentions). The results were obtained through a questionnaire conducted in six European countries divided into two groups according to familiarity with Industry 5.0 employing Multi-Group Confirmatory Factor Analysis (MGCFA). The findings implied the existence of significant relationships between familiarity with Industry 5.0 and quality performances through certain mediating variables. Additionally, a difference was established between the two groups of countries: High Familiarity Level and Low Familiarity Level. These conclusions benefit SMEs by facilitating change and technology adaptation. Positive perceptions drive the gradual adoption of digitalisation, boosting competitiveness through agility, innovation, and efficiency. The research offers insights for practitioners and policymakers to enhance SMEs’ competitiveness in Industry 5.0.

User Profiling in Human-AI Design: An Empirical Case Study of Anchoring Bias, Individual Differences, and AI Attitudes

People form perceptions and interpretations of AI through external sources prior to their interaction with new technology. For example, shared anecdotes and media stories influence prior beliefs that may or may not accurately represent the true nature of AI systems. We hypothesize people's prior perceptions and beliefs will affect human-AI interactions and usage behaviors when using new applications. This paper presents a user experiment to explore the interplay between user's pre-existing beliefs about AI technology, individual differences, and previously established sources of cognitive bias from first impressions with an interactive AI application. We employed questionnaire measures as features to categorize users into profiles based on their prior beliefs and attitudes about technology. In addition, participants were assigned to one of two controlled conditions designed to evoke either positive or negative first impressions during an AI-assisted judgment task using an interactive application. The experiment and results provide empirical evidence that profiling users by surveying them on their prior beliefs and differences can be a beneficial approach for bias (and/or unanticipated usage) mitigation instead of seeking one-size-fits-all solutions.

The Role of UTAUT2 in Understanding Technology Adoption: A Study of the Merdeka Mengajar Platform Among Indonesian Teachers

This research investigates the adoption of the Merdeka Mengajar application using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. The study aims to identify the factors influencing teachers' behavioral intentions and usage behavior regarding this educational technology platform. A total of 383 teachers from various levels in Malang were sampled from a broader population of 8,936. Statistical analysis uses SEM (Structural Equation Modelling) analysis techniques. The findings suggest that performance expectancy, effort expectancy, social influence, hedonic motivation, and price value significantly influence behavioral intention. However, facilitating conditions and habit do not show a direct significant impact on use behavior. These results indicate that while technological support such as infrastructure and internet access is necessary, it alone may not be enough to motivate consistent usage without internal factors like perceived usefulness or enjoyment of the app. Moreover, the habitual use of new technology may require additional support and time before it can significantly affect behavior. This study contributes valuable insights into the adoption of educational technologies, especially in the Indonesian context, where digital learning platforms are increasingly being integrated into teaching practices. Future research may explore how ongoing support and user experience improvements can further enhance the app's adoption.

A Data Mining-Based Method to Disclose Usage Behavior Patterns of Fresh Air Systems in Beijing Dwellings during the Heating Season

As the popularity of fresh air systems (FAS) in residential buildings increases, exploring the behavioral characteristics of their use can help to provide a comprehensive understanding of the potential for demand flexibility in residential buildings. However, few studies in the past have focused on the personalized usage behavior of FAS. To fill this gap, this study proposes a method based on data mining techniques to reveal the behavioral patterns of FAS usage and the motivations behind them, including motivational patterns, operation duration patterns, and human–machine interaction patterns, for 13 households in Beijing. The simultaneously obtained behavioral patterns, in turn, form the basis of association rules, which can classify FAS usage behavior into two typical residential user profiles containing user behavioral characteristics. This study can not only provide more accurate assumptions and inputs for behavioral stochastic models but also provide data support for the development and optimization of demand response strategies.

Safe Use of Screen Time Among Adolescents: A Randomized Controlled Study of the Efficacy of Yoga.

For students between the ages of 11 and 18, the estimated daily exposure to electronic media is around 1.5 hours, suggesting a growing trend in the amount of time spent in front of a screen. Increasing screen time use is related to sleep, mental health, cognitive, emotional, and behavioral disorders, unhealthy diets, depressive symptoms, poor quality of life, and substance-behavioral addictions. The current study is a parallel-group, simple randomized controlledtrial (RCT). A total of 100 participants were randomized into a yoga group (YG) (n=50) and an attentional control group (CG) (n=50) by using an online randomizer. The YG received yoga practice, whereas the partially active CG received an educational program on digital hygiene. Students were assessed for screen time use, Smartphone Addiction Scale-Short Version (SAS-SV), general well-being, depressive symptoms, and generalized anxiety following 12 weeks of intervention. The yoga group showed a significant decrease in the scores of SAS-SV, generalized anxiety, depressive symptoms, and screen time use after the intervention (p<0.001, p<0.05, p<0.01, and p<0.001, respectively). Furthermore, there was a statistically significant decrease in screen time use for all modes of total weekdays, weeknights, and weekends. Additionally, in the yoga group, the majority of the sub-factors of screen time use showed statistically significant changes; time spent on television showed a constant decrease in all modes such as weekdays, weeknights, and weekends. The result of the current study suggests that yoga practice hasa positive influence on screen usage behavior and associated health-related complications. More studies are required to understand if yoga practice can help mitigate the pleasure-seeking behavior that drives the excessive usage of screens.

Towards Sustainably Driven Telemedicine: Understanding the Determinants Affecting Elderly’s Behavioral Intentions and Usage Behavior

Towards Sustainably Driven Telemedicine: Understanding the Determinants Affecting Elderly’s Behavioral Intentions and Usage Behavior

Telemedical Interventions in Ambulant Psychotherapeutic Practices: Online Survey of Psychotherapists and Patients in Germany on the use of Digital Interventions in Psychotherapy

Psychotherapy is going digital. The study investigated the usage behavior and acceptance of digital psychotherapy interventions among outpatient psychotherapists and patients. 269 therapists and 157 patients answered questions in an online survey on the use and satisfaction of digital psychotherapy interventions (video therapy, apps, VR, sensor-based psychotherapy), affinity for technology, acceptance of technology, digital therapy relationship. All participants were satisfied with video therapy, stated that they used apps, VR, etc. less. Patients were more open, more tech-savvy and felt more competent in their use compared to therapists. Psychotherapists rated the digital therapeutic relationship better than patients. The use of digital technologies in psychotherapy is not yet established in outpatient care. Measures to promote acceptance are necessary to break down barriers.

Admire you, become you: exploring the role of coworker’s AI symbolization in motivating employee AI usage behavior

Admire you, become you: exploring the role of coworker’s AI symbolization in motivating employee AI usage behavior

Unraveling the dynamics of ChatGPT adoption and utilization through Structural Equation Modeling

ChatGPT, an advanced Artificial Intelligence tool, is getting considerable attention in higher education. ChatGPT significantly changes the student learning experience through its AI-aided support, personalized study assistance and effective educational experiences, and it has become an object of particular interest in this context. This research aimed to build a technology acceptance and usage model that encapsulates the elements influencing students’ adoption and utilization of ChatGPT, drawing on constructs from the ‘Unified Theory of Acceptance and Use of Technology’ and ‘Flow Theory’. The proposed model was found valid and prolific, with the credibility of the results relying on the self-reported surveys of 505 students from three universities in Pakistan. Structural Equation Modelling (SEM) was used to analyze data that confirmed the robustness and validity of the proposed model of the study. The study findings supported nine out of the ten proposed hypotheses. Perceived playfulness was declared the paramount predictor of behavioral intention, while perceived values and performance expectancy were the next-level predictors. Additionally, behavioral attention was a high and inspiring determinant of ChatGPT usage behavior, followed by attention focus. This analysis demonstrates a need for a thorough investigation of AI tools like ChatGPT in higher education.

Usage behavior related to the 9-Euro-ticket - impetus for psychiatric outpatients to increase activity and social contacts?

This study presents first descriptive statistics on the usage behavior relating to discounted tickets for public transportation as part of an initiative of the German Federal Government in 2022. During a three-month period, 103 psychiatric outpatients of the University Clinic - Charité Berlin provided self-reported data by completing a survey. In general results suggested a high usage rate of the so-called "9-Euro-ticket" of 89,3%. Due to its purchase, 37,9% (N=39) of the participants felt motivated to explore unknown places. Furthermore, approximately one third of the sample (34,9%; n=36) reported an increase of social contacts as a result of their ticket usage. A higher influence of costs on participants' activity levels was associated with an increase in main symptoms. Higher age was correlated with a decrease of patients' core symptoms due to their activity. Results indicate a high usage of reduced-price tickets for subsidized public transport by the patients concerned.

Exploring the Influence of Artificial Intelligence Usage on Ethical Decision Making Among Public Sector Employees: Insights into Moral Identity and Service Motivation

This study constructs a moderating mediation model to link public sector employees’ Artificial Intelligence (AI) usage with employees’ moral norms and ethical decision-making behaviors. Based on the theory of public service motivation, this study hypothesizes that the impact of AI usage on employees’ ethical decision-making behaviors acts through the mediating effects of employees’ service motivation, employees’ moral norms, and employees’ ethical perceptions and that the relationship between AI usage and employees’ service motivation, employees’ ethical norms, and employees’ ethical perceptions is moderated by the culture of the public organization. The selected data from 417 public sector employees in China supported most of the research hypotheses. The findings show that employee service motivation, employee moral norms, and employee moral cognition mediate the relationship between AI usage and employee ethical decision-making behavior. Public organization culture moderated the relationship between AI usage and employee service motivation, as well as AI usage and employee ethics. This study reveals the complex mediating and moderating relationships between AI usage and employees’ ethical decision-making behaviors in the public sector. It provides important theoretical and practical insights for further understanding and promoting public sector employees’ ethical behaviors in the era of AI.

Implementation of The Electronic Application for Marriage and Pregnancy (Elsimil) to Accelerate Stunting Reduction

Denpasar City is one of the areas that has succeeded in reducing the prevalence to 5.5%, one of which is by providing guidance to prospective brides to reduce the risk of stunting cases by implementing the ELSIMIL application. This study was conducted to obtain an overview of the factors that influence the behavior of using the ELSIMIL application in Denpasar City so that it can be an effort to accelerate stunting reduction. Data were collected from 14 informants, both users and policy holders. Data collection methods through interviews, observations, and document studies with validity testing through source triangulation and method triangulation. The results of the description of the ELSIMIL application implementation show that performance expectations, effort expectations, social influences, and facility conditions affect usage behavior along with behavioral intentions according to UTAUT theory. Performance expectations, which include perceived usefulness, motivation, relative advantage, and belief in the future potential of the application, are the main factors supporting usage behavior. Effort expectations include perceived ease and complexity of information can be a factor that supports behavioral intentions. Social support from family and friends and application conditions can also influence ELSIMIL usage behavior

Developing Questionnaires for the Technology Acceptance Model (TAM): A Study on the Teachers’ Acceptance of Using Online Education

Since the Movement Control Order (MCO) in 2020, Malaysia's Ministry of Education (MOE) has recommended online learning sessions. Traditional teaching methods need to be replaced with more modern approaches. This change allows students to participate in distance learning, making online learning more widely accepted in education. However, the challenges of online communication have intensified due to the pandemic. Some teachers may find the complexity of online platforms an obstacle and may use this as an excuse to avoid using technology. Another issue is the lack of computer proficiency, which can create a fear of technology. Additionally, the convenience of learning from any location at any time should be considered. Previous studies have shown that user motivation plays a crucial role in accepting technology. This study aims to develop an instrument that measures teachers’ acceptance of online learning based on the Technology Acceptance Model (TAM). The article will focus on developing a questionnaire to assess the following variables: teacher’s Perceived Usefulness, Perceived Convenience, Perceived Ease of Use, Attitude, Intention to Use, and Usage Behaviour in accepting technology. The study used a quantitative approach. The pilot test was conducted on the instrument for 40 secondary school teachers in Perak. The data were analysed both descriptively and inferentially. The reliability and validity results were significant and are expected to aid future researchers in developing better assessment tools. Keywords: Instrument development, questionnaire, Technology Acceptance Model, teachers’ acceptance, online education.

MENTAL HEALTH IMPACTS BY ELEVATED DIGITAL SCREEN TIME DURING COVID-19 PANDEMIC

Introduction – The length of time invested in and the various activities carried out utilizing digital devices are referred to as screen time. The COVID-19 pandemic's restrictions on social interactions exacerbated the overuse of digital gadgets for everyday tasks like socializing, education, shopping, working, meeting, and entertainment. Increased screen usage has been thought to have harmful impacts on psychiatric health in studies. Methods – Several journal articles were used as references, with the inclusion criteria of literature that discuss the mental health impacts of elevated digital screen time during the pandemic of COVID-19 and its recommended measures to minimize its adverse mental health outcomes and the promotion of healthy digital habits. Results – During the pandemic of COVID-19, the unprecedented digital life led to higher numbers of anxiety, sadness, confusion, and unpleasant feelings including irritation and violence. it is crucial to know how screen use time evolved during the COVID-19 period of crisis so that the design of health promotion and intervention may be planned and informed. This study aims to discuss the mental health impacts of elevated digital screen time on the pandemic of COVID-19 and strategies to minimize its adverse mental health outcomes, and the promotion of healthy digital habits. Discuss – Boosting collaborative research involving worldwide nations to tackle COVID-19-related public health concerns can improve global evidence and future policies. Increased screen usage has been displayed to have harmful impacts on psychiatric health in studies. Excessive digital use has been linked to attention deficit disorders, social and emotional cognitive impairment, social withdrawal, phantom vibration syndrome, and major psychiatric diseases such as depression, anxiety, and gaming addiction. Conclusion – It is critical to promote good digital practices and good applications of digital media to avoid the negative effects of increased screen time. To prevent hazardous screen usage and other behaviors that may influence the mental health and welfare of COVID-19-affected people, it is critical to harness the evidence available and adopt multimodal strategies. Keywords: mental health, screen time, COVID-19.

The Effect of IFMIS Symbolic Adoption on Post-Acceptance Use Behavior: Moderated Analysis

Integrated Financial Management Information System (IFMIS) is a a significant milestone in a country's budget reform agenda. The government of Indonesia has launched an IFMIS for work unit level called Sistem Aplikasi Keuangan Tingkat Instansi (SAKTI) in 2022. This research seeks to test the effect of TAM determinants on symbolic adoption and subsequent post-acceptance usage behaviour with individual absorptive capacity as moderator. This research is quantitative research, using PLS-SEM disjoint two-stage approach to assess symbolic adoption, a higher order construct consisting of four dimensions. 93 samples from ministry of public works and housing are analysed and found that both TAM determinants have a positive impact on symbolic adoption and symbolic adoption positively affect post-acceptance use behaviour. Individual absorptive capacity enhances the impact of symbolic adoption on explorative use, but does not moderate the effect on extended use. This research contributes to the field of symbolic adoption by demonstrating how individual absorptive capacity can shape the relationship between symbolic adoption and post-acceptance usage behaviour.

Analysis of The EwacsPro Auto Dispatch System in XYZ Company Using Technology Readiness Level (TRL) and Technology Acceptance Model 2 (TAM2) Adoption

Fleet Management System (FMS) is a system that quite good to manage the haul cycle and can be useful for reducing the idle time of the conveyance. The development of an FMS with an Auto Dispatch System (ADS) that utilizes digital information technology can make management easier. ADS is the system which has assignment logic systems for transport equipment, either directly suggesting to the operator through computerized systems or through dispatchers. XYZ Company developed its own ADS, called Early Warning &amp; Control System Pro (EwacsPro), which has been tested on 1 of 4 total pits they have in ABC jobsite, using the LTE Communication network, which is the first in Indonesia. The problem behind this research is that the system has never been tested for readiness. This research aims to analyze, evaluate and explore the level of readiness of the EwacsPro system using the Technology Readiness Level (TRL) method and also analyze the level of operator acceptance of the use of the EwacsPro system adopting Technology Accetance Model 2 (TAM2) using Structural Equation Modeling (SEM) analysis. The research will provide recommendations to the XYZ management for improvements related to EwacsPro at ABC Jobsite so that it can provide benefits according to the technological design which has been made, using Business Model Canvas (BMC) tool. The EwacsPro system reached level 9 TRL with a fulfillment rate of 80%. There are 7 variables that significantly influence EwacsPro's Usage Behavior directly and 1 variable that significantly influences using 2 influencing moderator variables of the TAM2 analysis. The R-square test also shows that all values are above 0.75, which means that there is a strong influence from the measurement of the exogenous latent variable on the endogenous latent variable.

Acceptance Of Accounting Information Systems For Non-Profit Organizations

This study aims to determine how much users accept accounting information systems when preparing financial reports. Quantitative methods with path analysis techniques are used in this study to facilitate analysis. The population and samples in this study were 30 respondents from users of the accounting information system of the Brayat Pinuji Foundation social organisation with ten social care units spread across the territory of Indonesia. The results obtained from this study are a significant positive effect of perceived convenience on perceived usefulness: perceived convenience on attitude of use, perceived usefulness on attitude of use, perceived convenience on the intensity of usage behaviour, perceived usefulness on the intensity of usage behaviour; usage attitude on the intensity of usage behaviour; and the intensity of usage behaviour on the real use of accounting information systems.

IoT Sensor Network Electricity Consumption Behaviour Using ClusterAnalysis Algorithm for Network Environment

The Internet of Things (IoT) sensor network plays a crucial role in monitoring electricity consumption behavior by collecting real-time data from various connected devices. Utilizing cluster analysis algorithms, researchers can effectively segment and analyze consumption patterns within a network environment. By grouping similar usage behaviors, these algorithms reveal insights into energy efficiency, peak usage times, and anomalies in consumption. The paper presents a novel methodology for analyzing and classifying users' electricity consumption behavior, utilizing the Clustering Behavior Analysis Weighted Classification (CBAWC) algorithm. By leveraging cluster analysis techniques and weighted classification, the proposed approach allows for the segmentation of consumers into distinct clusters based on their consumption patterns. Through the application of CBAWC, the study provides insights into the diverse behaviors exhibited by consumers, ranging from moderate to high consumption levels, varied peak hours, and peak days. The classification results demonstrate the effectiveness of the algorithm in accurately assigning users to their respective clusters, enabling stakeholders to better understand consumption trends and tailor energy management strategies accordingly. Through the application of CBAWC, consumers are segmented into distinct clusters based on their consumption patterns. For example, clusters exhibit varying average daily consumption levels, with Cluster 1 consuming around 300 kWh, Cluster 2 consuming approximately 450 kWh, and Cluster 3 consuming about 280 kWh on average. Additionally, clusters display different peak hours per day and peak days per month. The classification results demonstrate the algorithm's effectiveness, with high accuracy scores ranging from 0.86 to 1.00 across different user groups.